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+736.000000 5144.264160 +737.000000 4907.322754 +738.000000 4310.609375 +739.000000 4971.517578 +740.000000 4815.629395 +741.000000 5393.541992 +742.000000 5906.814941 +743.000000 4883.022461 diff --git a/ch01/gen_webstats.py b/ch01/gen_webstats.py deleted file mode 100644 index 61d0b738..00000000 --- a/ch01/gen_webstats.py +++ /dev/null @@ -1,38 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# This script generates web traffic data for our hypothetical -# web startup "MLASS" in chapter 01 - -import os -import scipy as sp -from scipy.stats import gamma -import matplotlib.pyplot as plt - -from utils import DATA_DIR, CHART_DIR - -sp.random.seed(3) # to reproduce the data later on - -x = sp.arange(1, 31*24) -y = sp.array(200*(sp.sin(2*sp.pi*x/(7*24))), dtype=int) -y += gamma.rvs(15, loc=0, scale=100, size=len(x)) -y += 2 * sp.exp(x/100.0) -y = sp.ma.array(y, mask=[y<0]) -print(sum(y), sum(y<0)) - -plt.scatter(x, y) -plt.title("Web traffic over the last month") -plt.xlabel("Time") -plt.ylabel("Hits/hour") -plt.xticks([w*7*24 for w in range(5)], - ['week %i' %(w+1) for w in range(5)]) -plt.autoscale(tight=True) -plt.grid() -plt.savefig(os.path.join(CHART_DIR, "1400_01_01.png")) - -sp.savetxt(os.path.join(DATA_DIR, "web_traffic.tsv"), - list(zip(x, y)), delimiter="\t", fmt="%s") diff --git a/ch01/performance_test.py b/ch01/performance_test.py deleted file mode 100644 index f2111732..00000000 --- a/ch01/performance_test.py +++ /dev/null @@ -1,22 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - - -import timeit - -normal_py_sec = timeit.timeit('sum(x*x for x in range(1000))', - number=10000) -naive_np_sec = timeit.timeit('sum(na*na)', - setup="import numpy as np; na=np.arange(1000)", - number=10000) -good_np_sec = timeit.timeit('na.dot(na)', - setup="import numpy as np; na=np.arange(1000)", - number=10000) - -print("Normal Python: %f sec" % normal_py_sec) -print("Naive NumPy: %f sec" % naive_np_sec) -print("Good NumPy: %f sec" % good_np_sec) diff --git a/ch01/utils.py b/ch01/utils.py deleted file mode 100644 index 7b2ec21b..00000000 --- a/ch01/utils.py +++ /dev/null @@ -1,19 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import os - -DATA_DIR = os.path.join( - os.path.dirname(os.path.realpath(__file__)), "data") - -CHART_DIR = os.path.join( - os.path.dirname(os.path.realpath(__file__)), "charts") - -for d in [DATA_DIR, CHART_DIR]: - if not os.path.exists(d): - os.mkdir(d) - diff --git a/ch01_3rd/chapter_01.ipynb b/ch01_3rd/chapter_01.ipynb new file mode 100644 index 00000000..214c853c --- /dev/null +++ b/ch01_3rd/chapter_01.ipynb @@ -0,0 +1,1655 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy and SciPy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.13.3'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.version.full_version" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([0,1,2,3,4,5])\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6,)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1],\n", + " [2, 3],\n", + " [4, 5]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = a.reshape((3,2))\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most often you are working on views to speed up your code, ..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1],\n", + " [77, 3],\n", + " [ 4, 5]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b[1][0] = 77\n", + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... which means that `a[2]` is also changed:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 77, 3, 4, 5])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want a copy, `copy`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1],\n", + " [77, 3],\n", + " [ 4, 5]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = a.reshape((3,2)).copy()\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-99, 1],\n", + " [ 77, 3],\n", + " [ 4, 5]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[0][0] = -99\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 77, 3, 4, 5])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ahhh, `a[0]` is not changed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 4, 6, 8, 10])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = np.array([1,2,3,4,5])\n", + "d*2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 4, 9, 16, 25], dtype=int32)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Works differently for normal Python lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[1,2,3,4,5]*2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exception: unsupported operand type(s) for ** or pow(): 'list' and 'int'\n" + ] + } + ], + "source": [ + "try:\n", + " [1,2,3,4,5]**2 # does not work on normal lists\n", + "except TypeError as e:\n", + " print(\"Exception: %s\"%e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing\n", + "One can use a list as an index itself, which will then pick the elements individually from that dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([77, 3, 4])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[np.array([2,3,4])]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([77, 3, 4])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[[2,3,4]] # normal lists work, too" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True, False, False, True], dtype=bool)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a>4" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 4, 3, 4, 4])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[a>4] = 4\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 4, 3, 4, 4])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.clip(0,4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Handling non-existing values (NaN, etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., nan, 3., 4.])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.array([1, 2, np.NAN, 3, 4]) # let's pretend we have read this from a text file\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True, False, False], dtype=bool)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.isnan(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use it to select only those fields that are not NaN:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4.])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[~np.isnan(c)]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.5" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(c[~np.isnan(c)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Speed" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normal Python: 1.988895 sec\n", + "Naive NumPy: 1.316497 sec\n", + "Good NumPy: 0.016554 sec\n" + ] + } + ], + "source": [ + "import timeit\n", + "normal_py_sec = timeit.timeit('sum(x*x for x in range(1000))',\n", + " number=10000)\n", + "naive_np_sec = timeit.timeit(\n", + " 'sum(na*na)',\n", + " setup=\"import numpy as np; na=np.arange(1000)\",\n", + " number=10000)\n", + "good_np_sec = timeit.timeit(\n", + " 'na.dot(na)',\n", + " setup=\"import numpy as np; na=np.arange(1000)\",\n", + " number=10000)\n", + "\n", + "print(\"Normal Python: %f sec\" % normal_py_sec)\n", + "print(\"Naive NumPy: %f sec\" % naive_np_sec)\n", + "print(\"Good NumPy: %f sec\" % good_np_sec)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Speed-up: 120.143562\n" + ] + } + ], + "source": [ + "print(\"Speed-up: %f\" % (normal_py_sec / good_np_sec))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "45.43400773412542" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1.621358/0.035686" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But: less flexibility:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int32')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1,2,3])\n", + "a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['1', 'stringy'],\n", + " dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = gen_web_traffic_data()\n", + "np.savetxt(os.path.join(DATA_DIR, \"web_traffic.tsv\"), \n", + " list(zip(x, y)), delimiter=\"\\t\", fmt=\"%f\")\n", + " \n", + "plot_web_traffic(x, y, fig_idx=\"01\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading in the data and cleaning it" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.genfromtxt(os.path.join(DATA_DIR, \"web_traffic.tsv\"), delimiter=\"\\t\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.00000000e+00 2.27333106e+03]\n", + " [ 2.00000000e+00 1.65725549e+03]\n", + " [ 3.00000000e+00 nan]\n", + " [ 4.00000000e+00 1.36684644e+03]\n", + " [ 5.00000000e+00 1.48923438e+03]\n", + " [ 6.00000000e+00 1.33802002e+03]\n", + " [ 7.00000000e+00 1.88464734e+03]\n", + " [ 8.00000000e+00 2.28475415e+03]\n", + " [ 9.00000000e+00 1.33581091e+03]\n", + " [ 1.00000000e+01 1.02583240e+03]]\n" + ] + } + ], + "source": [ + "print(data[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(743, 2)\n" + ] + } + ], + "source": [ + "print(data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(np.isnan(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(np.isnan(y)) # this is the dirt that we introduced above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we have to remove all data from `x` and `y` where `y` is `NaN`." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "x = x[~np.isnan(y)]\n", + "y = y[~np.isnan(y)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building our first model\n", + "We will be using scipy's polynomial fitting functions in this section." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "fp1 = np.polyfit(x, y, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model parameters: [ 2.58462016 996.50674246]\n" + ] + } + ], + "source": [ + "print(\"Model parameters: %s\" % fp1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use squared distance as the error." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "def error(f, x, y):\n", + " return np.sum((f(x)-y)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "319531507.008\n" + ] + } + ], + "source": [ + "f1 = np.poly1d(fp1)\n", + "print(error(f1, x, y))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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3UZ/xyb5W6ub7B8fZZnXO7zrf15ipdyPqZG17PpIkSZIkaZoylC9JkiRJkiR1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\nURZk/FeD6iuBf6T8dlo8oQO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i8H05dv9eg6Y4h/X2+5/oP3zzA/2Hb76vxdWnP4d/9weO6df/9N2y34GLKzmNTc5q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H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YK589iH\nfu+F2f+urSrTztYGPelD1SIET3lpTPs7W9S1rUk9p/p1IjUg6ea1cU1FqdofjPtS5SoI/LxfFLaQ\nexgrw8FdhNBvFZVKkEFjqqpIIfcevcJrDwAAAAAA/EQoHwAizI1BPgaJoicbvk8NzYTuU8MzAfzv\nvfW9UIfvZwP4EQ7fLyUKqziGdQIQg8YIMzcmsdRUlOoH4zfPJbVVZdrV2lAUYbVs2OHImcF532UE\nPAEznJ5zTbWBz3FwuHEeu3xtSgdP9ungyb5A9pnhj8Z11drb3qxf3XaPTjz//Ozv/+yXH1HtmjX+\nNcxnft4vClvIPYyV4eA+7ifMF5VKkEFjqqpIvvceOx5Yr2MvXnR07Fzw2gMAAAAAAL8RygeACHNr\nkI9BonCaSE/o/Jvn5616nxpO6dW3XlXGzvjdvLwQvl/ZwlUcDy8SCg1yMDasE4AYNEaYmZgMU1NR\nqrkrxv7ZLz+icZUX1Srxk+nMssEEAp6AGU7OuSYErcoQzJzHltPTO6DBkXEd2L2F721IkmILrj0X\n/lyM/LpfFLaQexgrw8F93E+4VRQqQQaJ6aoi+d57vK3yrKt9d157AAAAAAAQBNy1BYAIc2uQj0Gi\nYIta+H42dF+XUKJ+5r8J3+cuu4rjnh2bNTQ6EapgbFgnADFojLAyMRnmg813Spmb/YOYZWn9be6v\nNhoUk+mMnjp0OufPPwFPwJlCz7l311boS998veDjMqEmmEycx1Zy4vyw9h1LaX9ni2vHAMLMr/tF\nYQu5h7UyHNzH/YT5olAJMkjcqiqS673HQt7f69dU6OKViRUfx2sfHNMZO3T3oAEAAAAAMIlQPgBE\nmJuDfAwS+W8iPaHvXvrubOg+G8AnfI/FlMSseQNmYRC2CUBzB50++TMJfe75C/rjbzJojHBxOhmm\n/d136/y3/Vm1Ogj2HUvl1TeSCHgCThQa1JKkNy5P5PV5LY1Z+vlHNmr3I/cyyTjAnJ7HctHTO6Cu\nbU28D4Al+HG/KPO4KPcAACAASURBVGwh97BWhoP7CKHfKuyVIIPE7aoiK917LPT9/f3L47z2IXBh\n+Kp6egd0ZJHXaWdrg57kdQIAAAAAFAlC+QAQYW4O8jFI5J0ohu9nA/j1M/99d83dhO+xqDBMAFpu\n0GnXgw2yJH3lpR8ycIhQcDoZ5p7aSp13oV1hkP0uKAQBT6BwhQa18rmW6XzP3fqtDz+gyvISV55D\nGIRlxUsn57F89Jzq1972ZlePAYSVH/eLwhhyD2tlOLiPEPriwlwJMiiCUFWkkPc3r32wTaYzy57z\nL1+b0sGTfTp4so8xIgAAAABAUSCUDwAR5+YgH4NEZmXD99nQfWo4pdRQSt8b+V6ow/fZVe8J36MQ\nQZ4AlMug0+FvDUqS/uXDG/Qrj71Dk9MZBg4ReLsf2agT54c1ODKe8zbZyTAT16662LJgcxoAJeAJ\nOJNvWIdrmdyEccXLQiZ15uvwmUHt2bGZ/hywBD++Y8MWcg9bZTh4jyDy4sJYCTIoglRVpJD3N699\n8EymM3rq0Omc+909vQMaHBnXgd1bCOYDAAAAACKLUD4ARJwXg3wMEuVnbvg+NZTSuUvnQhu+X7Nq\nzWzgnvA93BLE0Fy+g05f+ubreuPyBINOCLTxyWn9+tGzOvrt7+e13dzJMBMutS0rqCs1T2dsHTkz\n6GgfBDwBM/IN63Ats7gwr3iZ76TOQly+NqWh0QmCYcAKvPyODVPIPdunfXJrXN8bvqpTF97KeVuv\nK8PBfwSRYUoQq4rw/g63fcdSeU+EPXF+WPuOpbS/s8WlVgEAAAAA4C9C+QBQBApZKbCQQb5CbqIH\nNVxnQtTC94n6hJrXNStRP7MCfnNdM+F7eCpIoTkGnZyJ8nd/GF0YvqpD3+jXH53qVzpj57xdw+2V\n+oOPP6RNd9W42LoZQV+peWh0wtFqgxIBT8BvBIJuisKKlytN6jRh7Hra6P6AKPPqO9ar+1+FWqpP\nW14a02R65ftEQZsEBSB8wlZVBMGVPacVoqd3QF3bmqj6AgAAAACIJEL5AFAE8l0p0ItBvqCH6/Ix\nkZ7Qy5de1rnhmdB9ajilc8PnCN8DLvE7NMegU+Gi9N0fBSutgrySwZFxffEbr7k60SQsKzWbCmYS\n8AQQBFGafLjYpM6x62l96PdecLzv6lXRuK3KZElESRDvf0kr92nnBvIXBvT9qAwHhAnnsfyEqaoI\ngs1pRaqeU/3a295sqDUAAAAAAARHNEaPAAArWmmlQK8G+cISrltMFMP32dB9oi6hRH1C61evJ3wP\nrIBBp/yF+bvfLX4HB/JdBXkpbk40CdNKzaaCmVEJeCIc/P4eQjBFdfLh3Emd0xlbtVVljlbOr60q\nU31Nhanm+YLJkoiqoNz/ysq3TzuZzmhr4x36jY6Ebq8u4/wMLCHf8xh935uCXlUEwZexbR05M+ho\nH4fPDGrPjs1F+zkEAAAAAEQXI/4AUGQWWynQq4GIsITrsuH71NBM6D41PBPAvzByIdTh+9kAPuF7\n5IFBy/mmMww65Sss3/1eCUoArpBVkJfi1kSTMK3UXF9TQcAToRGU7yH4Y6W+XTFMPiyJWdrZ2qCD\nJ/sK3seu1obQ9uWYLIli4ef9r7kK6dOe6ntLPb39gas+AgRBvuex3Y/cqy+ffp2+7xxBrSqC8Hhz\nbNLR/Q9p5rM6NDrhazVUAAAAAADcQCgfAIrU3JUCvRK0cN341Li+++Z3Cd8DiyCwt7ih0QkGnfJU\n6Hf/01/+Jz37LzZHZiJIkAJwTlZBXowbE03CtlJzsQc8EQ5B+h6C93Lp28XvqCqayYfJtrij7+zk\n1o0GW+MdJkuiGPlx/ysrbH1aIOgKOY/R911c0KqKIFzGJ9NG9jN23cx+AAAAAAAIEkL5AABP+DkQ\nOTd8nxq+GcAPY/i+tqL2Zui+LqFE/cx/E76HKQT2lmdqsCiX/UShSoGT7/7nXryo5168qNsqSvWB\n5jv1sw9t0IMb7wjd30AKXgDOZCBfcmeiSRhXai7WgCfCIWjfQ/BOPn27zvfcXTSTD5vqVivZFi/o\nfJNsi4c2nBa0ifJA1IWxTwsEmcmKb3MVc983KFVFEC6V5WbiBdWriCkAAAAAAKKHq10AgCe8GIgc\nnxrXy5denrfq/bnhc4TvgRwR2FuZqcGi5fYTpSoFJsLfb0+kdeTM93XkzPdVXhrTzz0c18ffe29o\n/gZSsAJw0xnb8SrIizG5upmJNvqxUnOxBjwRDkH6HoJ38u3bHf2nN4wcNywrXnZ3JDQ4Mp7XZ+Ox\nTXXq7ki42Cr3sGI34K2w9mmBoDJd8W2hYu/7+llVBOGztrpctVVljib01laVqb6mwmCrAAAAAAAI\nBkL5AADXmR6IjGL4fjaAXz/z33etvovwPTxHYG9l9TUVrg06Ra1KgRvh78l0Rl944TV94YXXQvE3\nkIIXgBsanXC8CvJiTK5uZqKNfq3UXGwBT4RD0L6H4B23VpNdSVhWvCwvjenA7i3L9r/mCkvfYyms\n2A2Yt1x1szD3aYtZFCrWRZWbgfy5x6DvC6wsZlna2drgqFrgrtYGvl8BAAAAAJEUjlEyAECoFToQ\nmdF1pa1BTcYGNDI5oB09n9X3Rl7WhZELsmW70FL3zA3fZ1e9J3yPICGwl5uSmDuDTlGsUuBW+Dsr\nDH8DKXgBODdWLza9upmpNvqxUnOxBTwRDkH7HoI33F5NdinlpTHVVpZ7ftxClZfGtL+zRV3bmtRz\nql+HF6lUtKu1QckQVSpaDCt2A2blUt1sOmNm0YiwVB8JuyhVrIsityq+LSaMfV8mk8APyba4o/uj\nya0bDbYGAAAAAIDgIJQPAHDdSgOIc8P3U9aApm78f9r6gWTdDN//7QW3W+oc4XuEVdACe0EeUHRj\n0CmKVQq8CI8E/W8QxACcG6sXm17dzFQb/VqpuVgCnnCH6fNfEL+H4A0/AvnSzETDX/6jbwV+0txC\njeuqtbe9WXt2bA5sH9QJVuwGzMinulnne+42csywVB8Jq6hVrIsqtyf9zxWmvi+TSeCnprrVSrbF\nC7ruSLbFeW8CAAAAACKLO7pFwrKsMknvkxSXtF7SVUlvSPq2bduvGT5Wo6T3SLpb0mpJFyX1S3rB\ntm1jd069fE4AnMkOIM4P3/drKvb6ouH7MLi94vaZ0P26ZiXqZ0L4zXXNhO8RSkEK7IVhQNH0oFNU\nqxR4FR4J8t8giAG4+poK1VaVGQ00mF7dzEQbTa/eX4ioBzxhllvnvyB+D8F9Xq4mu5igT5pbTknM\niuR7PcxVaICgyLe62dF/ekOlMUvpTOH3u4LQp42yKFasiyovzz9h6PsymQRB0d2R0ODIeF4LjTy2\nqU7dHQkXWwUAAAAAgL8I5fvEsqwmSQ9J2nLj/1sl1cx5SL9t2/caOE6dpH2SflbSHUs85gVJv2Pb\n9hGHx9ol6WlJjyzxkLcsy/pTSb9h2/YlB8fx7DkBUePVytPXpq7p5Usv69zwOaWGUvrOUEoXK09r\n0g5f+D5mr1br3S1qXd9C+B6RFYTAXtgGFE0OOgWtSoEpboS/lxLUv4GfAbilzvnTGVt33VZh7HVx\nY3Wzkpilna0NjipSmF6934moBjxhhtvnP4K4xcnL1WSXEuRJc8Uo7FVogCAopLqZk0C+FKw+bRRF\nsWJdVHl9/gly35fJJAiS8tKYDuzesuw17VxBuKcLAAAAAIDbGEnxkGVZ2yXt0UwQf9EwueHj/ZSk\nL0iqX+Gh75X0XsuyeiT9a9u2x/I8zmpJByR9bIWH3iHpVyR92LKsj9u2/Tf5HOfGsTx5TkDUuLXy\n5sLwfWo4pXPD53Rh5IJsLTLwGOBxxJi9WmWZuMrs+Oz/l2c2KqZafejupkCGPQFT/A7shXFA0dSg\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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_web_traffic(x, y, [f1], fig_idx=\"02\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, our web traffic cannot be modeled with only one dimension. What about two?" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "f2p = np.polyfit(x, y, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.05605675e-02 -5.29774287e+00 1.98466917e+03]\n" + ] + } + ], + "source": [ + "print(f2p)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "f2 = np.poly1d(f2p)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "181347660.764\n" + ] + } + ], + "source": [ + "print(error(f2, x, y))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3UZ/xyb5W6ub7B8fZZnXO7zrf15ipdyPqZG17PpIkSZIkaZoylC9JkiRJkiR1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\nURZk/FeD6iuBf6T8dlo8oQO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6zGc+ozvuuEOVlZV5/0iA6fVXU2v6x/sGfTOojd1f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\nWlp0/PhxcweORLVk8WJ94hPDU/GLiorMHXuMQjZrmHjtZtOyo02rFtbZPme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pYfk5R7O7\nyJpe7AX8zi9FYbijEI1a7FpypiBNk7aC9yX/CmuzZ77C2hRuIlRUWRYrSKgo19CfVTT9ZmankfqF\n17v0i7+4XuWTiljbeRs7E40vSOH7qMpUnJypkuQsxVIzVRmr09L51+jOhVerrmpKoU8PMCLfmsqP\nf/+a5l/h8EnlwA/XgWeffbY+8pGP6OGHH876Nfv379fWrVv13ve+N69jR6NRTwbyJTMTzceyu35u\n4py6+gb1xR8+H7rP9dEmet2VFEfVP5iUpJF/ppm4PgrbfbKJQRP52LSrXX969UxbA5hWXDvbdiPB\n68f7Qn2vAQAAgodQPgDA16xO/Rrrzx/cpfajvb6ccuW14E1RNMIUA0jy3nPTKWH5OdNMTGvZtKtd\nqxfNY6EVgeCHojDc42ajFruWZBaEadJ28b7kX2Fs9kwbSqbyau7O1BReGiuSJPUNDKk0FtVQMhWY\n600ToaJlDTWu/z5MDFLIBU2/Z7LbSP0fv23jd/o2diY6JR2+T3QMh+4TncMB/Jfeesl34fupk6aq\nvqp+ZOJ9vCqueHVcM8rPU+eJkzSkwLZ8r23cZKqmUiheHw60YsWKcUP5knTfffflHcr3OrsTzcce\ny8T6uYlzCvLnej7Gvu6O9vRr3ZZWbX/5rZweb+f3GKb7ZLcHRBztGdCG39h7jTT/5uVQ71gJAACQ\nSSSVShX6HIDQiEQicUnPp//9+eefVzzuvxtC5O/48eP69a9/PfLv733vezV16tQCnlEwvNx5Qu/7\nP1td+V5+CdHuP9xN8AaeFJbnZhh+zteO9epdX/6V7eM8ufp9LLIGENc83i68wx0TTVIdzc415v/6\ncaut4vKqd88OZNhuKJnSVV/6ue1p0k+t+UBgXru8L/mLW+8hXpFuMNqc4fo51yZ5E8ewwu3rHrtr\nIL/+y/e4fi/ytz94znY4PBdBe9+2i89Cs6w+j5saa327M1G28P2+t/YpmUpOfAAPGR2+HwngV8d1\nfsX5ikR4fsO8Ql2X5HN++V5PzJic0uorTjXehHGtJx9DQ0Oqra3VoUOHsn7N5MmT9frrrxfk9+jk\n/aGdaz+n1s9NXY/6+XPdCW5fH4XlPtlU7SUfFaXF6uqz3gxg9/Fpv/iL63RRdYXt4wB2UeMCAP9K\nJBK69NJLR//RpalUKlGIc/Hv2C8AQOi5Udgd/b38MA3D61NiEF5heW6G4ec0Na3F7akvgFvYMQZu\nTCln15Ls/DpN2km8L/lLWHY6mChUcbRnQM3b9qt52/6soQoTx/CTuqopamqstRx+cfv5YmdHl3wx\nWfF0w9NLmVZpQtB3Juob7NPeN/cq0TEcuk8H8P0avh879b6+qp7wPVzjl+sSN2sqfvfGG2+or69P\ns2bNyutxRUVFuu222/TlL38569f09vbqhz/8oT72sY/ZPc2cudEwYmWi+YJZZ+mfbr5c559V5sh9\nuJVzysQPn+tuKcT1UVjuk6srSlVZFrN9LZ8Pu4F6E4F8yd87VgIAAIzFlQ0AwJdMBJHytXVvp9Zt\nSfhiGgbBG3hVWJ6bQf45TS2OssgKIOicbNQibDc+u1vUN12TX+gCcEKQmz37B5O6Y+POnIMxmZrk\nTRzDj6yEiq6fU6W1S9zfqdPt0F++Tb9B3kWERmpz7D6PW7Yf8MTORH2DfdpzeM9pU+9bO1sJ3wOG\n+OW6xFRNJZlKGTgbb+rr69OPfvQjbdy4UT/96U/V1NSk+++/P+/j3H777RlD+WVlZVq2bJlWrFih\n6667zsQpT8jNhpGS4qjWL1/gqYnm+Z7TeLzyuV5ohfw9Bvk+WTIzaCIfpqbc2z1OZVlM1RWlE35d\nkO/hAABAsJCEAQD4kokgkhVMwwAQdiamteS6yAoAQeBEoxZhu/H5bZo0MJ4gNnuu25LIe1Ll2CZ5\nE8fwIy8GnTIpxCCFXJt+3ZgSW2g0Upvhx52Jghi+HwngVw////MqziN8D8/xy3WJqZrKm939qpxm\n4IQ8IpVK6cknn9T999+vhx56SMeOHRv5u82bN+v//t//qylTpuR1zDlz5ujaa6/VE088IUl697vf\nrRUrVuimm25SRUWF0fMfTyEaRrw40bykOKr/+ZFLteXZQzpuIzgc1B0H8+GV66Mg3ien2R00kY/F\n88/Vd3930PZxrrt4un7y3OuWHz/RjpVhuIcDAADBEu5VVQCAbxUyQMQ0DABhZmJay0SLrACA8RG2\nm5ifpkkDYZIupluRbpJPpVK2j+Hngr0Xg05juT1IIZemXzenxBYajdRmeHlnonT4Ph26T3QmlOhI\n6KUjLxG+B1xm4trGrc9rUzWV3v5gNHe/8sor+s53vqONGzdq3759Gb+mu7tb3//+97V8+fK8j//Z\nz35W1113nW6//XbNmTPH7ulaUsiGEa9NNO/o6rMVyJeCveNgrrx8fRQUdgZN5Gvlwjo9mnjd1n/T\nkuKorUC+lH3HyjDdwwEAgGAJbvUVABBohQwQMQ0DVrG1IoLC7rSWbIusAIDcELabmF+mSQNhYzdY\n0LL9gFJ2zyEgjfZeCzqN5vYghYmafgsxJbaQaKQ2wws7EwUpfD9t0rTh0H1VXPHq+Mj/J3wPvzNx\nbePWdYmpmsrkEv/GC44fP65NmzZp48aN2rp1a06P2bhxo6VQ/rJly7Rs2bK8H2eKVxpGvDLR3Auf\n6340tqZ1vJffoxusDJrIV1NjrS6qnmL7vqF/0N41abYdK8N2DwcAAILFv3fNAIBQMxFEsoopDsgX\nWysiaOxMa8m2yAoAyB1hu9z4YZo0ECZDyZQ272q3dYyHnzqoiOy9dwWt0d4rQafR3B6kMFHTbyGn\nxBYKjdT2ubkz0ejwfaIjodbDrb4O38er46qffmrqfX1VPeF7BJKJaxs3r0tM1VTOKS8xdEbuGBwc\n1C9/+Ut95zvf0fe//3319vbm9fhf/epXOnjwoGbOnOnQGTrDTw0jbvD7joNuD3zKVtOaWurv36Nf\n5DtoIl+jd4q0e99g6jzGCuM9HAAACA6udgEAvmQiiGQHUxyQC7ZWRJBZmdYy3iIrACA/hO1y5+Vp\n0kCYdHT12Q6BHTMwmZFGe+e5OUhhoqZfr0yJdRuN1PY5sTNR32CfXjj8wvDU+47EyAR8wveAP5m4\ntnHzusRUTSXqg9d4KpXSrl279MADD+i73/2u3njjDVvHeuCBB7R69WqDZ2hGtqC23xpG3ODXHQfd\nHvg0UU3reJ/9+7Gg79xoyuhBE3c+8JReeL3LyHHH1iLt3DeYPI/RwnoPBwAAgoNQPgDAtwrZvc8U\nB0yErRURdPlOa6HxBADMImyXPy9OkwbCxEvN7V46lyBya5BCLk2/YZ4SSyO1PXaexyn1ayDSrosv\n6NUXf701EOH7dOg+XhVXvDquc6ecS/jeg9yepBx2pq4n3LwuKWRNxQ379+9XS0uLWlpa9MILLxg7\n7v3336/Pf/7znnnfmyio/aH4O3zVMOIGv+04WIiBT/nWtKwKw86NJs2eXq4f3fVurdq4U49b/G8z\n0U6RVu4b7Ljpqppxp9mH+R4OAAAEA4lCAIBvFap7nykOyAVbKyIMRk9radl+QJsyFILGW+wFANhD\n2A6An3ipud1L5xJUTof+brziPN1x3YV6s/tk1tBp2KfE0kht30TP43T4vj96QAORgxqItmkgckCD\nkTekSFKb9kvySfaV8L2/uT1JGcNMXU+4eV1SqJqKk9588009/PDDeuCBB/TEE08YP35RUZEuuugi\nHT9+XNOmTTN+/ImMbrYpKY7qm4+9rAd/O3FQ24SgNbL6ZcfBQg18slLTsiJMOzeaUlIc1YY8rutv\nuXqm7rz+QvUPJXNq0sv3vqGkOKr+QeuNpj/f/YaGkinu4QAAQGBRfQAA+JqVINKUSUU6cXLI8vdk\nigMmwtaKCJvZ08u1ZnG9Vi+ax0Q2AHBRSXFU37z1Kn3hB8/pB0+/OuHXE7YDUEjVFaWqLIvZmto5\nbXKxIoroaK/1Y9Bo7w47ob8ll5+rd1SUntH0W1FarFlnl6ntrR794JlD+sEzhyRlD512dPWFfkos\njdT2pJ/HD+zYN2743k9Gh+9HAviE732rEJOU05jKb+baphDXJW5PRHZCX1+ffvzjH+uBBx7QI488\nooEBe5/3mVx++eW67bbbdMstt2jG/8/encdJWZ35Av+91Xs3NGs3e7NKoAtBQTZRBAkg0I02tBiD\nmtyMGScz3iRm5k6uEzNejcaY3GRyE6NO1JksGA00i9BsKoIQFBEEgWpQEIEGGrpZel/pOvePppvq\norZ3f89bv+/n40equt6ltlPnfc5zntO3r+H7jybcZBsruW0iqywrDtpR8EnPmJYa8bpyoxHM7tfH\nuv853j5Y8p+7dD2XSNdYvIYjIiIiN3DXlRQREcUdtbP37580CN+8dSjm/nq75mOyigNFw6UVKV4l\neBTXBDo5uExEThdugF4BIAIex2Q7InKKBI+CxeMH6qpOee+EQRCArn1wor11tK7o8st7b0Jyoqdj\n0m9lfTN+v/1LrN53BofOVl+3TbikU6Oqu7qhSiwnUsemoaUBn138DL5yH3wVPpRUlOBQuQ+lacch\nIFfyfffU7tdVvc/NymXyvYvYVUmZVfmvMaJvY0e/RO2YSt7YfgD0VS02gt/vx/bt27Fs2TKsWLEC\n1dXX9wn06tOnD5YuXYqHHnoI48aNM3z/sYg22cYqbp3I6vQVB+0q+GTF540rNxrD7H59tP0fK68x\n4FmEv8biNRwRERG5AZPybaIoSgKAEQByAfQH0A1AE4DLAL4AsEcIUWfwMdMBTAMwEEAfAJUAzgD4\nWAhxzuBjjQbgBTAAQDKAswCOA/hICGFY9NrK50REzqWlOoAM1TBITlxakUhuHFwmJ+CkEIok2gB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+0PdX3olvwCxsKIiIjsE98RaIdTFKULgL8Lunt1lM2CK+ur7WkFP/6GGI9zQQhRr+FYt2g4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\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6sxUxtv7YvQX4xw/Lee4l+3NKowYiIiIiMp/8s8tERGnK7M42yUK6MkxI2bWzGiVnZkjc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\nPTmnQqWEgwE8dNe1hsda+3b1OlrorSI374U5LiYR7M5HZVk9F0UrFJr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JiQvY/+IQnszz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R9Xzv5poXK3sxTF7i9+89R34huXbsbhPVujraWp8h2sod3bfjD+6R//eUXaXvhav3jxSvzmqe9E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\nVtTArUc29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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_web_traffic(x, y, [f1, f2], fig_idx=\"03\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\numpy\\lib\\polynomial.py:583: RuntimeWarning: overflow encountered in multiply\n", + " scale = NX.sqrt((lhs*lhs).sum(axis=0))\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\ipykernel\\__main__.py:3: RankWarning: Polyfit may be poorly conditioned\n", + " app.launch_new_instance()\n" + ] + } + ], + "source": [ + "f3 = np.poly1d(np.polyfit(x, y, 3))\n", + "f10 = np.poly1d(np.polyfit(x, y, 10))\n", + "f100 = np.poly1d(np.polyfit(x, y, 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ifqMT/Z3pW6+v3ecbVbn/K7zfY2ZejeiTmbb85EkSZIkSdOUoXxJkiRJkiSp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\nzKsoCzL+q0H1tcA/Un473Te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MPHGI3/38atWLxDX5+L3PzYVLLgEIKizv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\n5eePHQrA7t1wxRXw5puB1U/M/pqBP/wUR2xTQP2FU7CB/Lb6q3cwe2K2X2U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26PW9srtreG7iM5fO+wOjTyyJHKSBqlYf1H6bijjtOEIcdrdPJIOWwO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\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Gx3cWbvnb3fq0QrUNmKFzMhWj9Ymrx/VlwsCetmoHTkpw8eC80SyaOjgq91hJCS4euHoU\nr20+RHWj+XUJPQlvnBxfIG1hQgghhPUkKN/+NnV6nKppWo5Syu8dpKZpbmB2p8V/DfNYbwOHgfbo\n4SGapo1RSm0JsP68To9XKqVOhnmsv3I6KB9gPgGC8i1+TUJER1ISjdnZpFRVBV7ngLGgLBGcBLVG\nxm6dTk4S7akbRfyThrLQjJZRl4/sbcp5SOeyiIRkHBShmDFwo7Nod/QLYWcyA5gQzmJG0Gln7QMa\n7501Mm6zxFolXoPvOwLwJfhe+LDbDGdmZFL2DdANxsqs/MK5/LXPtVQeMT0gPysrG8/ZU0gvvITk\n/iPRtNB19LzaE3xn3TIWfvEmKa3Nhs/h877DeX78TLJvvoF7559n2n1CNOo9vpyaxbzleAuVqytR\nLYr8a/P9rnP4MBTdeYy1q8bgaUw647nN5IQ8xliq+ADvvpcsgV//OrxxFsOeHd5RlyQZ8hW4Tag2\nGAmO90dP+XvLBQP56MsKDpxsCHsbJ/Zp6W0biFTnZCpGP9Nr/7zWtn3aZ/dKj9o9VnlNY1QC8iG8\nhDdOjy+QtjAhhBAiNqQVxP781TCT/Cxrdz7gOzffYaXUl+EcSCnl0TRtNXCdz+IrgUAB7N/o9PjD\ncI4TYN0rNE1zKaU8fta18jUJETWNubnBg/IlU35USFCrMXbrdHKSaE/dKOKbNJSFx2gZ9fa2I6ac\nh3Qui0hIxkERihkDN3yFmw1TiHglM4AJYR0zsgiaEXTame+AxnjMEhsNvsH3xRWnA/CdGHzfM7Xn\nGVnvJfhehMOuM5wZzaQcboCulVn5hXP5a59LHzGN6k//bnjfSUlJzJw5k5tuuolzJ05l5hPrw9ou\np6Ga/+/TV7hl0+uGg/Gb3AmsHHkJz0+YRXHvId6FG4+wv/Yz09pio1HvaWd1FnMj9cDm8mYqV1dS\n9VEVlR9VUre1DoC04WldgvK3bIHf/x5efFHR0uI/YL+ETJrRSApSZxlLJR+QT1YWfOc7kB7irbIi\nQPe+2YWUnahn9a5jhvYTbvkbadZ4p/ZpRdI2EAnfew+zEm/YvU87GvdY0Wz7DpbwJl7iC6QtTAgh\nhIgNiR6xv6GdHrcCwe7ARnV6/KnO433CmQHswYY2R3wspdQOTdNOAD1PLUoHBgF7zDzOKXpekxBR\n05CbS84ef1/xUyRTvukkqNUYu3Y6OUm0p24U8UsaykIzo4x6Y9thclITqWyQzmVhPck4KEIxu9Mp\n3GyYQsQjmQFMCGuYHaRkNOjUHxnQ6F99Sz3bK7Z3BN3HU/B9Yb73/xJ8LyJh1xnOjGRS1hOga2VW\nfuFMgdrnkvIGkpg3iJaKfRHtd9q0aSxcuJCioiJ69vR2JR+uCp29O7W5kX/7/FW+s3YpWc31ER27\nXWVKBovPu4rnxs+iIqNnl+fNbouNRr3HyizmRuqBx18/zlc/+4r6Ev+fWf2OepqPNuPulcSrr8Kj\nj8KHH7Y/G7h8acFNCVmMI3DCtPNcJ/mfX3sD8rOzA78+KwN0kxJcPH3r+cx5/F/sOFIT0T4gvPI3\nkqzx/Xuk8n+3ns+Q/AzHzTxlpG0gEu33HmYn3oDu06cd7bZvf/eH8RJfIG1hQgghROxI7739FXV6\n/FmAbPLtOqcj3q3zeF+F2B8AmqZlAf1CbBvKHk4H5bcfy1/EsiWvSYhoa8zNDb6CZMo3nQS1GmPX\nTicniubUjSL+SENZeMwoo6oaWrn+/AG8vKEs4n1I57KIlGQcFKGY3enk1OnqhTCDzAAmnM6MzPPR\nFK0gJSNBp4F09wGNnYPv2wPwnRx875v1vjCvkPz0fAm+F6ax8wxn980u5MDJBl3t35EE6FqVlV84\nU7D2ufSR06n8aF/Y+xo1ahQ33HADCxcuZODArt+bYO0obk8b1255lx+ueZHetSfCPqY/Zdm9eWbi\n1SwZczn1ScH7NcxsizW73mNV5mYz6oGuNFfAgPx2f/tJJb/8OJ/9+/Wd32Zy/Abll7qT2d5PcWhi\nDc/8xEOCO/D7FIsA3aQEF08sHM+lj3wU0fYQXvkbST/qgZMN3PnyJo5UN0ZttoBoWbxW5xfIoPZ7\nj2gNDO4OfdpmtKEH4+/+MF7iC6QtTAghhIid7t0CbXOapmUAt3davCLEZp0z6+utaXVe/5wwj3NM\nKaU35UApguMIcQAAIABJREFUMDGCY0XrNQkRVSGD8iVTvqkkqNU4u3Q62T34QI9oTN0o4o80lIXH\nrDLqG6N6GwrKl85lESnJOChCMbPTyerp6oWwE5kBTDiZ2ZnnoyHaQUqRBJ0G0p0GNLYH3/tmvS8u\nL2Zf5T4JvhdCBzvPcJaU4OKpWyYGDYb1FWmArlVZ+YW9eDwe1qxZw8svv0xZWRm33965u9orWPtc\n+ohpVH70XNDjuLN7kz5iGi8+9GNmzpgSfF1/7ShK8Y0vP+Gnq59nyAljia+29BnKXybN581zL6LN\n5Q57OzPbYs2q9/zt9klMPSfPlHMKxqx6YNaULLQkDdUcuI6ydXEl+8nXfY6byQH2s4d0NpPN9t4a\n+y84RuOwE7RXIypqm4L228QqQDfa5a+RflR/GfzNnC3AbHsqavnb2v08u2afZcf0vfeI5sDgeO/T\nNqMNPRB/94fxEl8gbWFCCCFEbElQvr09BPTxeVwJPB1im5xOj8t1HrPz+pmaprn8ZOc3ehx/2wSa\nFM6q16SLpmn5gN4WjSG+D2pra6murjZyGsIh6urqaOjZdXpLX+roUWqOH4fERIvOKr4tXfsVfVIj\n72Rc+umXfHv6kNArxjGttcnQe3h6P41UV+vfz4GT9azacph3S45S03i6cT8zJYHLR/Zm9ti+9MuR\nAHcRXzxKsbq41NBv76PiUr4/tR+uOA9OMKuMGpDpYtHkPqzaclj3trPGnEVuUpvU53zU1dUFfSzO\ntGB0T17/3N9kYWFuPyZXvn9x7sbz8li+0VhgwcRBPfjxjAL5rohuq6K2iRTVTB8jtw6qmX1HjpGX\nkWzaeQnni2a9p6VN8acPd3fUUVPgzO+waub1z/fw+ud7mDXmLL47YyiJ7tjU/x99bxc7y8p1/cZ2\nlpXzm9c2cedl4eVO+f28c/nTh+6I6uy+FpyXR11t1wAiJ6tvqWfniZ3sOL6j42/78e2UVpc6Lvi+\nR0oPRuSOYHjucEbkjuDcnucyIncEeWl5XYPvPVBT4+zP0qMUx+uaaWhuJTUpgdz0pLi/j3eiVBTn\n9HCf0TapV2ZKAqk0U10dnQyvd102kJsm5LNq8yHe8dOOesXI3sw61Y7aWF9LYwTH+PGMAqqqq/ls\n38mwt5H7EOdRSrFhwwaWL1/OypUrOXz49HV3zpw55OV5u0R96zxB2+dS86nqP5zaAzvOWJyQnkNu\n4cXkjp5ORv9z0TSN4cOGhPVd8W1HGV26nR+8/QyjDu7U+1LPsLX/uTwz/QbWDh0Pmnaq4zf8a6jZ\nbbG/n3cu33+xjr3HIq9brt1xgNH5SVG/zpxRD1TQo1KjLk3RHOS2KVA9MH1iOrWf1AbcbiyVEZ3j\nVrKZ655C8rij9Jmyh9S8Onp0Wuf4yUrSNf9l9IGT9by3ZX9E95PvbdnPTRPyDfVjRbP8NdqPGsx7\nW/ZTVV3N/XNGxew+BbreV/W2sEvR997DjPpEMNHu045lvdWjFJcMzuD1z83/rvq7P4yX+AJpC4uc\n9HEJIYRz1dYGvp+wmqaUsxpmuwtN0+YByzst/p5S6okQ220EzvNZNFsptUrHcbOgyxxuWUqpmk7r\nzQH+4bPoc6XURHTQNO33wI98Fv1eKfUfftaz5DXppWnafwP3GdnHo48+SkFBgZFdCAfJ27yZC+8L\n/pV556mnaMiLfvYKIYQQQgghhBBCCCGEMzS2NXKg6QBljWWUNpZS1lhGWWMZ5c3ljgu+z3RnUpBS\nwICUAR1/BSkFZCdkS+Z7IYSIgZqaGpYtW8aaNWuoqPCfDfyWW25h/vz5uve9atUqnn76adLS0rjg\ngguYOnUqo0ePxu0OPxN9ZynHjjHy+ecZsHp1xPsAOD58ODuvu46KceNArj/6KXAdceHe5iahOIGE\nbQm4jrmo+1kdrRfqDzxOfiGZlFeCz2g0lwupIinsfWZnN3LVVXu58sp9ZGU16z4nIYQQQgghhHOU\nlpZy5513+i4apZQqjsW5SKZ8G9I0bSzwfKfF7wB/CmPzjE6P9Sa9aAiwz84B7EaP4+9Ynfdp1rHC\nfU1CRFWoTPkAKcePS1C+EEIIIYQQQgghhBDdUDwH37f/X4LvhRDCXpKSknjrrbdobAzc/frxxx9H\nFJQ/depUevXqxfjx40lKCj+Y2h9XUxNDV67knOXLSWhqing/x0eMYOf111MxZowE40cg8b1EEr5I\nIKE4AdcJV5fnE7YlRBSU3zqqFV4Jvs5YqlgdxiT2BQXVzJnzFdOmHSApyaP7XIQQQgghhBDCCAnK\ntxlN0wqA1zkzEH0/cJOKbFoDvdtE2rJvxblFup2zeitE3GrMzQ25TsqxYxaciRBCCCGEEEIIIYQQ\nIlYk+F4IIYRdJCcnc/755/Pxxx8HXGfv3r2UlZUxYMAAXfvOzs5mypQpxk5QKfp+8gmFzz5LWoBM\n/uE4Pnw4O268kWOjR0swvgFJ/0wiYXvgEJOEbZGFn7QNb0MlKLTWrp9NKxo7yaSV4J/beecdZc6c\nrxg3rkI+YiGEEEIIIUTMSFC+jWialg+8C/TzWXwEuFwpFW4rQ22nx6k6T8Pf+p33acZx/G3j7zhm\nHCvc16TXE4Qcs9/FEOAf7Q8mTZrEiBEjTDgVYXd1dXWsW7eOGk0jM8j4mn8+9xzVbjfz5s2T74YB\nFbVNLHxqneH9vHDHZPIykk04I2d79L1drNpyWPd2s8acxZ2XnRP2+gdO1nPbs5/pPk67v/7b+fTL\nieRyZH8epThe10xDcyupSQnkpifhklbluORRimue/JSaRv3ZhNplpiTwyncu6DbfkWiVUfK7i0xd\nXR3r16/veDxp0iTS09NjeEbREa3vx8HKBlZtPsQ7JUfPKAcyUxK4YmRvZo3tG7fXumiI5e/Y7GO3\ntCn+9OHusMq7WWPO4rszhpLoljIrnsh3wJgnP/qK5RsPRrz9gvH9+Pb0ISaekfNI3airaNR7nPRd\njWXbi5PKxLqWOnYe38mO4zvYcWIHO47vYPvx7ZRWl8bkfIzomdKTEbkjGJ47vONvRO4IeqX2kuD7\nEKxqWxPR09KmuO/VbXy272TY20wc1IP754ySOpkN2Pnzs0sdq76+PmhQPniz5d94441+6zzRKudc\nW7aQctddJHzyie59t9uTN4A/XXYrH587CTwabI54V34FaouNtL5iVh0rHJFcZw7NPMSR7UcCPu8u\ndfO/a9zUh1Et7lwP3DlhJ3Xr6tASNQ5kZPDByR58QQ7FZNEYIKwlOVmRNbKMPlP2ktS7hreAtza7\ndb0mX779TKUn6lj03OcR76vd07dOoKCnvdpHrfyexaK/wuh9VaRC3XtEcj0Kl1l92rGst0Z6bD2C\nfUbxFl/gpPYFO+kufVxCCBGPtm/fHutT6CBB+TahaVpP4J/AMJ/Fx4CvKaV26dhVNALY66JwHH/b\nWBmU7+816aKUKgfK9WzTuXMiIyODrKwso6ciHGLfvn0cUIpgofaJFRU8/PDDPPzwwxQWFnLttddy\nzTXXSIC+TukZikYticr6loj3kZOWyKA+vXC7pNPkZ7PPY9fJNj76MvwsNNOH5fGz2eeRlNB1+tJA\nlq0+wJGGyN/vZVuOc++skRFvb0d7Kmp5YV0pyzYeOOP7nJOWyILx/blpykDO7iUNAfFmWmEBz/xr\nb8Tbz5pQQE52tolnZG/RLKNyus/bGDXp6elxVd+NdrmclZXFiILe/HimorymkbqmVtKTE8jPTJE6\niQ6xvH5G89j3zpvAwql1vLB2P0v97L9ofH8WSt0gLjW3evj35z87da0LXRY8ve4Iu0628dQtE3XV\nx+NZ0ZRzeGLNoci3v2AYWVnd87cl9yThM1rvafMoXtxUQaWB++IXNlXw45njLKk3lDfWGLqHb6cS\nUsjKytS9nd2ui3XNdWw/tp3i8mJKKkooriimuKKYfZX7LDm+mXJTcynML6Qwz/s3Mm8khfmF5Kfn\nx/rUHGlPRS1PrztCONfwzp5ed4SFU4dLOWsTv194Afe/VswL60IPqlk4uYD7ZhdKXcwm7lmxlVXb\nK9HzO1y1vZLsrFIenDc6KucUzTqWUkr3YKl58+aRlZVFdXV1wHU+/vhjbrjhBr91HtPb56qr4Re/\ngMcfB48n7H36Uv378/Tl/8ZDvSbicbmhMaLdhOSvLdbIPdygjEzD/VvhiuQ603pFK0d+FzgoHyBn\ndwJ7hrWF3FfneuCQXw1Bc2lkTcni4/Vunrkk8Lb9+8P3vgeLFmn84eMqXlhXS7UJdVPffqZclWhK\nfTe3Rw5ZWfZKsmFGP2q4jjS00UASZ1n0HphxX6WH3nsPPfUJPedgRp92LOutRo4NsOTbU+iZnszL\n60sjvj+Mt/gCaQszR7z1cQkhRDzLyMiI9Sl0kKB8G9A0LRt4B/Bt2TmJN0N+sc7dVXV6nKdz+84t\n69VKKX+tHUaP4+9YlQHWs+o1CRFVa9asoRCCBuX39/l/cXEx9913H/fddx+jR4/m2muv5dprr2XY\nsGEBtxdebpfGgvH9DQW1Fo3vb4sbZjtISnDx1C0To9rp1OZRLNt4wMhpsnTjAe6+akRcfG7NrZ6g\n73dlfQvP/Gsvz/xrr3TyxaGFk40F5S+cMtDEs7E/K8ooIawul90ujbOy7dVZ5wSxvH5adeyze6Vz\n76yR3H3VCBm40Y3c/1qxruAWgI++rOD+14qjFkTkNIPzMlg4uSCiTu+Fkwu6ZTCk3JNYr7ym0XBQ\nTGV9C+U1jZbUI9KTzelaMLKfWFwXfYPviytOB+BL8L0IxGjA1Qtr98ddEgqnSkpw8eC80SyaOtg2\nA4JEaO3B75F4YV0pi6YONvXzjFYda+/evbzyyiv8/e9/54EHHmDmzJm6zislJYWrr76av/3tbwHX\nOXz4MHv27OHSSy/t8pxp7XNKwZIl8KMfweEIsyX36AE//zna977HrYnJ7DM5+LUzf22xRu/hjPZv\n+aMpGFDuYnipi+FlblKaNX5zfaPu60z2hdmQoEFr4FnBh5e6+exsRf32vjRXZNLzMv8ZKzvXA3t+\nrWfH/6dPhzFjYMuWM7e5+GK4806YNw8STm1+3+xCDpxs0P2e++Pbz5SfmUJOWqLhAN38zBTD52U2\nM/pR9ahrinxmYL3MuK8K5oZJA/ju9CE0t3kiuvcIVZ+IhFl92rGstxo99jvFR7l31khD94fxFl8g\nbWFCCCFE7EhQfoxpmpYJvAVM8FlcDXxDKfVFBLvsnFVfb1RW5/UDZenvvDxP07Q0pVS9BceK1msS\nImqUUqxZs4ZQzaD9AizfunUrW7du5Re/+AVjx47tCNAfOnSo2acaNySo1VzR7nRyWvBBNDW3erij\nI4tOaC+sK+XAyQbJhBpHpKFMP+kYF9Ek5bIzxPJzisWxZeBG92G3ICIniyRQY/qwPO6bXRjFs7In\nufbFhlmBKlYFvNgpSCka18Xa5lp2HNsRF8H3vdJ6nQ66zyukMN/7fwm+jz5JQhGfZKCss9hpYIzZ\ndayysjKWLFnCkiVLWL9+fcfyJUuW6A7KB7juuuv8BuX379+f8ePHM23aNAYPHhxwe8Ptc7t3e1Of\nv/OO7nMHIDkZfvhD+M//hJwc7zlBWOdUXtPEq5v1ZxP21xZrxj2c0f4tAM0DAypOB+EPK3OT0Xi6\njPKgSG/Qf51xp7vJnJhBzdqagOsMLUnjYPH5eBqTAMgcv5/EHmeGEISqB2qaN/h+0SJISoIbb4R/\n/3cYP77ruu2DQu5aupkVX0SeFRrO7GeKtwDdzsz4noXLrAG94YjG/VCCS+PmCwZyywWDTGtj8a1P\nfLbvBNf9ZW3E+7p+UgGHqxoM1UliWW81+9hG7g/jLb5A2sKEEEKI2JCg/BjSNC0deAOY4rO4FrhS\nKbXe/1YhdR5qrjdit3Nrit+h60qpak3TDgF9fRYPAbbqONbZ4RzLz/KovCYhomnLli0cPnyYULeT\n/UM8D7B582Y2b97MPffcw3nnnce1117LNddcw5AhQ8w41bghQa3GtXlUl46laHU6OS34IJokE6oA\naSiLlHSMi2iQctkZYvk5yXdERJOdgoicTmbXCZ+Ua7Fhh8zzesRLkFJtcy3bK7Z3BN23B+A7Ofje\nN+t9YV4heemRTHIbHn9tR7H+TO1EklDENxkoa392GxhjRh2rtLSUpUuXsnTpUj799FO/26xcuZLG\nxkZSUvQNfLv88svJycmhsrKSvn37cs0113DdddcxYsQIPvroo7D3o7t9rrERHn4YHnoImpp0nXOH\nBQvgt7+Fszt3PYd3Ts2tHqoaWkxpizXrHi7S/q12hfvc/OSVwN8BFxrDDrjZlKr/OtNjRo+gQfn5\nDR6ScNN46nHNpoH0vPTM7vlw6oE33ggVFXDbbZAfYixhUoKL71061HBQPpzZzxRvAbq+jPSj6mH1\nbAFm3w/NG9eXX88fQ2qS29T9tnO7NCYPzo34sxjeJ5OiJz/pMuBowfj+3KQjSVEs6612qjPbMb7A\nyD2XtIUJIYQQsSFB+TGiaVoqsAq42GdxPTBTKfWJgV1v6/T4Ap3bXxRif52f8w3Kv4Awg/I1TRsO\n5PosqgcC3dFa+ZqEiIqysjKys7M5WFUVdL1+gAYEnvTxTJs2bWLTpk3cfffdTJgwoSNA/+wADY/d\njVODWmPdodmeyWWZn6wxvo04ZnY6OS34IFokE6poJw1lxsRrx3isrw/dkZTLzhDLz0m+I8ZJ2RaY\n3YKI4oHMrhOalGvRFazMs1Pm+XA5KUipPfjeN+t9cXkx+6v2W3YOZolF8H1n4bYdxYpd6heShEKI\n2LJTkJ+ROtazb6/Hs/lV3n/z1TMy4gdSXV3N22+/zdVXX63rOElJSTz++OMUFBRw0UUX4XK5OvYX\nibDa595/H77zHdgV4STnY8bA//4vzJhh6JzMaos18x4ukv4tX7v7tdGmKdwq8PVveKmbTee0UVnf\nrOs7nj09G/7n9OM63Gwhm83ksJkcviQDD6ffm7qt/cmZuhNXoqdjWTj1wNRU78QH4YpGP5MdA3TN\nZPR7Fg6rB+KacV+lAbddfLalddpIP4sdR7oOkKmsb+GZf+3lmX/tDbvvKJb1VrvVme0SX2DWPZe0\nhQkhhBDWc3bkmkNpmpYCvArM8FncCMxRSq02uPsNwAmg56nHZ2maNkwp9WUY5+UCpnZa/GaQTd4C\nrvB5PAP4S5jnOaPT47eVUh5/K2LtaxIiKmbNmkVKSgrNy5bBiy8GXC8ZyAPKIzjG559/zueff85d\nd93F+eefT1FREQsWLOjWGfSdFtQa6w7N5lZP0PcqkkaccDkx+CAaJBOq8CUNZaJdrK8P3ZmUy84Q\ny89JviORk7ItNDsFEcUbmV0nMCnXoiPcMs9pmeftGKQUT8H3eWl53qD7vEIK8ws7/m9l8H1nsWw7\nCofd6hdmBQemJEYnM6sQ8c5OQX56r5UtJw5Sv3MN9TvX0Hz0K9/457AsWbJEd1A+wMKFC3VvE5HK\nSvjpT+HppyPbPjcXHnwQFi0CtzllpBltsWbfw+np3+qsMRn29fEw5HDg92d4qfcaff9rJTx/2+Sw\nrtkeD2xqzqa0by5vH87mC9WD3WTgIXAd1NOYRP32vmSM8Q5YiFY9MFr9THYJ0I0Gvf2okbB6tgAz\nZvT6t4sG8QuL72uj9Vm8sK6UAycbeOqWiUF/47FMnma3xG2xji+I1j2XtIUJIYQQ1pGgfItpmpYE\nLAe+5rO4CZirlHrP6P6VUq2apr0G3Oqz+N+Au8PY/ArOzHz/lVJqS5D1VwC/93k8V9O0HKVUZRjH\n+qafffll8WsSImrcbje9J04MGpQPMJDIgvJ9bdiwgQ0bNnDXXXcxbtw4FixYQFFREcOHDze4Z+dx\nQlCrHTo0m1s93PH8Z2E3KobbiBOueJn23gjJhCoCkYay7ssO14fuTMplZ4jl5yTfkchI2RY+OwUR\nxat4nV0nUlKumU9vmXfLBYMck3m+XayClHyD74vLiyk5ViLB91EW67ajUOdmx/qFGcGBALMe/RcL\nJsigRREfrJzJwi5BfuHWsVqOl1F3KhC/pTzy+gDAq6++SkNDA6mpNqzr/uMf8N3vwuHD+rdNSIDv\nfx/+67+gRw/zzw1jbbFm3sN5Wj00flHLt7/K5uZLJ7K0+XiX/q3s1AQaWzw0tfrPe7ejoC1oUP6A\nchdpjbB2zwnuf62YB+eNDrju0aPw7LPw1FPw1VcJQOB1/anZOIj00QeYcW70gtWj1c8U6wDdaAun\nH7VPVorfjOyhxGq2AKMzet18wSDzTkaHaH0WH31ZEfI3HsvkaXZM3Bar+AIr7rmkLUwIIYSIPgnK\nt5CmaQnAEuBKn8UtQJFS6m0TD/U8ZwawL9I07XdKqeMhtvuZn/0EpJTap2nax5zORJ8K/AC4P9h2\nmqZNByb7LKrEO3NAMJa8JiGirSE/P+Q6BXinhzDLF198wRdffMEvfvELRo4cSVFREUVFRYwaNQpN\n6z6d5HYNarVLh+b9rxXrnhIxnEaccDW3eiivbjS0j1gEH5hJMqGKUKShrHuxy/WhO5Ny2Rli+TnJ\nd0Q/Kdv8CxSYZJcgItF9SLlmrkjLvBsmDeCl9WW6jxergJdoBynFW/B9YX4hI3uNpDC/kMI8bwC+\n3YLvA4l121Egdq5fmBEcCFDZIIMWhfPFYiYLuwT5BapjKaVoOVZK/c5/eQPxj5mXGbm2tpY33niD\nBQsWnLHcykERXZSXw513wt//Htn2U6fCn/4EhdZkH4+kLdbIvZfLAwOPuhhe6ubkzV+xZm0NbdVt\nAAz4yQDu/a3//q1fv7E94HVmxwAPM9cFOSYaw8rcfHFOGy+sK2XR1MFn/A49Hnj/ffjLX2DlSmgx\ncKvQfDSbS3qey19uGRzVa5jRYOxA/UxOSABmVLB+1DaP0lXfgtMDcWNR7thxRi89An0WdU2tfO33\nqyPap7/fuK9YJk+zc+I2q+ML7HrPJYQQQgh9pFfOIpqmuYEXAN+5AluB65RSq8w8llLqfU3T3gcu\nPbWoF/CkpmnXKaX8DpXXNO1O4BKfRceAP4RxuJ8DH/s+1jTtdaXUZwGO0xN4ptPih5VSVcEOYvFr\nEiJqWtLTaU1JIaExcPDxL2+/nYzWVlauXElVVdCfhm4lJSU88MADPPDAA5xzzjkUFRWxYMECxo8f\n320C9O0W1GqHm+v2DpFIhGrECYfezlN/7NBIZpRkQhVC+LLD9aG7k3LZGWL5Ocl3RD8p284UKjDp\nhkkFtggiEt2HlGvmirTMu/78AUwflmd55nkjzAhSqm2upaSihJIKb9B9cUUxJRUljg6+bw+6d1rw\nvT+xbjsKxu71C6PBgZ11l0GLIn7EciYLuwT5+daNlFK0VOylfsca6nauofWEsVmKglmxYkVHUH4s\nBkV0UApeeAF+8AM4cUL/9r16wW9/C7feCjbvx4p0IMj17yUxfUsCqc3e11fHmZPSV37ofeyvfyvY\ndWZX/zbaNIVbBX7fhpd6g/IBXli7n3tnjaS8HP761/as+LpeSlCJu4eSFOXolGgHY9s1AZiZ/H3P\n3C5N90DcWy4YxMNv7YhNuUPsZvQyU+fP4perSgztr/03Hki0BrWEtW0Mjx0OK+IL7HzPJYQQQgh9\nJCjfOv8HXNtp2c+BTZqmDdK5ryNKqVDphH8KfAoknXpcBCzTNO1OpVRHqiVN0zLxZpO/p9P29yil\nQs57pZT6l6ZpS0/tn1PHe0/TtG8DS3wD5jVNmww8Bwzx2cVXwKOhjmPlaxIiqjSN+vx8skoD31CN\nSE/n2f/9X5qamnj33XdZsmQJK1eupKbG3K/vrl27eOihh3jooYcYNGgQCxYsYMGCBUyePBmXSzp0\nrGCXm+tIz6Fj+xCNOKFE0nnqy26NZJGSTKhCiHZ2uT50d1IuO0MsPyf5jugjZdtpegKThvfJNBSU\nH61MYSI+SblmHiNl3ssbynjnR9Po3yM1KpnnoymcIKX24Pvicm/QfXGFNwC/tMq87MBWicfg+0Bi\n3XYUiBPqF0aCAwOJ50GLIr7YYSYLOwT5pSW5aTqyuyMjfuvJw4b3GUhqaiqzZ8/m2muv5aqrrorp\noAgADh6Eb30L3ngjsu3vuAMeeghyc807pyiKdCCI0ugIyPenZmMNrVWtJGR3rWcHu840JsO+Ph6G\nHHafsbwy3cOOAg87B7SxbZA3IF8p+OvSejY/p/jHPzRDWfE76zGgjgd/nsotN1tTX7UiGNtuCcD0\niiR7fbgDca89fwDPfbKPr//Rf0b3qJc7PucbzRm9rNbmUSzbaGwg19KNB7j7qhEBP+tYzjDg9NkN\nzGDXey4hhBBC6Cc9JNa5xc+y35z60+sS4MNgKyilNmqadhuw2GfxXGCWpmnrgTK82ebPB7I6bf4n\npdRfdJzPN/EG2p936nEW8BLwG03TNgPNwDBgVKftTgIzlVL14RzE4tckRNQ09OoVNCif/d4MYMnJ\nycyaNYtZs2bR2NjIO++8w5IlS/jHP/5BbW2tqee0b98+HnnkER555BH69evH/PnzKSoq4qKLLsLt\ndofegYiIHW6urWjE8T1W50a+/cfrDL0Pc8b25XfXjLVtI5kedplOWQgRe3a4Pggpl50ilp+TfEf0\nkbLNS29g0o4jxgZnRztTmIgvUq6Zx2iZt2RDmeHM87HkdmlkprZRVrudjw86P/g+Pz2/I+i+PfA+\nXoPv/bGy7Ugvp9QvIgkODCXeBi12V5EERTpJLGay6PyeDsxNj0mQn1KKDRs2sHTpUpYuXcqRvebN\nmNFZcnIyV111Fddddx2zZs0iPd17zjEdFNGeHf/f/x0qK0Ov39no0fDkk3DhhcbOIwYiGQiyo6CN\nKzckBl7BA1X/qiJ3pv/BCffNLuSr8lrW7u06E8GOgjZyqzV2FHjYUdDGjgFtHOmp4FRR01aXRO26\n/tRuLqD1ZDp7dJ15EO420s89QsZ5+0nud5IDvQtIT7dmMFm8BWObyYxZM4INxG3zqJgPxvJlxoxe\ndlHLkNr6AAAgAElEQVRe02joXh28AyLKaxqDDiiJ5QwD8TC7QaTsfM8lhBBCCP0kKD+OKaVe0DQt\nCW8m+oxTixOAQC0Y6tS6/6HzOHWapl2FN1j+Mp+nBpz68+cr4Aal1E6dx7LkNQkRTfX5+cFX8BOw\nn5KSwpw5c5gzZw4NDQ289dZbLFmyhNdee426ujpTz+/gwYM89thjPPbYY/Tu3Zt58+ZRVFTE9OnT\nSUiQy4ZZ7HJzbUUjTrBGvn45xjKJ5Gcmx01DqV2mUxZCxJZdrg9CymWniOXnJN+R8EnZdprRWaL0\niJdMYcI6Uq6Zw8wyL5zM87FW01TD9mPbKS73Bt23B+DHS/B9YX4hvdJ6xfrUYsqqACC9nFS/0Bsc\nGK54GbTYHZkRFGl3Vs9kEew9nTuuH5MG9WT9vq4By4FEEuTX1tbGp59+yvLly1m2bBmlwZIzGZSa\nmspVV11FUVERM2fOJDMzs8s6sRgUAUB5OXz3u7B8uf5t09Lg/vvhBz+AxCBB6jbkafFQv72ewWP0\nZ3ve1b8Nj6ZwqcDXo8oPKwMG5ScluPiv2YVc9ejHXZ5beXELr0xv6QjCB1AejYY9edRuGUDD7nzw\nmNfH4s6qJ3NcKRljynCnN3cst3owWTwFY5shGrNm+Jst4L/+sS025U4ITrivCqWuqdWS/cRyUEt3\nHlBj13suIYQQQkRGoivjnFLqr5qmfQQ8gDervL+7Sg/wPvBrpdQHER7niKZplwPfAr4HBLprOgw8\nD/xSKRVRJLFVr0mIaGnIC5FF61Sm/EBSU1OZN28e8+bNo76+njfffJMlS5awatUq6uvDmngibEeP\nHuXJJ5/kySefJDc3l7lz57JgwQIuu+wykpKSTD1Wd2OXm+toNuKE08hn9D2Il+CsdnaYTlkIEVt2\nuT4ILymXnSGWn5N8R8IjZZuXkcAkveIlU5iwnpRrxkWjzPMX8GK1eAq+d6kcEj0FJKkBJHoGkqgK\nSPQM4NIBQ3lqYXSydDqZVQFAejmtftE5OPCVz8uoajD2nsRbu5hRTsg6H42gSLuyaiaLcN7TZz/Z\nB8A5+RnsKg89C7Ge9765uZn333+fFStWsHLlSsrLy0NuE6m0tDRmzZpFUVERV155JRkZGQHXtXpQ\nRIfly+E734GKCAYiX3YZ/OUvMHiw/m1jwNPioeazGio/rKTyw0qq1lThafRw8YmLdWd7rk+BEwVu\neu33BFyn8qPgMw70SPc/iKGlUzRI1adDqNk4iLZaM2e3UqQMriBzXCmpQ46iBfjpxGIwWTwEYxtl\n1awZMSt3dLDDfVWk0pPNCe0KZz+xHNTSXQfU2PWeSwghhBCRkaB8iygVZGh79I+9B7hJ07R04GKg\nP5APVAKHgPVKqcMmHEcBfwb+rGnaSGAU0BdIOnWcPcBapVTgFoXwj2XJaxIiGup7hciudeIE1NZC\nkAbVdmlpaSxYsIAFCxZQV1fHm2++ybJly1i1ahW1taEbl/U4fvw4zzzzDM888wzZ2dn86le/4vvf\n/76px+hO7HJzHa1GHL2NfJGKh+AsX4Pz9GfRaSeZUIWID3a5PggvKZedIZafk3xHwiNlm5fRwKSc\n1EQqG0IHHzo9gEvElpRrxjm9zKtpqqGkoqQj6L49AN/ZwfcFJHoKOoLv3WT7Xd+KLJ1OZGUAkB5O\n/a21Bwd+86JBXPywsXxC8dYuFimnZJ23KijSDqyayULve7qrvJbzB/VgVL9sVmw6GHGQX11dHW+9\n9RbLly/n9ddfp6qqKrwXFQEtKZWRky/hlz9axNe//nXS0tLC2s6qQREdTp6EO++ExYv1Hyw7Gx55\nBG67DTR7B0m3nGjh0J8PnQ7Cr+va5V21porcK3N1Z3se3ZTC4T8eDLhOzec1tFa3kpDl/3qan5lC\nTlpiyAFrrdWppgXkuzMayRhdRsbYMhKyG0KuH8vBZE4OxjbKqlkzLC93uplwf+PB5KQlkp8Z/u8/\nloNautuAGrvecwkhhBAiMnJF7kZOZaZ/26JjlQAlFhzHstckhFka8vNDr1RaCiPDb3ho8yiqW12M\nm/Z1Lrp8Jk8nwnv/fJelS5fy6quvmt4oXFVVRW6u/2kyRXjscnMdrUacSBr5IuX04KzO9GbRAcmE\nKkQ8scv1QZwm5bIzxPJzku9IaFK2mROYVNnQYkoQkRChSLlmjFPKvPbge9+s990l+D4Yq7J0OokZ\nbUdZKQnkpiebeFbO+a0F0tjSZsp+4q1dTA+nZZ23KijSDqyaySKS93TDvpMM653J5/derivI78SJ\nE7z22musWLGCt99+m8bGRl3H1UNLTidt6CTShl/MFZdfzv/dfpGu765VgyI6vP22N6D+0CH9B7r6\nanjiCejbV/+2saDB3nv2ggq8yskPK8m9Mld3tudjCccCBuUn9UuixyU9ggblu10aC8b3DznrVcbo\nMmq/MDK71ams+GNLSR1ajuYK8mZ0IoPJrGdV9nrLy51uKNzfeDBF4/tH9P7GclBLdxlQE4tBF0II\nIYSIHuf2ZgohhEOFzJQPYQflB88CNJT7f/8nnn76ad577z2WLVvGypUrOX78uJHTByA5OZmZM2ca\n3o8/Tphm2Ax2ubmORiOOkUa+SDg5OMufpASX7iw6se5UFEKYxy7XB3GalMvOEMvPSb4joUnZZk5g\nEkQeRCSEHlKuGWO3Ms9f8H1xeTFl1WWm7N9KvdN7MzJvJIV5hfROHcrj7zZGHHwfjGTpPJMZbUfV\nja1M+vU/Tc1abrffml5OH1QQa07LOm9VUKRdWDGThRXv6aFDh1i5ciXLly/nww8/pK3NnME0/rhS\nMkg9Zwrp515EysBxaAmJEdexrBoUQUMD3HUXPPaY/gPk58Pjj0NRke2z4/tK7JFIxtgMar8IPFP1\nv17cz0tTmzqud+Fme86emg0aoCC5fzI5l+SQMz2HnBk5pAxOQQvjfVo4uSDk9TrprCoSe1XTciwr\n7NcNkJfvoWnIV2SGmRU/kO48mCwWrMpeb1m5082F8xsPuv0UIwNyRDTFctCFEEIIIczXPVvrhBAi\nhhp79kS53WjBGnD37w+6D/1ZgL7OlVdeyZNPPslHH33E0qVLWbFiBUePHo3oNVxxxRVkZelrsAvF\nKdMMm8VON9dmN+JYGZDv9OCsQPRm0RFCxA87XR+czOxBflIuO0MsPyf5jgQnZZu5gQdODMwSziPl\nWuRiVeZVN1WzvWJ7R9B9ybGSuAi+L8wv7Ph/btrpWRt/uaqEFE/k73EwkqWzK6NtR2B+1nKn1y+c\nPqgg1pyWdd6qoEi7sGLQSbTe0927d7NixQqWL1/O2rVrDR0jlLSsHqScM4WEIReQUjAGzZ1gSh0r\n2oMiPEpR8ckGsm7/Jsk7Ipg0/cYb4dFHwaGzMWdOyw4alD/goMb/vNf1ehcq23NiTiKFSwvJGJdB\nytnhBeF3Njgvg4WTC4L+PjQNMsaUcfL90LNcaRp8/evwrW/BhKlNTPvdl7rPqbPuOpgsFqzMXm/F\nYCwR3m88kIWTC+Te3eZk0IUQQggRP+SuRwghrOZ2o/r1QysNcsMc5DljWYASuOyyy7jssst4/PHH\nWbNmDcuWLWPZsmUcPOh/Wkx/ioqKwl7X18svv8yQIUOYOHFiR4Oi06YZNpNdbq7NbMQxo5FPD6cH\nZ4USbhYdIUR8scv1wYmiPchPymVniOXnJN+RwLp72WZ24IHTArOEc0m5FplolnnxFnxfmF/IyF4j\nKcwvpDDPG4DvG3zvT7TbHiRLZ1dG2o78MStruZPrF04fVBBLTss6b2VQpF1Ee9BJNN7T999/nx/+\n8Ids3brV0H5D6d27N/Pnz6eoqIhp06ahudym17GiOijC4+HFm+7i9jf/j+Q2nZ9vr17w5JOwYIEp\n56eXp8lD9fpqKj+spPLDSnJm5DDoF4N07aO51cNiTwWXBVnHrTTOOehm6+A23de7vPl5YZ9LcTF8\n8gnccceZy++bXciBkw1B+xDTCw9y8sMR4PF/Tn36wO23e//OPtu7rM0jg8mcxsrs9TIDkHXC+Y13\nNn1YHvfNDj0QR8SWDLoQQggh4ofUaoUQIgY8AwbgChaUHyRTvllZgNxuN9OmTWPatGn84Q9/YP36\n9SxdupRly5axb9++gPtKSEhg9uzZuo4P0NTUxLe+9S1qamro378/c+fOZdacq1m8L42PvzoZ1j5i\nPc2w2ex0c21WI44ZjXx6OD04K1yhsugIIeKLna4PTmH1ID+nl8tmzyRgV7H8nJz+HYmG7l62mRGY\n5MtpgVnC+aRc08eMMq+6qZqSihJKKrxB98UVxZRUlDg6+L496D7c4PtArGh7kCydXUXSdhSMGVnL\nnV6/cPKgglhyWtZ5K4Mi7SLag06i8Z7m5ORELSD/rLPOYsGCBRQVFXHxxRfjdrvPfN7kz9XsQREt\nbQqA5BMnOO+xx+i9aZP+Hc6dC3/+M+TnR3xOenmaPFSvOx2EX/1pNZ5Gz+nnGz26g/Lvf62Yla6T\nXEIaLgLfiw0vdbF1sHe2bDNn6Th2DF56CZ57Dj7/HFwumDkT+vY9vU5SgounbpkYtJ3MndZC2jlH\nqN95ekOXy5sVf9EimD0bEhM7bSODyRzHyuz1MgOQdcL5jfuKt4R38U4GXQghhBDxQYLyhRAiBjz9\n+wdfIUDAfrSyALlcLqZMmcKUKVP47W9/y8aNG1m2bBlLly5l165dZ6x72WWX0aNHD93H/+CDD6ip\nqQHgwIEDPP744zz++OO4UjJJHTqJtGEXkHr2eLSEpKD7ieU0w9Fgl5trsxpxrOy0tkPnqRAiduI9\nqNgu1wcnMDaLUPfqjIj2TAJChNKdyzYzAhh8OS0wSwgjnFrvC7fM81BPi1ZKs6uUPj2PsaH2GAV/\n2C7B90FY0fYgWTq70tt2FA4zspY7uX7h9EEFseDErPNWBkXaSTQHnZj1XlTWN3fUp8877zwGDhzI\n/iAJk/QYOHAg8+fPZ/78+Vx44YW4XNFrf/BXVzIreLq51cN9r27j5i/XM+7xx0murta3o+xsePxx\nWLgQNGvrb1/97CsOPhp4huia9TW01bXhTncHXMdXR/9cKpTlexhYHni7PifO/LyNXO+am+GNN7yB\n+K+/Di0+Mc8eDyxeDD/72ZnbJCW4eHDeaBZNHcwLa/ez1E870PQbWnj+v72Z8G+7Db75TQjVdSmD\nyZzFyuz1Zg3aADhc1eC4ez+rhfMbLxrfn4XS1us4MuhCCCGEiA/SsiyEEDGgBgwIvkKAhl8rsgBp\nmsaECROYMGECDz74INu2bevIoF9cXExRUVFEx16xYoXf5Z7GGuq2vUfdtvfoe8efSezZL/TriME0\nw9Fip5trMxpxrOq0tkvnqRDCet0lqNhO1we7M2sWoXhm9UwCQgTS3cs2owEMnTktMEsIvZxe7+tc\n5vkG37dopbSc+rfNdaxjmxO1UFIbw5MOk1v1INEzgEQ1kERPAYlqALdNmsqv515oyfGj3fYgWToD\nC9V2FAmjWcudXr9w8qCCWHBi1nkrgyLtJJqDTsx6L+5/rYTnb5tMUoILTdOYN28ef/zjHyPeX2Fh\nIfPmzWP+/PmMGzcOLcpB6MHqSl8b0dvQvtuDp3+99DMuffxBJn/2pv6dXHEFPPNM6EjvKMmZnhM0\nKF+1Kqo+raLn13qGtT/f7/LOAWcG5Zdne9hR0MbOAR52FrRxLFt13V7H9U4p2LjRG4j/0kveDPmB\nPPcc/PSn/sc8nN0rnXtnjeTuq0Z0GbiB0rhtBkyd6s2SHw4ZTOYsVmevN9rmUdnQwoRfvevIe79Y\nCfYbl8EMsWFGUgEZdCGEEEI4n6ZU15tCIUR0aJpWCGxrf7xt2zYKC7tn43l3U11dzQcffNDx+Ip9\n+0j94Q8Db+B2Q2MjJJxuXG7zqC6NEXrlpCXy+b2XR3wjvmPHDs466yyys7N1bdfW1ka/fv04evRo\nwHUScwvou+iJsPe56OKzLZ1m2Ap7j9XZ6uY6koYDM76nodit81QIYY1QQcW+4q2csNv1IZjOdZ5L\nLrmErKysqB1vT0Utlz7yUcTbf/CTGbZ576JF70wC4A3y6Y4zCQhrOalsM9M9K7aall3407svlUz5\nIi45pd4XqN5T1VjF9mPbKS4vpriimJKKErYc3cbh2sBBYXblL/g+0VOAm671O6NtTnpEu+0hHtuc\noqW51cPEX71LdWPkA8XM/O44tX5hRrnn1FlF9NpdXsPXfr/a8H7++eNpDM3PNOGMQrNDu36sROt+\nNNh72lpVTuOBYjIKLwnreAsnF3QM2F+9ejXTp08P+1wBJk2axPz585k3bx7Dhg3TtW2k9JQZkWh/\nTw68v4bGa65n6Al9s1PUJybz4CW3s2jxw5ydlxGVcwxH87FmPsn7JOg6BfcUMPhXg0Puq/N3buQ+\nF1NKEthR4GHngDaO+wnC76zz79hfuV1+VGPxYm+gfXFxGC/ylA0bYOLE8Nc3QtqZnOWXq0oMBcrr\nrReb2ebRWby1+Yv4Es2kAt2lnm8XVvdxCSGEME9xcTGjRo3yXTRKKaXjzso8zkqrIIQQccITKlN+\nWxscOgQFBR2L7JAFaPjw4RFtt3bt2qAB+QCpwy7QtU+rpxm2gt0yGrhdmu7vihlTVI7qm8WBygZH\ndZ4KIaJLb2fPC+tKOXCyIW46e+x2fbATK2YRcjqZSUDYVXct2+6bXUjZiXpW7wqS6jAMksVZxCsn\n1fuqmqrYWbeT0sZSyhrLeGz5Y+w8uZMD1foC1+wgP6031TV9SFQFp4LvC04F34cfsGpl5mkz2h6C\nac8OLEI7XtdkKCAfzP3uOLV+YSQbptNnFdHLiVnnzSizisb3t/V3OJBozWTh+54qpWg++hUNu9ZR\nv3stLeXe9zmlfyEJ2fkhj+k7K+9FF11EXl4eFRWB6yFut5tp06Yxf/585s6dS3+Ls8BHEhCtx/Rh\nedw3ayQ89hhn/fjHuFv1lfFb+gzlh7N+wp7c/qSuKzXU5tJyvIXKjyupWl3FoAcGkZCh73eb1CuJ\n9FHp1G2rC7hO5YeVYe2rc/9cySAPJYOadZ1P+/WuobntjHLb0+ymflcfmnf0p3ZPLsqj/7f+3HPW\nBeU7fYaa7sZo9nq99eJIZgAKl9Pa/CWQunuwYobaSPrrhRBCCBFbEpQvhBAxoEIF5QOUlp4RlF/X\nZKyDy+z96LFy5cqQ66TpDMqvrG/haHUDvdISSEpKivTUoirSBhen31wbbeR77MbxFPRMk8YqYTpp\nBHUuCSr2cvr1wWxtHsWyjcaC3uJxkJ+v9gCdSPgGJggRTd2tbEtKcPH0recz5/F/seNITcT7cWpg\nlhCh2LHeV9VYRUlFCSUVJRRXnM5+78Tg+z4ZfSjMK6Qwr5CReSMpzPf+e6Im0ZTM01a2ORltewi2\nX6n/hM+sz7y6ocXU+oBT6xd6BhVYEQBkR/mZKeSkJRrOOm/14EargyLtxMigk0BaW1sZ2LCbE+/+\nhfpd62mr6Vp3qN+9jqwJs8PaX/uAfbfbzdy5c3nqqafOeD45OZnLL7+c+fPnM3v2bHr16hXWfqMh\nkrpSuBZOLuC+af1JuuE6WLYMt45tPWg8OWUBf7h4IS3uREB/m0vz0WYqV1dS+VElVR9VnRFM3/PK\nnvS8vKeelwNAzoycoEH5NetraKtrw50e/NWadb178PXtrNpyGNWm0bivF7Ul/WjY1RvVYixk46WX\n4JFHwKqusmj8rkV0DM7LYOHkgojaByOpF+sdtKGXE9r8u9uAye7MSUkFhBBCCGEtCcoXQogY8IST\nPWX/frj44o6HTswCBKCUYsWKFUHXcWflkdR7iO59f7F5K9fPvpwrr7ySOXPmcOWVV9Kzp/6GWbN1\n9wYXsxr5nNh5Kuypu/8mnU6CikUgdphFyO5kJgEh7CkpwcUTC8dz6SMfRbwPJwdmCRFIrOt97cH3\n7UH38Rh83zPVf5tJU3ODKce1ss3JSNtDINOH5XHf7ELT9tcdmPWZX/Pkp1wzcYDcn58SalBBdw4A\n+v/ZO/PwJs5r/38kWbblncUGDDZgdptA2ANJ2MHsJM12U5Kmve3N0qRp0yVtljZJ2/Q2TW/apk3S\nJukvSds0+wZmNVughCVACGD2xdiAwTbGu+RF0u+PiTdsyRppJI3k83meeWxLo3dehDTzzjnf8z2h\n6jofaFGkHtGyk0VDQwP3ffN2amtrXe5jPe65KL+1ePzGG2/klVdeIT4+noULF3LjjTcyf/584uM9\n7x7jL3xZKwHcMq4fuYcvuhZPFxyFSRPg5ElV456P78kPF/2QHemj2jzuaczFUedg95jd1B52/f9Z\n/mm516L8c3855/J5Z4OTiu0VdJ/tfmwtrndOJ3yw2krNoSxqjvTBURvl85hNXLoEq1bBDTdoNqRH\nhGqHmnDCEyMib9zrfVkXe1K0MWdEL97b4919ll5j/l21YLIro0dTAUEQBEEQ9IGI8gVBEIJBbCz0\n6KFEylxx5kybP0PVBchmszFnzhxqa2spKirqcJ+YIddgMKgP0G3dsIaqqireffdd3n33XUwmE9df\nfz1Lly5lyZIlZGRk+Dp9VUjApYVAB/kEoSPkOxkeiKhYcEUodxEKBNJJQAgEod6FJpjzF2GWILQn\nUOu+jsT3ecV5nKtyLdjSK33i+iii++SsZuG9O/G9K0I15uRN7MEVck/oHVp8dgAqbY1yf66Cri4A\nClXXeYmXKmjRycJisTB37ly3HXpthQdw2KoxRsd1Ol5r8fjMmTPJyclh9uzZREVpJ5rWAl/XSokW\nM3sen9P+HsgA/PWv8IMfQH29qjFzhl3Ho/MeoNLF++xJzMUYZcQQ4f4+rOLTClXzaiJxamKHj1uG\nWEiankTS9CTix3RecOHL9a6hLJaavFRqDvWlsVz7+7j4eLj1Vhik3vdKM0K1Q00oo8aISK17vVZr\nMXdFG79ZddinsfUW8+/KBZNdlWCbCgiCIAiCoG9ElC8IghAs+vd3L8rPz2/zZ6i6AFksFl566SVe\neOEFdu7cyccff8xHH33E8ePHm/eJGTpZ9bhJMWY2rFzV5jG73c7mzZvZvHkzDz30ECNHjmTJkiUs\nWbKECRMmYDT6L7AhAZe2BCvIJwhNyHcyPBBRseCOUO0iFCikk4DgT0K9C41e5i/CLEFowR/rvtbi\n+7ziPA6VHury4ntXhGrMSW3sITLCSH2jo/nvNu7AOr5u6RktPjtXIvfn7hEBUOgWN0q8VB2dFdAu\nXbrUrSgfhx3rqT3EZk7z6HhN4vGoqCgWLlzo09z9gZZrpTb3+JWVcPfd8M47qsaqjrTwxOx7+WDk\nTHBjuORpzCVxaiI1B2pcPl+5qxK71Y7JYlI1z8jkSGJHxuKodygi/GnKFtVXXcGF2uudvSaSmsOK\nEL++KEnVsTzBYIA5c+CuuxR3/JgYzQ8h6BRvjYg8ca/317r4yqKNcIz5d/WCya6ImEkJgiAIguCO\n8FQfCIIghAIDBsDeva6fP3Wq3UOh6gIEYDQamTx5MpMnT+a3v/0tD764gn++8x51BfuJ6qdeVJI9\nIIrf7drldp+DBw9y8OBBfvOb39C7d28WL17MkiVLmDVrFhaLtuIyCbi0J5hBPkGQ72R4IKJiwR2h\n6ugaKKSTgOAPQr0Ljd7mL8IsQWjBl3WfgxoajAUU1hVwX84KzlQeC2nxfVZKFpk9M8lKySIrOYsR\nySM0E9+7I1RjTmpiD+ndY/zaISXUO8h4i6+fnY6Q+3PXiABIIVSLGyVe2p66ujoMBgORkZGA5wW0\nixYtwmg04nA4XA1N7fEdHovy9V6w75cY2Zdfwi23QCsDJU842HcY9y/8EWe6pTY/ZnBCn1IDUQ0G\nTqcq/ydqYi5J05I4/8J5l887651U7qyk2/RuquYKMHbHWEyx6sT8HdHZ9c7ZaKTmSB9qDqViy08G\np/ZrgBEjFCH+HXdA376aDy/oHC2MiNy51wdq3RpuMX8pmOx6hGNhiSAIgiAI2qLvCIMgCEI4k5Hh\n/vkORPmh6gJ0JQaDgYduncmKQhNM+S+vxkgs3a9q/wsXLvDKK6/wyiuvEBMTw9y5c1myZAmLFi0i\nOTnZqzk0IQEX9+ghyCd0LeQ7GT6IqFhwR6g6ugYK6SQgaE2od6HR6/xFmCUICp6s1xzUUG8soMFQ\nQEOrn3ZDSxfCV77w5yy1oyPxfWZyJt0s6oVmWhHqMSdPYw/+EO7opQOLWrQqIvDls+MOuT9vjwiA\nWgj14sauHi+9ePEiq1atIicnh3Xr1vH666+zeOmNqgtor732WrZu3eryONZTe3DaGzCYzG7nEwoF\n+5rGyJxOePVVePBBsNk8fq3TYODYzTdzz/Cvc8FqZuB5I8POGhl61sSQsybirQaO9rPzv8uUMdXE\nXJKmdu4mX7GlwitRvhaCfOj8eud0GChbNxJng7ZxFJOlnnu+Zeab3zQwfrzbxgRCmKOlEdGV7vWB\nJNxi/lIw2fUIt8ISQRAEQRC0R7LrgiAIwaIzUX5BATQ0gLltwDhUXYCuxNdk72d/f8nrY9fW1vLx\nxx/z8ccfYzAYmDJlCosWLWLRokVkZWVhUBnVlICLZwQzyCd0LeQ7GT6IqFjojFB1dA0E0klA0JpQ\n70Kj9/l3dWGWILRerzmopt5Y6FZ8Hyo0ie+bRPd6EN+7IxxiToGMPeitA4un+KOIwJvPjifI/Xlb\nRADUlnAobuwq8VKn08n+/ftZsWIFOTk57Nq1C6fT2fz88uUrWF6RprqAduHiJS5F+QZzFNEDRuOw\nVmOKc3/dDYWCfa1iW3ENNrjzPnjzTVWvsyUlseeHP6TcPpZv/iOKtLMmohvav2cZRUbMDdBgVhdz\niewViWWYBetRq8t9yj8tVzVnf+DuemeMtGMZfJHawxpY2BsdWDKKiRt5DsugYh7/+fQuca4QXBNO\nRkThFPOXgsmuSbgVlgiCIAiCoD3BX6kKgiB0VQYNcv+83a4I86/YL9RdgFrjbbL3xzMH0Of29V3x\ndNEAACAASURBVJrMwel0sm3bNrZt28YjjzxCenp6s0B/xowZREe7F6KFU8AlFNqsh8IcheASTt9J\nQUTFQueEuqOrP5FOAoKWhHryN5Tm31WEWYIAUG4r51DJIfKK8zhYkscly1asznzshrJgT0013c3d\nGdVnFKP7jG4W3utZfO+KcIo5+Ru9dmBxhz+LCNR+djxF7s/bIgKgjpHiRn1itVrZtGkTOTk55OTk\nUFhY6HLf9z9eTs9eX8Ng9NzR/NNjJcSkj2jzmDE2iZhBE7EMuYbo/qMxmqM8GisUCva1iJGNqzpH\nr9lT4cgRVa/bPWAUpU88RF23bkTsgCGnXcsLzHYDg4qMHEl38OrWU6quJUnTkjoW5Zsgfnw8idcn\nqpq3P+jsehc7osgnUX5k6mXiss4RM7wIU0x98+Phdt4W1BNORkThFPOXgsmuSTgVlgiCIAiC4B/k\nKi8IghAsOnPKBzh1qkPxfji4AIH3yd5NG3Kpq6vzy5wKCgp48cUXefHFF4mJiWHWrFksWrSIhQsX\n0rdv+2BqOARcQqHNeijMUdAH4fCdFFoQUbHgCeHg6OovpJOAoBWhnvwN9fkLQqjTWnyfV5Kn/F6S\nx/mq8+131vmyLTU+tdnxPiM+g9r8WtKi04iLiGPGjBkkJCQEe4o+Ey4xJ3+j9w4sVxKIIoLICCO/\nXDqSpVen8u7uQtYcvEB1nd2Xacv9+RWIAMg9UtwYfM6fP8/KlSvJyclh/fr11NbWevS62srL1Bcd\nJ6rvcFXHW10Ac+YvYuyoLIqSRrL5ciIGg7rCp1Ap2Pc1RnbL/lx+s+FvGOptnr/IYMD+2OO8kTGb\n2d0uA2DP7Py8PrTQxJF0h+prSdLUJIpeLsIQaSBhYgKJ0xJJmpZEwuQEIuL0c95yt1ayDCzBGN2A\nw2buZJQWIrpXE5t5jtjM85i7dfyd8eS8LYZG4Uu4GRGFU8xfCia7JuFUWCIIgiAIgn/Qzx2sIAhC\nVyM9HYxGcDhc73PqlNshwsEFyJtkb3Z2NsePH2fFihV88sknbN26FYe799FLamtrWbFiBStWrADg\n6quvbnbRnzBhAkajMaQDLqHQZj0U5ijoi1D+TgodI6JioTPE0dU10klA0IJQT/7qcf4ilhDCFVXi\ne52TGp/a7HiflZxFVkoWI3qOaON8X1lZyabSTX6bQ7DPFeEQc/KWzt77UOrA0oS/iwhcmSlogdyf\ntyACIEFvOBwOvvjiC1asWEFOTg579uzxeqzak7tUi/IBpn73GR5flKm6+AhCr2DfmxiZpd7Gr3Jf\n4uaDG9QdLDkZ3nwT05w5PHm5gv9s2QyAM8HJxZ4OepW6jqkMLzSxHOU8peZa0n1ed0ZvGk3CpARM\nFs+7JmiJ0wk7d8Lbb8MNN8D06a73dbVWeqQ0gjfecH8cY6yN2BHnic08T2TvCgxulladnbfF0Cj8\nCUcjonCJ+UvBZNcknApLBEEQBEHwD7K6EwRBCBaRkZCWBmfOuN6nE1F+E+HgAqQ22Tt48GAeeugh\nHnroIS5dusTq1av55JNPWLNmDdXV1X6Z4759+9i3bx+//vWvSUlJYf78+Vw7cy6OOjPGqBifxg50\nwCUU2qyHwhwF/eHvIGiwhTFdEREVC54gjq6ukU4Cgq+EevJXT/MXsYQQLpTbyskrbhHdNwnww1V8\nH2j0dq4Ih5iTp3j63odaBxZ/FhF0ZqagBSJSakELAdCcEb0kjiH4RE1NDRs2bGDFihWsXLmSoqIi\nTca1nthFt6nfUP26pgLarlCwrzZGNri0gBc//i1DL6k8R0+dCm+9BampAJhNbc8Z+f3tbkX5g84Z\nMdnB/pWu3tOCNHMPM92mB34N5nDA9u3w/vvwwQdQWKg8Xl7uXpTfxJVrpdtvp0NRviGykZghF4jN\nOkd0/0sYjE6P5udKuCmGRl2HcDQiCpeYvxRMdl3CpbBEEARBEAT/INFMQRCEYJKRoYkoP5zwJtnb\no0cP7rjjDu644w7q6urYvHkzn3zyCcuXL+fcuXN+mWdxcTFvvPEGb7zxBhhNRKdlYRk0EcugCZi7\n91U1VjACLqHQZj0U5ijoD38FQfUmjOlqiKhY8JSu7Ojqiq4gTBD8S6gnf/UwfxFLCKFKOIrvmwX4\nKcrPpOikYE+tGTlXBA817/3tE9NYfeCCT8cLdAcZLYoIOlpf2x1O1a7UahGRUnt8FQC9t+cskRFG\nOYcIqigsLCQnJ4ecnBw2btyIzWbT/BgNJfk0VhYTkZCi6nWtC2i7QsG+pzGyGw9u5LerXyDKUafu\nAI8+Ck89BRGuJQSn0+1M2mN2+XxUo4GBRUZO9GvpbhzogrTOsNth27YWIf75Dpa3H30Ef/0rRKu8\nDM2cCT16wKVLytt4/YxGDkTvxzLkIkaz+o7PHQk3xdCoaxGubuzhEPMXx/SuS7gUlgiCIAiC4B/0\ntfIWBEHoamRkwCY3rc67oCjfV6KiosjOziY7O5sXXniBL774olmgv2/fPv8c1GHHdmY/tjP7ubzx\nVSK698WSMR7L4IlE98vEYHIdoIbAB1xCoc26HucoDumhgdZBUBHG6AMRFQtq6UqOrp7QFYQJgv8I\n9eRvsOcvYgkhFLhsvdwsvG8W4BfnUVStjfNtIOkb37eN631mcqbuxPcdIeeK4KH2vX9rV6HPxwxk\nBxm7w8kHe8/6NMY/d5zh/T1nKbe2XT/2TojmyIUqX6foFhEptccXAVATcg4ROqOhoYHPPvuM1atX\ns2rVKg4cOBCQ49oK84jLUifKh/YFtOFcsN9ZjCyqoY6n1v+N/9q/Tt3APXrAv/4F8+a1e8rhbOvo\nnt/f7naoKouTpJq273OgC9I6orERtmxRhPgffggXL7rfv7IS1qyBG25QdxyzGR57TGmWfdtt0LNn\nBI99ZObNnV4I8l0IN8XQqGsRrm7s4RLz17tjuuQ2/Uc4FJYIgiAIguAfRJQvCIIQTAYNcv/8yZPg\ndIJBbo69wWAwMHbsWMaOHctTTz1FQUEBy5cvZ/ny5WzevJmGBu8DWO5oLDtHVdk5qnZ/giEyBsvA\nMYqLfsZYTLHt2692GHBxOpUuCnv3wrFjcPy40je1pARKS8FqVaLIdjtYLBAfDwkJ0Lcv9OsHAwZA\nZqayZWSAsSVIFQpt1vU0R3FIDz20CoKKMEZfiKhYEHxHD8IESQSFHqGe/A32/EUsIeiJ1uL7vOI8\nDpUeClnxfYK5F6N6ZzE+dVRIie9dIeeK4OHNe68FgeogU1xl8+kaCFDX6KCusa2QsLy2wedxPcHf\nIqVQxRsB0JXIOUS4kqKiItasWcOqVatYt24dlZWVfj+m2WzG1HcklsFfdaBN6u3VOK4KaMO1YP/K\nGNl7ewqpsDaSceksL3zyW0aU5Ksb8Npr4e23lbxCB1yqqW/zd3mik0vxDnpUKfHPS/EOjqU5ONbP\nztF+dop6OnFecZsfyIK01jQ0wObN8N57ivN9aam617/9tnpRPsBDD7X9W0vhph4NjQT/Es5u7OEQ\n89erY7rkNv1PuBSWCIIgCIKgPSLKFwRBCCYZGe6fr6iAy5ehe/fAzCfMSU9P54EHHuCBBx6goqKC\ntWvXsnLlSlatWkWp2mishzjra6k9uo3ao9sAiOw9WHHRzxhHZJ+h3DF5YEvQ4/RpWL0a1q2D7duh\nuNizg1RXK2J9gI66ASQkwKRJMHky9hkz+WRXtU//Jn+72mjh4qbFHMUhPXTRKggqwhh9ogdRsSCE\nOsEQJkgiKHQJ9eRvMOcvYgkhWIST+F5xvs9iQOJQBiQM46peI5nSfzQ9YtoXvIcqcq4IHr68974S\nqA4ygRL/+wN/ipRCHbUCIFfIOUQ7QrH42G63s2vXLlatWsWqVavYu3dvQI6bnJzMwoULWbx4MTNn\nzWb6n3aEbAFwIGisaqT+Qj0xQ2LaPTewZywPzxvOseJqkj5+n9+s/Qtx9VZV49ct+z5Rrz2r2Lu7\nwFp/xbXEAJ9c20BDhJNj/RxcSnR2/MIrCNQ1qb4eNmxQHPE//hjKyrwfa8UKqKmBWB9Pk1oKN/Vk\naCQEDr27sftKqMf89eSYLrnNwBIOhSWCIAiCIGiPiPIFQRCCSWeifIBTp0SU7wcSExO59dZbufXW\nW5sTECtXriQnJ4cvv/zSb8etv3CC+gsnqPjsbSJjE6g4PJW9/45m1IEDRBw96p+DVlZCbi7k5mL6\n5S/ZHGnhs/6jWTtkMusHT6TCEq9qOH+72mjh4lZe20BRhZV+3donKzxBHNJDH1+DoCKM0T/h6nYm\nCOGGJILCg1BP/gZr/iKWEPzNZetl8kryFAF+cV7z76Eqvs9KySKzZyZZKVlkJWcxInlEyDrfq0HO\nFcEjWIL8QApIAyX+1xp/iZTCiSYBUF2jg/f3eG9uIecQ3wi14uPS0lLWrl3LqlWrWLNmDWW+qJVV\nMHr0aBYtWsTixYuZMGECxlZdXUO5AFhrnE4ndQV1VHxWQcW2Ciq3VVK9v5r48fGM2zmuw9c8/cFe\nsv/8BMv2rVF1rAbiOcIjJI5aRrobQT6AJbL9tWTLaPUCe39ek6qqYM0axQ1/5UolJaIFtbWQkwO3\n3eb7WFoIN/ViaCQEHr26sWtNqMX8Wxfl/eqGLP66+RT/3hU8x3TJbQaPUC8sEQRBEARBW0IzIisI\nghAueCrKHz/e/3PpwphMJiZPnszkyZP59a9/TWFhIatWrSInJ4f169djs9k0PZ4BWAh8v6aS2Tk5\nmo7tCXH1VuYe38Hc4ztoNBjZOnAMb4/KZsPgiTSaPFsa+NPVRqux5/9pK7eOT/Mq+SUO6aGPr+5D\nIowRBCEUCaY7Y0fHtjuckggKE0I9+RuM+YtYQtCScBTfZyVnkZmc2fwzMTox2FMLCnKuCB5avPfe\nEkgBaUp8NEkxZp/NDwKJFGp6jt3hZP3hiz6NIecQ7wiV4mOHw8HevXub3fB37dqF0+mZq7kvREVF\nMWvWLBYtWsTChQtJT093uW+oFwBrQcW2Cs4+f5aKbRXUn6tv93z13mrstXZMMaY2jxfs/JJbH7yN\nrOJT6o5HJof4BXX0gq0V8LD7/XvERqoavyP8UZBWXKw42X/0EaxfD3V1mg7fzNtvayPKb8IX4aZW\nhkb+NF0S/Iee3Ni7Ou6K8m4e1w8DkHv4osvCm/TuMX6J4UpuM/iEWmGJIAiCIAj+QUT5giAIwaR7\nd0hIcG/bcUpdQFXwnbS0NO655x7uueceamtr2bRpU7OLfmFhodfjRgDfBH4KDNZorr4S4XQw49Qe\nZpzaQ0lMEh+MnMm7o+Zyqkc/t6/zp6uNVmNX2Rq9Sn6JQ3r44K37kAhjBEEINYLpzuju2L0Tojly\noUrVeHpPBAWz8CHYhHryN9DzF7GE4A1N4vu84q8E+CWKAP9C9YVgT001/RL6NYvum4T3XVl87wo5\nVwQPLd57bwmkgNRkNPjsQh0IPHEHFtoj55DgoHcX2suXL5Obm8uqVatYvXo1xcXFfj8mQO/evZvd\n8GfNmkVsrGff5VAvANaChtIGSt51/XlyNjqp+ryKpGmtOgi99x697voW6dYaVccq5BZO8T84Udzx\naw7X4HQ6MRhc39ca3TznKVoVpJ0+DR9/rAjxt20Dh8PnIV2Smgq33AK33+6f8b0RbmplaORP0yXB\nf/hqRCT4jidFeU0djG6fmMZ90wZRb3c0xxDPXKrhXzvO+CWGK7lNQRAEQRAE/SCifEEQhGBiMChu\n+fv2ud5HRPlBJSYmhoULF7Jw4UJeeOEFDhw4QE5ODitXrmT79u0eOQsZgTuBXwAe9EYIGsm15dy7\n60Pu3fUhO9JG8tdJN7E5Y7zyOW2FVq42rkRt/nBxU5P8Eof08EOt+5AktbsOXVlcK4QHwXRn9OTY\n3p5L9ZgICmbhg14I9eRvoOcvYgnBHWXWsmbX+1AX35scPZnSfzTj+44S8b0XyLkieATrPQuGgNRX\nF2p/8cn9U4iNipB7MR+Qc0hw0JsLrcPhYN++faxdu5bVq1fz2WefYbfbNT9OR4wbN65ZiD9mzBiM\nRu/WzqFeAOwrCVMSOt2nYluFIsqvq4Mf/QheeIEoFcdoII4j/JRj8VM41s/BsbQ6igYbyfnTVLeC\nfK3wpSDN4YBf/xo+/BC+/FLDSXVAWhrcfLOyXXMNePmR9htaGRr503RJ8C/eGhEJvqO2KO+tXYWc\nL7fxyjfGA/CLTw76NYYruU1BEARBEAT9IHdcgiAIwaYzUf7Jk4Gbi+AWg8HAqFGjGDVqFI8++igl\nJSWsWbOGlStXsmbNGioqKtq9ZgrwZ2BswGfrG9cUHuSawoMcTh7AS9fcwsrh12E3Ku1xfXW18UTU\n5g8XN0+SX+KQHt546j4kSe3wR8S1QjgQTHdGtcf2Br0kgoJZ+KBH1CR//dWK2xcCmbwWsYQAbcX3\neSUtAvxQFd+bnemYnWmYHf2JdCi/G4llbspAHs8O/jk7FJFzRfAIxnsWLAGpLy7U/iIpxszIvklB\nXxuEOnIOCTx6cqF96623WLVqFevWrQuYG77FYmHOnDksWrSIhQsXkpqaqsm4oV4ADFBfXE/ljkoS\nr0/E3M2s6rWRyZFYhlqwHrO63KdiW4Vi4HTrrbBnj6rxz1mG8btrHmHbsGRKE6zQ6tRbUlPnd1MR\nXwvSjEZYscJ/gvwBA1qE+BMntvMo0hVaGBppZbokBBe1RkSC73hblPeLTw5SVGHzawxXcpuCIAiC\nIAj6QiJtgiAIwWbQIPfPi1O+bklOTubOO+/kzjvvpKGhgW3btrFy5UpycnIoPnKE54C7gj1JHxlR\nks/zK57lR1v/yd8m3cR7V8322tVGjaht8ag+vkzbJZ0lv8QhXQBJaoczIq4VwolgujN6c2y16CER\nFMzCB73jLvnrz1bcWhGI5LWIJboWZdaydq73h0oOhZ343hV6OGeHKnKuCB5avPeJlggWXpXKv3fp\nX0DqjQu1P/HV8EErQr17mh7PIaH+nnaGnlxon3vuOXbv3q3JWO4YMmQICxYsYP78+UybNo3oaP9c\nc/Tq/tzRZ9pgd1Kzv4aK7RVU7qikcnsltlM2AEZ+MpKeS3qqPk7itYluRfnmzTk4x/4OQwfGQO54\nbdxi/nf6f1MfYQbad/31t6mIVgVpN94IWn7cBw1SRPi33AJjx+pbiN8ak9Hgs6GRXq7BgjZ4akQk\n+IYvRXlvf16o+jVqY7iS2xQEQRAEQdAXohQSBEEINhkZ7p8vKICGBjCrc1cRAovZbGb69OlMnz6d\nZ6dMofHuu4koLdVkbGtEFKe7p3K6WypF8T0pi0mkMiqW8rxN2M4fIQaIB1KANKOJweZohjbYiHZo\n16a4f/kFfrP2BX607xN6jHtWceRR0btVrahtxf4i+iRGU1Rh83bKLnGX/BKHdAH0mdQWfEfEtUI4\nEUx3Rl+OrQY9JIKCWfgQKrRO/tY3Ovzeiltr/Jm8FrFEeBJO4vt+Cf3ISs5iQOIw3tvBV+L7dIzE\nqB5LD+fsUEXOFcFDi/f+lnFpPL4ok/+Zqi8BaUeodaH2N94aPmhFuHRP09M5JFzeU3fozYU2Ozvb\nL6L8qKgopk+f3izEHzJkiM9jqinW0Iv7c4efaSc88r6FwWeNmOo7fl3l9kqvRfkXXmu/pjTQwCD+\nRr/aD1SNVxkZw8MLvs+aYde63c8bU5FFo/rw6s7O179a3vfdeCM89phvYwwdqojwb74ZRo8OHSH+\nlSyblO7TeT/Y12BBCEWCsX5WE8OV3KYgCIIgCIK+EFG+IAhCsOlMlO9wwOnTSsRQ0DfV1XD//fCP\nf/h0ga2OtLCt/2i2DhzL3tThHE3uj91oarOP0+ng7La3cFz5Yocd6mowAll9RzD76nk82bOChO3/\ngWPHfJiVQo+LZ+H22+H3v4dnnoFZszx6nTeitqIKm1+E+e6SX+KQLoC+ktp6J5Qc8ERcK4QTwXRn\nDGQSKpiJoGAWPoQiUvjUMSKWCF2axPdNovu8kjzyivO4WHMx2FNTTVpCGpnJmWQlZyk/U5SfCVEJ\nAJwormLNti0+H0eS994j54rgodV7rxcBaWe0dqG+7197OHKhKijzWDYpPWjrpHDsnhbsc0g4vqeu\n0JsL7bx583j66ad9Hgegf//+LFiwgAULFjBjxgxiY7X5jvpSrBEs92e3n2kDmK1Ol4J8gModlV4d\nN+HahHaPRXOBTJ4igSOqxjrQaxD3L/0ZBd3cd6P11lTkwVlDWHb9cNUFaXY77NoFvXp1nha7kuHD\nlRSZ2hRHZqYiwr/5Zhg5MnSF+K3JSI5j2aR0r2IWwbwGC0KookVRnrd4GsOV3KYgCIIgCIK+kFWV\nIAhCsBk8uPN9jh0TUb7e2bcPbrvNa+F7vcnMusGT+DhrBlsGjv2qnayb/S+ewlFb7vJ5B3B26GQ+\nHDmL7tcN5PHX/w75+bByJXz4IXz6qRIF95Y9e2D2bMjOVgT6I0e63NUXUVtRhY0lo1NZ/uV5b2fa\nDnfJL3FIF5oIdlLbFwIhlA81BzwR1wrhRH2jg/d2q2973Bpv3RkDnYTSJBHU2AhVVW03m00pfHU4\nlPWQw6Fk5qOjlc1iYfWOc/SpLKE8Oh6rOUp15t6XwodQRAqfOkbEEvrnUu2lZtF9OInvm4T3rcX3\nrpDkffCRc0Xw0Pq9D5aAVC1OpzNogvxpQ5N5YnFWUI4drkWEwTyHhOt76gqtXWidTidHjhxhzZo1\nzJ8/n+HDh6saZ9KkSSQkJFBZqV4EHhERwdSpU5uF+MOHD8egoVo5VIs1PPlMn0h1kFFkcvl85eeV\nOBodGFX+e2KGxhDRPYLGMuXz0TNyO8Md/0tEo7rz9T/GLOTpmd+mLiKy0319MRXxtCCtuhpyc2H5\nciU1UVICP/0p/Pa36o5nMChu+c880/m+11yj7HvDDeGbVnticRZnL1tV3YcH8xosqCeUzHDCHS2K\n8rzF0xiu5DYFQRAEQRD0hWRLBEEQgk3//hAZCfVu7FU0cDgX/Mjrr8O990JdneqXOnv3pvrbdzOt\nchhlMYkev856qvPWxNEDxwKtgjYDBihO/vffDxcvwj/+Aa++6tvna+1aWL8eHnwQnnwSEtoLPnx1\n1E2Jj2LTj6fz5o4zvLO7kCqb7wkwV0k0cUgXmghFYUwghPKhmlQNpqu4ILTGl4Ra03f8vd2FVPp4\nLfTWnTGQSahOE0FOp7KeOXVK2U6fhqIi5bELF1p+1tR4dfz7v9oAbBGRXI6O53JMApct8VyI68G5\nhBTOJyRzPiGZcwkpnE1Moc4c1fx6bwsfQhEpfHKPiCX0QWvxfV5xHodKD4W0+D4rJYvMnorrfVZy\nFiOSR3QqvneFJO/1gZwrPMTpBKsVystbCu2s1rY/m35vbFT2dziUn1f+HhEBZjNPGU2kXzrL/mIr\njSYTDcYI6k1mas3RWCOjqDFbsJqjqYmMxmqOwmkwevTe61lIFcjOR60J9j3ij97bF7ZFhME6h3S1\nwkwtCtAcdTV8ujaH323dxJo1aygsVAqua2treeyxx1SNZTabmT17Nh9++KFH+6empjaL8GfNmkVC\nB3FcLdBrsYbT6aSuoI7KXZVU7arCnGIm/Sfpbfbx5DN9MtUOe1wb6jhqHNQcrCH+6niX+7i6RqQ/\nnI7RbCd573NEvfkXVf++qkgLj8z7Hjkjpnr8GnemIq3naGh0nffoqCDtxAlYtQpWr4aNG9unvlas\nUC/KB9ei/IgImDlTeX7pUujjvkFAWBAZYeSVb4x3G6dtTbCvwaFKMNZzoWaGEyiCubYOZlc4T2O4\nktsUBEEQBEHQFyLKFwRBCDYmk+KWf+iQ632OHg3cfATPaWyEn/wE/vhH9a/t1QseeQTDPfdwsbKB\nsue2qHq57dQet8+b4rpj7qkE1TsM2vTqpcz9xz+G//wH/v53nO+8g8FmU/1PwW6HP/wB3noLnn0W\nli1rdpPVwlG3SdT2+KJMvnntAK57ZpNP44H7JFooO6QL2hIqwphACeW9SaqeLK7mF4uz6BZrDpoI\nRcvzkASlBW/xJaHW2XfcW7xJKAUyCdWcCGpshJMn4eDBlu3wYUWIb7UGZC7RjfX0qb5En+pLLvdx\nYKAwqRcnu/fjRI80TvRI4/KmKHpOmQgW/Tvm+oIUPrknUGIJPYs/A8ml2kstrvfFec2/i/jeNZK8\n1wddVlhltSqFdFcW1TU9Vl4OFRXKz6atUdv1SARwj4r96yOjMXdPwvBcD+jevd1WYo5lS6mddRfq\nKTTGUhzXjUsxiSTERDJ/ZB/mjezNsN7xIX1/5IrhveO5UGlrt969eWw/lgVRQFbf6OBH7+5jxf4i\nr14fCkWEwTiHdMXCTF8K2ar2rqQmbxN1Rcf4jtPR7vk1a9aoFuUDzJs3z6Uo32QyMWXKFBYsWMD8\n+fMZNWqUpm74rtBLsUbD5QYqdyoC/KrPq6jcVUlDccv/XezI2DaifE8/0yf7tv//u5LKHZUdivI7\niw3cNc9Ev/u+Bdu3d3qM1hxKGch3l/6M/O59PX6NK1ORjubY2+Lkkatdj2WzwZYtihB/1So4fryT\n+R5SbvMHDfJ4ugBMmKAI7ouKIDYWFixQ3PAXLICkJHVjhQOREUaevvEqvnN9Bm/uOMP7HXyugn0N\nDlWCIYwPVTMcf6OHIoVgd4XzNB4ruU1BEARBEAT9IKJ8QRAEPTB0qHtRvjjl64+qKrjlFsUpXg1R\nUfDww0qP1lglUBRb51Q1hN1aRd1594Ua0QPGtkmyuAzaGAxw/fVw/fUYnn2W3MWLGb9zJ91Uzegr\nLlyAO++El1+GF1+EkSM1cdRtXVTQJ9HidxfHUHRIF/xDKAhjAuk+5k1SdcfpMhY8vxUInpuP1uch\nQVCDrwk1td9xNXiTUPJ3EsrgdJBRdo7RRcd4sKQGfrUPvvzSq25EgcaIk/7lF+hffoGZTR2N1jyv\nFOBmZcG4cTB+vKIiuPpqMLt2VgwlpPDJM/wpltBDgjoYhKP4Pis5i8zkTL+J793R1ZP39ltocgAA\nIABJREFUeilqCTthldMJZWWQnw9nzrT/eeaMIrIPMSLrbUr848KFDp9PBm76amui0WCkNDaJ4rju\nFMd2Y1Ncd6qSetIvaxDjr8mk1/AMpZNmcnKzyYG/8Ffno2lDk3nlG+MxGQ26+D41odV6OhSKCAN9\nDumKhZm+FLLVl56h7vwRl89v376diooKEhM976QKkJ2d3ebv3r17k52dzYIFC5gzZw7dunkV5fUa\nPRVrnHvhHPk/z3f5fM2hGhqrG4mIi2g+vieUJjipiHWQWOM6rla5vZK+97YI5D2JDZx8413ic54D\nW5VH82jC/p3/4blxd5Cf7/nrOjIVUWMI8PyG49w2agzr1xlZvRo2bIDaWlXTZsUK+MEP1L3GaFQ8\ngRITYfZsiJZGTQAM7BnL44syeWTBCF1dg0ORYAnj9dphJJjoqUhBi+5yvuBpPFZym4IgCIIgCPpB\nRPmCIAh6YNgw98+LU76usJ8vwjY3m9i8A+peOH8+vPACDBzY5mG1AZ2G4tNKBNru2pXHMnBMm789\nCtokJ/NQdTUFKC5xPwS86vS6dSuMGQOPPkrNd77nzQjtaCoqCJSLY6g4pAv+R+/CmEC5j/mSVG0i\nWG4+Wjl7B7NNrRCaaJFQ8+Y77gmdFai5QusklNnewKii41xTcIBJhQe5+vxREupVZvL1jt0O+/cr\n22uvKY/FxMDkyTB1qrJNmhSybvpS+KQOLcUSekpQ+1PQ3CS+zyv+SoBfogjwi2uKNRk/kKQnpjeL\n7oMlvndFV03e67WoJeSEVfX1cOIEHDmibEePtvxeWRns2emCCKeD3tVl9K4ua/vEp8CLrf62WCA9\nHQYMUET6V/7s00eJB/mAP+5rrrzO6OmartV6OpSKCANxDunKhZneFrJZBlxN9RerXD5vt9vZsGED\nX/va11SNm56ezl133UVmZibZ2dkBc8N3hZ6KNRImdrLGckD13mqSpiap+0wb4GSqg7HHOz4fGyIM\nOGpb4vadxQZMDjs/2vpPvrvjfc+O30RsLPztb5iWLeNFFYJ6bw0BGhoMHDnSgz17Uli7rTc/L/Ht\neuSNKB+UJr1Cx5iMBl1dg0ONYArj9dJhRC/orUhBi7ykt6iN4UpuUxAEQRAEQR+IKF8QBEEPDB3q\n/vmiIsWZPb59y1MhcJwqqWbl+59ywyPfJq1ChQNjUhL88Y/wjW906HimNqAT3X8UaQ++he3Mfqyn\ndmM9tQd7ZWtRioHoAS39ZCMjjCRZIjsd99y5c+Tl5QHwe+DPwN3Az1Gc3lTR2Ai//CX933mXsRO+\nzd6+I9SO0IbWRQWBcHEMBYd0IbDoURgTSPcxX5OqHY0XKDcfrZy9g92mVgg9fE2oaVEM4wpPCtQ6\nwtcklMHpYOSFk0w7vYfJBfsZe+4olkb9u+BrTm2tYiW4YYPyt9msdC6aN0/ZRo70u0uuVkjhk+d0\nKFxP9O7/WS8Jai0FzaW1pc2u9+Ekvm8S4GcmZxIfpe/7+a6UvNdTUYs7dCescjrh7FnYt69lO3AA\nTp1SitAE37FalaIGV+YckZGK0cOQIco2dGjL7/36eSTY1+q+JiE6glvHp+m6c4OW6+lQLCL05zmk\nKxdmelvIFp0+CoPRhNPh+ny5Zs0a1aJ8gNdff131a/yBP4o1HHUOqg9UEz8uXnWxQfz4ztdelbsq\nSZqapPozfSLVwdjjyu+X4xycSHVwMtXOT346hgHTemCymJr3dRcb6FNZwvPLn2XCOTddlDti5Eh4\n7z0YPhzw3VTE1RwbK6Oxnk6mMj+ZO/OTsdm0i41t2QIVFYrrvSDogWAJ4/XUYUQv6LFIwde8pLeo\njeFKblMQBEEQBEEfiLJEEARBD3Qmygc4dgzGjfP/XIR2NCXsd+Vs5d/vPEZyjeft1Y8NHsWAdcuJ\nHOheCK42oGOMtBAzZBIxQybhdDppLDuH9fRebKf34miwYYppiWbXNzq49197OhXhrFu3rs3fdSjC\n/NeBn6A456sN7ZmPHuH9Yw/zxthFPDv1G9RGqk+0XekE4YuL46JRfYg2G7E7nJ0GsvTukC4EBz0J\nYwLlPqZFUrUjAuXmo4Wz95XnIX86AgvhgRYJNX8J8sGzAjWXr1W5ZulWW8HU03uZdnovU0/vpWdt\nhdfHDlsaGmDjRmV7+GFITYXsbFi8WPkZExPsGbpECp86xx9O3P5KUHt6ffNF0NxafJ9X0iLAD1Xx\nfWvX+6yULEb0HKF78b0rukryXi9FLbrH6YSTJ2HnTvjiixYR/qVLwZ5Z16a+3rVoPzoaBg9uEekP\nHw5ZWdiHDacYc/O5vUdslM/3R/FRJnK+dx31dofHMZZgoPV6uisUEXpKVyvMdDqdHDx4kNzcXNat\nW8eNX7uJaUNHq1qPzRg1kGMTJ7Jjx3aX+6xduxan0xlUp3tf8LVYw9wI3U7YOfrHMxgP2ajeU03N\nwRqcjU4mnZiEZZC6eJy5uxnLEAvW41aX+1TtqgLUfxb3DG2kJNHByb4OyuKd8NV/2Y/HWtoI8t3F\nBmae2MX/rfwD3WxVqo7Nf/83/PnPHd4nemMq0nqOjnoTtsIe2E73xHo6mcayOHVzU0FjI6xZA7fd\n5rdDCILHBFMYr6cOI3pAr0UKvuQlfcGbGK7kNgVBEARBEIJP+GY9BUEQQolhwzrfR0T5QaEpYV+y\ndSdvv/M4Payet17/y+Rb+cN1y/ivfZU8PdD9vr4EdAwGA+Ye/TD36EfC+CU4nc52+3giwlm7dm2H\nj1cBv0DpqP4E8D+AqcM9O8bodPKtPSuYfWIXP1r4ELvSRqp4dcdOEN64OALk7C8iZ3+RKhGUHh3S\nBSGQreK1cMBzRSDcfLRoL9t0HvKHsFIIT3xN0Pxzez4ffnFOm8lcwbJJ6T59Tj1Zs/Qrv0D2se3M\nPb6D8ecOY3I6vD5el+T8eXjtNWWzWBT3/BtvhEWLoFu3YM+uDf4ofAoX/OXE7Y8EtZrrm6eCZjsV\nNBgL+OvuVXxwqpjePUo5XHpIxPc6pysk7/XouqgLqqth927Yvl3ZduyAEnXvkxBkbDY4eFDZWmEC\n7AnJnO2RzvGeaZxNzWBmxhDWOXtQHeVd4V+93cnUZzc3/63H+yF/FJeHcxGhWrpCYWZRURG5ubnk\n5uayfv16Lly40PxcdHQ0777/bdWFbE+fmeNWlF9QUMCRI0cYMcK3jqPBwtsii8RqAz98L4q+pUYi\nHAaK/5nfbp+qPVWqRfkA8RPj3YryK3cpsX61n8WL3Z1c7N6+68GV43T0+TDbG3j40zf4n88/VnVM\nLBZ46SW4665Od/XUVMThgN//u5iKHYOwnu5J3dnu4AhMEWJ6OtR1wcZ5gj4JljA+kDH+UEHPRQre\ndpdLTYrmrV2Fqo/nawxXcpuCIAiCIAjBQ78RL0EQhK5Ez56QlATlbhzYjx0L3HyEZp5akUfp1h28\n9dajJNbVePSa6kgLP1z4Q9YNnQx4Ljr1Vmh+Ja7clNzNw263k5ub63bcC8B9wEvAX4DrVc4rreIi\nb//7EV6ZeCPPXX8HdRGRHr2uIycItS6OV+KNCEpPDumCEMhW8f52rguEm4+v7WVvnZDGYx8d0FxY\nKYQnmiTU9pyl0qb9d2/a0GSeWJzl8zgdrVkyLp1l8eEtZB/fTmZx4Ns5hy1WK3z0kbJFRMDs2bBs\nGdxwA8T5z7XQU7QsfAon/OnErWWC2pvCgSsFzU3i+wZDIQ3GM9QbCmgwFuIwtNzbllXD0Wqfph0Q\nmsT3zQL8MBbfd0a4Ju+D5bqoyy5Lly7B5s2waRNs2wYHDoC9vbhQCA/6VZbQr7KE6af3tHn8fHxP\njvdM51BKBodTBnIoZSCnu/fFbnRvxVDX2LbgUo/3Q1oXl4d6EaHW56FwLMysqKhg8+bNbNy4kQ0b\nNpCXl+dy302bNmHEobqQbc6cOfzyl7/scMxu3boxd+5cHI7QLWj2tsiiMsZJr8uKIN8VVXu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w0AhgwZwsyZM5k6bTrvnkugvtG3ZLQ7EZSvBNQxUegSBLpVvDduYZ4QzEC5O3Gtv4WVQmAJtMug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kcejQIcrLyykpKdF8XtnSJoS1feEQGxtLUVERJSUl3WXGjBk4HPraJJEKoEmLj6GjQ85M2doK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\nL8Uk8Cpt50oAA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_web_traffic(x, y, [f1, f2, f3, f10, f100], fig_idx=\"04\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, 100 was a bit overkill. " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Errors for the complete data set:\n", + "\td=1: 319531507.008126\n", + "\td=2: 181347660.764236\n", + "\td=3: 140576460.879141\n", + "\td=10: 123426935.754101\n", + "\td=53: 110768263.808878\n" + ] + } + ], + "source": [ + "print(\"Errors for the complete data set:\")\n", + "for f in [f1, f2, f3, f10, f100]:\n", + " print(\"\\td=%i: %f\" % (f.order, error(f, x, y)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But maybe we just haven't found the right dimension, so let's play a bit.\n", + "\n", + "**Note**: if you don't see an interactive slider below the following code block, you need to enable the interactive widgets by running\n", + "\n", + "```jupyter nbextension enable --py widgetsnbextension```\n", + "\n", + "in the console and restart `jupyter notebook`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9f7f3b7630742a199cc15af6415e5a7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "A Jupyter Widget" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import interactive\n", + "import ipywidgets as widgets\n", + "\n", + "def play_with_dim(dim=1):\n", + " f = np.poly1d(np.polyfit(x, y, dim))\n", + " plot_web_traffic(x, y, [f])\n", + " print(\"Error for d=%i: %f\" % (f.order, error(f, x, y)))\n", + " \n", + "interactive_plot = interactive(play_with_dim, dim=(1,100))\n", + "output = interactive_plot.children[-1]\n", + "output.layout.height = '500px'\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stepping back to go forward...\n", + "It seems like there is some change between week 3 and 4. What if we treat the time before and after differently?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error inflection=134390576.310656\n" + ] + } + ], + "source": [ + "inflection = int(3.5*7*24) # calculate the inflection point in hours\n", + "xa = x[:inflection] # data before the inflection point\n", + "ya = y[:inflection]\n", + "xb = x[inflection:] # data after\n", + "yb = y[inflection:]\n", + "\n", + "fa = np.poly1d(np.polyfit(xa, ya, 1))\n", + "fb = np.poly1d(np.polyfit(xb, yb, 1))\n", + "\n", + "fa_error = error(fa, xa, ya)\n", + "fb_error = error(fb, xb, yb)\n", + "print(\"Error inflection=%f\" % (fa_error + fb_error))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sNFxcur+OhvEphjNxLlnFKpMmjjN0VACAMFhJj16+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I8Mw4gB+AWAD5c9nAdwuRDitxJ3dTcqA9ivMQzj20KIfQ2e90WT16lJCLHVMIynMFGJPgXg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\nlEy3cRL8YmVAxy0rA1FeVLWRHdTmV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XT3Z5v+O0CXc9frawFVzgPdOala6iYgH7AWuO5m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\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WpX27lQg7e/XWnnbc+PhrmHLTU5hx6zM4947nMOPWZ0J9PZSsj+PzZ0zw/O+Xrt4m8nvb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\nOt+4aarBh2sqinWzB+/1Euk/xmt7R48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2k2MYRq5pmv1x7K4whv1MtC+nficg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\nHtu1HTdFwo2xBstb2ru1ftt+DYXCDrfsHK9f14fDpnZ09FjaxvaOHg2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fa = np.poly1d(np.polyfit(xa, ya, 1))\n", + "fb = np.poly1d(np.polyfit(xb, yb, 1))\n", + "\n", + "plot_web_traffic(x, y, [fa, fb], fig_idx=\"05\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How perform our higher-degree models with our \"inflection\" version?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0Tqz8HeW+uYES+zqUe/sJlN0T9mF6k2q/ABxVU/4g5XtzLnAbZSXqnYEXU2JttilwRuM+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\nmJm/66Pb2/uoU3Vmo/9fdHN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uwG/GM0rg0sIiIiIiIi8hWG/4mIiIiIiIiIiIiIiMgmuUKVDGcSkT0G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\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yRlXVtlhfUFGUDwAPAsFH0F7gVaAZKAMu4/wIBArwBQKj+X5Gw+vkAP8HXDrp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\n6ru5Y4GTR26arkOJJgbj4d4JRV17Hw/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N33/7FN+6KXT7UDI5hSdvLufx43+EXz2FqU/J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hV+lx14nzh9+nT27t3rdf4tW7b0TGsJpLLmTsbq7uCi85dx7tKlLFmyhDdLjby6swqr/00EwOiG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v2gghJMcpDR08sfscWvu8+NWNS/H668DJo6twW4rnXIy3Afvl+Pk3poXDUh0tMP3mAeDzDwEx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\n+r/Ir3ayf+Lum2PXDRtjeHDgou8PDw7Erhs2xhN33xwP7NzW8YKdifGx1h5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_web_traffic(x, y, [f1, f2, f3, f10, f100],\n", + " mx=np.linspace(0, 6 * 7 * 24, 100),\n", + " ymax=10000,\n", + " fig_idx=\"06\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, higher degree models don't give hope for a bright future. \n", + "What if we had trained them onlz on the last week?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\ipykernel\\__main__.py:4: RankWarning: Polyfit may be poorly conditioned\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\numpy\\lib\\polynomial.py:583: RuntimeWarning: overflow encountered in multiply\n", + " scale = NX.sqrt((lhs*lhs).sum(axis=0))\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\ipykernel\\__main__.py:5: RankWarning: Polyfit may be poorly conditioned\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Errors for only the time after inflection point\n", + "\td=1: 22140590.598233\n", + "\td=2: 19764355.660080\n", + "\td=3: 19762196.404203\n", + "\td=10: 18942545.482218\n", + "\td=53: 18293880.824253\n" + ] + }, + { + "data": { + "image/png": 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oHey8MmW3og8CZwEPRMS1EfGxiNhmXHs6+cbrHp/oZ6VuvH90jHVWx/y2432N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\nSJIkSZKkHnRa+d/gf0nSePK9wlN3t1/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4YNhbZvuOEGRUVd3102AABAyVStWlWDBw/2mDl9+rQWL15sUiMA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\n7vH4WNqXX35ZBQUFFiUCAAAAgCDCyv8AACtxTYGfp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H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\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wQWnHDsNhN42Qqv4X3Dd54pRaPVuH1/xXjr13yZcoxMKFUnq6b13Zj5xy0JykJNg08Zx0\nrZ42RHPH9GlZwX+J8D8AAACAmNXCXt0BAAAAABBcTz0l5eQYj3v9dalnz9rx/GwRAIxZLBa99NJL\n6tSpk+HYd999Vw8//HAYqgIAADEvKcn7fcL/aOGa0v1+7fZizVm5xbfB27dLDzxgOOz1U6T3f/W/\nDzwmtXfcLvcOl8pyrpDUznD+6NH7dM01ib7VJDW74P+Km87SppnDNXPUKUpv7/ufM6ZYLN7vN9bJ\nAwAAAACiHOF/AAAAAACa6PvvpenTfR9fWirdcIM0aJD0zTehqwsAYkXHjh31yiuvyGzUsVHSjBkz\ntHbt2jBUBQAAYppR5/+ysvDUAUShQLrfZ+XkK29/ufdBHo90002Sw+F1WJldmvKbnz5uWzNZtoMn\nqmj5LyRlGNbSoUOxXnmlow9V13K5PVqWW+Dz+GiQGGeVxezbqQYxK1yd/z2e4KwDAAAAAD4i/A8A\nAAAAQBPt3Su1bev/vE8/lU47Tfq//5MqK4NfFwDEksGDB+u+++4zHOd2u3XFFVdo3759YagKAADE\nLKPw/+HDBD3RYgXa/T5rw25JtWH6wtJK7SgqU2FppVzu/31PvfqqtGqV4Tp3DZW+T679fXLN75Tk\n+I32vbxLcmUazjWbHfr3v9spIcH3uovKqlRSUeP7hCiQGGeNdAmRF67wvxFTC9+EAQAAACDoeMUH\nAAAAAEATnX66tGWLdM890vz5/p0W7nTWnmL/2mvSU09J558fujoBoLm744479Mknn+i9997zOq6w\nsFCZmZl6//33ZbFYwlQdAAAtk8flkaPYoZp9NXLsc9T9qtlfo+4PdJepuYYdjcL/Hk/tLm5/ksNA\nDAhG9/tXN+6R2+PRm1/srRemT0mw6cqT2ujWW6YYBhi+6CQ9cUbt7xOcg5TivEb7V66Rq/QBn2p4\n6KEq9e2b7Ffd5dVOv8ZHWkqCTalJ8ZEuI/KiJfwPAAAAAEFG538AAAAAAAKQmCg99JC0aZM0YID/\n83fulIYPl66+WiouDn59ABALzGazXnjhBR1//PGGYz/88EPde++9YagKAICW6eCHB7Wu4zqtta3V\n+s7rtbHvRn194df6dsK32nX7Lu15aI9cZX7sjI42RuF/qbb7P9DCBKP7fVmVU8+u++6YdUoqatTh\nr3NlLfJ+ipdb0g0jJZdFinP1VvuaqSrL3aiK/0yRZDd8/qhR+3Tbbf4F/6Xm10V/XEaaLOZmugEr\nmAj/AwAAAIhRhP8BAAAAAAiCX/9aWrdOevJJKdn/nyPrxRelk0+WnnuutpEkAKC+4447Tq+++qqs\nVuPgzT333KMPPvggDFUBANDymFuZVVNUI3l53eLY5whfQcGWlGQ8hvA/WqBQdr/v/cMOTcj9p+G4\nHzRKgzf9RScn9tKtGU8rrnS/Dq4aIsl4k3BaWpFee62jmnIoSWpSvFISbP5PjJDMgd0iXUJ0IPwP\nAAAAIEYR/gcAAAAAIEgsFunGG6Vt26Rx4/yf/+OP0nXXScOGSf/5T/DrA4Dm7swzz9TDDz9sOM7j\n8SgzM1N79+4NQ1UAALQs9o7G3bWbdfjfl87/ZWWhrwOIMgcOh+b72ux26b73/yaLQScEh1K0S5N0\n0ZcXafGLCzWl+4na/6pH8gw1fIbNXqbVa9qrVaum1WgxmzQ2I61pk8Msc0BXpbdPjHQZ0SFc4X+6\neAAAAAAIM8L/AAAAAAAE2S9+Ib3+urRypdS1q//z16yRTj1Vuuceqbo66OUBQLM2ZcoUXXLJJYbj\niouLdfnll8vpDF2HUgAAWiJbR+Pu1zX7asJQSYj4Ev6n8z9akBqXRzOWb9b4hRtCsv4VX76pvj/s\nNhy3U9fLqdqjFh1fObRmwCaVHpzkwxPcajPyS43LWqV7/7lVefvLm1Rn5oAmvMETZoN7dNDs0b0i\nXUb0sFi833e5wlNHU46bAAAAAAAvCP8DAAAAABAio0ZJW7ZIU6caNxv7OYdDmj1b6ttX+uST0NQH\nAM2RyWTSc889p/T0dMOx2dnZmjFjRhiqAgCg5bC2tsqc4P0FTsx3/if8jxZk9tvfKCsnPyRrH3e4\nULd//ILhuBL9Wvt0Yd3HHpNHD1adKreMQ9VJ/TYqscchlVTUaHF2nobOW6MZyzfL4fSv63v3Dq2b\nvAEgc0DXxbEXhQAAIABJREFUJs/9VaoP/0363zMWTegvu5UISJ1wdf4HAAAAgDDjlR8AAAAAACHU\nurX0yCPSZ59JGRn+z//2W+ncc6VJk6SDB4NfHwA0RykpKXr99ddlt9sNxz788MNauXJlGKoCAKDl\nsHf0/v9gxw/NOPyfmGg8hvA/WpCN34XmzQiPXJq2drpSqr0HsN2yaLumSEcF/Rd6WusLdTF8hq3j\nNrU9r/iY61k5+Zq0dKPfGwBmj+6lwT06+DXnSDf+ps5958+DtHraEE08J10pCfVPXklJsGniOela\nPW2I5o7pQ/D/5wj/AwAAAIhRvPoDAAAAACAM+vWTcnJqNwL4kiX5uWeekU4+WXr5ZcnjCX59ANDc\n9OvXTwsWLPBp7O9//3t99913oS0IAIAWxN7JIPzfnDv/2+21v7wh/A8ExCOPTi24X1d8s99w7B6N\nV4VOqPv4E3ONXlF/w3mmuEJ1ujxfpkYOB1i7vVhzVm7xtWRJkt1q1qIJ/X3u4n90N/5A5qa3T9TM\nUado08zhWj99mFbdeq7WTx+mTTOHa+aoU5TevglvNLUEhP8BAAAAxCjC/wAAAAAAhInVKk2dKm3d\nKo0e7f/8oiLpyiulsWPZAAAAkvTHP/5RV1xxheG4gwcP6rLLLpPD0YyDiAAARJEGO/+bJFt7mxJ6\nJRieDBD1kpK83yf8DwSkSm9o3r9yDMdVmDpot66u+/h7a5UecA/V0acANMhUqY6X/0fmeKfXYVk5\n+crbX+5LyXXsVrPmjunTpG78gcyVJIvZpM5tWunE1CR1btNKFrPB56GlC1f4nzfpAAAAAISZNdIF\nAAAAAADQ0nTtKq1YIb35pnTzzVJhoX/zBw5Uo53rAKAlMZlMevrpp5Wbm6v//Oc/Xsd+/vnnuv32\n2/Xoo4+GqToAAGLXL274hdqPaS97R7tsHW21/+xgk9kaI33HWreWDhxo/H5ZWfhqAWJMueVjXb9+\niU7y8i12xM0jOuoPe9rK8XWl3PFm3Vl1lip9iDi0PS9XcZ18C/VnbditmaNO8Wns0Y50458+oqeK\nyqpUXu1UYpxVqUnxhqH8QObCD9HS+Z838QAAAAAEWYy8AwcAAAAAQPNiMtV28N+2TbrxRt9/Dnjq\nqbWnBwAAaiUlJen1119Xq1atDMc+9thjeuONN8JQFQAAsa3dBe3UaUIntbuwnZL6Jimuc1zsBP+l\n2vC/N3T+B3zSOq5+UL/K/I2Sy+ZrxifGc1ec1EqfnX6P+n/aT8njO+lv9p7ao2TDea165Cq5X4nP\nNb6RWyCXu+md2wPpxk8n/xCzWLzfD1f4HwAAAACCLIbehQMAAAAAoPlp00Z68klp3Tqpd2/vY00m\nadEiyWbzPg4AWpo+ffro73//u09jr7vuOu3YsSPEFQEAgGaN8D9aCJfbo8LSSu0oKlNhaWVAIfiG\nXNirY93va0x7VGy7R4+/51Irp/d55TZp7rB7Nb7fSZLNqj/ln6x/Hupg+DxLmx3q8Nsf/KqxpKJG\nRWVVPo0N9ecLQWbU+d/lCk8dAAAAABBkxmfiAQAAAACAkDvzTCk3V5o/X5ozR6pq4OfON90knXFG\n+GsDgObgmmuu0ccff6znnnvO67iysjJdeuml+vTTT306LQAAALRAhP8R43YVH1ZWTr6W5RaopKKm\n7npKgk1XntZBJwXpOZf1P17LcvfKqR+1zz5b5++q0Agf9uE+cs7FKko+WZkDu8nlkn75S2n9eoNJ\nlgPqeGWeTBb/A/nl1d53I3j7fI3NSNNVA7spvX2i389FiBmF/+n8DwAAAKCZovM/AAAAAABRwmaT\n7rxT+uYbafjw+ve6dJHmzo1MXQDQXDzxxBPqbXSMiqQvv/xSU6ZMCUNFAACgWSL8jxjlcLo1Y/lm\nDZu/Vouz8+oF2aXaLvhv5u4NyrNSEmzqf0I7Xdq/vYrj5sitIj24ynjetg6pWtLvWl1xxvFKb5+o\nVq2kpUulJ56QrI22NnSqw+82y5bsaFKtiXENL+zL52txdp6GzlujGcs3y+EkTB5VwhX+93ACBAAA\nAIDwIvwPAAAAAECU+eUvpfffl158Uerwv1PtH39cSk6ObF0AEO0SEhL0xhtvqLVRYE/SwoUL9eKL\nL4ahKgAA0OwkJXm/T/gfzZDD6dakpRuVlZMflueNy0iTRy59UzVHDvNOjd8infaD8by7LrhNTotV\n723+Qff+c6vy9pfLZKo9DXHtWukXvzh2Tpuzv1BC9/Im1ZmSYFNqUvwx1/39fGXl5GvS0o1sAIgm\n0dL532QKz3MAAAAAtBiE/wEAAAAAiEImk5SZKX37bW13uzFjmr7WDz78cB0AYsVJJ52khQsX+jT2\n+uuv1+bNm0NcEQAAaHaMNhKWlYWnDiCI5qzcorXbi8P2vCsHdNWN79yof+18TzandN9HxnNe63O+\nNqb1kiSVVB7bVf+ss6RNm6Rzz/1pTu9zStXm7KIm1zkuI00W87Hh7KZ8vtZuL9aclVuaXAuCLFrC\n/wAAAAAQZIT/AQAAAACIYu3a1Xa3a6qNG6Vu3aTbb5fKm9YEDwCanSuuuEI33HCD4biKigpdfPHF\nOnDgQBiqAgAAzYZR+J/O/2hmdhUfDlvHf0nKHNBVL297VItyF0mSJuZKvzzofU65LV4PD/59g/eO\n7qrfqZO0apV0661Sr17SS0stATVWzxzY7ZhrgXy+snLylbefN2CiAuF/AAAAADGK8D8AAAAAADHK\n6ZQmTZIcDmnePKl3b+m99yJdFQCExyOPPKKMjAzDcXl5ebrsssvkdDrDUBUAAGgWCP8jxoQz+D+4\nRwedeEKuZq6eKUlKrJZmrTWe98zpY7Q/sW2j94/uqm+zSfPnSzk5Up/01soc0LVJtWYO6Kr09onH\nXA/085W1YbckyeX2qLC0UjuKylRYWimX2xPQuvCTxeL9vssVnjoAAAAAIMiskS4AAAAAAACExqOP\nSl9++dPH330njRghjR8vLVggdeoUsdIAIOTi4+P1+uuvKyMjQ6WlpV7HfvTRR5o2bZoWLFgQpuoA\nAEBUI/yPGOJye7QstyCgNexWsxxO4y7pmQO6alDvHzT6lYl116ZskDoZNMI/0CpZi84YY7h+Vk6+\nJg7qXhfYT/xfbn/26F4qOFiptduLDdc4YnCPDpo9utcx14Px+Xp14x65PR69+cVelVTU1F1PSbBp\nbEaarhrYrcFNBwiycHX+97CpAwAAAEB40fkfAAAAAIAY9N130qxZDd979VWpZ09p0SJOOAcQ27p3\n767nnnvOp7GPPvqonn/++dAWBAAAmgfC/4ghRWVV9QLoTeFwuvWPzNM0rl8XJcfX7y+YkmDTxHPS\ntXraEF1+ljR+2Tg53bWnah1XLv1lnfH6T545Xoc8vr1BcaSr/tEsZpPuvaSXRp3a2ac1Mgd01aIJ\n/WW3HhuXCMbnq6zKqWfXfXfMOiUVNVqcnaeh89ZoxvLNPm2oQADCFf43YjKF5zkAAAAAWgw6/wMA\nAAAAEGM8HunGG6WKisbHlJRIkydLS5dKCxfWbgYAgFg0ZswY3XbbbZo/f77h2Ouvv149e/bUgAED\nwlAZAACIWklJ3u8fPlz7wotAJ5qB8mpnUNa5IeuLut8nx1s1/JSOGn/68erXrZ0sZpMKDhVoxOIR\nOlR9qG7c9Gwp2eF93R8SU/Xs8aeo4Kk/KOWcK5XU77cyefneeiO3QNNH9JTFbNKu4sPKysnXstyC\nekH7uP+F+quPCtenJNg0LiNNmQZd94P1+TKSlZOvgoOVjW5CQBBES/gfAAAAAIKMV5EAAAAAAMSY\n116T3nvPt7HZ2dKvf117SkBVVWjrAoBIefDBBzV8+HDDcQ6HQ2PGjNH3338fhqoAAEDUMur873ZL\nlZXhqQUIUGJc8PsBHqpyalnuXl329AbNWvGN9peXaORLI1VwqKBuzPEl0s05xhtkfiy/Tme8vlme\n6nId/HCR9r/9sNyOxr+/SipqtLekQjOWb9aw+Wu1ODvvmA771U53XfB/1Kmd9fHtQ7Rp5nDNHHWK\n1+C/FJrPV2PWbi/WnJVbwva8FofwPwAAAIAYRfgfAAAAAIAYUlMjTZvm/5x7763dBLB6dWjqAoBI\nslqteuWVV9S9e3fDsYWFhfrd736nKnZEAQDgE3eNW9XfV6vsizId+NcB/bDkB+U/lK+dd+yMdGlN\nZxT+l2q7/wPNQGpSvFISbCFb/8WcnTr18Qv19b6v612/9yOT7G6P17mHla5CDdM3FevrrlV8+4l+\nWHqbag7saXTeba99paycfJ/q++fXhbprxRa5DGo5ItSfr5/LysnXjqLDKiyt1I6iMhWWVvpcKwwQ\n/gcAAAAQowj/AwAAAAAQQ2w26Z13pAED/J+7fbs0bJh07bXSgQPBrw0AIqldu3Z6++231dqHMF9O\nTo7++Mc/yuMhdAMAQGN+fP9HZbfP1sf2j7W+y3ptytikzRdt1rfXfKtdd+7Snof3yFXhinSZTUP4\nHzHC5faoqKxK552cGpL1PfLogO1xFVZ/Vu/6KUXS1V83MukoeZqkV9RN23S/pOS66zUH8lW49FaV\nf5vd4LzPvzvoV53+dNi3mE0am5Hm1/qBGvnYJzrzgY90/iMf68wHPlK/+z7Qvf/cqrz95WGtI+aE\nK/zP60YAAAAAYUb4HwAAAACAGHPqqdK6ddITT0hJSf7Pf/556eSTpRde4OeXAGJLr1699MILL/g0\ndsmSJXrsscdCXBEAAM2XOcEs5wGn1zGOfY4wVRNkhP/RzO0qPqx7/7lV/e77QGc+8JGW5e4NyXNK\nrVkqt350zPX7P5TM8v6GQql6a6MGaYlOlHSJpI2Setfd9zgqtX/Fgzq4+tmgbMrNysk3DNO73B4V\nllZq0K+OC/h5/qh21g+hl1TUaHF2nobOW6MZyzfL4aRDfZNYLN7vu8K0Qc1kCs9zAAAAALQYhP8B\nAAAAAIhBFot0003Stm3S737n//z9+6UJE6QLLpB27Ah+fQAQKZdcconmzJnj09h169bR/R8AgEbY\nO9oNxxD+RygcCWjvKCpTYWmlXG7+vnaEw+nWjOWbNWz+Wi3OzlNJRU3InlVmeV+ltleOuX5mvnTx\nf4zn/1eT9aB+rZq6yMKvJG2QdHn9gR6PTEEKT2dt2N3g9Z9vlrjmuY1BeV4wZOXka9LSjWwAaIpw\ndf4HAAAAgDCzRroAAAAAAAAQOl26SMuWSW+/XbsZoKDAv/mrVkl9+kizZknTpkk2W2jqBIBwmjlz\npr788kstX7680TFz5szRzJkzgxY0AgAg1vgU/v+hmYb/fTlCjfB/2O0qPqysnHwtyy2oF2pPSbBp\nbEaarhrYTentEyNYYWQ5nG5NWrpRa7cXh/xZleZN+tH25LE3PNKDq4zn79dZelKjtF0//15LlPSy\npIGSblfc8ScpZcg1Add7xBu5BZo+oqcs5tq/4zucbs1ZuUVZOflBe0YorN1erDkrt2jumD6RLqV5\nIfwPAAAAIEbR+R8AAAAAgBbgt7+Vtm6VbrnF/9PGq6qk//s/KSNDWr8+NPUBQDiZzWYtWbJEvXv3\nPuZe69attXz5cs2aNUtmo7AIAAAtmCXZIlOc9xcXNftC13U8pBJ9CJCXlYW+Dkgy7mZfUlGjxdl5\nGjpvjWYs39xiO6TPWbklLMF/h2mniu0PSqZjP88j/iuda5Cj98ikNbpZL6qbl1G3SOY1aj/qTpnM\nlsAKPkpJRY2Kyqok/bRZItqD/0dk5eQrb395pMtoXgj/AwAAAIhR/PQKAAAAAIAWIilJWrBAysmR\n+vb1f/4330hnny3deKNUUhL8+gAgnJKSkrRixQq1a9eu7lr37t21fv16XXLJJRGsDACA5sFkMhl2\n/3fsa6ad/+322l/e0Pk/LPwNaGfl5GvS0o0tbgPAkVMRQs1pKlJR3N3ymCqPuWd2Sw/40PV/heUK\n3a7RchpEFRJ7u2VNTqn7uH+3tn7X25Dyaqek4GyWSEmw6bqzT1ByvDUYpRnK2rA7LM+JGYT/AQAA\nAMQowv8AAAAAALQwp58uff659Ne/SgkJ/s31eKR//EPq2VN6/fXajwGguerevbtee+01WSwWDR8+\nXJ9//nmDpwEAAICGxWz4X5Jat/Z+n/B/WDQloL12e7HmrNwSooqiU6DB/7EZXbTiprO8jnHpsIrs\ns+UyHWzw/pWbpVOLvD+nWnbd4rpf38n76Rq2477Wcb8prfs4c0BXPXLZr70v7qPEOGvAmyWev/Z0\nrZ8+TJtmDtes0b10af/jg1KbkTdyC+Ry80aMz8IV/ufNMQAAAABhRvgfAAAAAIAWyGqVpk2TtmyR\nLrrI//k//CBddpk0erS0m8ZzAJqx8847Tx999JHefffdeqcAAAAAYw2G/82SLdWmxFMTZe9k0D0/\nmhH+j7hAAtpZOfnK218e5Iqik8vt0bLcgoDW+PDbIvXs3EYpCbYG73tUo2L7faox72nwvt0p3bPa\n+Dl/143KVzevY0y2veqYWai2iTZNPCddq6cN0dwxfdSlbUKj9fkqJcGm1KT4gDdLZP93vzq3aSWL\n2SSpdnNCOJRU1KiorCosz4oJ0dL532QKz3MAAAAAtBjhOX8OAAAAAABEpRNOkN55R3rtNemWW6R9\n+/yb/8470urV0r33Sn/+c+2mAgBobs4999xIlwAAQLPU5eYu6jC+g+wd7XW/bO1tMlliIOhI+D/i\nAg1oZ23YrZmjTglSNdHF5faoqKxK5dVOlVc7VVJRE9B6JRU1OlBerbEZaXpn06569zxya7/tb6q2\nfNPo/Os3Sukl3p9xSEmaqxlex8TFubX0TbsGnX2uUpPi68L1kmQxmzQ2I02Ls/OM/0CNGJeRJkkB\nb5Z4I7dA00f0rKuve4fWyhzQNeCvWV+UVztD/oyYYbF4v+9yhacOAAAAAAgyfiQPAAAAAEALZzJJ\n48dLF1wg3XmntHChf/MrKqS//KV2fu/eoakRAAAAQPRpd0HzPTXn6PB0Ypz1mKCxkpK8L1BWFtoC\nW7hgdLP/eUA7Fhw5DWFZbkHAgf+fK692KnNA12PC/3u0VBXWjxud17pamtn47Tp/1e06oPZexzzz\njFmXjejQ6P3MAV0DCv9nDuymorKqoGyWKCqrUuc2requzR7dSwUHK7V2e3FAaxtJjCPi4bNo6fwP\nAAAAAEHGK0MAAAAAACBJattWevpp6eqrpcmTpW3bfJ87dSrBfwAAAADRr7HwdEqCTWMz0nTVwG5K\nb59I5/8IC1VAu7lyON2as3JLSDvLJ8ZZ1blNK406tbOk2o0X7+1/T4Va5nXebZ9KqRXe196nVP1N\nU72OmTpVuuoq7+sE0mE/c0BXpbdP1I6i4Gzc+XkHfrvVrEUT+of031NKgk2pSfEhWTsmEf4HAAAA\nEKMMXu0AAAAAAICW5pxzpC++kO65R4qLMx7frZt0990hLwsAAAAAmszhdGvG8s0aNn+tFmfnHRMs\nL6mo0eLsPA2dt0Yzlm+WOzHR+4KE/0Pq58HqSK8TSQ6nW5OWbgxp8P/oUPkNQ06UJH1W+pkWFSzy\nOq/DYem29cbr36u7VK7GN9S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7T+P92WXg/a1F/PL5oeyq2uV1rMXjJK3+Hhye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\ni3YNqvoyvc5lclYjtkpf50sGm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vD3pZ3Z+2ejKtJXp3eibjCZo7sh/0yZojGq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\nqRfGdGuGBwe3q+0/vfvuu8jNretozRjD2rVrsXHjRkyePBnTp09HkyZNA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o5CX0GhcwHc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HrTo0hL897vGomIKIlkZOhvo7LzP++DREREREQkGdMcRERkiJ2dxyOdl5uJHd/+pKcDGrLD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\nd2MowE51RERWaQVjIlKpIFIrlhCRS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fb1 = np.poly1d(np.polyfit(xb, yb, 1))\n", + "fb2 = np.poly1d(np.polyfit(xb, yb, 2))\n", + "fb3 = np.poly1d(np.polyfit(xb, yb, 3))\n", + "fb10 = np.poly1d(np.polyfit(xb, yb, 10))\n", + "fb100 = np.poly1d(np.polyfit(xb, yb, 100))\n", + "\n", + "print(\"Errors for only the time after inflection point\")\n", + "for f in [fb1, fb2, fb3, fb10, fb100]:\n", + " print(\"\\td=%i: %f\" % (f.order, error(f, xb, yb)))\n", + "\n", + "plot_web_traffic(\n", + " x, y, [fb1, fb2, fb3, fb10, fb100], \n", + " mx=np.linspace(0, 6 * 7 * 24, 100),\n", + " ymax=10000,\n", + " fig_idx=\"07\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting training and testing\n", + "Let's use 30% of the web traffic data after the inflection point as test data that we do not train on." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fbt2(x)= \n", + " 2\n", + "0.05404 x - 50.39 x + 1.262e+04\n", + "fbt2(x)-100,000= \n", + " 2\n", + "0.05404 x - 50.39 x - 8.738e+04\n", + "Test errors for only the time after inflection point\n", + "Error d=1: 6492812.705336\n", + "Error d=2: 5008335.504620\n", + "Error d=3: 5006519.831510\n", + "Error d=10: 5440767.696731\n", + "Error d=53: 5369417.148129\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\ipykernel\\__main__.py:11: RankWarning: Polyfit may be poorly conditioned\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\numpy\\lib\\polynomial.py:583: RuntimeWarning: overflow encountered in multiply\n", + " scale = NX.sqrt((lhs*lhs).sum(axis=0))\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\ipykernel\\__main__.py:12: RankWarning: Polyfit may be poorly conditioned\n" + ] + } + ], + "source": [ + "frac = 0.3\n", + "split_idx = int(frac * len(xb))\n", + "shuffled = np.random.permutation(list(range(len(xb))))\n", + "test = sorted(shuffled[:split_idx])\n", + "train = sorted(shuffled[split_idx:])\n", + "fbt1 = np.poly1d(np.polyfit(xb[train], yb[train], 1))\n", + "fbt2 = np.poly1d(np.polyfit(xb[train], yb[train], 2))\n", + "print(\"fbt2(x)= \\n%s\" % fbt2)\n", + "print(\"fbt2(x)-100,000= \\n%s\" % (fbt2-100000))\n", + "fbt3 = np.poly1d(np.polyfit(xb[train], yb[train], 3))\n", + "fbt10 = np.poly1d(np.polyfit(xb[train], yb[train], 10))\n", + "fbt100 = np.poly1d(np.polyfit(xb[train], yb[train], 100))\n", + "\n", + "print(\"Test errors for only the time after inflection point\")\n", + "for f in [fbt1, fbt2, fbt3, fbt10, fbt100]:\n", + " print(\"Error d=%i: %f\" % (f.order, error(f, xb[test], yb[test])))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Errors for only the time after inflection point\n", + "\td=1: 22140590.598233\n", + "\td=2: 19764355.660080\n", + "\td=3: 19762196.404203\n", + "\td=10: 18942545.482218\n", + "\td=53: 18293880.824253\n" + ] + }, + { + "data": { + "image/png": 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Jym5FHwROAe6NiCsj4mMRseG49nTyjdc9PtHPSt14//AY66yO+W3H+xpTdXel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\nN9Ffktq65v5reNOpb+KZbD+ZB+BV672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1JWz39d0h7DMFpnca7GknZIGqpMmuSvqy/pA0lrDMMo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\nACgALl68qGnTplnW1as3RRs2SIcPS4sXS08+Kb3xhlS1qvczwjtM01S/r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AAFtJSfbHWfkfAIDD0qKFtHCh9P770tixUlpauBuF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\nz5hZsGCBHnjgAbVo0cLBZgAAAAAAoMSJifF+nJ3/AaBESHo0Sb+++uufH7i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G2OaWLVum\nd955x4FGAELBwIH2mZkzpdTUwHcBAAAAEILY+R9AiMvK8Wj4si26ZtIazVmb9JfBf0lKScvWnLVJ\najXxIw1ftkVZOZ4gNQ2OqAiXZndvdsoTAC7Y45bLMl7PrXBt8Qfhc3Nz1adPH0mTJJX1mo2JsfTs\ns8W+pCTJsiz1ebOP1u5Za5t1G7cW37xY9c6q55+LAwAAAAgpDP8DAAAACFt9+vTRhRdeaJvr16+f\nMjMzHWgEINgaN5auu8575vBh708HAAAAAFCC2Q3/p6c70wMATiMrx6Oe8zZq/vo9PuXnr9+jnvM2\nlsobABI7Ntb/Hr5a97aso4TYSDXY7fZ+kktKuLr4O//PmjVLmzZVkdTFNvvYY0a1axf7kpKkSZ9N\n0tyv5/qUnX7DdLU+r7V/LgwAAAAg5DD8DwAAACBsRUREaPr06ba5HTt2aPLkyQ40AhAKBg2yzzzz\njJSbG/guAAAAAEIMO/8DCGGjVm3Vmh3JhTpnzY5kjVq1NUCNQludSnEa0aaBvhzxL/V6+RJVeKa2\n4judpcjKkadk45vFKzLh1M8L49dff9WwYWMkPWebrVfP8ukJlb5YtX2Vhrw3xKds30v7qnez3v65\nMAAAAICQxPA/AAAAgLB25ZVX6rbbbrPNjRkzRvv27XOgEYBg+9e/8p4A4M2uXdKKFc70AQAAABBC\nYmK8H2f4H0CQ7Eo+7vOO/yebv36Pkg6l+rlR+HC7jGo1StBFD9XRxYsbq8WBFmr2dTOdP/F8VfxP\nRbliXapwbYViX2fIkCE6erSfpDq22enTjaKji31Jbfl1i25deqssWbbZ686/Ts/8+5niXxQAAABA\nSCsxw//GmLONMW2NMfcYYwYbYx7Of9/WGHN2sPsBAAAACJwJEyaobNmyXjOpqakaPHiwQ40ABJMx\n8mlntUmTAt8FAAAAQIhh538AIaqog/9/nP/57jMey/VY2n8kXT8cPKb9R9KV67EfJA9nxmVU9qKy\nqjmopi5860K1PNxStYbUKtaaH3/8sebN+1LSw7bZm2/O25yiuA6mHlTbhW11POu4bfbvZ/1dr3V+\nTRGuiOJfGAAAAEBIC+u/9RtjzpLUR9Jdsrm12hiTJOllSc9blnUo4OUAAAAAOKZatWp64okn9PDD\n3n/wsmjRIt13331q1aqVQ80ABEu3btKjj0r79585s26d9Pnn0mWXOdcLAAAAQJAx/A8gBOV6LC3Z\nVLynli7etE/Dbqgvt8v88dmJpwks2bRPKWnZf3yeEBupTk1r6PbLaqtOpbhiXTccuKJcckUVfW/M\n7Oxs9enzgKTnJUV6zcbHW5o82XjN+CIzJ1MdX+uo3UfOfFPHCRXKVNDqW1croUxCsa8LAAAAIPSF\n7c7/xph7Jf0kaZSk8yQZm9d5+dmfjDE9g1AZAAAAQAD169dP9evXt8317dtX2dnZtjkA4S06Wurb\n1z7H7v8AAABAKWM3/J+e7kwPACjg4LGMvwznF0VKWrYOHsuQJGXleDR82RZdM2mN5qxNOmXtlLRs\nzVmbpFYTP9LwZVuUleMp1rVLuqlTp+q775pLammbTUw0Ouec4l3Psizdt/o+rdu7zjYb4YrQki5L\nVLdi3eJdFAAAAEDYCMvhf2PMLEmzJMUpb7Dfyn+dyYnjRlKspOeNMbMD3RMAAACAcyIjIzVt2jTb\n3NatWzVjxgwHGgEItl697Od6li6Vfv7ZmT4AAAAAQkBMjPfj7PwPIAhSM3P8tk5Wjkc9523U/PV7\nfDpn/vo96jlvIzcAnMG+ffv0+OPTJT1tm23WTLr//uJf8+lPn9a8b+b5lH3uhufUqg5PugUAAABK\nk7Ab/jfGjJTUU38O/Sv//SFJqyVNlPRY/muipFWSkvXXmwSMpB7GmFGOlgcAAAAQUK1bt9bNN99s\nmxs5cqQOHDjgQCMAwVSxotSjx+mPuVzSzTdL69ZJ1as72wsAAABAENndIZyWJlne9hwDAP+Li47w\n2zqjVm3Vmh3JhTpvzY5kjVq11S8dSpqBAwcqLW2UpIpecy6XpVmzJLe7eNdb/v1yDftgmE/ZAc0H\nqOfFPYt3QQAAAABhJ6yG/40xF0gaob8O8X8tqY2kapZltbMsa4hlWYn5ryGWZbWXdI6kG/OzpsC5\njxpj6gfjvwUAAABAYEycOFGxNj/IP3r0qB555BGHGgEIpgEDJGP+/HVcnNS/v/TDD9Lrr0vNmwev\nGwAAAIAgsBv+l6SMjMD3AIACqsSXUUJsZLHWSIiNVGpmjs87/p9s/vo9SjqUWqwOJc27776rN944\nJKm7bbZvX6OmTYt3va8PfK3bl94uS/Y3oV1f93pNuG5C8S4IAAAAICyF1fC/pFGS3Mob3Jek6ZKa\nWZb1lmVZZ3wGnWVZHsuy3pZ0iaSp+vMGAJekJwLaGAAAAICjatWqpeHDh9vm5s2bp08//dSBRgCC\n6fzzpY4d83b3Hz9e2rdPmjxZqlMn2M0AAAAABIUvw//p6YHvAQAFuF1GnZrWKNYanZvW0MINe4u1\nxvzPdxfr/JIkMzNT99//kKSZttnq1S2NGVO86x04fkDtFrZTarb9DRgNKjfQok6LFOHyzxMjAAAA\nAISXsBn+N8ZEKW/3/hO7/i+1LKuft6H/k+XfBDBA0hLl3QBgJN2YvzYAAACAEmLQoEGqW7eube7B\nBx9Ubm6uA40ABNPMmdKuXdKQIVJCQrDbAAAAAAgqX4b/09IC3wMATnJb81rFOr/rpbW0ZNO+Yq2x\neNM+5Xrsd50vDSZMmKAff4yUVNE2O3WqUXx80a+VkZOhDos6aO9R+5s3zoo5S6u6rVL5MuWLfkEA\nAAAAYS1shv8ltZAUqz937R9YjLUG5q8hSTGSriheNQAAAAChJDo6WlOnTrXNff3113rhhRccaAQg\nmKpUkaK47R8AAACAJMXE2GcY/gcQBOdVLlvkGwBua15LcdFupaRlF6tDSlq2Dh7LKNYaJUFSUpIS\nExMlfSPpAkkvnjHbpk3eUyeLyrIs3bPyHq3/eb1tNtIVqaW3LNV5Fc4r+gUBAAAAhL1wGv4/N/+r\nJekry7KK/Ly6/HO/LPBR7WL0AgAAABCC/vOf/6hdu3a2ueHDh+vQoUMONAIAAAAAAEHHzv8AQtjI\ntg11Vb3KhTrnqnqVNbJtQ6Vm5vilg7/WCWf9+vVTRsaJmyB+l9RT0pWStv4lFxPj0fTpkjFFv9a4\nteO0YMsCn7Izb5ypf9b+Z9EvBgAAAKBECKfh/4L/wt3lh/WSzrA2AAAAgBJi8uTJio6O9po5fPiw\nHn30UYcaAQAAAACAoPJl+D89PfA9AOA0oiJcmt29mc9PALiteS3N7t5MUREuxUVH+KWDv9YJVytX\nrtTq1atPc2StpCaShknK+3Ni1CiXahdjq8ml25Zq+IfDfcoOunyQ7ml6T9EvBgAAAKDECKfh/9wC\n7/3xr033GdYGAAAAUELUqVNHQ4cOtc29+OKL+uKLLxxoBAAAAAAAgoqd/wGEuKgIlxI7Ntb/Hr5a\n97aso4TYyL8cT4iN1L0t6+h/D1+txI6NFRWRN/ZRJb7MKdnCSoiNVJX4MsVawylHNx7V3kl7dfyb\n47I8ll/WTEtLU79+/bwksiU9pb///Sbdf3+uBgwo+rU27d+kO5bd4VP2xr/dqPHXji/6xQAAAACU\nKOF0y3Zygfd/88N6Bdc45If1AAAAAISgRx55RK+88op++umnM2Ysy9KDDz6ozz77TC5XON0jDQAA\nAAAACiUmxj7D8D+AEFCnUpxGtGmgYTfU18FjGUrNzFFcdISqxJeR22VOybtdRp2a1tCctUlFvmbn\npjVOu3YoSn4tWXsn7pUkRVaOVIXWFVTh2gpKaJ2gmHN9+L3+NBITE7V7927b3IsvDlfLlm7b3Jns\nP7Zf7Ra2U1q2/Z83jao00oJOC+R2Ff16AAAAAEqWcJpq+TH/q5HU0BhzQVEXyj+3cYGPfihOMQAA\nAAChKyYmRs8++6xtbsOGDZo9e7YDjQAAAAAAQNAw/A8gzLhdRtXKx6hulXhVKx/jdTj/tua1inWt\n2y6rXazznXT4/cN/vM9OztbBRQe1/d7tWl9nvT6v+7l2j7Mf4i9o+/btmjBhgm3urrvuUsuWLQvd\n94T07HS1X9RePx/72TZbObayVnVbpXLR5Yp8PQAAAAAlTzgN/6+XdETSiee1TTPGFPqW8/xzphb4\n6Gj+2gAAAABKqPbt2+v666+3zQ0dOlS//vqrA40AAAAAAEBQuFxSmTLeM+npznQBAD87r3LZIt8A\ncFvzWqpTKc7PjQov12Np/5F0/XDwmPYfSVeuxzolk3UoS8e/Pn7GNTJ+zFD2b9k+X9OyLD3wwAPK\nzvZ+TkJCgsaPH+/zuqe7zt0r7tYXv3xhm410RWrpLUt1bsK5Rb4eAAAAgJIpItgFfGVZVq4xZqmk\nu5V3A8A1khYYY3pYluXTd+CMMWUkvSjpWv15E8FSy7JyA9EZAAAAQGgwxmjKlClq1KiR1x/gpKSk\n6OGHH9Z///tfB9sBCEUZGfbzQAAAAADCVGxs3l/6z4Sd/wGEsZFtG2rf4XSt2ZHs8zlX1auskW0b\nBrCVvV3JxzV//R4t2bRPKWl/fg83ITZSnZrW0O2X1f7j5oSUD1Ns16vQuoLP13799df1wQcf2ObG\njh2rKlWq+LzuycZ8PEavbX3Np+wLbV9Qy1pFf8IAAAAAgJIrnHb+l6RRkjLz3xtJXSRtNcbcY4wp\ne6aTjDFljTE9JH0rqZvyBv+NpCxJowNbGQAAAEAoqFevngYNGmSbe/XVV/Xhhx860AhAKEpJkRIT\npZo1pc8+C3YbAAAAAAERE+P9OMP/AMJYVIRLs7s38/kJALc1r6XZ3ZspKiI44yNZOR4NX7ZF10x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IiQoqK8Z9LTnekCADgty7KUvt3778XbauXqcEa25qxNUsv2Q3T8+GMaoMlez7mlyvvq39/+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\n9P4eAAAAAIQ5y7L0yiuvq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wAAANAcRUVFKSsrS927d1dBQYHH2Jtv\nvlkDBgxQx44d7UkOQINuuUWaPVv65ZeqY6dTuukm6b77qjYAAAAAAM2KVfG/VNX9v2XL0OcCQOl9\n2ytz6SZb1wtHcfq3279V+lvpVYX/Hh4wEiPpdm8mHD++Vuf7KIehti1/f7LJKWl/0L0XnKCdBSUq\nKq1QYqxTbZLjvH7iwaTBXbR1r0s56/O8ipekAZ1aa9LgLl7HB4zO/wAAAACaOTr/RzDDMF4zDMOs\n8+sXP+dqbRjGM5J2SFos6WVJj0p6WtJbkjYZhvGFYRhDg5D3MMMwvpS08cDcTx9Y62VJSyTtMAzj\nGcMwUgNcx7Z7AgAAAJq7jh076sknn7SMKygo0FVXXSW3221DVgA8iYuTpk2r+nnwYGnNGmnmTAr/\nAQAA0Ex5W/wPwBaBdJlv2zLOp3jbi9MP2FG4Qxe9dpFcX7ikDZ5jR0k6ymrCrl2lCy6wXLd6Q8Cx\nbZLVtmW814X/khTjdChjVB+v/92k922vjFF9GnyaQEhYFf/T+R8AAABAE0fxf4QyDOMiSSOCNNdf\nJK2RdL08P2nwNEnzDMN41TAMn9smGIaRZBjGHElzJZ3qIbTVgVzWGIZxnq/rHFjLlnsCAAAAIslV\nV12liy++2DIuJydHM2fOtCEjAFaGD5e+/FJ6912pc+dwZwMAAACEEMX/QKMzaXAXDejU2qcxAzq1\n1qLbBjTu4nRJrnKXLnntEm393zbpo6pz0YrWKTrlkFiHpLu96WZ/zz2SI/T3EeN0aOqQblo8bqBG\n909TSkJ0respCdEa3T9Ni8cN1NQh3Wz/vQUAAACA5s4Z7gRgP8MwUiTNDtJcAyW9raonDVYzJa1S\nVWf+FEk9JdXswp8uqYVhGJeYpulVO0/DMKIkvS6pbquCPEnfSton6ZgDa1V/8nG4pHcMw/izaZpL\nG9s9AQAAAJHGMAw9//zz+uqrr7Rz506Psffee6/OPfdcdevWzabsANTHMKRTPW2/BwAAAJqL+Hjr\nGIr/AVtVd5mfvGCtspflWsan922vSYO7HCxOH33G0cr+erPmrdqq/OLyg3EpCdEa1qud0vt1UFqq\n/f3dTNPUte9eq2Ur90gv/ChVXq8YLdUUTVFv9dZjekwf6sOD8cMkHWvVrT4tTRoRlN5/XktLTdTE\nQSdq/AUnaGdBiYpKK5QY61Sb5DifniYQdHT+BwAAANDMUfwfmWZIOvLAzwWSkv2ZxDCMdpLeUu0i\n+S8kjTFN88cacbGSxkqaLql62/9gSVMk3evlco+qduF/uaTbJT1vmmZZjbVOlPSCfn8yQKyktw3D\n6Gaa5vZGdk8AAABAxGnTpo1eeOEFXXTRRR7jysrKdMUVV2jZsmWKjY21KTsAAAAAQMTypvO/yxX6\nPADUEkghf2MsTq90m7r34wc057/zpFe+lCo7KVYfaYr+oz5qIUm6S3fJLbcWaZFiJU1r2VLat8/z\nxHfeKTnDU/4R5TDUtqUXG6gAAAAAAEFB8X+EMQzjz5KuOXBYIel+STP9nG6ypMNqHH8p6c+maZbU\nDDJNs1TSPw3DyJU0v8al2w3DeM40zc0WOR8t6dY6p4ebpvlO3VjTNH8wDONPkj7R7xsA/iBpkqS/\nN5Z7AgAAACLZ4MGDde211yozM9Nj3HfffacHHnhAjzzyiE2ZAQAAAAAiljfF/3T+B8ImkEL+xlCc\nvjGvUNnLcpW5co42a4o0f6pU1EdxqtRUrVavA4X/kuSQQ/foHsWrXHfHfqWOVoX/hx8uXX11iO+g\nCaHzPwAAAIBmzhHuBGAfwzASJWXUOPWEpP/6Oddxkq6scapM0lV1i+RrMk3zbUkv1zgVq6qifCuT\n9Ht3fUl6qb7C/xrruCRddSCnatce2ETQIJvvCQAAAIhoM2fOVFpammXctGnT9Pnnn9uQEQAAAAAg\nolH8D4RcpdvU9n0ubdhZoO37XKp0+16EXV3If2ybZLVtGR+2Dv7eKqtwa8L81Tp7Ro6e+WKRcs3H\npXVnSmvvUZwq9bBWq5fyDxkXq/16Rdt0Wmmp9SK33SbFxYUgewAAAABAY0Tn/8jyiKSOB37eKOkB\nSX39nOsySVE1jt8yTfMnL8ZNU+0C+0sNw7ihoQJ7wzDiJQ2rZw6PTNNcbxjG25IuPXDKeSDnKR6G\n2XJPAAAAAKTk5GRlZWXpzDPPlOmh25ZpmkpPT9d3332nww47rME4AAAAAAACEu9FV3CK/wG/VHe9\nf3PVVuUXlx88n5IQraG92unyfh2UlpoYxgxDo6zCrTFZK5SzPk8V2q282IdkuuKlea8oTm49qtXq\noUO7+sfqN/XQnUrQFutFWraUrr8+BNn/rtJt+vy0hbCi8z8AAACAZo7i/whhGMZpkm6scWqsaZou\nw+qNb8OG1Dl+0ZtBpmn+aBjGMv2+6SBR0rmS3m1gyHmSarZa+co0zXVe5viifi/+l6S/ynPxv133\nBAAAAEBS//79ddddd2naNM/7e7ds2aLrrrtOb7zxhgJ4DwMAAAAAQMOcTik6WiovbzjG5bIvH6AZ\nKKtwa/KCtcpellvv9fzicmUu3aTMpZuU3re9Jg3uohinw5bc7Chon7xgrXLW58mtEuXFTlGldkuv\nviZVtNcpyqu38D9Bm9VddypOeV6tseeqMSo1o9XGbQY9/0jdtAEAAAAAjR3F/xHAMIxYSf+WVP1J\nycumaX4cwHxHSOpR41SFpC98mGKJaj9x4C9quFD+/HrGeutzVeVW/d95T8MwDjdN87e6gTbfEwAA\nAIADJk+erA8++EDfffedx7h58+YpMzNTo0ePtikzAIFwu6v+6bCnZgMAAAAIjoQEad+hxbgH0fkf\n8FrNrvfeyF6Wq617XcoY1SekGwDsKmivXseUqd3RT6rM8ZP03yukbSMkSZ+ptf6pY3WLNhwck6x1\n6q67Fa39Xq2x6bC2GmScoqJHPg1q/o1504ZX6PwPAAAAoJlrRO/AEEIPSOp84Oc8SXcEOF/XOsff\nm6ZZ5MP4L+scd/Fhra+8XeRATqu9XMvOewIAAABwQGxsrF599VXFxMRYxt56661at87bB4EBCJev\nvpJOPVXKzg53JgAAAICPEhI8X6f4H/Baddd7X+Ssz9PkBWtDkk9ZhVsT5q/W2TNylLl0U63Cf+n3\ngvazpi/RhPmrVVbhDmi96sL5fc45KnZ+Lu05WnrvX7Vi5qudntYxkqQUrVQP3e514f9vSa103ZCJ\nKopNCGr+1Zs2Gir8ryt7Wa7GZK0I+PfLVhT/AwAAAGjiKP5v5gzD6CVpXI1T/zBNc3eA055Y53hD\nvVEN+9livppOsGktO+8JAAAAQA1du3bVo48+ahlXXFyskSNHqrS01IasAPhq82Zp5EjptNOk5cul\n8eOlIl+21QMAAADhFh/v+TrF/4BXqrve+yN7Wa427Qrum0m7C9or3abeXLVVRVGfaV/0/0mVUdJb\nr0plyYfEvqk/aoXWq7vGyymXV/P/ktJWw9If00+tOwQ9/8a2acMvVp3/AQAAAKCJo/i/GTMMwynp\n35KcB059YJrm/wVh6mPrHPv6yc3mOsd/MAzjsLpBhmG0ktQqwLXqxh/XQJwt9wQAAACgfrfeeqvO\nO+88y7j//ve/uueee2zICIC3CguliROl44+XXnvt9/O//io9/nj48gIAAAB8ZtX53+VdYS4Q6fwt\n/D84/uu6X70Gxu6C9p0FJfrNtVa7o2dVnfhsorT11Hpjr9ULuk3Xy6Hyeq/X9WPrjhqe/pi2pBzh\nMc6f/Bvbpo2QofM/AAAAgCaO4v/m7R5JPQ78XCTp+iDNm1LneKcvg03TLJRUUud0Sy/WKTZN09dP\nDOrmVt869a0VqnsCAAAAUA+Hw6GXX35Zbdq0sYydNWuW3nvvPRuyAuCJ2y29+KJ03HHS1KlSSd13\nxZIee0zautX+3AAAAAC/WBX/0/kfsFTd9T4Q81ZtVaU7OAXa4Sho37B7s/Jip8g0yqTcU6XP7qs3\n7k49phc0RlHyrkP/N0edqBGXPaq8JO960Pmaf2PbtOE3Ov8DAAAAaOac1iFoigzDOFHSxBqn7jNN\n85cgTZ9U59ifNicuSXE1jg99xmHw1qmpvnWCuZbVPfnMMIw2klr7OOyYmgeFhYXav39/MNJBI1dU\nVOTxGAAAu/HaBF/Ex8dr9uzZGjp0qMe43r1768gjj+T/cYEwKyyUxo9P0m+/NdxbwuWSxo0r0/PP\n17MzIEx4bQIANCa8LgGNS0JsrMcvT8vy81XCe1E0c4G+NuUVlirOLNMR8QEkYZbplx271DopNoBJ\nqsz7+mcdEe//RoJ5X63X2AHHWAceUFRepOvfH6FKY49Ukiy9lS2ZUXWiTE3T3bpL3j8u74vj+uje\n4XcrISZOCfL+frzN322a+mxtbkC/Vzlrc3XTGUfJEebi+5jS0lpf2tdVWVmpIv4uhw+MggLL4o+i\noiJV8t9VyPC+CQDQ2PDaFJkKCwvDncJBFP83Q4ZhOCRlSqr+NGSlpH8GcYm6hfL+fIPvklSzJUHd\nOYO5jqc5g72W1T354wZJkwKZYPny5dqxY0eQ0kFTsnz58nCnAABALbw2wUpUVJQuuugivfvuu4dc\nMwxDf/3rXzVy5Eht3rxZmzc3km5iQAQbPry9nn66p8eY11+PUe/eX6lTp3ybsvINr00AgMaE1yUg\nvPoVFelwD9d/27RJqxbA+8oEAAAgAElEQVQvti0foDHw57Vp/EmBr7vmmy8Dn0RSZwWYjztXixd7\n1xHfbbr1+C+Pa+2+76tOvP+UlJ9WKyZKFXpWf9doZXqdwpYzz9TuW27R7U6npEqvx1Ul5X3+Nx3v\n29SHqlTOkiWBThKwTps26QQP110ulxbzdzl8EJ+Xp3MtYlauXKm9BQW25APeNwEAGh9emyJDbm5g\nT0sLpoZbs6Epu1VSvwM/V0gabZqmj58C+MSf7f+NeYzdawEAAACo4YorrlBaWu0vRg877DBNnjxZ\nV1xxhZxO9rEDjcVZZ+UqLc26qP/f/+4qk3fNAAAAaOQqYz13GXeUldmUCYCmaM6OOfpq31dVB6tH\nSN9dWet6rEr0ukb4VPi/8YILtOof/5DJ52Hes3ryAB9QAAAAAGjiKP5vZgzDOFrSlBqnnjBN879B\nXqbusyv8eWhj3TH1PQ/DrnXsXgsAAACAB9HR0brjjjsUe6Do4uSTT9aTTz6p7t27hzkzAHVFRUnX\nXLPGMm7duj/oiy+OtCEjAAAAwH9Wxf9RpaU2ZQKgqcnZm6O5v82taheX/0dp4bO1riepQP/RhRqq\nt7ye88eRI7V6zBjJQVkHAAAAAOB3bA9vRgzDMCRlSEo4cGqjpAdCsBTF/4Gt5atnJM31ccwxkt6p\nPjjllFN0wgmeHm6I5qKoqKjWY4ROOeUUJSYmhjEjAECk47UJgXA4HCouLtbo0aNlWHXsAhA2Z50l\nLVtWroULoz3GvfFGb915Z2fFxdmUWAN4bQIANCa8LgGNS9w770hLljR4PTUhQWeddZZ9CQFhEIzX\npmdzftZbq371O4ehvY7S2AHH+D2+Wl5hqdIzlgU8T02DurfV9QOPVXTU759Vrdi+Qs/MfUaHFR6m\nya9P1r/Ku+l/pSkHr/9Bu/S+/qKTtcLrdab/ZazmdR4kfRd4ztlj+qp1kufNTW7T1PBnv1JBSYXf\n6yTHOTX376fKEebP8WJWrvR4PSEujr/L4RNjyxbLmN69e6uyTx8bsolMvG8CADQ2vDZFph9//DHc\nKRxE8X/zMkbS2TWOx5qm6QrBOvvqHLf2ZbBhGEk6tFA+34t1EgzDSDRNs8iH5dp4sU59a4Xqnnxm\nmuZOSTt9zKfWcVJSklq0aBGMdNDEJCYm8u8eANCo8NoEX1x//fXhTgGAl2bOlD78UCovbzgmN9eh\nzMwWGj/evry8wWsTAKAx4XUJCLOUFI+XnaWl/BlFxPHntWlYv+P0zBfb/F5z2Kmd1KJF4IUziUmm\nSowY5Rd7eLPqoxeW7dBPeyuVMaqPYpwObdm3RZctuEwJ+Ql64uUn1HFXRz0up8apQOuVrHbaokU6\nR8frf17NX+6I0h0X3qZ3TxwoBeGb/pSEaHU8IlVRDuuC/DO7tFfm0k1+rzWod3ultGzp9/igseg6\n4HA4+LscvklOtgxJTEyU+O/KNrxvAgA0Nrw2RYakpKRwp3AQz4drXibX+Pk9SRsMw+jo6ZekI+rM\n4awnLqZOzE91jjv4mGfd+D2mae6tG2Sa5m5Jdc+3D3Cturk3dD4k9wQAAAAAQHN07LHSrbdaxz38\nsLRjR+jzAQAAAPwSb/Fg6OJie/IAmrijWycpva+vX+tWSe/bXmmpwemYGeUwNLRXu6DMVVPO+jxN\nXrBWhWWFGjxnsMp3lGvmyzPVcVdHSVKyKvS4vtO5WqkvdLrXhf8uZ6zG/HViVeF/kAzr1c6rwn9J\nfv87Ozi+n69fsYeI1ZMHTNOePAAAAAAgRCj+b15qfiJ5gaRNXvyaU2eOo+qJObFOTN1nVxzrY55H\n1zn+wUNssNdq6Lkbdt4TAAAAAADNzsSJUmqq55jCwqo4AAAAoFFKSPB83RWKB24DzdOkwV00oJNP\nD1vXgE6tNWlwl6DmEWhBe0NeXfaLhr42Ult/2qqZL81Uh121C9+P0lq9qz+pvbZ4Nd/+2ERdMeJB\nLTnm5KDm6UtBfmPZtAEAAAAA8Izif/hjTZ3j7oZhWHwaWsvpFvN5unaqt4sYhpEoqbuXa9l5TwAA\nAAAANDstW0oPPWQd9+9/S99+G/p8AAAAAJ9ZFf/T+R/wWozToYxRfbwuJk/v214Zo/ooxhncEoZA\nCto9yXe+ou++/UYzX5qp9rtrz5+ib9VDtylW+7yaKy8xRZde9qhWtAv+xgdfC/Iby6aNgND5HwAA\nAEAzR/E/fGaa5nZJ39c45ZTU34cpBtY5ft9D7AcWYz05Q1W5VfvWNM3f6gu0+Z4AAAAAAGiWRo+W\nulh832+a0u238107AAAAGiGK/4GginE6NHVINy0eN1Cj+6cpJSG61vWUhGiN7p+mxeMGauqQbkEv\n/K/mT0G7J4VRn6rcvVBPZT6lP+75Y61rqVqq7rpbTnn3pJDclodraPrjWtcmLWj5VXObpsoq3D6N\naSybNkKKDyQAAAAANHFO6xA0FaZppvg6xjCMgZIW1zi12TTNjl4Mna/aXfWvlvSRF+sdL6lvjVNF\nFuM+lOSSFH/g+FTDMI43TXOdFzleVed4vkW8XfcEAAAAIMQKCwuVlJQU7jSAiON0Sk88IZ13nue4\nJUukd96RLrnElrQAAAAA78THe75O8T/gl7TURE0cdKLGX3CCdhaUqKi0QomxTrVJjlOUw6JLexBU\nF7RPXrBW2ctyA5qrxPGjdkf/UzIq9MZpb+jGD288eO0Iva/Omi5D3hXc/9i6o0Zd+qDykloFlFND\n5izfom35JT4X51dv2hh9xtHK/nqz5q3aqvzi8oPXUxKiNaxXO6X36+DzkwVsYdX5HwAAAACauCa0\n/RqNTLakyhrHfzUM4zgvxt1d5/gN0zRLGgo2TbNY0jyLOQ5hGEYnSUNqnKqQ9H8Ww2y5JwAAAACh\nU15ernvuuUddunTRnj17wp0OEJHOPVe68ELruHHjpNLS0OcDAAAAeM2q8395uVRRYU8uQDMU5TDU\ntmW8jm2TrLYt420p/K9W9ykEyXG+90msMHYqL2aKZFT9PTCv75t6La6q0L+d3tDxeszrwv8VR52g\nEZc9GrLC/2o56/M0ecFav8ZWb9pYOfEcfTX+bH18+5n6avzZWjnxHE0cdGLjLPz3Bp3/AQAAADRx\nFP/DL6Zp/iTp5RqnYiS9ZBhGXENjDMO4WLW78ZdJmuzFcg9IKq9xfJVhGBd5WCdO0osHcqqWaZrm\nz54WsfmeAAAAAATZhg0bdPrpp2vatGnKzc3VmDFjZPJlHhAW06dXPQXAk59/lp5+2p58AAAAAK9Y\nFf9LkssV+jwAhEx1Qfv7t57h0zi3irUz5kG5jX2/n/z8Nj1fMlCGsnWsZns9V8mfz9EVlz6k/XH2\nPLUye1muNu0q8nt8ODdt+IXO/wAAAACaOYr/EYhJkvbWOD5N0seGYRxfM8gwjFjDMG6WNLfO+Bmm\naW62WsQ0zY2Snqxzep5hGDcZhlGzwF+GYZwg6ZMDuVTbLe8L8m25JwAAAADB9corr6hnz5765ptv\nDp576623lJGREcasgMh1/PHSDTdYxz30kJSXF/p8AAAAAK94U/xfXBz6PACEXNuW8UpJiPYq1lSl\ndsXMULnjl4PnnFt66eIlp+p9/UUD9IL3C48cqegFCxSbkuxjxoHJ/pqvsA+iWQgAAACAJo7if/jN\nNM2tkv6qqm731U6X9INhGN8YhvG6YRgfSNoi6Z+San56slDSfT4sd4+k92scR0t6StIWwzDeNwzj\nDcMwVkhaq9qF/2WShpimub0R3hMAAACAAO3fv1+XX365Ro0apcLCwkOu/+Mf/9APP/wQhswATJok\nHXaY55h9+6riAAAAgEYhPt46huJ/oFmIchga2qudV7H5ziy5opZJko7Pkx77SNo651u9bQ7XefrI\n+0VvuEF69VVFxcV6vXawzFu1VZXuCCl6p/M/AAAAgGaO4n8ExDTNJZKGSKrZp8+Q1EfSpZLOk9S6\nzrA5kv5mmmalD+tUHpjv9TqX2kg6X9JwSb0PrF1tp6SLTdP83Nt1Dqy1RDbcEwAAAIDAbNu2TT17\n9lR2dnaDMS6XSyNHjlRJSYmNmQGQpFatvCvsf+45ac2a0OcDAAAAWPKm87/LFfo8ANgivW97y5jC\nqI/ldr+pa1dKX7wg/fgv6c4vpcOLfSykv/9+6emnJYfD67WDKb+4XDsLIuTzMavifzr/AwAAAGji\nKP5HwEzTfE9SV0nPStrrIfRrScNM07zMNM0iP9YpNE3zb6oq9P/aQ+geSbMldTVN8wNf1zmwli33\nBAAAAMB/RxxxhDp16mQZ9/333+vuu++2ISMAdd1wg9S5s+cYt1u64w6+ewcAAEAj4E3xP53/gSar\n0m1q+z6XNuws0PZ9LnX4Q2LDRfimqe6/LtRj7z2p7dOlFxZIp231c+Enn5QmT65VlH506yTbNwAU\nlVbYuh4AAAAAIDSc4U4A4XWgy33Az70zTXOnpOsNw7hV0umSOkg6QlKRpF8lfWua5qZA1zmw1jxJ\n8wzDSJPUS9KRkhIl7ZC0WdIXpmmWBWEd2+4JAAAAgO8cDodeeukl9ejRQ7/99pvH2Oeff17jxo3T\nH//4R5uyAyBJ0dHS9OnS4MGe4z76SHr/femCC+zJCwAAAKgXxf9As7Qxr1DZy3L15qqtyi8uP3g+\nJSFal5x0lE7p2ErLf9kjSWpduFdD13yi4avf1zF7PH/eZCkqSnrpJenyy+u9PGlwF23d61LO+rx6\nrwdbYmyElIfQ+R8AAABAMxch7+5glwNF94ttWmuTpJAX39t5TwAAAAB8c/jhh+vll1/W+eef32BM\nly5dNGfOHAr/gTC58ELpnHOkRYs8x91xR1VcdLQ9eQEAAACHoPgfaFbKKtyavGCtspfl1ns9v7hc\nL335i5yVFRq1e43O+HyBzvr5GzlNd+CLx8VJc+dKgwY1GBLjdChjVB+POQZLSkK02iTHhXQNAAAA\nAIA9HOFOAAAAAACAQJx33nm6/fbb6712/fXX65tvvlG3bt1szgpANcOQnnhCclh8CrVunfTss/bk\nBAAAANQrPt46xuUKfR4AAlZW4daYrBUei+qP2b1F9yx5UV/NvkoPvjhR52xYFpTCf7NlS+16a4E2\nnDJA2/e5VOluuNN8jNOhqUO6afG4gRrdP00pCbV3xKckRGt0/zQN690uoJyG9WqnKIdFR/zmgs7/\nAAAAAJo5Ov8DAAAAAJq8hx9+WIsXL9a3334rSWrVqpUyMzN1ySWXhDkzAJLUtas0Zoz03HOe4x54\nQLr8cumww2xJCwAAAKgtOlqKipIqKxuOofM/0CRMXrBWOevzDjmfUObShes+16Xff6yTf/0hqGua\nUVH64bRzddvJ6VqfUyrlfCapqoB/aK92urxfB6WlJtY7Ni01URMHnajxF5ygnQUlKiqtUGKsU22S\n4xTlMLQxr1DzVm71O7f0fh38HgsAAAAAaFwo/gcAAAAANHmxsbGaM2eOevXqpZNPPlmvvvqq2rUL\nrCMagOB68EFpzhxp//6GY/bsqYqbOdO+vAAAAICDDENKSJAKChqOofgfaPQ25hXW7vhvmur16zpd\nunqRBq37XEllwX2Ch9mpkz485QLd17KX8pJaHXI9v7hcmUs3KXPpJqX3ba9Jg7soxln/4/GiHIba\ntjz0KSRHt05Set/2Hp9k0JAYp0Ovfr3Z4+aDZoXO/wAAAACaOYr/AQAAAADNQufOnfXFF1+oW7du\nioqKCnc6AOpo00aaOFG66y7Pcc8+K02YIKWm2pMXAAAAUAvF/0CjVOk26+2IX5/qAvkU134N//5j\njfj+Ix27x/+u+fUxExJkjBih8lFXafTPscr5aZdX47KX5WrrXpcyRvVpcANAQyYN7qKte131PtHA\nk7IKt9ebDwAAAAAAjR/F/wAAAACAZuOkk04KdwoAPLjllqri/o0b679+7rnSjBkU/gMAACCM4g/t\nuF0Lxf+Araq7+L+5aqvyi8sPnk9JiNbQXu0O6WZf6Tb15qqtuvDHz/XIh0+rRWlRUPNZfUySjr/r\ncUWPTJeSk/XA/NXK+cm3bvw56/M0ecFaTR3SzadxMU6HMkb10eQFa/16AoAU2OaDJoPO/wAAAACa\nuWb6bg4AAAAAAACNTWys9Pjjh57v3Fn6z3+kDz6Quna1Py8AAADgoIQEz9ddLnvyACJcWYVbE+av\n1tkzcpS5dFOtwn9Jyi8uV+bSTTpr+hJNmL9aZRVuSdLOghJ1+t+3mrVwetAK/wuNRE3vJ/3priPU\n5r8bFH3d36Xk5IMbE/yRvSxXm3b5nl+M06GpQ7pp8biBGt0/za8C/urNBxGL4n8AAAAATRzF/wAA\nAAAAALDNkCHSgAFVP7dqJf3zn9Lq1dIFF1g35wMAAABCzqr4n87/QMiVVbg1JmuF14X12ctyNSZr\nhcoq3Crek6/p/5mpaHdlQDmYcmiXTtUaPaj7zKs0fptTd136kg5POrzWuoHI/nqz32PTUhN1Wd/2\nBzc9+Ly2n5sPmgQ+XAAAAADQzFH8DwAAAAAAANsYhjRzpnTrrdJPP0k33yxFR4c7KwAAAOAAiv+B\nsJu8YK1y1uf5NKa6m/0RUyep/b7f/F67WO20UWP0lV7XGj2sR/UXzdJnqsit0GXnXqYFCxZIkird\npt5ctdXvdSRp3qqtqnT734U+nJsPmjQ6/wMAAABo4ij+BwAAAAAAgK169pRmzarq/A8AAAA0KvHx\nnq9T/A+E1Ma8Qr+L2n95/V0lZmb4PK7ciNEOnadv9aSWK0u5ukwupeoxdVa2+khaLmms9uzZo4su\nuki33Xabtu7er/zicr/yrJZfXK6dBSV+jW0Mmw8aLTr/AwAAAGjmKP4HAAAAAES89957T5988km4\n0wAAAAAAhJtV53+Xy548gAjlb+F/Ummxpr3/pE9jvj5KGjNYOvbGBH2aMkr71F2SoRI5NFHd9L7a\nHoiMk/SspDcktdSsWbP03n/e8yvPuopKK/wat7OgJKybD5o0Ov8DAAAAaOIo/gcAAAAARKyKigqN\nHz9eF154oUaOHKlt27aFOyUAAAAAQDhZFf/T+R8ImUC62U/49AW1259nGbc7PklP9ItWlxukU8dI\nL/SWclPzddcl05WvKO2TU7erh77WH+oZPVzSKo0Ycb0uGXKJX3nWlRjr9Gucv5sGQjVPo0LnfwAA\nAADNnH/vJAEAAAAAaOJ+/fVXjRw5Up9//rkkKS8vTyNHjtQnn3wip5O3ywAAAAAQkSj+B8LG3272\nAzau1MjvP7KMW96uswZeWSBXdJ3mD26Hfn17uu5WD7kUpS1KbHCOli1X6YUXHlN8QpxSEqID6r6f\nkhCtNslxfo31d9NAqOZpVKyK/+n8DwAAAKCJo/M/AAAAACDifPTRR+rZs+fBwv9qn332me6///4w\nZQUAAAAACLv4eM/XKf4HQsafLvQtSgr16Pv/tIwrjo7V1Rc7Di38l6QFE6T8s7VeLTwW/hvGD/rg\ng85KSkpSlMPQ0F7tfM63pmG92inK4V+X+jbJVZsPAhHI5gMAAAAAQPhQ/A8AAAAAiBiVlZW67777\ndP755ysvr/5HwT/yyCN6//33bc4MAAAAANAoWHX+d7nsyQOIQP50ob//kwy1LdxtHXf2sfqh9Y+H\nXlh3pvTtJC9WcmnChNXq16/bwTPpfdv7kOmh0vt18HtsuDcfNGp0/gcAAADQzFH8DwAAAACIGO+8\n846mTJki0+JLviuuuEJbtmyxKSsAAAAAQKNhVfxP538gZHztZv+nDcs0bM0nlnGbTjpWT/Rde+iF\nwlRp7v9JirKco3fvLD344KW1zh3dOsnvDQDpfdsrLbXhpwx4O0dA4wPYfAAAAAAACB+K/wEAAAAA\nEWPIkCEaMWKEZdzu3bv1t7/9TeXl5TZkBcAbu3ZJo0dLOTnhzgQAAADNGsX/QNj40s2+patAj3zw\ntGVcRWK8/jxwo8y6lRFuQ3rpJanyKMs5EhMX6OOP/yajno7ykwZ30YBOrb3KudqATq01aXAXn8bU\nJ9ybDxotOv8DAAAAaOYo/gcAAAAARAzDMPT888/ruOOOs4z98ssvde+999qQFQBP3G7p+eelzp2l\nzEzpxhsl9uUAAAAgZCj+B8LK22L2Bz5+Tm2K9lrG3XmutDHFfeiFD26Tdl3oxUobtGDBkUpJaVnv\n1RinQxmj+nidd3rf9soY1UcxzuCUaoRz8wEAAAAAIDwo/gcAAAAARJQWLVpo7ty5iouLs4ydPn26\nFixYYENWAOqzapV02mnS2LHSnj1V59aulZ58Mrx5AQAAoBmLj/d83eWyJw8gQnnTzf689V9qyA9L\nLOf6slOCZnWv58/sz6dIyx/1Ipsy3Xbbcp11Vm+PUTFOh6YO6abF4wZqdP80pSRE17qekhCt0f3T\ntHjcQE0d0i1ohf/Va4dz80GjROd/AAAAAM2cM9wJAAAAAABgtx49euipp57SmDFjLGOvvPJKrVq1\nSh07dgx9YgAkSfn50sSJ0uzZVZ3/63rgAelvf5PatbM9NQAAADR3Vp3/S0qq/ifV0YwLZ4EwmzS4\ni7budSlnfd4h11oV79PUD/9lOUeFErV+/11KLXhKu1rs+v2Cq6X02muSohscW+3EE1/WjBmjDzlf\n6Ta1s6BERaUVSox1qk1ynKIchtJSEzVx0Ikaf8EJ9V4PlerNB6PPOFrZX2/WvFVblV/8+yPzUhKi\nNaxXO6X366C01MSQ5QEAAAAAsAfF/wAAAACAiHTttdcqJydHr776qse4vXv3asSIEfr8888VExNj\nU3ZA5Covl3r2lH75peGYoiLpttukuXNtSwseNFT4AgAA0CRZFf9LVd3/EymgBUKlupv95AVrlb0s\nt9a1Bxc9q9TifZZzbNAN6rjjTD33XFc9OOxBfZf2nWRKejlDKk+zHB8X95Fycv4qo0YX+Y15hcpe\nlqs36ymuH9qrnS4/UFwf5TDUtqXFU0RCIFybDxodOv8DAAAAaOYo/gcAAAAARCTDMDR79mytWLFC\n69at8xi7fPly3XXXXZo1a5ZN2QGRKzpauu466d57PcfNmyd9+KF03nn25IVDeVv4AgAA0KR4U/xf\nXEzxPxBi9XWzP33lpxq07nPLsbvVVzv0F0lSq6JWmpE1Q5MunaQvtnWVdgz3YvUtmjevhVJT/yBJ\nKqtw17sRoVp+cbkyl25S5tJNSu/bXpMGd1GMM3xPBwnX5oMmg+J/AAAAAE0cz6MEAAAAAESspKQk\nzZ07V/Hx1l+IPvnkk3rrrbdsyArAHXdInTtbx910k1RSEvp8UFtZhVsT5q/W2TNylLl0U63Cf+n3\nwpezpi/RhPmrVVbhDlOmAAAAfvDi/aFcrtDnAUDS793sV47prqeWvmAZX64k/U93SPq9+/uOlB36\nzlkhfe5NU4cKjR79qS68sJ+kqvc/Y7JWNFj4X1f2slyNyVrB+6Bwsur8DwAAAABNHMX/AAAAAICI\n1rVrV82ePdur2GuuuUY///xziDMCEBMj/etf1nEbNkiPPx76fPA7Cl8AAECz523nfwD2MU1F3XiD\nHHt2W4Zu0M0qU+uDx6XOUk0a8qgK570kKc5y/DHHZOnZZy8/eDx5wVrlrM/zKd2c9XmavGCtT2Ng\nIzr/AwAAAGjiKP4HAAAAAES8K6+8UldffbVl3L59+3TppZeqhFbjQMj96U/SiBHWcQ8/LG3aFPp8\nUIXCFwAA0OxR/A80PnPmSPPnW4bt0mn6TefUOvfEoCf086I7pNLjLcdHR+fos88uUFRUlCRpY16h\n1xuf68pelqtNu4r8GuuvSrep7ftc2rCzQNv3uVTpjtAidzr/AwAAAGjmKP4HAAAAAEDS008/ra5d\nu1rGrVq1SnfccYcNGQF44gkpOdlzTEmJdPPNNO6zQ1MrfAEAAPALxf9A47Jtm3TTTZZhJUai1usO\nSb8Xfr998tv6qOBwactVXiz0m1591dCRRx5x8Iy/738Ojv96c0DjvbUxr1APLfxBvacs0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/P/1n3f+Hp5W/p9JwzcW6TW3Jf9egyUd6i4+2Km98qjbOnaSF08cQ/If/MPkf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\nzaqDpczw3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VU/t2jRAqtWrcLMmTNlb+/NN9/EVVddhVOnToVcRs+M9D56D9QVY4bZkvQe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axmRfjRLm5euQDRa/QHNG/Zlqg7YGd1Sdas861fPcXMYhqzAsSxMKOIK1yRRlVt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\niThA3FGXayBRGO0Mv3zTH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Errors for only the time after inflection point\")\n", + "for f in [fb1, fb2, fb3, fb10, fb100]:\n", + " print(\"\\td=%i: %f\" % (f.order, error(f, xb, yb)))\n", + "\n", + "plot_web_traffic(\n", + " x, y, [fbt1, fbt2, fbt3, fbt10, fbt100], \n", + " mx=np.linspace(0, 6 * 7 * 24, 100),\n", + " ymax=10000,\n", + " fig_idx=\"08\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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I1oHV61J2YToK+AawJCLOi4ijI+KhAx3p5BvUd3yi75W6+f6OcbZZnfPbzvc1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\nrHoBsG9m/rWPbm/qo07V6Y3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rknBg/2/9QCAAAbKRr0I0mSDI2I8yPijIjY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1Vl1P0AAAAAAAAICeVOt0AwAAAABUUCVoUF/6A9HA6NXnjzyman0urwIAQDv8+c9/zuDgYKfb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\n0aJFSZJ/+Zd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AAAAgmrjdNb9P0AAA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3fDDTBjBjzyCOTnZy6f1FhZNJAkNXg9esDzz8PgwbBmzTePde4Mzz0H++4bTjZJkiSpxqIp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\nrGgAAAAqaOvWrZo2bZp++eUXv+fabDb94x//0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BgwYxaNAgjh49yhdffMHnn3/OsWPHnDL/li1b2LJlC8888wx9+/Zl6NCh9OzZ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\nggYiIiIiIiIiIiIiIiIiJU5BAxERkZtcWbh7sKM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_web_traffic(\n", + " x, y, [fbt1, fbt2, fbt3, fbt10, fbt100], \n", + " mx=np.linspace(0, 12 * 7 * 24, 100),\n", + " ymax=100000,\n", + " fig_idx=\"09\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## So, when will we hit 100,000 hits per hour?" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fbt2(x)= \n", + " 2\n", + "0.05404 x - 50.39 x + 1.262e+04\n", + "fbt2(x)-100,000= \n", + " 2\n", + "0.05404 x - 50.39 x - 8.738e+04\n", + "100,000 hits/hour expected at week 10.836350\n" + ] + } + ], + "source": [ + "fbt2 = np.poly1d(np.polyfit(xb[train], yb[train], 2))\n", + "print(\"fbt2(x)= \\n%s\" % fbt2)\n", + "print(\"fbt2(x)-100,000= \\n%s\" % (fbt2-100000))\n", + "\n", + "from scipy.optimize import fsolve\n", + "reached_max = fsolve(fbt2-100000, x0=800)/(7*24)\n", + "print(\"100,000 hits/hour expected at week %f\" % reached_max[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch01_3rd/data/web_traffic.tsv b/ch01_3rd/data/web_traffic.tsv new file mode 100644 index 00000000..87787147 --- /dev/null +++ b/ch01_3rd/data/web_traffic.tsv @@ -0,0 +1,743 @@ +1.000000 2273.331055 +2.000000 1657.255493 +3.000000 nan +4.000000 1366.846436 +5.000000 1489.234375 +6.000000 1338.020020 +7.000000 1884.647339 +8.000000 2284.754150 +9.000000 1335.810913 +10.000000 1025.832397 +11.000000 1140.241089 +12.000000 1478.341797 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3998.185547 +726.000000 4358.066406 +727.000000 4323.617188 +728.000000 4157.835938 +729.000000 4630.654297 +730.000000 4415.905273 +731.000000 4411.992188 +732.000000 4725.586426 +733.000000 4364.381348 +734.000000 4800.028809 +735.000000 4749.926758 +736.000000 5144.264160 +737.000000 4907.322754 +738.000000 4310.609375 +739.000000 4971.517578 +740.000000 4815.629395 +741.000000 5393.541992 +742.000000 5906.814941 +743.000000 4883.022461 diff --git a/ch02/.gitignore b/ch02/.gitignore new file mode 100644 index 00000000..c0bfc79f --- /dev/null +++ b/ch02/.gitignore @@ -0,0 +1,2 @@ +decision.dot +.ipynb_checkpoints/ diff --git a/ch02/README.rst b/ch02/README.rst index e2cb729a..1fba0065 100644 --- a/ch02/README.rst +++ b/ch02/README.rst @@ -6,50 +6,8 @@ Support code for *Chapter 2: Learning How to Classify with Real-world Examples*. The directory data contains the seeds dataset, originally downloaded from https://archive.ics.uci.edu/ml/datasets/seeds -chapter.py - The code as printed in the book. - -figure1.py - Figure 1 in the book: all 2-by-2 scatter plots - -figure2.py - Figure 2 in the book: threshold & decision area - -figure4_5_sklearn.py - Figures 4 and 5 in the book: Knn decision borders before and after feature - normalization. This also produces a version of the figure using 11 - neighbors (not in the book), which shows that the result is smoother, not - as sensitive to exact positions of each datapoint. - -figure4_5_no_sklearn.py - Alternative code for Figures 4 and 5 without using scikit-learn - +chapter_02.py + The code from the book (with a few extras) load.py Code to load the seeds data -simple_threshold.py - Code from the book: finds the first partition, between Setosa and the other classes. - -stump.py - Code from the book: finds the second partition, between Virginica and Versicolor. - -threshold.py - Functional implementation of a threshold classifier - -heldout.py - Evalute the threshold model on heldout data - -seeds_knn_sklearn.py - Demonstrate cross-validation and feature normalization using scikit-learn - -seeds_threshold.py - Test thresholding model on the seeds dataset (result mention in book, but no code) - -seeds_knn_increasing_k.py - Test effect of increasing num_neighbors on accuracy. - -knn.py - Implementation of K-Nearest neighbor without using scikit-learn. - -seeds_knn.py - Demonstrate cross-validation (without scikit-learn) diff --git a/ch02/chapter.py b/ch02/chapter.py deleted file mode 100644 index c68b45ab..00000000 --- a/ch02/chapter.py +++ /dev/null @@ -1,164 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - - -from matplotlib import pyplot as plt -import numpy as np - -# We load the data with load_iris from sklearn -from sklearn.datasets import load_iris -data = load_iris() - -# load_iris returns an object with several fields -features = data.data -feature_names = data.feature_names -target = data.target -target_names = data.target_names - -for t in range(3): - if t == 0: - c = 'r' - marker = '>' - elif t == 1: - c = 'g' - marker = 'o' - elif t == 2: - c = 'b' - marker = 'x' - plt.scatter(features[target == t, 0], - features[target == t, 1], - marker=marker, - c=c) -# We use NumPy fancy indexing to get an array of strings: -labels = target_names[target] - -# The petal length is the feature at position 2 -plength = features[:, 2] - -# Build an array of booleans: -is_setosa = (labels == 'setosa') - -# This is the important step: -max_setosa =plength[is_setosa].max() -min_non_setosa = plength[~is_setosa].min() -print('Maximum of setosa: {0}.'.format(max_setosa)) - -print('Minimum of others: {0}.'.format(min_non_setosa)) - -# ~ is the boolean negation operator -features = features[~is_setosa] -labels = labels[~is_setosa] -# Build a new target variable, is_virigina -is_virginica = (labels == 'virginica') - -# Initialize best_acc to impossibly low value -best_acc = -1.0 -for fi in range(features.shape[1]): - # We are going to test all possible thresholds - thresh = features[:,fi] - for t in thresh: - - # Get the vector for feature `fi` - feature_i = features[:, fi] - # apply threshold `t` - pred = (feature_i > t) - acc = (pred == is_virginica).mean() - rev_acc = (pred == ~is_virginica).mean() - if rev_acc > acc: - reverse = True - acc = rev_acc - else: - reverse = False - - if acc > best_acc: - best_acc = acc - best_fi = fi - best_t = t - best_reverse = reverse - -print(best_fi, best_t, best_reverse, best_acc) - -def is_virginica_test(fi, t, reverse, example): - 'Apply threshold model to a new example' - test = example[fi] > t - if reverse: - test = not test - return test -from threshold import fit_model, predict - -# ning accuracy was 96.0%. -# ing accuracy was 90.0% (N = 50). -correct = 0.0 - -for ei in range(len(features)): - # select all but the one at position `ei`: - training = np.ones(len(features), bool) - training[ei] = False - testing = ~training - model = fit_model(features[training], is_virginica[training]) - predictions = predict(model, features[testing]) - correct += np.sum(predictions == is_virginica[testing]) -acc = correct/float(len(features)) -print('Accuracy: {0:.1%}'.format(acc)) - - -########################################### -############## SEEDS DATASET ############## -########################################### - -from load import load_dataset - -feature_names = [ - 'area', - 'perimeter', - 'compactness', - 'length of kernel', - 'width of kernel', - 'asymmetry coefficien', - 'length of kernel groove', -] -features, labels = load_dataset('seeds') - - - -from sklearn.neighbors import KNeighborsClassifier -classifier = KNeighborsClassifier(n_neighbors=1) -from sklearn.cross_validation import KFold - -kf = KFold(len(features), n_folds=5, shuffle=True) -means = [] -for training,testing in kf: - # We learn a model for this fold with `fit` and then apply it to the - # testing data with `predict`: - classifier.fit(features[training], labels[training]) - prediction = classifier.predict(features[testing]) - - # np.mean on an array of booleans returns fraction - # of correct decisions for this fold: - curmean = np.mean(prediction == labels[testing]) - means.append(curmean) -print('Mean accuracy: {:.1%}'.format(np.mean(means))) - - -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler - -classifier = KNeighborsClassifier(n_neighbors=1) -classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)]) - -means = [] -for training,testing in kf: - # We learn a model for this fold with `fit` and then apply it to the - # testing data with `predict`: - classifier.fit(features[training], labels[training]) - prediction = classifier.predict(features[testing]) - - # np.mean on an array of booleans returns fraction - # of correct decisions for this fold: - curmean = np.mean(prediction == labels[testing]) - means.append(curmean) -print('Mean accuracy: {:.1%}'.format(np.mean(means))) diff --git a/ch02/chapter_02.ipynb b/ch02/chapter_02.ipynb new file mode 100644 index 00000000..9ef9e030 --- /dev/null +++ b/ch02/chapter_02.ipynb @@ -0,0 +1,786 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 11\n", + "\n", + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Magic command to get inline plots:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We load the data with `load_iris` from `sklearn`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "data = load_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`load_iris` returns an object with several fields" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "features = data.data\n", + "feature_names = data.feature_names\n", + "target = data.target\n", + "target_names = data.target_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use NumPy fancy indexing to get an array of strings:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "labels = target_names[target]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot all the 2D projections of the features (since there are only 4 of them):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axes = plt.subplots(2, 3)\n", + "pairs = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n", + "\n", + "# Set up 3 different pairs of (color, marker)\n", + "color_markers = [\n", + " ('r', '>'),\n", + " ('g', 'o'),\n", + " ('b', 'x'),\n", + " ]\n", + "for i, (p0, p1) in enumerate(pairs):\n", + " ax = axes.flat[i]\n", + "\n", + " for t in range(3):\n", + " # Use a different color/marker for each class `t`\n", + " c,marker = color_markers[t]\n", + " ax.scatter(features[target == t, p0], features[\n", + " target == t, p1], marker=marker, c=c)\n", + " ax.set_xlabel(feature_names[p0])\n", + " ax.set_ylabel(feature_names[p1])\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learning a classification model (a decision tree):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import tree\n", + "\n", + "tr = tree.DecisionTreeClassifier(min_samples_leaf=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fit` performs the learning (fits the model):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=10, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tr.fit(features, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the decision tree (using an intermediate file):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "Tree\r\n", + "\r\n", + "\r\n", + "0\r\n", + "\r\n", + "petal width (cm) <= 0.8\r\n", + "gini = 0.667\r\n", + "samples = 150\r\n", + "value = [50, 50, 50]\r\n", + "\r\n", + "\r\n", + "1\r\n", + "\r\n", + "gini = 0.0\r\n", + "samples = 50\r\n", + "value = [50, 0, 0]\r\n", + "\r\n", + "\r\n", + "0->1\r\n", + "\r\n", + "\r\n", + "True\r\n", + "\r\n", + "\r\n", + "2\r\n", + "\r\n", + "petal width (cm) <= 1.75\r\n", + "gini = 0.5\r\n", + "samples = 100\r\n", + "value = [0, 50, 50]\r\n", + "\r\n", + "\r\n", + "0->2\r\n", + "\r\n", + "\r\n", + "False\r\n", + "\r\n", + "\r\n", + "3\r\n", + "\r\n", + "petal length (cm) <= 4.65\r\n", + "gini = 0.168\r\n", + "samples = 54\r\n", + "value = [0, 49, 5]\r\n", + "\r\n", + "\r\n", + "2->3\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "8\r\n", + "\r\n", + "sepal length (cm) <= 6.25\r\n", + "gini = 0.043\r\n", + "samples = 46\r\n", + "value = [0, 1, 45]\r\n", + "\r\n", + "\r\n", + "2->8\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "4\r\n", + "\r\n", + "petal length (cm) <= 4.45\r\n", + "gini = 0.049\r\n", + "samples = 40\r\n", + "value = [0, 39, 1]\r\n", + "\r\n", + "\r\n", + "3->4\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "7\r\n", + "\r\n", + "gini = 0.408\r\n", + "samples = 14\r\n", + "value = [0, 10, 4]\r\n", + "\r\n", + "\r\n", + "3->7\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "5\r\n", + "\r\n", + "gini = 0.0\r\n", + "samples = 29\r\n", + "value = [0, 29, 0]\r\n", + "\r\n", + "\r\n", + "4->5\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "6\r\n", + "\r\n", + "gini = 0.165\r\n", + "samples = 11\r\n", + "value = [0, 10, 1]\r\n", + "\r\n", + "\r\n", + "4->6\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "9\r\n", + "\r\n", + "gini = 0.165\r\n", + "samples = 11\r\n", + "value = [0, 1, 10]\r\n", + "\r\n", + "\r\n", + "8->9\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "10\r\n", + "\r\n", + "gini = 0.0\r\n", + "samples = 35\r\n", + "value = [0, 0, 35]\r\n", + "\r\n", + "\r\n", + "8->10\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n", + "\r\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import graphviz\n", + "tree.export_graphviz(tr, feature_names=feature_names, rounded=True, out_file='decision.dot')\n", + "\n", + "graphviz.Source(open('decision.dot').read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluating performance on the training set (which is **not the right way to do it**: see the discussion in the book)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 96.0%\n" + ] + } + ], + "source": [ + "prediction = tr.predict(features)\n", + "print(\"Accuracy: {:.1%}\".format(np.mean(prediction == labels)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Leave-one-out crossvalidation (a good way to do it):" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = []\n", + "for i in range(len(features)):\n", + " train_features = np.delete(features, i, axis=0)\n", + " train_labels = np.delete(labels, i, axis=0)\n", + " tr.fit(train_features, train_labels)\n", + " predictions.append(tr.predict([features[i]]))\n", + "predictions = np.array(predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the LOO predictions with the gold set:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy (with LOO CV): 95.3%\n" + ] + } + ], + "source": [ + "print(\"Accuracy (with LOO CV): {:.1%}\".format(np.mean(predictions.ravel() == labels)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can do the same leave-on-out cross validation with scikit-learn's `model_selection` module:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.953333333333\n" + ] + } + ], + "source": [ + "from sklearn import model_selection\n", + "\n", + "predictions = model_selection.cross_val_predict(\n", + " tr,\n", + " features,\n", + " labels,\n", + " cv=model_selection.LeaveOneOut())\n", + "print(np.mean(predictions == labels))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The seeds dataset\n", + "\n", + "A slightly more complex dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from load import load_dataset\n", + "\n", + "feature_names = [\n", + " 'area',\n", + " 'perimeter',\n", + " 'compactness',\n", + " 'length of kernel',\n", + " 'width of kernel',\n", + " 'asymmetry coefficien',\n", + " 'length of kernel groove',\n", + "]\n", + "data = load_dataset('seeds')\n", + "features = data['features']\n", + "target = data['target']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now try a _nearest neighbors_ classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Everything is similar to what we had before" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean accuracy: 83.8%\n", + "Mean accuracy: 86.7%\n" + ] + } + ], + "source": [ + "kf = model_selection.KFold(n_splits=5, shuffle=False)\n", + "means = []\n", + "for training,testing in kf.split(features):\n", + " # We learn a model for this fold with `fit` and then apply it to the\n", + " # testing data with `predict`:\n", + " knn.fit(features[training], target[training])\n", + " prediction = knn.predict(features[testing])\n", + "\n", + " # np.mean on an array of booleans returns fraction\n", + " # of correct decisions for this fold:\n", + " curmean = np.mean(prediction == target[testing])\n", + " means.append(curmean)\n", + "print('Mean accuracy: {:.1%}'.format(np.mean(means)))\n", + "\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "classifier = KNeighborsClassifier(n_neighbors=5)\n", + "classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)])\n", + "\n", + "means = []\n", + "for training,testing in kf.split(features):\n", + " # We learn a model for this fold with `fit` and then apply it to the\n", + " # testing data with `predict`:\n", + " classifier.fit(features[training], target[training])\n", + " prediction = classifier.predict(features[testing])\n", + "\n", + " # np.mean on an array of booleans returns fraction\n", + " # of correct decisions for this fold:\n", + " curmean = np.mean(prediction == target[testing])\n", + " means.append(curmean)\n", + "print('Mean accuracy: {:.1%}'.format(np.mean(means)))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_decision_space(clf, features, target, use_color=True):\n", + " from matplotlib.colors import ListedColormap\n", + "\n", + " clf.fit(features[:, [0,2]], target)\n", + "\n", + " y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1\n", + " x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1\n", + " X = np.linspace(x0, x1, 1000)\n", + " Y = np.linspace(y0, y1, 1000)\n", + " X, Y = np.meshgrid(X, Y)\n", + " C = clf.predict(np.vstack([X.ravel(), Y.ravel()]).T).reshape(X.shape)\n", + " if use_color:\n", + " cmap = ListedColormap([(1., .7, .7), (.7, 1., .7), (.7, .7, 1.)])\n", + " else:\n", + " cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])\n", + "\n", + " fig,ax = plt.subplots()\n", + " ax.scatter(features[:, 0], features[:, 2], c=target, cmap=cmap)\n", + " for lab, ma in zip(range(3), \"Do^\"):\n", + " ax.plot(features[target == lab, 0], features[\n", + " target == lab, 2], ma, c=(1., 1., 1.), ms=6)\n", + "\n", + " ax.set_xlim(x0, x1)\n", + " ax.set_ylim(y0, y1)\n", + " ax.set_xlabel(feature_names[0])\n", + " ax.set_ylabel(feature_names[2])\n", + " ax.pcolormesh(X, Y, C, cmap=cmap)\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_decision_space(knn, features, target)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "classifier = KNeighborsClassifier(n_neighbors=1)\n", + "classifier = Pipeline([('norm', StandardScaler()),\n", + " ('knn', classifier)])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_decision_space(classifier, features, target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random forests\n", + "\n", + "See the excellent paper [Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?](http://jmlr.org/papers/v15/delgado14a.html) by Delgado et al. (2014) to understand why we recommend random forests as the default classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import ensemble\n", + "rf = ensemble.RandomForestClassifier(n_estimators=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, use cross-validation to evaluate:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF accuracy: 86.7%\n" + ] + } + ], + "source": [ + "predict = model_selection.cross_val_predict(rf, features, target)\n", + "print(\"RF accuracy: {:.1%}\".format(np.mean(predict == target)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not in the book, but we can also plot the decision function for a random forest:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_= plot_decision_space(rf, features, target)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch02/extra/create_tsv.py b/ch02/extra/create_tsv.py deleted file mode 100644 index e6d7b4fd..00000000 --- a/ch02/extra/create_tsv.py +++ /dev/null @@ -1,18 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import milksets.seeds - - -def save_as_tsv(fname, module): - features, labels = module.load() - nlabels = [module.label_names[ell] for ell in labels] - with open(fname, 'w') as ofile: - for f, n in zip(features, nlabels): - print >>ofile, "\t".join(map(str, f) + [n]) - -save_as_tsv('seeds.tsv', milksets.seeds) diff --git a/ch02/figure1.py b/ch02/figure1.py deleted file mode 100644 index 4ec6fff8..00000000 --- a/ch02/figure1.py +++ /dev/null @@ -1,42 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from matplotlib import pyplot as plt - -# We load the data with load_iris from sklearn -from sklearn.datasets import load_iris - -# load_iris returns an object with several fields -data = load_iris() -features = data.data -feature_names = data.feature_names -target = data.target -target_names = data.target_names - -fig,axes = plt.subplots(2, 3) -pairs = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)] - -# Set up 3 different pairs of (color, marker) -color_markers = [ - ('r', '>'), - ('g', 'o'), - ('b', 'x'), - ] -for i, (p0, p1) in enumerate(pairs): - ax = axes.flat[i] - - for t in range(3): - # Use a different color/marker for each class `t` - c,marker = color_markers[t] - ax.scatter(features[target == t, p0], features[ - target == t, p1], marker=marker, c=c) - ax.set_xlabel(feature_names[p0]) - ax.set_ylabel(feature_names[p1]) - ax.set_xticks([]) - ax.set_yticks([]) -fig.tight_layout() -fig.savefig('figure1.png') diff --git a/ch02/figure2.py b/ch02/figure2.py deleted file mode 100644 index 0b69d395..00000000 --- a/ch02/figure2.py +++ /dev/null @@ -1,63 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -COLOUR_FIGURE = False - -from matplotlib import pyplot as plt -from sklearn.datasets import load_iris -data = load_iris() -features = data.data -feature_names = data.feature_names -target = data.target -target_names = data.target_names - -# We use NumPy fancy indexing to get an array of strings: -labels = target_names[target] - -is_setosa = (labels == 'setosa') -features = features[~is_setosa] -labels = labels[~is_setosa] -is_virginica = (labels == 'virginica') - -# Hand fixed thresholds: -t = 1.65 -t2 = 1.75 - -# Features to use: 3 & 2 -f0, f1 = 3, 2 - -if COLOUR_FIGURE: - area1c = (1., .8, .8) - area2c = (.8, .8, 1.) -else: - area1c = (1., 1, 1) - area2c = (.7, .7, .7) - -# Plot from 90% of smallest value to 110% of largest value -# (all feature values are positive, otherwise this would not work very well) - -x0 = features[:, f0].min() * .9 -x1 = features[:, f0].max() * 1.1 - -y0 = features[:, f1].min() * .9 -y1 = features[:, f1].max() * 1.1 - -fig,ax = plt.subplots() -ax.fill_between([t, x1], [y0, y0], [y1, y1], color=area2c) -ax.fill_between([x0, t], [y0, y0], [y1, y1], color=area1c) -ax.plot([t, t], [y0, y1], 'k--', lw=2) -ax.plot([t2, t2], [y0, y1], 'k:', lw=2) -ax.scatter(features[is_virginica, f0], - features[is_virginica, f1], c='b', marker='o', s=40) -ax.scatter(features[~is_virginica, f0], - features[~is_virginica, f1], c='r', marker='x', s=40) -ax.set_ylim(y0, y1) -ax.set_xlim(x0, x1) -ax.set_xlabel(feature_names[f0]) -ax.set_ylabel(feature_names[f1]) -fig.tight_layout() -fig.savefig('figure2.png') diff --git a/ch02/figure4_5_no_sklearn.py b/ch02/figure4_5_no_sklearn.py deleted file mode 100644 index adc83d73..00000000 --- a/ch02/figure4_5_no_sklearn.py +++ /dev/null @@ -1,79 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -COLOUR_FIGURE = False - -from matplotlib import pyplot as plt -from matplotlib.colors import ListedColormap -from load import load_dataset -import numpy as np -from knn import fit_model, predict - -feature_names = [ - 'area', - 'perimeter', - 'compactness', - 'length of kernel', - 'width of kernel', - 'asymmetry coefficien', - 'length of kernel groove', -] - - -def plot_decision(features, labels): - '''Plots decision boundary for KNN - - Parameters - ---------- - features : ndarray - labels : sequence - - Returns - ------- - fig : Matplotlib Figure - ax : Matplotlib Axes - ''' - y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1 - x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1 - X = np.linspace(x0, x1, 100) - Y = np.linspace(y0, y1, 100) - X, Y = np.meshgrid(X, Y) - - model = fit_model(1, features[:, (0, 2)], np.array(labels)) - C = predict( - model, np.vstack([X.ravel(), Y.ravel()]).T).reshape(X.shape) - if COLOUR_FIGURE: - cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)]) - else: - cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)]) - fig,ax = plt.subplots() - ax.set_xlim(x0, x1) - ax.set_ylim(y0, y1) - ax.set_xlabel(feature_names[0]) - ax.set_ylabel(feature_names[2]) - ax.pcolormesh(X, Y, C, cmap=cmap) - if COLOUR_FIGURE: - cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)]) - ax.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap) - else: - for lab, ma in zip(range(3), "Do^"): - ax.plot(features[labels == lab, 0], features[ - labels == lab, 2], ma, c=(1., 1., 1.)) - return fig,ax - - -features, labels = load_dataset('seeds') -names = sorted(set(labels)) -labels = np.array([names.index(ell) for ell in labels]) - -fig,ax = plot_decision(features, labels) -fig.savefig('figure4.png') - -features -= features.mean(0) -features /= features.std(0) -fig,ax = plot_decision(features, labels) -fig.savefig('figure5.png') diff --git a/ch02/figure4_5_sklearn.py b/ch02/figure4_5_sklearn.py deleted file mode 100644 index 55ac0c80..00000000 --- a/ch02/figure4_5_sklearn.py +++ /dev/null @@ -1,85 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -COLOUR_FIGURE = False - -from matplotlib import pyplot as plt -from matplotlib.colors import ListedColormap -from load import load_dataset -import numpy as np -from sklearn.neighbors import KNeighborsClassifier - -feature_names = [ - 'area', - 'perimeter', - 'compactness', - 'length of kernel', - 'width of kernel', - 'asymmetry coefficien', - 'length of kernel groove', -] - - -def plot_decision(features, labels, num_neighbors=1): - '''Plots decision boundary for KNN - - Parameters - ---------- - features : ndarray - labels : sequence - - Returns - ------- - fig : Matplotlib Figure - ax : Matplotlib Axes - ''' - y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1 - x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1 - X = np.linspace(x0, x1, 1000) - Y = np.linspace(y0, y1, 1000) - X, Y = np.meshgrid(X, Y) - - model = KNeighborsClassifier(num_neighbors) - model.fit(features[:, (0,2)], labels) - C = model.predict(np.vstack([X.ravel(), Y.ravel()]).T).reshape(X.shape) - if COLOUR_FIGURE: - cmap = ListedColormap([(1., .7, .7), (.7, 1., .7), (.7, .7, 1.)]) - else: - cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)]) - fig,ax = plt.subplots() - ax.set_xlim(x0, x1) - ax.set_ylim(y0, y1) - ax.set_xlabel(feature_names[0]) - ax.set_ylabel(feature_names[2]) - ax.pcolormesh(X, Y, C, cmap=cmap) - if COLOUR_FIGURE: - cmap = ListedColormap([(1., .0, .0), (.1, .6, .1), (.0, .0, 1.)]) - ax.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap) - else: - for lab, ma in zip(range(3), "Do^"): - ax.plot(features[labels == lab, 0], features[ - labels == lab, 2], ma, c=(1., 1., 1.), ms=6) - return fig,ax - - -features, labels = load_dataset('seeds') -names = sorted(set(labels)) -labels = np.array([names.index(ell) for ell in labels]) - -fig,ax = plot_decision(features, labels) -fig.tight_layout() -fig.savefig('figure4sklearn.png') - -features -= features.mean(0) -features /= features.std(0) -fig,ax = plot_decision(features, labels) -fig.tight_layout() -fig.savefig('figure5sklearn.png') - -fig,ax = plot_decision(features, labels, 11) -fig.tight_layout() -fig.savefig('figure5sklearn_with_11_neighbors.png') diff --git a/ch02/heldout.py b/ch02/heldout.py deleted file mode 100644 index e381e706..00000000 --- a/ch02/heldout.py +++ /dev/null @@ -1,41 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# This script demonstrates the difference between the training accuracy and -# testing (held-out) accuracy. - -import numpy as np -from sklearn.datasets import load_iris -from threshold import fit_model, accuracy - -data = load_iris() -features = data['data'] -labels = data['target_names'][data['target']] - -# We are going to remove the setosa examples as they are too easy: -is_setosa = (labels == 'setosa') -features = features[~is_setosa] -labels = labels[~is_setosa] - -# Now we classify virginica vs non-virginica -is_virginica = (labels == 'virginica') - -# Split the data in two: testing and training -testing = np.tile([True, False], 50) # testing = [True,False,True,False,True,False...] - -# Training is the negation of testing: i.e., datapoints not used for testing, -# will be used for training -training = ~testing - -model = fit_model(features[training], is_virginica[training]) -train_accuracy = accuracy(features[training], is_virginica[training], model) -test_accuracy = accuracy(features[testing], is_virginica[testing], model) - -print('''\ -Training accuracy was {0:.1%}. -Testing accuracy was {1:.1%} (N = {2}). -'''.format(train_accuracy, test_accuracy, testing.sum())) diff --git a/ch02/knn.py b/ch02/knn.py deleted file mode 100644 index 89ebfdb4..00000000 --- a/ch02/knn.py +++ /dev/null @@ -1,46 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np - -# This function was called ``learn_model`` in the first edition -def fit_model(k, features, labels): - '''Learn a k-nn model''' - # There is no model in k-nn, just a copy of the inputs - return k, features.copy(), labels.copy() - - -def plurality(xs): - '''Find the most common element in a collection''' - from collections import defaultdict - counts = defaultdict(int) - for x in xs: - counts[x] += 1 - maxv = max(counts.values()) - for k, v in counts.items(): - if v == maxv: - return k - -# This function was called ``apply_model`` in the first edition -def predict(model, features): - '''Apply k-nn model''' - k, train_feats, labels = model - results = [] - for f in features: - label_dist = [] - # Compute all distances: - for t, ell in zip(train_feats, labels): - label_dist.append((np.linalg.norm(f - t), ell)) - label_dist.sort(key=lambda d_ell: d_ell[0]) - label_dist = label_dist[:k] - results.append(plurality([ell for _, ell in label_dist])) - return np.array(results) - - -def accuracy(features, labels, model): - preds = predict(model, features) - return np.mean(preds == labels) diff --git a/ch02/load.py b/ch02/load.py index e508a682..e0c4a356 100644 --- a/ch02/load.py +++ b/ch02/load.py @@ -10,22 +10,30 @@ def load_dataset(dataset_name): ''' - data,labels = load_dataset(dataset_name) + data = load_dataset(dataset_name) Load a given dataset Returns ------- - data : numpy ndarray - labels : list of str + data : dictionary ''' - data = [] - labels = [] + features = [] + target = [] + target_names = set() with open('./data/{0}.tsv'.format(dataset_name)) as ifile: for line in ifile: tokens = line.strip().split('\t') - data.append([float(tk) for tk in tokens[:-1]]) - labels.append(tokens[-1]) - data = np.array(data) - labels = np.array(labels) - return data, labels + features.append([float(tk) for tk in tokens[:-1]]) + target.append(tokens[-1]) + target_names.add(tokens[-1]) + features = np.array(features) + + target_names = list(target_names) + target_names.sort() + target = np.array([target_names.index(t) for t in target]) + return { + 'features': features, + 'target_names': target_names, + 'target': target, + } diff --git a/ch02/seeds_knn.py b/ch02/seeds_knn.py deleted file mode 100644 index c18d9592..00000000 --- a/ch02/seeds_knn.py +++ /dev/null @@ -1,36 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from load import load_dataset -import numpy as np -from knn import fit_model, accuracy - -features, labels = load_dataset('seeds') - - -def cross_validate(features, labels): - '''Compute cross-validation errors''' - error = 0.0 - for fold in range(10): - training = np.ones(len(features), bool) - training[fold::10] = 0 - testing = ~training - model = fit_model(1, features[training], labels[training]) - test_error = accuracy(features[testing], labels[testing], model) - error += test_error - - return error / 10.0 - -error = cross_validate(features, labels) -print('Ten fold cross-validated error was {0:.1%}.'.format(error)) - -# Z-score (whiten) the features -features -= features.mean(0) -features /= features.std(0) -error = cross_validate(features, labels) -print( - 'Ten fold cross-validated error after z-scoring was {0:.1%}.'.format(error)) diff --git a/ch02/seeds_knn_increasing_k.py b/ch02/seeds_knn_increasing_k.py deleted file mode 100644 index 7cd8b3f9..00000000 --- a/ch02/seeds_knn_increasing_k.py +++ /dev/null @@ -1,48 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# Basic imports -from __future__ import print_function -import numpy as np -from matplotlib import pyplot as plt -from load import load_dataset - - -from sklearn.neighbors import KNeighborsClassifier - -from sklearn.cross_validation import cross_val_score -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler - - -features, labels = load_dataset('seeds') - -# Values of k to consider: all in 1 .. 160 -ks = np.arange(1,161) - -# We build a classifier object here with the default number of neighbors -# (It happens to be 5, but it does not matter as we will be changing it below -classifier = KNeighborsClassifier() -classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)]) - -# accuracies will hold our results -accuracies = [] -for k in ks: - # set the classifier parameter - classifier.set_params(knn__n_neighbors=k) - crossed = cross_val_score(classifier, features, labels) - - # Save only the average - accuracies.append(crossed.mean()) - -accuracies = np.array(accuracies) - -# Scale the accuracies by 100 to plot as a percentage instead of as a fraction -plt.plot(ks, accuracies*100) -plt.xlabel('Value for k (nr. of neighbors)') -plt.ylabel('Accuracy (%)') -plt.savefig('figure6.png') diff --git a/ch02/seeds_knn_sklearn.py b/ch02/seeds_knn_sklearn.py deleted file mode 100644 index ac89bb59..00000000 --- a/ch02/seeds_knn_sklearn.py +++ /dev/null @@ -1,90 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# Basic imports -from __future__ import print_function -import numpy as np -from load import load_dataset - - -# Import sklearn implementation of KNN -from sklearn.neighbors import KNeighborsClassifier - -features, labels = load_dataset('seeds') -classifier = KNeighborsClassifier(n_neighbors=4) - - -n = len(features) -correct = 0.0 -for ei in range(n): - training = np.ones(n, bool) - training[ei] = 0 - testing = ~training - classifier.fit(features[training], labels[training]) - pred = classifier.predict(features[ei]) - correct += (pred == labels[ei]) -print('Result of leave-one-out: {}'.format(correct/n)) - -# Import KFold object -from sklearn.cross_validation import KFold - -# means will hold the mean for each fold -means = [] - -# kf is a generator of pairs (training,testing) so that each iteration -# implements a separate fold. -kf = KFold(len(features), n_folds=3, shuffle=True) -for training,testing in kf: - # We learn a model for this fold with `fit` and then apply it to the - # testing data with `predict`: - classifier.fit(features[training], labels[training]) - prediction = classifier.predict(features[testing]) - - # np.mean on an array of booleans returns the fraction of correct decisions - # for this fold: - curmean = np.mean(prediction == labels[testing]) - means.append(curmean) -print('Result of cross-validation using KFold: {}'.format(means)) - -# The function cross_val_score does the same thing as the loop above with a -# single function call - -from sklearn.cross_validation import cross_val_score -crossed = cross_val_score(classifier, features, labels) -print('Result of cross-validation using cross_val_score: {}'.format(crossed)) - -# The results above use the features as is, which we learned was not optimal -# except if the features happen to all be in the same scale. We can pre-scale -# the features as explained in the main text: - -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)]) -crossed = cross_val_score(classifier, features, labels) -print('Result with prescaling: {}'.format(crossed)) - - -# Now, generate & print a cross-validated confusion matrix for the same result -from sklearn.metrics import confusion_matrix -names = list(set(labels)) -labels = np.array([names.index(ell) for ell in labels]) -preds = labels.copy() -preds[:] = -1 -for train, test in kf: - classifier.fit(features[train], labels[train]) - preds[test] = classifier.predict(features[test]) - -cmat = confusion_matrix(labels, preds) -print() -print('Confusion matrix: [rows represent true outcome, columns predicted outcome]') -print(cmat) - -# The explicit float() conversion is necessary in Python 2 -# (Otherwise, result is rounded to 0) -acc = cmat.trace()/float(cmat.sum()) -print('Accuracy: {0:.1%}'.format(acc)) - diff --git a/ch02/seeds_threshold.py b/ch02/seeds_threshold.py deleted file mode 100644 index 6b1f87d0..00000000 --- a/ch02/seeds_threshold.py +++ /dev/null @@ -1,33 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from load import load_dataset -import numpy as np -from threshold import fit_model, accuracy - -features, labels = load_dataset('seeds') - -# Turn the labels into a binary array -labels = (labels == 'Canadian') - -error = 0.0 -for fold in range(10): - training = np.ones(len(features), bool) - - # numpy magic to make an array with 10% of 0s starting at fold - training[fold::10] = 0 - - # whatever is not training is for testing - testing = ~training - - model = fit_model(features[training], labels[training]) - test_error = accuracy(features[testing], labels[testing], model) - error += test_error - -error /= 10.0 - -print('Ten fold cross-validated error was {0:.1%}.'.format(error)) diff --git a/ch02/simple_threshold.py b/ch02/simple_threshold.py deleted file mode 100644 index d174f283..00000000 --- a/ch02/simple_threshold.py +++ /dev/null @@ -1,25 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from sklearn.datasets import load_iris - -data = load_iris() -features = data['data'] -target = data['target'] -target_names = data['target_names'] -labels = target_names[target] -plength = features[:, 2] - -# To use numpy operations to get setosa features, -# we build a boolean array -is_setosa = (labels == 'setosa') - -max_setosa = plength[is_setosa].max() -min_non_setosa = plength[~is_setosa].min() - -print('Maximum of setosa: {0}.'.format(max_setosa)) -print('Minimum of others: {0}.'.format(min_non_setosa)) diff --git a/ch02/stump.py b/ch02/stump.py deleted file mode 100644 index 0dfaec85..00000000 --- a/ch02/stump.py +++ /dev/null @@ -1,55 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from sklearn.datasets import load_iris -data = load_iris() -features = data.data -labels = data.target_names[data.target] - - -is_setosa = (labels == 'setosa') -features = features[~is_setosa] -labels = labels[~is_setosa] -is_virginica = (labels == 'virginica') - - -# Initialize to a value that is worse than any possible test -best_acc = -1.0 - -# Loop over all the features -for fi in range(features.shape[1]): - # Test every possible threshold value for feature fi - thresh = features[:, fi].copy() - - # Test them in order - thresh.sort() - for t in thresh: - - # Generate predictions using t as a threshold - pred = (features[:, fi] > t) - - # Accuracy is the fraction of predictions that match reality - acc = (pred == is_virginica).mean() - - # We test whether negating the test is a better threshold: - acc_neg = ((~pred) == is_virginica).mean() - if acc_neg > acc: - acc = acc_neg - negated = True - else: - negated = False - - # If this is better than previous best, then this is now the new best: - - if acc > best_acc: - best_acc = acc - best_fi = fi - best_t = t - best_is_negated = negated - -print('Best threshold is {0} on feature {1} (index {2}), which achieves accuracy of {3:.1%}.'.format( - best_t, data.feature_names[best_fi], best_fi, best_acc)) diff --git a/ch02/threshold.py b/ch02/threshold.py deleted file mode 100644 index d621a350..00000000 --- a/ch02/threshold.py +++ /dev/null @@ -1,55 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np - - -# This function was called ``learn_model`` in the first edition -def fit_model(features, labels): - '''Learn a simple threshold model''' - best_acc = -1.0 - # Loop over all the features: - for fi in range(features.shape[1]): - thresh = features[:, fi].copy() - # test all feature values in order: - thresh.sort() - for t in thresh: - pred = (features[:, fi] > t) - - # Measure the accuracy of this - acc = (pred == labels).mean() - - rev_acc = (pred == ~labels).mean() - if rev_acc > acc: - acc = rev_acc - reverse = True - else: - reverse = False - if acc > best_acc: - best_acc = acc - best_fi = fi - best_t = t - best_reverse = reverse - - # A model is a threshold and an index - return best_t, best_fi, best_reverse - - -# This function was called ``apply_model`` in the first edition -def predict(model, features): - '''Apply a learned model''' - # A model is a pair as returned by fit_model - t, fi, reverse = model - if reverse: - return features[:, fi] <= t - else: - return features[:, fi] > t - -def accuracy(features, labels, model): - '''Compute the accuracy of the model''' - preds = predict(model, features) - return np.mean(preds == labels) diff --git a/ch07/.gitignore b/ch03_3rd/.gitignore similarity index 100% rename from ch07/.gitignore rename to ch03_3rd/.gitignore diff --git a/ch03_3rd/chapter_03.ipynb b/ch03_3rd/chapter_03.ipynb new file mode 100644 index 00000000..c1325eac --- /dev/null +++ b/ch03_3rd/chapter_03.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# This code is supporting material for the book\n", + "# Building Machine Learning Systems with Python\n", + "# by Willi Richert and Luis Pedro Coelho\n", + "# published by PACKT Publishing\n", + "#\n", + "# It is made available under the MIT License\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the magic command `%matplotlib` to see the plots inline:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boston dataset\n", + "\n", + "Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_boston\n", + "boston = load_boston()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first regression attempt:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "lr = LinearRegression(fit_intercept=True)\n", + "\n", + "# Index number five in the number of rooms\n", + "x = boston.data[:, 5]\n", + "y = boston.target\n", + "\n", + "# lr.fit takes a two-dimensional array as input. We use np.atleast_2d\n", + "# to convert from one to two dimensional, then transpose to make sure that the\n", + "# format matches:\n", + "x = np.transpose(np.atleast_2d(x))\n", + "lr.fit(x, y)\n", + "\n", + "fig,ax = plt.subplots()\n", + "ax.set_xlabel(\"Average number of rooms (RM)\")\n", + "ax.set_ylabel(\"House Price\")\n", + "xmin = x.min()\n", + "xmax = x.max()\n", + "ax.plot([xmin, xmax],\n", + " [lr.predict(xmin), lr.predict(xmax)],\n", + " '-', lw=2, color=\"#f9a602\")\n", + "ax.scatter(x, y, s=2)\n", + "fig.savefig('Regression_Fig_01.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "mse = mean_squared_error(y, lr.predict(x))\n", + "print(\"Mean squared error (on training data): {:.3}\".format(mse))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "rmse = np.sqrt(mse)\n", + "print('RMSE (on training data): {}'.format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2 = r2_score(y, lr.predict(x))\n", + "print(\"R2 (on training data): {:.2}\".format(r2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Repeat, but using all the input variables now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = boston.data\n", + "\n", + "lr.fit(x,y)\n", + "\n", + "mse = mean_squared_error(y, lr.predict(x))\n", + "print(\"Mean squared error (on training data): {:.3}\".format(mse))\n", + "rmse = np.sqrt(mse)\n", + "print('RMSE (on training data): {}'.format(rmse))\n", + "r2 = r2_score(y, lr.predict(x))\n", + "print(\"R2 (on training data): {:.2}\".format(r2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see how well we do, we plot _prediction vs. gold reality_:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots()\n", + "ax.set_xlabel('Predicted price')\n", + "ax.set_ylabel('Actual price')\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], ':', lw=2, color=\"#f9a602\")\n", + "ax.scatter(lr.predict(x), y, s=2)\n", + "fig.savefig(\"Regression_FIG_02.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will use **cross-validation** for evaluating the regression quality:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, cross_val_predict\n", + "kf = KFold(n_splits=5)\n", + "p = cross_val_predict(lr, x, y, cv=kf)\n", + "rmse_cv = np.sqrt(mean_squared_error(p, y))\n", + "print('RMSE on 5-fold CV: {:.2}'.format(rmse_cv))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now compare a few different regression models on _both training data and using cross-validation_:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge \n", + "\n", + "for name, met in [\n", + " ('linear regression', LinearRegression()),\n", + " ('elastic-net(.5)', ElasticNet(alpha=0.5)),\n", + " ('lasso(.5)', Lasso(alpha=0.5)),\n", + " ('ridge(.5)', Ridge(alpha=0.5)),\n", + "]:\n", + " # Fit on the whole data:\n", + " met.fit(x, y)\n", + "\n", + " # Predict on the whole data:\n", + " p = met.predict(x)\n", + " r2_train = r2_score(y, p)\n", + "\n", + " kf = KFold(n_splits=5)\n", + " p = np.zeros_like(y)\n", + " for train, test in kf.split(x):\n", + " met.fit(x[train], y[train])\n", + " p[test] = met.predict(x[test])\n", + "\n", + " r2_cv = r2_score(y, p)\n", + " print('Method: {}'.format(name))\n", + " print('R2 on training: {:.2}'.format(r2_train))\n", + " print('R2 on 5-fold CV: {:.2}'.format(r2_cv))\n", + " print('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "las = Lasso(normalize=True) \n", + "alphas = np.logspace(-5, 2, 1000) \n", + "alphas, coefs, _= las.path(x, y, alphas=alphas) \n", + "\n", + "fig,ax = plt.subplots() \n", + "ax.plot(alphas, coefs.T) \n", + "ax.set_xscale('log') \n", + "ax.set_xlim(alphas.max(), alphas.min()) \n", + "\n", + "\n", + "ax.set_xlabel('Lasso coefficient path as a function of alpha') \n", + "ax.set_xlabel('Alpha') \n", + "ax.set_ylabel('Coefficient weight') \n", + "fig.savefig('REGRESSION_FIG_03.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## E2006 Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_svmlight_file\n", + "data, target = load_svmlight_file('data/E2006.train')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute error on training data to demonstrate that we can obtain near perfect scores:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(data, target)\n", + "pred = lr.predict(data) \n", + "\n", + "print('RMSE on training, {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('R2 on training, {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, we do not do so well on cross-validation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kf = KFold(n_splits=5)\n", + "pred = cross_val_predict(lr, data, target, cv=kf)\n", + "\n", + "print('RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we try _an Elastic net_:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Edit the lines below if you want to switch method: \n", + "met = ElasticNet(alpha=0.1)\n", + "met.fit(data, target)\n", + "pred = met.predict(data)\n", + "\n", + "print('[EN 0.1] RMSE on training: {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('[EN 0.1] R2 on training: {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not a perfect prediction on the training data anymore, but let us check the value on cross-validation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred = cross_val_predict(met, data, target, cv=kf)\n", + "\n", + "print('[EN 0.1] RMSE on testing (5 fold): {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('[EN 0.1] R2 on testing (5 fold): {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now use `ElasticNetCV` to set parameters automatically:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import ElasticNetCV\n", + "# Construct an ElasticNetCV object (use all available CPUs)\n", + "met = ElasticNetCV(n_jobs=-1)\n", + "\n", + "met.fit(data, target)\n", + "pred = met.predict(data)\n", + "print('[EN CV] RMSE on training, {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('[EN CV] R2 on training, {:.2}'.format(r2_score(target, pred)))\n", + "\n", + "pred = cross_val_predict(met, data, target, cv=kf)\n", + "print('[EN CV] RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('[EN CV] R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a a pretty good general-purpose regression object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Construct an ElasticNetCV object (use all available CPUs)\n", + "met = ElasticNetCV(n_jobs=-1, l1_ratio=[.01, .05, .25, .5, .75, .95, .99])\n", + "\n", + "pred = cross_val_predict(met, data, target, cv=kf)\n", + "\n", + "print('[EN CV l1_ratio] RMSE on testing(5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred))))\n", + "print('[EN CV l1_ratio] R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the final result:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(target, pred, c='k', s=1)\n", + "ax.plot([-5,-1], [-5,-1], 'r-', lw=2)\n", + "ax.set_xlabel('Actual value')\n", + "ax.set_ylabel('Predicted value')\n", + "fig.savefig('REGRESSION_FIG_05.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:py3.6]", + "language": "python", + "name": "conda-env-py3.6-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch07/data/.gitignore b/ch03_3rd/data/.gitignore similarity index 100% rename from ch07/data/.gitignore rename to ch03_3rd/data/.gitignore diff --git a/ch03_3rd/data/download.sh b/ch03_3rd/data/download.sh new file mode 100755 index 00000000..4ef2ba7c --- /dev/null +++ b/ch03_3rd/data/download.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +curl -O https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.train.bz2 +bunzip2 E2006.train.bz2 + diff --git a/ch04/.gitignore b/ch04/.gitignore index c4c0b18a..ecd790dc 100644 --- a/ch04/.gitignore +++ b/ch04/.gitignore @@ -4,3 +4,7 @@ wiki_lda.pkl.state *.npy *.pkl topics.txt +.ipynb_checkpoints +data/dataset-379-20news-18828_EQZOQ.zip +data/wiki_en_output.tfidf_model +wiki_lda.pkl.id2word diff --git a/ch04/Topic modeling.ipynb b/ch04/Topic modeling.ipynb new file mode 100644 index 00000000..27619d46 --- /dev/null +++ b/ch04/Topic modeling.ipynb @@ -0,0 +1,599 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Topic Modeling\n", + "\n", + "We start with importing `gensim`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**IMPORTANT**: You cannot run this example only from within the notebook. You must first download the data on the command line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import gensim\n", + "from gensim import corpora, models, matutils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the usual imports:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from os import path\n", + "\n", + "\n", + "# Check that data exists\n", + "if not path.exists('./data/ap/ap.dat'):\n", + " print('Error: Expected data to be present at data/ap/')\n", + " print('Please cd into ./data & run ./download_ap.sh')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will generate 100 topics as in the book, but you can changes this setting here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "NUM_TOPICS = 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "corpus = corpora.BleiCorpus('./data/ap/ap.dat', './data/ap/vocab.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Build the LDA model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model = models.ldamodel.LdaModel(\n", + " corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_topics_used = [len(model[doc]) for doc in corpus]\n", + "fig,ax = plt.subplots()\n", + "ax.hist(num_topics_used, np.arange(42))\n", + "ax.set_ylabel('Nr of documents')\n", + "ax.set_xlabel('Nr of topics')\n", + "fig.tight_layout()\n", + "fig.savefig('Figure_04_01.png')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We can do the same after changing the $\\alpha$ value: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ALPHA = 1.0\n", + "\n", + "model1 = models.ldamodel.LdaModel(\n", + " corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=ALPHA)\n", + "num_topics_used1 = [len(model1[doc]) for doc in corpus]\n", + "\n", + "fig,ax = plt.subplots()\n", + "ax.hist([num_topics_used, num_topics_used1], np.arange(42))\n", + "ax.set_ylabel('Nr of documents')\n", + "ax.set_xlabel('Nr of topics')\n", + "\n", + "# The coordinates below were fit by trial and error to look good\n", + "ax.text(9, 223, r'default alpha')\n", + "ax.text(26, 156, 'alpha=1.0')\n", + "fig.tight_layout()\n", + "fig.savefig('Figure_04_02.png')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the topic model\n", + "\n", + "We can explore the mathematical structure of the topics:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "doc = corpus.docbyoffset(0)\n", + "topics = model[doc]\n", + "print(topics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is not very informative, however. Another way to explore is to identify the most discussed topic, i.e., the one with the highest total weight:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "topics = matutils.corpus2dense(model[corpus], num_terms=model.num_topics)\n", + "weight = topics.sum(1)\n", + "max_topic = weight.argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the top 64 words for this topic.\n", + "Without the argument, show_topic would return only 10 words" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "words = model.show_topic(max_topic, 64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to visualize the results is to build a _word cloud_. For this we use the `wordcloud` module:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from wordcloud import WordCloud\n", + "\n", + "wc = WordCloud(background_color='white', max_words=30, width=600, height=600)\n", + "wc = wc.generate_from_frequencies(dict(words))\n", + "\n", + "\n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.imshow(wc, interpolation=\"bilinear\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NEWS DATA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, repeat the same exercise using alpha=1.0.\n", + "\n", + "You can edit the constant below to play around with this parameter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nltk.stem\n", + "\n", + "nltk.download('stopwords')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "english_stemmer = nltk.stem.SnowballStemmer('english')\n", + "stopwords = set(nltk.corpus.stopwords.words('english'))\n", + "stopwords.update(['from:', 'subject:', 'writes:', 'writes'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to add a little adaptor class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class DirectText(corpora.textcorpus.TextCorpus):\n", + "\n", + " def get_texts(self):\n", + " return self.input\n", + "\n", + " def __len__(self):\n", + " return len(self.input)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.datasets\n", + "dataset = sklearn.datasets.load_mlcomp(\"20news-18828\", \"train\",\n", + " mlcomp_root='./data')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We preprocess the data to split the data into words and remove stopwords:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "otexts = dataset.data\n", + "texts = dataset.data\n", + "\n", + "texts = [t.decode('utf-8', 'ignore') for t in texts]\n", + "texts = [t.split() for t in texts]\n", + "texts = [map(lambda w: w.lower(), t) for t in texts]\n", + "texts = [filter(lambda s: not len(set(\"+-.?!()>@012345689\") & set(s)), t)\n", + " for t in texts]\n", + "texts = [filter(lambda s: (len(s) > 3) and (s not in stopwords), t)\n", + " for t in texts]\n", + "texts = [[english_stemmer.stem(w) for w in t] for t in texts]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also remove words that are _too common_:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "usage = defaultdict(int)\n", + "for t in texts:\n", + " for w in set(t):\n", + " usage[w] += 1\n", + "limit = len(texts) / 10\n", + "too_common = [w for w in usage if usage[w] > limit]\n", + "too_common = set(too_common)\n", + "texts = [[w for w in t if w not in too_common] for t in texts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "corpus = DirectText(texts)\n", + "dictionary = corpus.dictionary\n", + "try:\n", + " dictionary['computer']\n", + "except:\n", + " pass\n", + "\n", + "model = models.ldamodel.LdaModel(\n", + " corpus, num_topics=100, id2word=dictionary.id2token)\n", + "\n", + "thetas = np.zeros((len(texts), 100))\n", + "for i, c in enumerate(corpus):\n", + " for ti, v in model[c]:\n", + " thetas[i, ti] += v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compare all documents to each other **by the topics the contain**:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.spatial import distance\n", + "distances = distance.squareform(distance.pdist(thetas))\n", + "large = distances.max() + 1\n", + "for i in range(len(distances)):\n", + " distances[i, i] = large\n", + "\n", + "print(otexts[1])\n", + "print()\n", + "print()\n", + "print()\n", + "print(otexts[distances[1].argmin()])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Modeling Wikipedia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data\n", + "\n", + "Note that you **must have run the `wikitopics_create.py` script**. This will take a few hours" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gensim\n", + "if not path.exists('wiki_lda.pkl'):\n", + " import sys\n", + " sys.stderr.write('''\\\n", + "This script must be run after wikitopics_create.py!\n", + "\n", + "That script creates and saves the LDA model (this must onlly be done once).\n", + "This script is responsible for the analysis.''')\n", + " \n", + "# Load the preprocessed Wikipedia corpus (id2word and mm)\n", + "id2word = gensim.corpora.Dictionary.load_from_text(\n", + " 'data/wiki_en_output_wordids.txt.bz2')\n", + "mm = gensim.corpora.MmCorpus('data/wiki_en_output_tfidf.mm')\n", + "\n", + "# Load the precomputed model\n", + "model = gensim.models.ldamodel.LdaModel.load('wiki_lda.pkl')\n", + "\n", + "topics = np.load('topics.npy', mmap_mode='r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the number of topics mentioned in each document\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lens = (topics > 0).sum(axis=1)\n", + "print('Mean number of topics mentioned: {0:.3}'.format(np.mean(lens)))\n", + "print('Percentage of articles mentioning less than 10 topics: {0:.1%}'.format(np.mean(lens <= 10)))\n", + "\n", + "# Weights will be the total weight of each topic\n", + "weights = topics.sum(0)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve the most heavily used topic and plot it as a word cloud:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "words = model.show_topic(weights.argmax(), 64)\n", + "\n", + "wc = WordCloud(background_color='white', max_words=30, width=600, height=600)\n", + "wc = wc.generate_from_frequencies(dict(words))\n", + "\n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.imshow(wc, interpolation=\"bilinear\")\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fraction_mention = np.mean(topics[:,weights.argmax()] > 0)\n", + "print(\"The most mentioned topics is mentioned in {:.1%} of documents.\".format(fraction_mention))\n", + "total_weight = np.mean(topics[:,weights.argmax()])\n", + "print(\"It represents {:.1%} of the total number of words.\".format(total_weight))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve the **least** heavily used topic and plot it as a word cloud:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "words = model.show_topic(weights.argmin(), 64)\n", + "\n", + "wc = WordCloud(background_color='white', max_words=30, width=600, height=600)\n", + "wc = wc.generate_from_frequencies(dict(words))\n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.imshow(wc, interpolation=\"bilinear\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we can measure how often this topic used:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fraction_mention = np.mean(topics[:,weights.argmin()] > 0)\n", + "print(\"The least mentioned topics is mentioned in {:.1%} of documents.\".format(fraction_mention))\n", + "total_weight = np.mean(topics[:,weights.argmin()])\n", + "print(\"It represents {:.1%} of the total number of words.\".format(total_weight))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py deleted file mode 100644 index 7f6ac2b3..00000000 --- a/ch04/blei_lda.py +++ /dev/null @@ -1,86 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from __future__ import print_function -from wordcloud import create_cloud -try: - from gensim import corpora, models, matutils -except: - print("import gensim failed.") - print() - print("Please install it") - raise - -import matplotlib.pyplot as plt -import numpy as np -from os import path - -NUM_TOPICS = 100 - -# Check that data exists -if not path.exists('./data/ap/ap.dat'): - print('Error: Expected data to be present at data/ap/') - print('Please cd into ./data & run ./download_ap.sh') - -# Load the data -corpus = corpora.BleiCorpus('./data/ap/ap.dat', './data/ap/vocab.txt') - -# Build the topic model -model = models.ldamodel.LdaModel( - corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=None) - -# Iterate over all the topics in the model -for ti in range(model.num_topics): - words = model.show_topic(ti, 64) - tf = sum(f for _, f in words) - with open('topics.txt', 'w') as output: - output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for w, f in words)) - output.write("\n\n\n") - -# We first identify the most discussed topic, i.e., the one with the -# highest total weight - -topics = matutils.corpus2dense(model[corpus], num_terms=model.num_topics) -weight = topics.sum(1) -max_topic = weight.argmax() - - -# Get the top 64 words for this topic -# Without the argument, show_topic would return only 10 words -words = model.show_topic(max_topic, 64) - -# This function will actually check for the presence of pytagcloud and is otherwise a no-op -create_cloud('cloud_blei_lda.png', words) - -num_topics_used = [len(model[doc]) for doc in corpus] -fig,ax = plt.subplots() -ax.hist(num_topics_used, np.arange(42)) -ax.set_ylabel('Nr of documents') -ax.set_xlabel('Nr of topics') -fig.tight_layout() -fig.savefig('Figure_04_01.png') - - -# Now, repeat the same exercise using alpha=1.0 -# You can edit the constant below to play around with this parameter -ALPHA = 1.0 - -model1 = models.ldamodel.LdaModel( - corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=ALPHA) -num_topics_used1 = [len(model1[doc]) for doc in corpus] - -fig,ax = plt.subplots() -ax.hist([num_topics_used, num_topics_used1], np.arange(42)) -ax.set_ylabel('Nr of documents') -ax.set_xlabel('Nr of topics') - -# The coordinates below were fit by trial and error to look good -ax.text(9, 223, r'default alpha') -ax.text(26, 156, 'alpha=1.0') -fig.tight_layout() -fig.savefig('Figure_04_02.png') - diff --git a/ch04/build_lda.py b/ch04/build_lda.py deleted file mode 100644 index a0ee9c5f..00000000 --- a/ch04/build_lda.py +++ /dev/null @@ -1,89 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License -from __future__ import print_function - -try: - import nltk.corpus -except ImportError: - print("nltk not found") - print("please install it") - raise -from scipy.spatial import distance -import numpy as np -from gensim import corpora, models -import sklearn.datasets -import nltk.stem -from collections import defaultdict - -english_stemmer = nltk.stem.SnowballStemmer('english') -stopwords = set(nltk.corpus.stopwords.words('english')) -stopwords.update(['from:', 'subject:', 'writes:', 'writes']) - - -class DirectText(corpora.textcorpus.TextCorpus): - - def get_texts(self): - return self.input - - def __len__(self): - return len(self.input) -try: - dataset = sklearn.datasets.load_mlcomp("20news-18828", "train", - mlcomp_root='./data') -except: - print("Newsgroup data not found.") - print("Please download from http://mlcomp.org/datasets/379") - print("And expand the zip into the subdirectory data/") - print() - print() - raise - -otexts = dataset.data -texts = dataset.data - -texts = [t.decode('utf-8', 'ignore') for t in texts] -texts = [t.split() for t in texts] -texts = [map(lambda w: w.lower(), t) for t in texts] -texts = [filter(lambda s: not len(set("+-.?!()>@012345689") & set(s)), t) - for t in texts] -texts = [filter(lambda s: (len(s) > 3) and (s not in stopwords), t) - for t in texts] -texts = [map(english_stemmer.stem, t) for t in texts] -usage = defaultdict(int) -for t in texts: - for w in set(t): - usage[w] += 1 -limit = len(texts) / 10 -too_common = [w for w in usage if usage[w] > limit] -too_common = set(too_common) -texts = [filter(lambda s: s not in too_common, t) for t in texts] - -corpus = DirectText(texts) -dictionary = corpus.dictionary -try: - dictionary['computer'] -except: - pass - -model = models.ldamodel.LdaModel( - corpus, num_topics=100, id2word=dictionary.id2token) - -thetas = np.zeros((len(texts), 100)) -for i, c in enumerate(corpus): - for ti, v in model[c]: - thetas[i, ti] += v - -distances = distance.squareform(distance.pdist(thetas)) -large = distances.max() + 1 -for i in range(len(distances)): - distances[i, i] = large - -print(otexts[1]) -print() -print() -print() -print(otexts[distances[1].argmin()]) diff --git a/ch04/wikitopics_plot.py b/ch04/wikitopics_plot.py deleted file mode 100644 index 04adf780..00000000 --- a/ch04/wikitopics_plot.py +++ /dev/null @@ -1,64 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from __future__ import print_function -import numpy as np -import gensim -from os import path -from wordcloud import create_cloud - -if not path.exists('wiki_lda.pkl'): - import sys - sys.stderr.write('''\ -This script must be run after wikitopics_create.py! - -That script creates and saves the LDA model (this must onlly be done once). -This script is responsible for the analysis.''') - sys.exit(1) - -# Load the preprocessed Wikipedia corpus (id2word and mm) -id2word = gensim.corpora.Dictionary.load_from_text( - 'data/wiki_en_output_wordids.txt.bz2') -mm = gensim.corpora.MmCorpus('data/wiki_en_output_tfidf.mm') - -# Load the precomputed model -model = gensim.models.ldamodel.LdaModel.load('wiki_lda.pkl') - -topics = np.load('topics.npy', mmap_mode='r') - -# Compute the number of topics mentioned in each document -lens = (topics > 0).sum(axis=1) -print('Mean number of topics mentioned: {0:.3}'.format(np.mean(lens))) -print('Percentage of articles mentioning less than 10 topics: {0:.1%}'.format(np.mean(lens <= 10))) - -# Weights will be the total weight of each topic -weights = topics.sum(0) - -# Retrieve the most heavily used topic and plot it as a word cloud: -words = model.show_topic(weights.argmax(), 64) - -# The parameter ``maxsize`` often needs some manual tuning to make it look nice. -create_cloud('Wikipedia_most.png', words, maxsize=250, fontname='Cardo') - -fraction_mention = np.mean(topics[:,weights.argmax()] > 0) -print("The most mentioned topics is mentioned in {:.1%} of documents.".format(fraction_mention)) -total_weight = np.mean(topics[:,weights.argmax()]) -print("It represents {:.1%} of the total number of words.".format(total_weight)) -print() -print() -print() - -# Retrieve the **least** heavily used topic and plot it as a word cloud: -words = model.show_topic(weights.argmin(), 64) -create_cloud('Wikipedia_least.png', words, maxsize=150, fontname='Cardo') -fraction_mention = np.mean(topics[:,weights.argmin()] > 0) -print("The least mentioned topics is mentioned in {:.1%} of documents.".format(fraction_mention)) -total_weight = np.mean(topics[:,weights.argmin()]) -print("It represents {:.1%} of the total number of words.".format(total_weight)) -print() -print() -print() diff --git a/ch04/wordcloud.py b/ch04/wordcloud.py deleted file mode 100644 index accca2d6..00000000 --- a/ch04/wordcloud.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import print_function -warned_of_error = False - -def create_cloud(oname, words,maxsize=120, fontname='Lobster'): - '''Creates a word cloud (when pytagcloud is installed) - - Parameters - ---------- - oname : output filename - words : list of (value,str) - maxsize : int, optional - Size of maximum word. The best setting for this parameter will often - require some manual tuning for each input. - fontname : str, optional - Font to use. - ''' - try: - from pytagcloud import create_tag_image, make_tags - except ImportError: - if not warned_of_error: - print("Could not import pytagcloud. Skipping cloud generation") - return - - # gensim returns a weight between 0 and 1 for each word, while pytagcloud - # expects an integer word count. So, we multiply by a large number and - # round. For a visualization this is an adequate approximation. - words = [(w,int(v*10000)) for w,v in words] - tags = make_tags(words, maxsize=maxsize) - create_tag_image(tags, oname, size=(1800, 1200), fontname=fontname) diff --git a/ch04_3rd/chapter_04.ipynb b/ch04_3rd/chapter_04.ipynb new file mode 100644 index 00000000..4f7e896c --- /dev/null +++ b/ch04_3rd/chapter_04.ipynb @@ -0,0 +1,1818 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Downloading the data\n", + "In this chapter we will use the StackOverflow data from https://archive.org/download/stackexchange (while downloading, you have a couple hours time to contemplate whether now would be a good time to donate to the awesome archive.org :-) )\n", + "\n", + "Since it is updated on a regular basis, you might get slightly different numbers. In this chapter we use this version:\n", + "```\n", + "stackoverflow.com-Posts.7z 08-Dec-2017 22:31 11.3G\n", + "```\n", + "After downloading it, you need to unzip it with [7-Zip](http://www.7-zip.de/download.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extracting and filtering it" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sample posts: C:\\repo\\ML_Book\\BuildingMachineLearningSystemsWithPython\\ch04_3rd\\2017\\data\\sample.tsv\n", + "sample meta: C:\\repo\\ML_Book\\BuildingMachineLearningSystemsWithPython\\ch04_3rd\\2017\\data\\sample-meta.json\n" + ] + } + ], + "source": [ + "import os\n", + "import re\n", + "from collections import defaultdict\n", + "from tqdm import tqdm_notebook as tqdm # we all love nice progress bars, don't we?\n", + "try:\n", + " import ujson as json # UltraJSON if available\n", + "except:\n", + " print(\"You can also use the normal json module, but you get a XXX speedup if you use ujson instead.\")\n", + " raise\n", + " \n", + "# TODO change before merging to master\n", + "#DATA_DIR = \"data\" # put your posts-2012.xml into this directory\n", + "DATA_DIR = r'F:\\Stack Exchange Data Dump - Dec 2017'\n", + "\n", + "YEAR = 2017 # will restrict the data to posts from this year\n", + "\n", + "fn_posts_all = os.path.join(DATA_DIR, \"posts.xml\")\n", + "fn_posts = os.path.join(DATA_DIR, \"posts-%i.xml\" % YEAR)\n", + "\n", + "fn_filtered = os.path.join(DATA_DIR, \"filtered-%i.tsv\" % YEAR)\n", + "fn_filtered_meta = os.path.join(DATA_DIR, \"filtered-%i-meta.json\" % YEAR)\n", + "\n", + "SAMPLE_DIR = '%i' % YEAR\n", + "if not os.path.exists(SAMPLE_DIR):\n", + " os.mkdir(SAMPLE_DIR)\n", + "\n", + "if not os.path.exists(os.path.join(SAMPLE_DIR, 'data')):\n", + " os.mkdir(os.path.join(SAMPLE_DIR, 'data'))\n", + "\n", + "fn_sample = os.path.abspath(os.path.join(SAMPLE_DIR, 'data', \"sample.tsv\"))\n", + "fn_sample_meta = os.path.abspath(os.path.join(SAMPLE_DIR, 'data', \"sample-meta.json\"))\n", + "print(\"sample posts: %s\" % fn_sample)\n", + "print(\"sample meta: %s\" % fn_sample_meta)\n", + "\n", + "CHART_DIR = os.path.join(SAMPLE_DIR, \"charts\")\n", + "if not os.path.exists(CHART_DIR):\n", + " os.mkdir(CHART_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 59GB in posts.xml is contain posts from 2008 to 2017. We will use only some posts from the last year, which provides enough fun for now. We could simply grep on the command line, but that would take quite a while." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting all posts from 2017 ...\n", + "... done!\n" + ] + } + ], + "source": [ + "year_match = re.compile(r'^\\s+]*CreationDate=\"(\\d+)-')\n", + "size = os.path.getsize(fn_posts_all)\n", + "\n", + "def get_year(line):\n", + " m = year_match.match(line)\n", + " if m is None:\n", + " return None\n", + " return int(m.group(1))\n", + "\n", + "print(\"Extracting all posts from %i ...\" % YEAR)\n", + "with open(fn_posts_all, 'r', encoding='utf-8') as fa, open(fn_posts, 'w', encoding='utf-8') as f_year:\n", + " # first two lines are the xml header and tag\n", + " f_year.write('\\n') \n", + " \n", + " right = size//2\n", + " delta = right\n", + " \n", + " # first find some post of YEAR\n", + " while True:\n", + " fa.seek(right)\n", + " fa.readline() # go to next newline\n", + " line = fa.readline()\n", + " \n", + " year = get_year(line)\n", + " \n", + " delta //= 2\n", + " assert delta > 0\n", + " \n", + " if year>YEAR:\n", + " right -= delta\n", + " elif year YEAR:\n", + " break\n", + " \n", + " # and write the closing tag\n", + " f_year.write('')\n", + "print('... done!')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2d5085de70f346cfa785e64d5f977988", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5113519), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Found 5113519 posts\n", + "#qestions: 2331406\n", + "#answers: 2776274\n" + ] + } + ], + "source": [ + "from dateutil import parser as dateparser\n", + "\n", + "from operator import itemgetter\n", + "from lxml import etree\n", + "\n", + "NUM_ROWS = 5113519 # counted by hand\n", + "\n", + "filtered_meta = {\n", + " 'question': {}, # question -> [(answer Id, Score), ...]\n", + " 'total': 0 # questions and answers finally written\n", + "}\n", + "\n", + "# Regular expressions to find code snippets, links, and tags, which might help in \n", + "# designing useful features\n", + "code_match = re.compile('
(.*?)
', re.MULTILINE | re.DOTALL)\n", + "link_match = re.compile('(.*?)', re.MULTILINE | re.DOTALL)\n", + "tag_match = re.compile('<[^>]*>', re.MULTILINE | re.DOTALL)\n", + "whitespace_match = re.compile(r'\\s+', re.MULTILINE | re.DOTALL)\n", + "\n", + "def extract_features_from_body(s):\n", + " '''\n", + " This method creates features from the raw post. It already contains all \n", + " features that we will use throughout the chapter.\n", + " '''\n", + " num_code_lines = 0\n", + " link_count_in_code = 0\n", + " code_free_s = s\n", + "\n", + " # remove source code and count how many lines\n", + " for match_str in code_match.findall(s):\n", + " num_code_lines += match_str.count('\\n')\n", + " code_free_s = code_match.sub(' ', code_free_s)\n", + "\n", + " # sometimes source code contain links, which we don't want to count\n", + " link_count_in_code += len(link_match.findall(match_str))\n", + "\n", + " links = link_match.findall(s)\n", + " link_count = len(links) - link_count_in_code\n", + "\n", + " html_free_s = tag_match.sub(' ', code_free_s)\n", + " \n", + " text = html_free_s\n", + " for link in links:\n", + " if link.lower().startswith('http://'):\n", + " text = text.replace(link, ' ')\n", + "\n", + " text = whitespace_match.sub(' ', text)\n", + " num_text_tokens = text.count(' ')\n", + "\n", + " return text, num_text_tokens, num_code_lines, link_count\n", + "\n", + "num_questions = 0\n", + "num_answers = 0\n", + "\n", + "def parsexml(fn):\n", + " global num_questions, num_answers\n", + "\n", + " counter = 0\n", + "\n", + " # iterparse() returns a tuple (event, element). Since we request only\n", + " # 'start' events, we pipe the result through an itemgetter that always returns\n", + " # the 2nd result.\n", + " it = map(itemgetter(1), etree.iterparse(fn, events=('start',)))\n", + " \n", + " # Get the element, in which we will parse the elements. While doing so,\n", + " # we will need the root handle to clear memory\n", + " root = next(it)\n", + " \n", + " for counter, elem in enumerate(tqdm(it, total=NUM_ROWS)):\n", + " \n", + " if elem.tag != 'row':\n", + " continue\n", + " \n", + " Id = int(elem.get('Id'))\n", + " PostTypeId = int(elem.get('PostTypeId'))\n", + " Score = int(elem.get('Score'))\n", + "\n", + " if PostTypeId == 1:\n", + " num_questions += 1 \n", + " ParentId = -1\n", + " filtered_meta['question'][Id] = []\n", + " \n", + " elif PostTypeId == 2:\n", + " num_answers += 1\n", + " ParentId = int(elem.get('ParentId'))\n", + " if not ParentId in filtered_meta['question']:\n", + " # question is not from the same year so we have already dropped it\n", + " continue\n", + "\n", + " filtered_meta['question'][ParentId].append((Id, Score))\n", + "\n", + " else:\n", + " continue\n", + "\n", + " Text, NumTextTokens, NumCodeLines, LinkCount = extract_features_from_body(elem.get('Body'))\n", + "\n", + " # We have to tell lxml that this element is not used anymore. Otherwise, memory will blow up.\n", + " # See https://www.ibm.com/developerworks/xml/library/x-hiperfparse for more information.\n", + " elem.clear()\n", + " while elem.getprevious() is not None:\n", + " del elem.getparent()[0]\n", + " \n", + " values = (Id, ParentId, Score, NumTextTokens, NumCodeLines, LinkCount, Text)\n", + "\n", + " yield values\n", + "\n", + " print(\"Found %i posts\" % counter)\n", + "\n", + "if any(not os.path.exists(fn) for fn in [fn_filtered, fn_filtered_meta]):\n", + " total = 0\n", + " with open(fn_filtered, \"w\", encoding='utf-8') as f:\n", + " for values in parsexml(fn_posts):\n", + " line = \"\\t\".join(map(str, values))\n", + " f.write(line + \"\\n\")\n", + " total += 1\n", + " filtered_meta['total'] = total\n", + " \n", + " with open(fn_filtered_meta, \"w\") as f:\n", + " json.dump(filtered_meta, f)\n", + " \n", + " print(\"#qestions: %i\" % num_questions)\n", + " print(\"#answers: %i\" % num_answers)\n", + " \n", + "else:\n", + " print(\"Skipping the conversion step, loading data from %s ...\" % fn_filtered_meta)\n", + " filtered_meta = json.load(open(fn_filtered_meta, \"r\"))\n", + " print(\"... done!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to select the answers that we want to keep per question. We do this in two stages:\n", + " * Stage 1: Chosing questions that have a positive and negative answer and then chosing the most positive and negative.\n", + " * Stage 2: Write out the features for those answers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a28a348cae54b28a498609f918a4480", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, description='Stage 1:', max=2331406), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "NUM_QUESTION_SAMPLE = 10000\n", + "\n", + "posts_to_keep = set()\n", + "found_questions = 0\n", + "\n", + "question = filtered_meta['question']\n", + "\n", + "# Sorting the questions before iterating over them is only done for\n", + "# reproducability.\n", + "for ParentId, posts in tqdm(sorted(question.items()), desc=\"Stage 1:\"):\n", + " assert ParentId != -1\n", + "\n", + " if len(posts) < 2:\n", + " continue\n", + "\n", + " neg_score_ids = []\n", + " pos_score_ids = []\n", + " \n", + " for Id, Score in posts:\n", + " if Score < 0:\n", + " neg_score_ids.append((Score, Id))\n", + " elif Score > 0:\n", + " pos_score_ids.append((Score, Id)) \n", + "\n", + " if pos_score_ids and neg_score_ids:\n", + " posts_to_keep.add(int(ParentId))\n", + "\n", + " posScore, posId = sorted(pos_score_ids)[-1]\n", + " posts_to_keep.add(posId)\n", + "\n", + " negScore, negId = sorted(neg_score_ids)[0]\n", + " posts_to_keep.add(negId)\n", + "\n", + " found_questions += 1\n", + "\n", + " if found_questions >= NUM_QUESTION_SAMPLE:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "42503a986c5a49f4a61e327f291d7179", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, description='Stage 2:', max=5113519), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "read: 4726479\n", + "kept: 30000\n" + ] + } + ], + "source": [ + "already_written = set()\n", + "sample_meta = defaultdict(dict)\n", + "\n", + "total = 0\n", + "kept = 0\n", + "\n", + "with open(fn_sample, \"w\", encoding='utf-8') as f:\n", + " for line in tqdm(open(fn_filtered, 'r', encoding='utf-8'), total=NUM_ROWS, desc=\"Stage 2:\"):\n", + " Id, ParentId, Score, NumTextTokens, NumCodeLines, LinkCount, Text = line.split(\"\\t\")\n", + "\n", + " Text = Text.strip()\n", + "\n", + " total += 1\n", + "\n", + " Id = int(Id)\n", + " if Id in posts_to_keep:\n", + " if Id in already_written:\n", + " print(Id, \"is already written\")\n", + " continue\n", + "\n", + " # setting meta info\n", + " post = sample_meta[Id]\n", + " post['ParentId'] = int(ParentId)\n", + " post['Score'] = int(Score)\n", + " post['NumTextTokens'] = int(NumTextTokens)\n", + " post['NumCodeLines'] = int(NumCodeLines)\n", + " post['LinkCount'] = int(LinkCount)\n", + " post['idx'] = kept # index into the TSV file\n", + "\n", + " if int(ParentId) == -1:\n", + " q = sample_meta[Id]\n", + "\n", + " if not 'Answers' in q:\n", + " q['Answers'] = []\n", + "\n", + " else:\n", + " q = sample_meta[int(ParentId)]\n", + "\n", + " if 'Answers' not in q:\n", + " q['Answers'] = [Id]\n", + " else:\n", + " q['Answers'].append(Id)\n", + "\n", + " f.writelines(\"%s\\t%s\\n\" % (Id, Text))\n", + " kept += 1\n", + "\n", + "with open(fn_sample_meta, \"w\") as fm:\n", + " json.dump(sample_meta, fm)\n", + "\n", + "print(\"read:\", total)\n", + "print(\"kept:\", kept)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utility functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def load_meta(fn):\n", + " meta = json.load(open(fn, \"r\"))\n", + " \n", + " # JSON only allows string keys, changing that to int\n", + " for key in list(meta.keys()):\n", + " meta[int(key)] = meta[key]\n", + " del meta[key]\n", + "\n", + " return meta\n", + "\n", + "meta = load_meta(fn_sample_meta)\n", + "\n", + "def save_png(name):\n", + " fn = 'B09124_04_%s.png'%name # please ignore, it just helps our publisher :-)\n", + " plt.savefig(os.path.join(CHART_DIR, fn), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading the features and labeling them" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "all_answers = sorted([a for a, v in meta.items() if v['ParentId'] != -1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An answer is labeled as positive if it has a score greater than zero." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([False, True], dtype=bool), array([10000, 10000], dtype=int64))\n" + ] + } + ], + "source": [ + "Y = np.asarray([meta[aid]['Score'] > 0 for aid in all_answers])\n", + "print(np.unique(Y, return_counts=True))\n", + "# We will need a couple iterations on X further down..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating our first classifier: kNN using only LinkCount as a feature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So how good is `LinkCount`? Let's look at its histogram." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "\n", + "X = np.asarray([[meta[aid]['LinkCount']] for aid in all_answers])\n", + "\n", + "plt.figure(figsize=(5,4), dpi=300) # width and height of the plot in inches\n", + "\n", + "plt.title('LinkCount')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Occurrence')\n", + "\n", + "n, bins, patches = plt.hist(X, normed=1, bins=range(max(X.ravel())-min(X.ravel())), alpha=0.75)\n", + "\n", + "plt.grid(True)\n", + "save_png('01_feat_hist_LinkCount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, so most posts don't contain a link at all, but let's try nevertheless..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training on LinkCount" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0424eeb432c9439ca5bbc4deccd24780", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Mean(scores)=0.50170\tStddev(scores)=0.01243\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import KFold\n", + "\n", + "N_FOLDS = 10\n", + "cv = KFold(n_splits=N_FOLDS, shuffle=True, random_state=0)\n", + "\n", + "scores = []\n", + "for train, test in tqdm(cv.split(X, Y)):\n", + " clf = KNeighborsClassifier()\n", + " clf.fit(X[train], Y[train])\n", + " scores.append(clf.score(X[test], Y[test]))\n", + "\n", + "print(\"Mean(scores)=%.5f\\tStddev(scores)=%.5f\"%(np.mean(scores), np.std(scores))) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using more features" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_feat_hist(data_name_list, filename=None):\n", + " if len(data_name_list) > 1:\n", + " assert filename is not None\n", + "\n", + " num_rows = int(1 + (len(data_name_list) - 1) / 2)\n", + " num_cols = int(1 if len(data_name_list) == 1 else 2)\n", + " plt.figure(figsize=(5 * num_cols, 4 * num_rows), dpi=300)\n", + "\n", + " for i in range(num_rows):\n", + " for j in range(num_cols):\n", + " plt.subplot(num_rows, num_cols, 1 + i * num_cols + j)\n", + " x, name = data_name_list[i * num_cols + j]\n", + " plt.title(name)\n", + " plt.xlabel('Value')\n", + " plt.ylabel('Occurrence')\n", + " \n", + " max_val = max(x.ravel())\n", + " if max_val>1000:\n", + " bins = range(0, max_val, 100)\n", + " elif max_val>100:\n", + " bins = range(0, max_val, 10)\n", + " else:\n", + " bins = range(0, max_val)\n", + " \n", + " n, bins, patches = plt.hist(x, bins=bins, normed=1, alpha=0.75)\n", + "\n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " \n", + " if not filename:\n", + " filename = \"feat_hist_%s\" % name.replace(\" \", \"_\")\n", + "\n", + " save_png(filename)\n", + "\n", + "\n", + "plot_feat_hist([(np.asarray([[meta[aid]['NumCodeLines']] for aid in all_answers]), 'NumCodeLines'),\n", + " (np.asarray([[meta[aid]['NumTextTokens']] for aid in all_answers]), 'NumTextTokens')],\n", + " '02_feat_hist_CodeLines_TextTokens');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the features vary in their value ranges, we need to standardize them using `StandardScaler()` so that kNN does not bias towards features having larger value intervals. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0049cacf49e44963820ea3d057ac594c", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Mean(scores)=0.60070\tStddev(scores)=0.00759\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "def get_features(aid, feature_names):\n", + " return tuple(meta[aid][fn] for fn in feature_names)\n", + "\n", + "X = np.asarray([get_features(aid, ['LinkCount', 'NumCodeLines', 'NumTextTokens']) for aid in all_answers], float)\n", + "\n", + "scores = []\n", + "for train, test in tqdm(cv.split(X, Y), total=N_FOLDS):\n", + " clf = make_pipeline(StandardScaler(), KNeighborsClassifier())\n", + " clf.fit(X[train], Y[train])\n", + " scores.append(clf.score(X[test], Y[test]))\n", + "\n", + "print(\"Mean(scores)=%.5f\\tStddev(scores)=%.5f\"%(np.mean(scores), np.std(scores))) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing more features\n", + "Let's create some more text based features like average sentence and word length, how many words are CAPITALIZED or contain exclamation marks.\n", + "\n", + "We simply fetch the post texts, calculate the statistics and add them to the `meta` dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import nltk\n", + "\n", + "def fetch_posts(fn):\n", + " for line in open(fn, 'r', encoding='utf-8'):\n", + " post_id, text = line.split('\\t')\n", + " yield int(post_id), text.strip()\n", + "\n", + "def add_sentence_features(m):\n", + " for pid, text in fetch_posts(fn_sample):\n", + " if not text:\n", + " for feat in ['AvgSentLen', 'AvgWordLen', 'NumAllCaps', 'NumExclams']:\n", + " m[pid][feat] = 0\n", + " else:\n", + " sent_lens = [len(nltk.word_tokenize(sent)) for sent in nltk.sent_tokenize(text)]\n", + " m[pid]['AvgSentLen'] = np.mean(sent_lens)\n", + " text_tokens = nltk.word_tokenize(text)\n", + " m[pid]['AvgWordLen'] = np.mean([len(w) for w in text_tokens])\n", + " m[pid]['NumAllCaps'] = np.sum([word.isupper() for word in text_tokens])\n", + " m[pid]['NumExclams'] = text.count('!')\n", + "\n", + "add_sentence_features(meta)\n", + "\n", + "plot_feat_hist([(np.asarray([[meta[aid][feat]] for aid in all_answers], dtype=int), feat) for feat in ['AvgSentLen', 'AvgWordLen', 'NumAllCaps', 'NumExclams']],\n", + " '03_feat_hist_AvgSentLen_AvgWordLen_NumAllCaps_NumExclams');" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "54194de8e01344ff99e273dba1ade15f", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Mean(scores)=0.60225\tStddev(scores)=0.00729\n" + ] + } + ], + "source": [ + "X = np.asarray([get_features(aid, ['LinkCount', 'NumCodeLines', 'NumTextTokens', \n", + " 'AvgSentLen', 'AvgWordLen', 'NumAllCaps', \n", + " 'NumExclams']) for aid in all_answers], float)\n", + "\n", + "scores = []\n", + "for train, test in tqdm(cv.split(X, Y), total=N_FOLDS): \n", + " clf = make_pipeline(StandardScaler(), KNeighborsClassifier())\n", + " clf.fit(X[train], Y[train])\n", + " scores.append(clf.score(X[test], Y[test]))\n", + "\n", + "print(\"Mean(scores)=%.5f\\tStddev(scores)=%.5f\"%(np.mean(scores), np.std(scores))) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# High or low bias?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import precision_recall_curve, roc_curve, auc, classification_report\n", + "\n", + "def plot_bias_variance(data_sizes, train_errors, test_errors, name, title):\n", + " plt.figure(num=None, figsize=(6, 5), dpi=300)\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('Data set size')\n", + " plt.ylabel('Error')\n", + " plt.title(\"Bias-Variance for '%s'\" % name)\n", + " plt.plot(\n", + " data_sizes, test_errors, \"--\", data_sizes, train_errors, \"b-\", lw=1)\n", + " plt.legend([\"test error\", \"train error\"], loc=\"upper right\")\n", + " plt.grid(True)\n", + "\n", + "def plot_pr(auc_score, name, precision, recall, label=None):\n", + " plt.figure(num=None, figsize=(6, 5), dpi=300)\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('P/R (AUC=%0.2f) / %s' % (auc_score, label))\n", + " plt.fill_between(recall, precision, alpha=0.5)\n", + " plt.grid(True)\n", + " plt.plot(recall, precision, lw=1)\n", + " filename = name.replace(\" \", \"_\")+'_pr'\n", + " save_png(filename)\n", + "\n", + "def plot_feat_importance(feature_names, clf, name):\n", + " plt.figure(num=None, figsize=(6, 5), dpi=300)\n", + " coef_ = clf.coef_\n", + " important = np.argsort(np.absolute(coef_.ravel()))\n", + " f_imp = feature_names[important]\n", + " coef = coef_.ravel()[important]\n", + " inds = np.argsort(coef)\n", + " f_imp = f_imp[inds]\n", + " coef = coef[inds]\n", + " xpos = np.array(list(range(len(coef))))\n", + " plt.bar(xpos, coef, width=1, alpha=0.75)\n", + "\n", + " plt.title('Feature importance for %s' % (name.split('_')[-1]))\n", + " ax = plt.gca()\n", + " ax.set_xticks(np.arange(len(coef)))\n", + " labels = ax.set_xticklabels(f_imp)\n", + " for label in labels:\n", + " label.set_rotation(90)\n", + " filename = name.replace(\" \", \"_\")+'_feat_imp'\n", + " save_png(filename)\n", + "\n", + "def measure(clf_class, parameters, name, X, Y, data_size=None, plot=None, feature_names=None):\n", + " if data_size is not None:\n", + " X = X[:data_size]\n", + " Y = Y[:data_size]\n", + "\n", + " train_errors = []\n", + " test_errors = []\n", + "\n", + " scores = []\n", + " roc_scores = []\n", + " fprs, tprs = [], []\n", + "\n", + " pr_scores = []\n", + " precisions, recalls, thresholds = [], [], []\n", + "\n", + " for fold_idx, (train, test) in enumerate(cv.split(X, Y)):\n", + " X_train, y_train = X[train], Y[train]\n", + " X_test, y_test = X[test], Y[test]\n", + "\n", + " only_one_class_in_train = len(set(y_train)) == 1\n", + " only_one_class_in_test = len(set(y_test)) == 1\n", + " if only_one_class_in_train or only_one_class_in_test:\n", + " # this would pose problems later on\n", + " continue\n", + "\n", + " clf = clf_class(**parameters)\n", + "\n", + " clf.fit(X_train, y_train)\n", + "\n", + " train_score = clf.score(X_train, y_train)\n", + " test_score = clf.score(X_test, y_test)\n", + "\n", + " train_errors.append(1 - train_score)\n", + " test_errors.append(1 - test_score)\n", + "\n", + " scores.append(test_score)\n", + " proba = clf.predict_proba(X_test)\n", + "\n", + " label_idx = 1\n", + " fpr, tpr, roc_thresholds = roc_curve(y_test, proba[:, label_idx])\n", + " precision, recall, pr_thresholds = precision_recall_curve(y_test, proba[:, label_idx])\n", + "\n", + " roc_scores.append(auc(fpr, tpr))\n", + " fprs.append(fpr)\n", + " tprs.append(tpr)\n", + "\n", + " pr_scores.append(auc(recall, precision))\n", + " precisions.append(precision)\n", + " recalls.append(recall)\n", + " thresholds.append(pr_thresholds)\n", + "\n", + " # This threshold is determined at the end of the chapter,\n", + " # where we find conditions such that precision is in the area of\n", + " # about 80%. With it we trade off recall for precision.\n", + " threshold_for_detecting_good_answers = 0.59\n", + "\n", + " if False:\n", + " print(\"Clone #%i\" % fold_idx)\n", + " print(classification_report(y_test, proba[:, label_idx] >\n", + " threshold_for_detecting_good_answers, target_names=['not accepted', 'accepted']))\n", + "\n", + " # get medium clone\n", + " scores_to_sort = pr_scores # roc_scores\n", + " medium = np.argsort(scores_to_sort)[len(scores_to_sort) // 2]\n", + " # print(\"Medium clone is #%i\" % medium)\n", + "\n", + " if plot:\n", + " #plot_roc(roc_scores[medium], name, fprs[medium], tprs[medium])\n", + " plot_pr(pr_scores[medium], name, precisions[medium],\n", + " recalls[medium], plot + \" answers\")\n", + "\n", + " if hasattr(clf, 'coef_'):\n", + " plot_feat_importance(feature_names, clf, name)\n", + " elif hasattr(clf, 'named_steps'):\n", + " for step, s_clf in clf.named_steps.items():\n", + " if hasattr(s_clf, 'coef_'):\n", + " plot_feat_importance(feature_names, s_clf, name)\n", + "\n", + " summary = {'name': name,\n", + " 'scores': scores,\n", + " 'roc_scores': roc_scores,\n", + " 'pr_scores': pr_scores,\n", + " 'med_precisions': precisions[medium], \n", + " 'med_recalls': recalls[medium], \n", + " 'med_thresholds': thresholds[medium]}\n", + " \n", + " return np.mean(train_errors), np.mean(test_errors), summary\n", + "\n", + "def bias_variance_analysis(clf_class, parameters, name, X, Y):\n", + " data_sizes = np.arange(40, 2000, 20)\n", + "\n", + " train_errors = []\n", + " test_errors = []\n", + "\n", + " for data_size in data_sizes:\n", + " train_error, test_error, _ = measure(clf_class, parameters, name, X, Y, data_size=data_size)\n", + " train_errors.append(train_error)\n", + " test_errors.append(test_error)\n", + "\n", + " plot_bias_variance(data_sizes, train_errors,\n", + " test_errors, name, \"Bias-Variance for '%s'\" % name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have learned in the previous chapter, when using features with different value ranges, it helps to standardize them." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def create_pipeline(**param):\n", + " return make_pipeline(StandardScaler(), KNeighborsClassifier(**param))\n", + "\n", + "bias_variance_analysis(create_pipeline, {'n_neighbors': 5}, \"5NN\", X, Y)\n", + "save_png('04_bv_5NN_all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maybe simplifying the feature space helps. Let's try out to use only `LinkCount` and `NumTextTokens`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lyAxeJ3hzkmWdHzbb1Vo/m9a3YXaYnaE9y8X8O0WUUuZm52+1juTD7XgxL/aOjyTZr9b6qFrr2bXWD9Vab661bhmrE0zGea7L72mqGO+xeFOSB9Va/2Goz2AYmHtekMHLlDw0rTv+ekIp5alJjm/ruifJn3RemOnwnrSWYtlhSVpLh3C/cZ8bu8zcuPfq1bG4LMn0tvZPaq3XTVAtOzE3jrmuXavp8s+uK59JJuN4FKRAFwxc2HleR/ff7Wm/WuuP01pjdYcZSV44hqXRA0bxTZT3d7SfOspSxkwp5dAkv9/WtSFDWN+y1nptkm+2dS1IcvKYFkeveGEGfwj5TK31J0PYr3PuLKWUOU0bm3+nnOcl2b+tvSqtZTYmnHmxt9RaV+3uAtcYmYzzXFfe01Qy3mOx1rp8qAFKx37/meT/dnT3zN+aSc7oaF88cKGn0cDdNO/Yw3GmtC7NjV1hbty79eJYHFj+8MyO7l76wk5ibhxTXb5W05WfXZc/k7maARcAACAASURBVEy68ShIge44Icm8tvYNtdYfDXHfSzrazx2bkpgEvtvRnltKWTAhlezslI72Z2utq4a4rzE/NXSOkc6f+y7V1hrTN7V1zU/rQZRNzL9Ty1kd7Y/30Idw8+LUMxnnuW69J3pD59+aB09IFR0GHh5+Ukf3kMZikq8laX/exYNLKUePSWH0GnMjY+3JGfxslq0ZfFfRhDI39pRhXavp8s+uK59JJut4FKRAd5zY0b52GPtel9Yv6B0eVUo5cNQVMRls3UXfrF30TYTRjPnObZ9RSvH7ahIppRyU5Hfburam9TDOobq2o/2Hu9nW/DtFlFIenMHfrkp661uC5sUpZDLOc11+T/SGzr81e+XvzMel9eyKHe4cuItgjwaWE/l6R7exODmZGxlrnV/Y+VKt9a4JqWTXzI29Y7jXarr5s+vWZ5JJOR59AIPueERH+4ah7jhwK/73O7ofPuqKmAyO6GhvzQgezDxORjPmf5RkZVvX/CSHjUFN9I7O8fG9YS47cn1He3dzovl36vjjJH1t7Ztrrf9voorZBfPi1DIZ57luvid6Q+ffmnfucqvuG/GYH2AsTg3mRsZMKWX/JH/U0d1LX9hJzI29ZLjXarr5s+vWZ5JJOR5nTHQBMEU8rKP902Huf2uSR7W1j0ryH6OqiMmgc83fb+/hoV278rullMvSenjogUnmpPWL8e4k30ry/6W1xu+QPwwMPHDskI7uW4dZ18+SLGprHzXQx8R4eSnljWnNZYuTbEmyIsltaa2f/tVhPmTxqI72SObE3R2vnfl3Chi4dfwlHd0j/XBrXmQsTMZ5rpvviQk2MG/9QUf3N3e17R68oZTysLSWw1mU1lrsK9MaP9cl+WKttXMJlD0xFieXpaWUS5I8Pq3l4+an9Yyze9JaHufrST5Va13ZfIhdMjcyll6QZG5b+84kXxnF8cyNk9twr9V05WfX5c8kk3I8uiMFxlkpZVEGTzJJcvswD9O5/ZEjr4jJoJSyT3a+tfjKERzqkWn9UXhkkv3Sut30oCRHDxz/o0luL6W8qZQyc4jH7Pz2xT211vXDrMuY7y2nJTk+rQ+3s5Psk2THQ+r+KsnXSynfKqU8fYjH6xwjw50Tb+toLy6lLOzcyPw7pZyYwR8KNiS5bITHMi8yFibjPNeV90TPeHkGP1/i3iTXjOA4L01yXFrz6Kwk+yc5PK2Q5q1Jbi6lXF1KecwwjjnasWg+7S2HJ1mW1kWyBUlmJjlgoP2iJB9M6/fuuwc+A+2RuZFx0PnZ+yO11l0t3zRU5sZJaoTXarr1s+vmZ5JJOR4FKTD+Oh8otX6Yt/omrW/B/v/t3XeYbEWZ+PHvBbzkHBQkCQuCCixBQMUFFARBQRBewQCoiAF1MfwMmDAia86uAUEEl9eAgKKIAUURCYpixEVAJQgClyj5/v6oc9eeM316uqfDzPT9fp6nHzh1QlV3163pU++pqlYr91EejYdjKT+6FlkAfG5Iea1G+TF3XkSs28Xx9Tpfr7/dsM7PPdsB342Id0fEvCmO7auOZOYdQH0B8XZ1xPZ38fGC2vbXMnPBEPOzXdRUxrGdG9V70gyLiA2Bt9SSP5KZ9w4pyycB50fEkV0e32+bWj9+RdedmvWWB44CLomIbqZ3sW3UwETEFpR7nVajmNbLtnFumk5fzai+u1Hek4xlfZzxAkiLgfpTM/+cxjXq56w4zbJoDETEfsDLa8lv6nG4+92Uocj/j7Jo179TIvzbAQcCnwRuq52zA6WjvP4Hsc46Pz6uAT4LvAjYifJk4GaUp6deAZxdO34eZYTKe6a47qjqiHVxMRARawJPryVP5+bWdlGDNI7tnPV4MRAR84FTmfjdXAX8V4+X+l/go5RpF3ekTLP0KGBn4LVMnvt8PvDxiHhxF9futy7Wj59H6ajXaN1PWbj4zcA+wDaUv7tbA/sC72dyR9qmwPciYoMprm3bqEGqjy74UWb2Ok3RIraNY6yPvppRfXdzqW2clfXRNVKk4as3HvWnTbpRb0C6GtKs8RMRW1GmlWn1XeBTXV7iTuA/gRMys94huMglwFcj4mjgI0xcd2Bz4Hhg/w55WOfnvguBPYBzMnNhwzHnU37Ub0eZQql1qO0bIuKCzDy94dxB1ZHWaQ/a1RHr4uLhEMo0IItcQVnHpFu2ixqGcWznRvWeNLM+R1mrYpEHgEN7eJr/bODTmXlBw/7fU9a8+EBE7AGcSFmPapFPRMRFmfmLDnn0WxfbdeasANze43U0fW8GPpuZTU8oXwqcERFvAd4GvJ7SiQblSe+vR8R2HX6n2jZqIKrg8nNrydN5YMe2ccz12Vczqu9uLrWNs7I+GkiRRq/px96gz9GYiYj1gW8x8Q/S1cBzO9xETJCZN1KegOnm2FuBwyLiRsrTMYvsFxFPyMyfdldy6/xck5ln9XDsxRGxI/AzylOCi7w3Ir6ZmQ90cZlR1RHr4nh6fm37+G7bRLBd1MjM5jZrunXLejxmIuKdwPNqyW/MzB93e43M/HIPx54dEY8DLqCsiQGwJPBe4CndXofe65X1cIZl5ru7PO5u4I0R8Tfg4y27tqGsZ9btemi2jZqufYHVW7ZvBb7a60VsG8fbIPpqakb13c3mtnFW1ken9pKG747a9rLTuEb9nPo1NeYiYi3gHCYupnw9sHvVCThMr6M8FdbqpR2Ot84vZqqhygcz8cfOZsCuDaeMqo5YF8dcFcRrnSv9AeCEEWRtu6ipjGM7Zz0eYxFxFGWUQKsPZub7hplvZl4JvKSWvHtE1BepbdVvXWx3vHVxFsvMTwBn1JJf1uEU20YNSn1ar1MyczrTIfXEtnHuGFBfzai+u7nUNs7K+mggRRo+f1ipLxGxGvA9Jj7t/w9gt8z807Dzr56gOK6W/JQOC4pb5xdD1TDz79aS92w4fBw7GDUz6je3387Ma4edqe2iujCO7Zz1eExFxIuAD9aSP5WZrxlF/pl5GvCHWnLTbwgYTudMrwuRa/SOrW3v2GGNMttG9S0i1gN2ryWPYpF5wLZxLhhgX82ovru51DbOyvpoIEUavltr28tFRK8LJK1V217QR3k0h0TEypTO6S1akm+hPN3w2xEWpb6o+JrA2g3H1uv8mtPIzzo/N32ntr1lw3F91ZGIWIHJP6za1RHb3zFWfZfPqiWP7OYW20V1No7t3Kjek0YoIp4HfJp/rT8B8AXgyBEXpd6mNv2GgP7b1Hqdvy0zH+zxGhq9Cyn3QYssSVmkux3bRg3CYUzsN/1VZl4y4jLYNs5SA+6rGdV3N8p7krGsjwZSpCHLzJuY+IMPYP0eL7NBbXvooxA08yJiRUrH9LYtybcBe2ZmfUqZocrMW+j+D2G9fq4ZEcv1mKV1fm66qrbdbR2pf99TqR9/c1VHJ7D9HXsHAiu2bP8d+OaoMrdd1BTGsZ0byXvS6ETEQZSgSWu/wMnA4dOc070fV9W2O3W4DLou2p7OAVUH2l9qyW3riW2j+lWNMq6vwzfKB3YWuaq2bds4Cwyhr2ZU390o70nGsj4aSJFG4/e17U7zWraz0RTX05ipnpg6C9ixJfkO4KmZeeHMlIr6XLBth2Zm5m1AfWqdjXvM6xG1bev83NBVHWHwbeLvOhxr+zu+6tN6fTEz7x9xGWwX1WQc27lRvicNWUQ8EziJ8lT/Il8BDp2hJz67/Q0B/m1fnM3GemLbOJ6exMTfXvdQAs2jNhvr/GJtSH01I/nuRnxPMpb10UCKNBq/qW0/rtsTq0a6Pnyzfj2NkYhYlvJU9U4tyXcBe2fm+TNUpnnA6rXkf3Q4pZ86v1ktr7uAK7s9XzNqjdp2Ux2p148te3wS5glTXK/TPtvfMRARmzKxjYQRPyVou6gpjGM7N8r3pCGKiH2ALwNLtSR/A3h2Zj4wM6Xq+jcE9FHnK9bFuWsk9cS2UcALattfz8ybZ6Acto2zyBD7akb53Y3qnmQs66OBFGk06usG7NLDuU9k4k3OLzPz732XSLNSRCwDnMHEOnI3sE9m/nhGClVsDTykZftB4PoOx/dT5+vHnj0b5sJUV3aobbdd9DszrwN+3ZK0FJM7xTvZpbb97Q7H2v6Op/rN7U8y848jLoPtohqNYzs34vekIYmIvSgjT1rbr28Bz5qBUX2tuvoNUbkIaO3QXLsKsE8pIpag1PtW1sU5ICLWYPITyp3qiW2jpiUiVgH2ryXPxLReYNs4awy5r2aU392o7knGsj4aSJFG42wmDsl8XBXJ7cZhte3TBlIizToRMR/4OrBbS/I9wDMy8/szU6r/8+za9iWZeUeH4+v19BnVD9JuHDbFtTQLVT8s6zcc53Y4pf691ucgbspnMybeUNxJWeSvie3vmImIJYFDaskzcXNru6ipjGM7N6r3pCGIiN2BrwHzW5K/CzwzM++dmVJBRKwFPLmWfG7T8VXA58xacld1EXgKsE7L9hWZ+eumgzWrHMTEPqy/03mqF9tGTddzgGVatq8CfjDqQtg2zh7D7qsZ8Xc3knuSca2PBlKkEcjMu4Cv1pJfP9V5VbR2v5ak+4FTBlg0zRIRsRSQwFNbku8DDsjMs2emVEX1A/9lteTTO52TmVcB57UkLQv8Zxd57czEm4kFlKc+NPu9Hnh4y/YDlCdcm5xcHbPI/hGxSZf5tMrMvLvpYNvfsbQXsHbL9u2Up6tHxnZRXRrHdm4k70mDV7UlpzOxc/AHlE6ge2amVP/nOKB1KqTb6PwwBsAXa9uHR0R9usV2XjfFdTQLRcRDgTfXks/MzIVN59g2qg/1kc/Hd6prQ2TbOAuMsK9mJN/diO9Jxq4+GkiRRucYSmO7yGHV/MRtVU93f4GJT4x9PjOvGE7xNFOqp6tPBvZtSb6fMsXCNweYzw4RUR8eOdU5m1IWUmtd1O4m4ONdnH50fTsituuQ12pMfqr8uMy8tZuyajAi4nnVzWov57wIeFst+YTMvLrpnMz8E3BiS9J84ISq7WvKZ18mPgVzL/D2Lop4DLa/46S+yPz/ZOad07mQ7aKGaRzbuRG/Jw1IRDyOMqd7a7v1Y+DpmVlfyLiffI6IiJV7OH5eRLyDyU+4vq/qBG+UmT9g4hPiawCfrqYDacrvlcCuLUn/AD7UbXnVv4h4ZEQ8vcdzHkapv62/T+8Fju3i9GOwbVQPIuLfgW1akh4ETujzmraNc9So+mpg5N/dSO5JxrE+zlu4cCaCqtLiKSLeB7y2Jek+4NXAZ1qH00fE5sDngMe3HHsTsEU1B6vGSEScyORpal7H9J6wvr7pKaaIOIxyY3A+5amqMzLzrw3HrgG8mPKU1Iq13YdmZldPBETEV4ADWpJuq66brXNpRsQOlJuPR7YcewWw5VQ/FjVYEXEusD2l/iVwblMndfVj62gmPrUHcA2wXWZ2Wi+CiFiXMqf0qi3JPwUOz8w/tBy3NHAE8AEmzul+bGbWfwQ25WX7OwaqIN/fmDhv+Q6ZeeE0r3cYtouLraoNWqrNrh0pC3Evcg3N893fkZmNC7+OYzs3yve0uBhmXYyIrYEfAq2deH+ktEOdpiNs5+5Of9sj4qoqny9Tfkec3260S0TMo8yz/lYmz7d+GbBjN+1cRGwD/IyJnd7fAF7Z2pZHxIqU39ZvAua1HPvizPzMVPksboZcH3eh1MfLgC8Bp1VBiHblWBE4lDISpf6Qz1sz850d3kbrdWwb56hR/J1uk+fHgJe3JH07M/fq9vyGa16FbeOcNKq+mpb8RvbdjeqeZNzqo4EUaYSqaPaZTBwSCHAD8AvK9CQbUZ6AaG047gV2y8zz0NiJiEE2xLtm5rkN+RxG6TBsdSPwO8oiYHcCKwGPAB7DxDq4yNGZ2c3TX4vyXJ4ybHTr2q6/Ar+i1O1Nq/xa3QI8bgYWj17sVYGUnVuSHgT+RJkb+FbKtAWrA1sx+aYWSl3aOTN/02V+u1DmsG79YbUQuAT4M+WmYxtgzdqp36RMR/IAXbD9HQ8R8TrKNAeL/CYzt+jjeodhu7jYqjo2NujzMidm5mFT5LMLY9bOjeo9LS6GWRcj4hgmjxqdrh9l5i5NO9u8j/spa1j8jfIbYh7ladRtmNjZvMiVwE6Z2Wkx5Xqez6F0yLe6H7iQ0q6uATyW0pa3+lRm1qdoFEOvj7tQAimtbgV+Q3nq+HZgBWA9ym/Ndp3on8nMF3dbENvGuWtUf6db8luaspj7ai3JB2Tm1/opgG3j3DWqvppaniP57kZ5TzJO9dGpvaQRqn4YBXBqbddawJ7AgcC2TPwBdwOwr514GpI1KZ3m+wHPBfYBtmByZ+GNlHrYdWchQDWSYS+gvgDbesDTKIuT1/8wXwHsYWfhrLEE5emTPSjt18GUxd/aBVG+D2zVbRAFoPoxuR+lji0yD9iuym8PJt9sfhk4qJebTdvfsVFfoHAYi8zbLmqgxrGdG9V70py3FKX9fCplofBnURZObtdReCqwbS8dhQCZeTJlPYPW0TVLUUYVLMqvtWNmIfAR4BW95KOhWhl4AmXqnOcCz6C0VfUgyp3AEb0EUcC2UT3Zj4lBlBsZzrp0to1qNKrvbpT3JONUHw2kSCOWmXdk5kGUH2wXdDj0ZuBTwGMy8zsjKZzG3U+ADwIXU54umMpCypMIrwA2zsxp/YispoDYHXgJZUhyk+soT5pvlZkXTScvDcRHKFMcNa5vUnMncBrlib3dMvNvvWaYmWdRfqR9mvKES5MLKE+FPbtpurEp8rH9ncMi4gnAZi1J9zL5yaZe2S5qJMaxnRvVe9Kc8g7g65S2qxu3UhaQ3T4zD8rMTvWoUWZ+gTJ64WTK75J2HgS+Bzw5M4+y03rG/B54D2XKq27X57mcMpXshpn52elkatuoLtXX4TspM+9re2RvbBvVk1F9d6O8JxmX+ujUXtIMi4hHUIZwrgMsD1xP6cD8abbM2SoNUkQ8hNIh+QhK3VsJWJryB+0WyvDKi3IICxpHxKMoNxfrUIa9X0sZ6n5B61ycmnkRsQrwaMpTKQ8FlqM8hLGAUk9+D/x6kD9wImI+5anEDYCHUerkNcAvM/PKQeVT5WX7q/9ju6hRGcd2bpTvSXNDRKwFbA6sS3kCfzlKMHoBpbP6MuD3mTnQDolqqpKdqnzXqvK7FrgwXetsVqkWG94E2Bh4OLAKsAwlwHILpePuosy8sfEi08/btlEzwrZRvRrldzeqe5K5XB8NpEiSJEmSJEmSJDVwai9JkiRJkiRJkqQGBlIkSZIkSZIkSZIaGEiRJEmSJEmSJElqYCBFkiRJkiRJkiSpgYEUSZIkSZIkSZKkBgZSJEmSJEmSJEmSGhhIkSRJkiRJkiRJamAgRZIkSZIkSZIkqYGBFEmSJEmSJEmSpAYGUiRJkiRJkiRJkhoYSJEkSZIkSZIkSWpgIEWSJEmSJEmSJKmBgRRJkiRJkiRJkqQGBlIkSZIkSZIkSZIaGEiRJEmSJEmSJElqYCBFkiRJkiRJkiSpgYEUSZIkSZIkSZKkBgZSJEmSJEmSJEmSGhhIkSRJkiRJkiRJamAgRZIkSZIkSZIkqYGBFEmSJEmSJEmSpAYGUiRJkiRJkiRJkhoYSJEkSZIkSZIkSWpgIEWSJEmSJEmSJKmBgRRJkiRJkiRJkqQGS810ASRJkiRJmusiYhfgh7XkXTPz3NGXRpIkSYPkiBRJkiRJkiRJkqQGjkiRJEmSBiwiNgSu7PLwB4C7q9dNwA3AVcAfgF8B52fmzYMvpSRJkiSpGwZSJEmSpJm1JLB89Vod2BTYqWX/woj4JfAV4KTMvGb0Reyf0x4tPhoCic/PzBNGXxpJkiSpf07tJUmSJM1u84BtgGOBKyPiixGx/gyXSZIkSZIWGwZSJEmSpNG4kzJVV7vXnynTet0/xTUeAjwP+ENEHDW8okqSJEmSFnFqL0mSJGk0Ls7MXaY6KCIeCmxfvXYHdmhz2LLAhyJie+CQzJwqACNpyKpp6ubNdDkkSZI0eAZSJEmSpFkkM/8OnFm93hIR2wJHAc9hciftwdV/nz26EkqSJEnS4sWpvSRJkqRZLDMvycznAftQpv+qOzgiXj3iYkmSJEnSYsNAiiRJkjQHZOY3ge2AG9vsfndEbDjaEkmSJEnS4sGpvSRJkqQ5IjOviohnAecAS7bsWgZ4B3DIdK4bESsAmwGbAqsDKwL3ALcAN1DWd7m+j6KPREQsCWxEeS8PB1aifE63VK8/AJdl5oMzVsg2ImJZ4NHA5sCqlM//AeAuSrmvBq7IzGsGnO9jgH8D1qJ873dRAnVXARdl5n2DzG82iYj1gK34Vz1ZGvgncCdwLeUzuDwz756pMo5aRDyEEqxdD1gTWBlYQKkTv8vM385g8SRJkmbUvIULF850GSRJkqSxUo0OubKW/KNuFpvv8vofBV5RS74X2KCbgEdELAU8Cdgb2AXYgqkXyb4COAX4aGb+o8tyHgO8rZtjOzgxMw/rkMcmwP6U9/MEYPkprncrJRD1/sz8eZ9l60tE7Ae8ANgDeEgXp1wL/BQ4HTgzM2+bRp5bUtbc2QNYp8OhdwDfA47LzAu6uO5hwBd6LU/NwP6NtBMRa1P+3RwMbNjFKfcCl1I+h69k5qVTXH8X4Ie15F2rRei7PX4QHpGZV3V7cETsA7wQ2JUSxGtyHfAN4L2Z+Ze+SihJkjTHOCJFkiRJmns+BLyMiaNS5gMBfLTTiRERwCeANXrMc2PgLcBrIuJ1mfmJHs8fqIhYnRIQ2brHU1cGDgAOiIgzgEMzc8Ggy9dJRKwPnAT8R4+nrgMcWL2OA97QQ55rA++nBBGmCpoBrAA8A3hGRJwOvCAzb+6xvLNGRBwJvJfyvro1H9i+eh0dEcuO0wiViNge+AiwY5enrA28FHhhRHwAePNsG90lSZI0LK6RIkmSJM0xmXklcGabXU/r4vRH0XsQpdVywMcj4lN9XGMQVqT3IErdPsCFEbHuAMrTlWq00k/oPYjST55bARcCz6a7IErdvsAFEbHpQAs2IhHxTuDj9BZEGWsR8Wzgx3QfRGk1H3gjcEY1LaAkSdLYc0SKJEmSNDd9nzJioNWOEbFEj0+JXw38Evgd8DfgdspaEStQRkD8O2UaqJVr570kIi7LzE92uPb1wK+q/1+BMqql1RWUKaQ66XYKoTuAi4DfA3+iTOF1O6XTd1VKAGlXyjokrTYBTo2InTPz/i7z6sfxlDUo6i4FzgUup6xLcR8lWLQqZc2XLSnrVyzZ5txGEbEdZfqoeof3g8B5wPmUaegWAMsC6wI7A0+u5bUJcFZEbJuZt7bJ6mb+9V3PZ/Ln/NfqmE7+d4r9Paumz3pTm12Lpni7lFK2OynrpKwIrE9Zs2YH4GGDLlOLO/jXZzYd8yj1oicR8VKg3b/bOymfyUWUabxup/y73wTYHdimdvzewAmUEV6SJEljzUCKJEmSNDf9rE3aipTFwy+f4tzLgM8DZ2Xmn6bKKCKWBo4A3kVZmHuRD0bE6U2LoGfmp4FPV9fYhcnrQRzetH5ElxYAXwK+CpzfzeLoEfF4ynRG27UkP56ybsj7+yjLlCJiJ0owp9WfgUMy86ddnL8q8FTgxcCUi11Wx3+FyUGULwDHdFjn4j0RsTFlCrg9WtI3pgSCnlk/ITPPAM6o8t2QyWsEvTUzT5iqzEPwZiaPwvkA8PbMvL3TiRExjxI8eBZlDZGBysyLKYHKaYmIDzI5kPJnoHENo2o6rw/Xkv8JvAP4ZId1d94QETtT2o3WgOgzI+IVmfmxngovSZI0xxhIkSRJkuamX1FGLdQXKd+IzoGUD2XmMb1klJn3AB+LiB9RRjEsCqYsDRwJHN3L9QbkWmCdzPxnLydl5vkR8UTgNGDPll2vjIgPD3lUyr617fuAPTKzq5EYmXkLcApwSkQs18Upn2DiouoPUNaEObmLvK6IiKdSOs6f37Jr/4jYITN/3k2ZZ1JErATsUks+ITNf2835mbkQuAS4JCLeBtwz2BJOX0S8FnhVLflGYM/MbDvKqwqInkoZMbTIDcBumXnZVHlm5o8iYlvKlGCtAZy3RcTxmXlnL+9BkiRpLnGNFEmSJGkOqjr8202V1HG9j4ZpmbrN89dMDpoM/En9Lstyb69BlJZz7wYOBe5qSV4PeMogytbBRrXtc7sNotRl5l2d9kfEIykjKVq9qZsgSkseCymjX35f29X1IvczbAMmT4X22elcKDP/WX0eM65a3+S/asl3AntPMcLsECYG1h4E9usmiLJI1X7sB9zbkrw6cHi315AkSZqLDKRIkiRJc9eCNmkrtUkbpC8xcVqptebiIuSZeQPwnVryTkPOdsXa9k1DzOv/MfF+70qmMXVZNV3ae2rJT42IZfoo26jUP28Y7mc+dBGxG2Vdktbpyu4HDszMizqcN49SJ1qdnJnn91qGzPwzcFIteb9eryNJkjSXGEiRJEmS5q5b2qQtO8wMqyfSb6gl7zjMPIeo/vT+sN9HvRN/h4gY+HTLVaf5/rXkEzLzgWle8qza9tKUhdhnu3ZBk2EHy4YmIrYBvs7k6fwOz8xvT3H6VpRF41t9ro/ifKu2vUM1dZgkSdJYco0USZIkae5q92BUT9MPVZ3u21avLShTg61IGdlS77BdZLXa9vq95DksEfFwysLxWwKbAitT3seyTF5wHOBhte1hv4+fAwe1bD8C+GxEHDnVVF092hJYtZbW88iDRTLz5oi4lfJ5LrI18KPpXnNE/kSZ/q61vh4XEZdn5nkzVKZpiYiNKAGt+iibozPzxC4usXNt+wHgwj6KdGVtexlgc+DSPq4pSZI0axlIkSRJkuauVdqkdbVuSESsDLwGeB4T100YVDlGJiIOAF5G6SzuZ9T9sN/H/1CmyWodNXQYsFdEnEAZbXBRZj7YZz5PaJP2sYjoZ7H0+uL2a/RxrZHIzAcj4ovAUS3JqwM/johzKNPUnZWZ/5iRAnYpItakTEP30Nquj2fmsV1eNhSJqAAADRxJREFUpl4nFgIXRMR0izW/TdqsrxOSJEnTZSBFkiRJmrvqow4AbpvqpIjYF/hvJnfMTtfKUx8yeBGxDmWthicN6JJDfR+ZeX1EHA18qLZrLeB11WtBRJxPGb1yAXB+Zt7RY1brtknbrNfyTmH1AV9vWN4F7ANsVEvfvXotjIjfUkbsXAScl5l/HG0Rm0XE8pRptOrTcn0N+M8eLlWvE0tRpvsapLlSJyRJknpmIEWSJEmagyJiPu0DKX+d4ryDKcGHJQdYnKYpwIammsbrh0zuYO7H0O+PMvPD1booxzbktwqwV/UCuD8iLgBOBf6ny9ETo+jQHupaPIOSmTdVC7R/jTIdWd084DHV6wiAiLgeOB34cmbO2PRlVT35CvDY2q4fA8/pceSSdUKSJKkPLjYvSZIkzU1b074j/s9NJ0TExsAXmBxEuQ84DXgVsBvwSMq6EssDS2TmvNYXcPUAyt+vE2gfRLmUEqTYD9iGsg7KSsD8Nu/j7aMqbKvMfD+l4/5k4O4pDl+KskD6x4CrI+J91SiFTtoF2BZbmXklsD3wYqCb0SYPq449NyIuiohdh1m+Dj4HPLWW9htg38zsdZo264QkSVIfHJEiSZIkzU2Pa5N2G3BFh3PeCyxdS/sO8ILMvK6HvGf0yfOI2JsS8Gl1A3BIZp7dw6Vm7H1U00c9NyKOBPYGdqUETB5JGSXRznLAa4F9IuIpmdkU0Gq3Ts6qmbmgz2LPWZl5P/AZ4DMRsS2wJ/AfwI6UQFuT7YDvR8RbMvPdwy9pERHvBQ6tJf8V2HOa32O9Tlyame1G6EiSJKkNAymSJEnS3FQPJEBZT2Nhu4OrUQxPryX/AtgnM+/rMe+Zfrr94Nr2A8DTM/PCHq+z2oDKM22ZeStwSvUiIlYFHg88kTK91xZtTtsU+FZEbJOZ97bZ3276rw0po3UWe5l5CXAJ8O6IWAJ4NOXz3hV4CpMDK/OAd0XEVZl58rDLFxEvB15fS76FEkS5ZpqX/Qewfsv2I6Z5HUmSpMWSgRRJkiRpjomITZg85Q/AmR1O+w8mj0Y5ttcgSkSsxwysiVKze237O9MIosDkBchnXGbeQllc/FvAGyJiU+AoyvodrVOyPRp4IfCpNpf5e5u0LTGQMkm1zshl1euTEbE0sD9wDCVg1eq4iDi1Gt0yFBFxIPCRWvLdlEDh7/q4dL1OrBwR62fmX/q4piRJ0mLDNVIkSZKkuedVTP4tfw9lYeom67VJO28aebebUmxkImI+sFYtuef3ERFLUtbNmNUy8/LMfBlwSJvdz2w4rV1QqV3gTTWZeU9mfhnYFvhlbffDKVOBDUVE7AycxMR/2w8AB2fmT/u8vHVCkiSpDwZSJEmSpDkkInanLIRdd3Jm3tjh1DXapN08jSI8axrnALR7ir++6H03BvU+9gJWmMZ5MyIzT2HyiJItGw4/H7izlrZ3NW3YKAzqu54xmXkH8PY2u5o+875ExBbA6UweNXZkZn5jAFmc0ybtuQO4riRJ0mLBQIokSZI0R0TERsCXmfw7/i7gbVOcXu9Yh/ZBiU75bwzs28s5LW5vkzadQEbf76Py6mmcM9P+UNteud1B1bop36klrwi8ZhiFamNQ3/VMq3/e0PCZ9yMi1qd8X/VrvzMz/3tA2fycydN77RQR7dZakiRJUo2BFEmSJGkOiIinAxcBq7fZ/frM/NsUl7iuTdpTesh/CeB4pj+y4JY2aT2vUVItzn5XLbnr9wEQEYcDu/Sa9yywdm270wikd7dJe11E7DTA8jS5ncmjUmbdejRdqH/e0Pkz71lErEYJoqxT2/X5zHzroPKp1nU5rs2uz0fEmoPKR5IkaVwZSJEkSZJmsYjYLiJOAs4AVmtzyImZ+fEuLtVuHZE3R8RKXZRhCeC/KQvWT9dfgVtraXtN81o/qW3vEhFdXSsi9gQ+Os18+xIRH4iIR03z3G2AJ9aSf9V0fGb+EvhaLfkhwGkRMa3vMSKWjogjIuJVnY6rFnCvL4y+R1WPRiYi9omI51cLyE/HK9ukNX7mvYqIZYEzgc1ru75J++n7+vUpoB5wXR/4dkSsO50LRsSaEfGuaspBSZKksWUgRZIkSZpFqo7JvSPiHRFxAWUUStNaBicAL+zmupl5HZMDEP8GnB0RG3Qoz6aUJ+YPr5LuZ/KIkG7yXwj8rJa8W0QcGxH1xeOnvFybtFMj4oCmEyJimYh4K2UdimWr5Nt6zLdfLwR+GxHnRMSLun3fEfE04NtMvn/70hSnvhi4spa2BvD9iHhfRDysy/x3iIgPAFdRAmobd3Ha+bXtRwKf61TXhmAjyiiqv0TEByNip26CORGxRkScCOxX23V5Zl40wPKdCDy+lnYB8KzMfGCA+QCQmXcDAdxT27Ut8IuIeHkV3OkoIuZHxNMi4ovA1cCbGMKUZ5IkSbPJUjNdAEmSJGkxsV1E1BcLX2R5YBVKZ+RDurjWnZTpvD7RYxneBny/lrYjcHlEnE4JtFwPLAM8HNidMgqi9b7hHZSAwHQ6xI8H9qylvQF4Q0RcR1k0vj4l1Bltpjj6IvBGJnborwB8JSJ+QXnK/3+B+4C1KB3FT2PitGi/q457/TTeR792q16fjojfAr+synMTsIAyfdpqlJEKuwObtbnGecCpnTLJzJsiYh/K99ra0b0U8FrglRHxM+DHlJEKt1AWO1+FMq3V1sB2wHSmfjoeeEkt7fnA8yPiRsoUWffV9l+cmYczeGsBr6peN1V15FLgL5TP+5/AcpQ6/VjKZ14PKCwEXj7gcm3fUNbzI6Kf6+6Vmde225GZP4uIIyhB2Hktu9YEPga8MyJ+RAno3EAJNq5AqRMbUv4tbU1psyRJkhYbBlIkSZKk0Vge2KrPa9wLnAK8pYs1USbJzB9ExHspwYtW84EDq1cnXwLeRZejYNr4GiWQ8+Q2+9am/ZoUk4JPmXlfRBxICRAsV9u9TfXq5Bpgb+CwKY4btiWALapXL34DHFRNodVRZv4mIh4LfB14TG33fGDn6jVQmXlRRHyBEjypW5P2wZkFgy5HG6tTAiW9TEW1EHh1Zp4znCJNMIi1ZOZ32pmZX4yIm4CTgFVru1cB9q1ekiRJqji1lyRJkjS7LQQupozA2DAznz+dIEqLoynBkIU9nPMA8B7g0GqKrmmpOv4PoASD+lKtAbIHcF2Pp14A7JiZV/Vbhmm4vs/zF1Kmg9qpacRBO5n5J2AH4IOU0Uz9uBg4q8tjXwp8BJgy4DMkNzF5hFOvrgb2zcwPD6A8s0ZmfosyuuQ0emsL6u6nrOny60GUS5IkabZyRIokSZI0sx6krFlwN6Xj9++UtSj+SBmN8dPMvHlQmVWBkLdExLeBN1OCEU0PWN0FfAP4r8wcyCLbmbkAeE5EvAM4iDJ11OaUqaxWoLupzRZd6ycRsRVlmqojKE/TN7mYMnXRl7oZyTEMmblZtWj8XsB/UIIbK3Vx6g3AV4HPZmbT9HBT5X0X8JqIOBY4kjLV2daUacQ6uZuy3sn3gNMzs76IfKc87wGOqtZXOYgyjdyjKeu0rMgUIyf6lZknRcSZlOnkdqOsR7IZE6e0audByminU4ATq7VFxk5mXgnsHxGbA6+kjNLpZv2bm4EfAucA38jMvw+vlJIkSbPDvIUL+3n4RJIkSdJcFhGrADsB61Om+bkf+AclkHNR1Rk+60XEkpSgzKKO+qWA2ymLrV+cmf2OBhm4auHzDSmd1xtQgirLUQJrt1FG2/x6WKNnImJlypoga1GmvFqZsl7I7cC1lDrw52EsfD5Tqve8CWUKrbUowbslKe95AXA5cFlm9jtyZ06KiHUpUxCuQakTywJ3UOrjX4A/ZOY1M1dCSZKkmWEgRZIkSZIkSZIkqYFrpEiSJEmSJEmSJDUwkCJJkiRJkiRJktTAQIokSZIkSZIkSVIDAymSJEmSJEmSJEkNDKRIkiRJkiRJkiQ1MJAiSZIkSZIkSZLUwECKJEmSJEmSJElSAwMpkiRJkiRJkiRJDQykSJIkSZIkSZIkNTCQIkmSJEmSJEmS1MBAiiRJkiRJkiRJUgMDKZIkSZIkSZIkSQ0MpEiSJEmSJEmSJDUwkCJJkiRJkiRJktTAQIokSZIkSZIkSVIDAymSJEmSJEmSJEkNDKRIkiRJkiRJkiQ1MJAiSZIkSZIkSZLUwECKJEmSJEmSJElSAwMpkiRJkiRJkiRJDQykSJIkSZIkSZIkNTCQIkmSJEmSJEmS1MBAiiRJkiRJkiRJUgMDKZIkSZIkSZIkSQ0MpEiSJEmSJEmSJDUwkCJJkiRJkiRJktTAQIokSZIkSZIkSVIDAymSJEmSJEmSJEkNDKRIkiRJkiRJkiQ1MJAiSZIkSZIkSZLUwECKJEmSJEmSJElSAwMpkiRJkiRJkiRJDQykSJIkSZIkSZIkNTCQIkmSJEmSJEmS1MBAiiRJkiRJkiRJUgMDKZIkSZIkSZIkSQ0MpEiSJEmSJEmSJDX4/w7N1V/trQ2IAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.utils import shuffle\n", + "X_simp = np.asarray([get_features(aid, ['LinkCount', 'NumTextTokens']) for aid in all_answers], float)\n", + "X_simp, Y_simp = shuffle(X_simp, Y, random_state=0)\n", + "\n", + "bias_variance_analysis(create_pipeline, {'n_neighbors': 5}, \"5NN\", X_simp, Y_simp)\n", + "save_png('05_bv_5NN_simp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does it help to reduce the model complexity by increasing $k$?" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k\tmean(scores)\tstddev(scores)\n", + "5\t0.6022\t\t0.0073\n", + "10\t0.6191\t\t0.0096\n", + "40\t0.6425\t\t0.0104\n" + ] + } + ], + "source": [ + "print('k\\tmean(scores)\\tstddev(scores)')\n", + "for k in [5, 10, 40]:\n", + " _, _, summary = measure(create_pipeline, {'n_neighbors': k}, \"%iNN\" % k, X, Y)\n", + " print('%d\\t%.4f\\t\\t%.4f' % (k, np.mean(summary['scores']), np.std(summary['scores'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It helps a bit, but do we really want to compare with 40 different samples each time?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_k_complexity(ks, train_errors, test_errors):\n", + " plt.figure(num=None, figsize=(6, 5), dpi=300)\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('$k$')\n", + " plt.ylabel('Error')\n", + " plt.title('Errors for different values of $k$')\n", + " plt.plot(ks, test_errors, \"--\", ks, train_errors, \"-\", lw=1)\n", + " plt.legend([\"test error\", \"train error\"], loc=\"upper right\")\n", + " plt.grid(True)\n", + " save_png('06_kcomplexity')\n", + "\n", + "def k_complexity_analysis(clf_class, X, Y):\n", + " # Measure for different k's: [1,2,..,20,25,..,100]\n", + " ks = np.hstack((np.arange(1, 21), np.arange(25, 101, 5)))\n", + " \n", + " train_errors = []\n", + " test_errors = []\n", + "\n", + " for k in ks:\n", + " train_error, test_error, _ = measure(clf_class, {'n_neighbors': k}, \"%dNN\" % k, X, Y, data_size=2000)\n", + " train_errors.append(train_error)\n", + " test_errors.append(test_error)\n", + "\n", + " plot_k_complexity(ks, train_errors, test_errors)\n", + "\n", + "\n", + "k_complexity_analysis(create_pipeline, X, Y) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we won't get much better with increasing values of $k$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using logistic regression\n", + "Creating some toy data to visualize how logistic regression works..." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import norm\n", + "\n", + "np.random.seed(3)\n", + "\n", + "NUM_PER_CLASS = 40\n", + "X_log = np.hstack((norm.rvs(2, size=NUM_PER_CLASS, scale=2), norm.rvs(8, size=NUM_PER_CLASS, scale=3)))\n", + "y_log = np.hstack((np.zeros(NUM_PER_CLASS), np.ones(NUM_PER_CLASS))).astype(int)\n", + "\n", + "plt.figure(figsize=(10, 4), dpi=300)\n", + "plt.grid(True)\n", + "\n", + "plt.xlim((-5, 20))\n", + "plt.scatter(X_log, y_log, c=np.array(['blue', 'red'])[y_log], s=10)\n", + "plt.xlabel(\"Feature value\")\n", + "plt.ylabel(\"Class\")\n", + "save_png('06_log_reg_example_data')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.09437188] [ 1.80094112]\n", + "P(x=-1)=0.05\tP(x=7)=0.85\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "def lr_model(clf, X):\n", + " '''\n", + " https://en.wikipedia.org/wiki/Logistic_regression\n", + " '''\n", + " return 1.0 / (1.0 + np.exp(-(clf.intercept_ + clf.coef_ * X)))\n", + "\n", + "logclf = LogisticRegression()\n", + "logclf.fit(X_log.reshape(NUM_PER_CLASS * 2, 1), y_log)\n", + "print(np.exp(logclf.intercept_), np.exp(logclf.coef_.ravel()))\n", + "print(\"P(x=-1)=%.2f\\tP(x=7)=%.2f\" %(lr_model(logclf, -1), lr_model(logclf, 7)))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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AgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGICBQAAAAAAAAAAAABiAgUAAAAAAAAAAAAAYgIFAAAAAAAAAAAAAGMD/AxnPBRz3JE9UAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_odds = np.arange(0.001, 1, 0.001)\n", + "plt.figure(figsize=(10, 4), dpi=300)\n", + "plt.subplot(1, 2, 1)\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 10))\n", + "plt.plot(X_odds, X_odds / (1 - X_odds))\n", + "plt.xlabel(\"P\")\n", + "plt.ylabel(\"odds = P / (1-P)\")\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.xlim((0, 1))\n", + "plt.plot(X_odds, np.log(X_odds / (1 - X_odds)))\n", + "plt.xlabel(\"P\")\n", + "plt.ylabel(\"log(odds) = log(P / (1-P))\")\n", + "plt.grid(True)\n", + "save_png('07_log_reg_log_odds')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_range = np.arange(-5, 20, 0.1)\n", + "\n", + "plt.figure(figsize=(10, 4), dpi=300)\n", + "plt.xlim((-5, 20))\n", + "plt.scatter(X_log, y_log, c=np.array(['blue', 'red'])[y_log], s=5)\n", + "plt.plot(X_range, lr_model(logclf, X_range).ravel(), c='green')\n", + "plt.plot(X_range, np.ones(X_range.shape[0]) * 0.5, \"--\")\n", + "plt.xlabel(\"Feature value\")\n", + "plt.ylabel(\"Class\")\n", + "plt.grid(True)\n", + "save_png('08_log_reg_example_fitted')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying logistic regression to our post classification problem" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C\tmean(scores)\tstddev(scores)\n", + " 0.001\t0.6369\t\t0.0097\n", + " 0.010\t0.6390\t\t0.0109\n", + " 0.100\t0.6382\t\t0.0097\n", + " 1.000\t0.6380\t\t0.0099\n", + " 10.000\t0.6380\t\t0.0097\n" + ] + } + ], + "source": [ + "print('C\\tmean(scores)\\tstddev(scores)') \n", + "for C in [0.001, 0.01, 0.1, 1.0, 10.0]:\n", + " name = \"LogReg C=%.2f\" % C\n", + " _, _, summary = measure(LogisticRegression, {'C': C}, name, X, Y)\n", + "\n", + " print('%7.3f\\t%.4f\\t\\t%.4f' % (C, np.mean(summary['scores']), np.std(summary['scores'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "C_best = 0.01" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Looking behind accuracy – precision and recall " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bias_variance_analysis(LogisticRegression, {'C': C_best}, \"LogReg C=0.01\", X, Y)\n", + "save_png('09_bv_LogReg')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Good answers...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature_names = np.array((\n", + " 'NumTextTokens',\n", + " 'NumCodeLines',\n", + " 'LinkCount',\n", + " 'AvgSentLen',\n", + " 'AvgWordLen',\n", + " 'NumAllCaps',\n", + " 'NumExclams'\n", + "))\n", + "\n", + "X_orig = np.asarray([get_features(aid, ['LinkCount', 'NumCodeLines', 'NumTextTokens', 'AvgSentLen', 'AvgWordLen', 'NumAllCaps', 'NumExclams']) for aid in all_answers])\n", + "\n", + "Y_orig_good = np.asarray([meta[aid]['Score'] > 0 for aid in all_answers])\n", + "Y_orig_poor = np.asarray([meta[aid]['Score'] <= 0 for aid in all_answers])\n", + "\n", + "X_new, Y_good, Y_poor = shuffle(X_orig, Y_orig_good, Y_orig_poor, random_state=0)\n", + " \n", + "name = \"LogReg C=%.2f\" % C_best\n", + "\n", + "print(\"Good answers...\")\n", + "_, _, good_results = measure(LogisticRegression, {'C': C_best}, '08_good_'+name, X_new, Y_good, plot='good', feature_names=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Poor answers...\n" + ] + }, + { + "data": { + "image/png": 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+CBHYu9HMtunk9ccbM1sduAL4BbB6m5vPS+SVv9nM3lt33WbJzpk3Ez2LO73mGmpm9nriM/oC7R9blyHOqf8wsy1qqs9c2Tn3WmJ0xljf2xzEiLnLzeyYlnPQFGD9xGOhOupZUdFIx1r2UTPbnziWfY7y1zuzLA4cTnynm9ZUn22IfexbxKjasazIy/vVli3LV2Dk97laHXXsQNFx6+fuXnb0vpRXdF6tOvqmaPtXVyy3E115jx6jcm9LrNoyG+0iMlQUPBER6YCZTTKzo4BTaL8xJG954Fwz27fD7Vem3uP5O4ELsqHjUo+fEIGy2r4nMzsE+DPlbhhHsyhwqpl9snqtBpeZ7QKcQ7lAQt5cwIlmtlOuzFWIwMnmHVbrIDP7cofbFjKzzxDD6YtS6I1mK+DaLFVOlTosRzSA5tMWtGsS8FHgr2a2cMWyZpOl+Lic9CSag2QZolG4LpsBV5tZ7XnwzWxuIgXXuzotAjjKzIrSlrRTly8Rx8i6vt+2jt/ZdcIPiN9i1bQ5ryR+A0W52YeCmW1HjEDarGJRCwDHmtkPs9RxtckakE/NXkMSzOzTRPC03U5FeasBF5nZ2yrWZyEiddRBtH8dZsAHgb+Y2fxV6tEDRdc306oUamZTzOznwC+p3slgVeBiM3tjxTq9g+iYsWIHm69CHE93r1KHumWdOdYsWP3LXtZlAkldBzkxUrdjWWAhlfKzH/MPFb3mdTWUfW1i2Vx03llFZGApIigi0qbsRvwXRJBhNLcRc0w8SlxILEVcwMydeO4cwHFmNs3d/1BjdR8jcv1OAx4nbhoXIm42ViduClO2BH5tZru5u9dYnwknC0q8L7HqKaJH9INEb8Flie9l2RJlHk6kPxjNVCKH+KPEnA1LEj3PUzf/BnzTzJ5w92PHev3xxszWBn4LzJlbNY2Y9+dB4jNYlsh/nLo+mkwEUFZ398ezxphzGDmp50tE/v37iRQyS2RlFo0W+JyZnebulW7UZsmCsN+oWMwiREPRq72DHOFZ4PV8Ru81/gwxCuge4rNfjkjLN0/B87cC/mxm27r7c+3WKWFRIn1bKsB0KzH/yiPECMFlibmJ8vvPIHiOGGH0GLE/v0C8p6WIz7OozssAp5vZBu7+eI31OYGY1yDvLiKtysNEIGh54nhUdA46wsz+4jEpadvM7EfAh8Z42vNEKq+HiM9vfmLfX4OKwQ6LSWGPp3iS1llm7WuPEtcGSxLzJaVGWs1BnJenufvZVeo3iLKA7R+I+V+KPER8ZvcRwYvlif28aD/6MNEQ9pGa6rgNcGzi9aYTI9geIObNWpI4pg16YLZ2WeDziDGe9m/iGuUR4hy5MsWN4HMCx5vZC+5+agf1mYtIAVm1x/f/A06keGTAICg653Y0pyH8b76EU4A9R3maE+eh/xLHsnl5+Z4ndQ6aG2ia2U7ewfwaZrYXcBzFv/sy5iZGAHc88r8Ldi1Yfqe739DTmkwAZrYY6QDvve6emvC9XTcRI81bLWJmy7j7fTWUX9a6Bcs7ur4qWca6xP2VyNBQ8EREpH1foThwcicxJPw0d38gv9JiMvfdsjLWyK2eDPzSzNZNbVvS48RN4pnE5Ih3Fz0xa/x9E9Gre+PEU14HfBz4zmgv6O7/ouUGJpuQN3XBtKkP8STwBVYFdswtu4hIXXChu49IsWAxGXVhw52ZfYDiwMn9RPq2Uzwx+XQ2aeuORPqYTRLbf8/MLnH3W4pefxyaDPya2YNGlxKfwYXuPr31yWb2CmK/P4SRN/2LESOIPk4EUFsbxqYSqZ9Oz990ZZ97A/g20VDUahKREmjrNt9XyiuJXOStniTyu/+WCOg+SAQEViDmpNgv2y5vQaJxfV1vf3L7H1PciPNvXv6cnm1dYTGB75uJHPn5oBRECrUjiWNWVd9h9kDlk0TQ6cSC384CZMd9dz+d2Y95byKd0mlhd6/U4zfhBSIw9XtiZM+t7j4j9cSs0XA7IoiQ6uW7AvBzRm8Ua8dHmT1w8iyRUvA4d789Ub8lgE8SvcHzaWXmBI4B2m7YMrPPMnrg5AyiAfyvRYG4LHXd9sA+RMq5yW1W43CKAyf/Jq4TTnf3BxOvPR+Rsu7LjPwdTSY6Wqzr7g+1WadO/IcIMrZahDh25J1FBKFGk1yf9eg/ieLAyXnE7/MCz00kno1yexfZfAqJbQ8wsws6aXjPmUI02LbeP99BnEvOSv3Ws7rVlqpz0JnZxsA3C1bPJH7Tx7j7jQXbfoxIq5k3Gfi5mV2dOj6P4RsUB06eJ86NTaIR8D5iH1yG+P3vy+zXSrsTAblBVbSvpXqHl3UUxeeIW4jv+0x3fyS/Mvtd70H8RvKjpOckgsHrtXOeNLOViGNSUeDkBiKIfx4xd87TRAP2msScXw1ePs7MQwTErir7+l1WdB34157WYuJYuWD5v2sq/w7SE6qvTBxreiWVoWA60cGzqjsKlhd9tiLjV78nXdFDDz30GOvBAE0YT9xMzUzUZSbRWDK5ZDlTiJvI1Pv6U5t1OpmY7HkvYI4O3tMkovEqNQnbNNqcqJYuTerezbLp3oTxrY/pwHsq1nMdovE0Vf6xlJxclbjpPLSgnOuAKXX/dkrWqxsTxud/px8rWdZupCeUfZJoUGlddnyZz56Xb5hSdVu/hs8qX9+zGWPiZKL35bdJH9cc+Hab9Xr7KJ//ceQmai0oYyEiLU7Rd7hjG/UpmjC+9XEpsGSF/bZosuZX1PS7+AjRo/cgYMEOy9iF6LWfqucWbZZVNGF86+N6YPk26vZ8QTnrtFm3rYEZBWVNBbbt4LNbDvg+cHbJ57+24PVnEAGRdq4TflpQ1pl17Fsd7ktFv6lSk8EXlHnsKJ/ZR0qWsQYxmi1VzsPt/MYZ+1zixMT0c/Xre8jVt+8TxhPnkqLP/0FKTqhOBOaeLijnfMDaqNMmpK9tHbgMWL1EGe8lzvuztiu6Bnt9G/WqfcJ44v6kaF9t6xjfUuYeBeVNJ4KVpb4LYiTdCQVl/abNOv25oJxniHPlqHUi7in/mts29Z1e2qvfTkvdpha8t0r3DnoUft6Ngs/7hJrKL7rP2reH73GJgjrUMsk6MSIvVf7PuvR+NGG8Hn17aM4TEZGSzGwOoiEj1dtpf3f/rBf0AM5z9+nu/iGiQSbvdbmJDMfyGXffxN1/5+4vtbHdrLrMdPejSOeoX4h0L0Bp39vd/ecVy/gp6RQI/+fu73P358sU4uHLxOiKvA2JG+Zh9H53P7rME939j0DquQsQQYBZfgW8s8xn7+53ErnTU+r4nbX23j8V2M3d7x2jTs+7+8HAJwqecmDZuTGy+S6+W7D6FODdnhttUlCnJ4C3Eo0kI14GOKbGeQyuAHbxxAiAAfJ7YGV3P8o7TCXh7ucAOwFPJFbXMZKn1TVEkOK/ZZ6c1e0LBavfXfZFzWwy0Qifur+5GdjGO0gR4+73uPtHKTFCJxtl9pNUMcB+7n5om9cJ7yc6WuS9oa5Jl/stG3FQlArpo+7+gzLluPutwM5EoDFvMaKDS12+7+4HuPsLNZY53h0IrJ1Y/jSwq7tfUqYQdz+LGIGYup7dnjg3lPVT0qPGLgJ2cPfUZMf5+vyM6Ewxa5TawKVvzEarHVWw+kZ3/1uHZf4wsWom0bHo6+7uZcrKfif7ER1N8vbJUquWqVODOI/lvQjs4e4/GKtO7j6VOE6c07K4799plpnglQWr60ivVDsz+7yZ+QA+ymbXyY8En6XT7A9lyyl63W6YCO9RpCcUPBGR8WxNMzuuhsdiJV/vXcQkg3lfc/fUDUEZnySGmOcdWLYAHyU1Vzvc/ddE+oK81Hwd0p5fuPspVQows12JeR/yjnf3Tue4OJJ0A3Xp/W8caWaNIO34OukGnFmNMf8GDijbgADg7meS/s3v3GbdRnMDEaybOeYzM1kA9ceJVZMpDqzk7U065dwdRICpnfq8lJWXSk20KpFWsKrniAbtp2soq2vc/b5OAuOJcm4gekLmvSVLV1eHp4F9OgjyHEWkWMl7bRtl7Et6ktIniQDZPW3WaTZebq6d95JOkfFld/9Nhy/9cdKpMIflOF00H8mJ7v6jdgrKvuO9iWBV3tuy+ZiquhE4uIZyhkY2x09RqryPuntbaaPc/c/EKK1keSXrtC3piZLvBXYv29kkq88lFHd86KvsHuZ0iuc1+FSHRR/AyPkaIDpsnd5uYdl10oeJeRjzPlaymI8XLXf3v7RRl5eIAF1RyqF+SKUqneWuntViYik6H6Q6mXSiqJw6zkNlTYT3KNITCp6IyHi2JJGDvuojNYF2ykGJZf9h5BwDpXnMeXFYYtWbs3zwvZbqmbl+lu9fOvMcnd+8tkrdND5BhUac7Gb2s4lVW5vZep2WO4Bm0MF34O6PAheM8pQvePvzgUCkeMtbMxu5UYePdtgj+rPEZK95e5nZwiW2L8oF/4lO6pONQEntn1DPBNDfcfdBajzphR8RKd9azQFsVlP5R7l72/nCs8askxOr1mrjd1HUAHeQtz9PQqdSAY27qDDqIfvtfDGxqtFG54+BZGaLEMGOvKeBT3dSprtfQcxjkDc38J5Oysw5qI5g5pDZjXSv+asYOWdOWd8i3Wi8ZTY33FiKRjN9upMRfFknqcvb3a5bzGxJM/s0EVjNz603yw/c/ewOyp5MOkh1E8WjS8eUXS+l7pn2y+ZHGa1OawNbJFbdQLrjx1h1eZbyHUN6YZmC5TOJtINSvwULlj9VU/lF5SxUU/llTIT3KNITCp6IiJRgZpuR7tF6RDu91wqcxcg0E1OIPKI9lfVMzk9iN4n05OJSzqnuPtYkuqPKJp1NTTr4I09M0tkOd78OuDKxavvEsvHqzAqNp0XpLh4gUmN14orEssnEnDZV/cXdL+1kQ3d/nJhIPW9uiie/BsDMVibdAH9rNtqmU8cR+fLzdikZ0CnixGTpE4q7TwfOTazavIbiXyKCM51KBSqnEHNdjSprWEs1qN7B7Gn2usbMtmLkBO8A38g6SlRxOiPPzXMA21Qst9/eRHqS9xPdvcqEukWjMfepUCZEY/75FcsYRkWf6zfbGZnZKru2LkqzOer3mKVAenNi1Z0VRoBBhc5SJe0yxkj5k8zsHDO7nbgGOQJYqqCsX5Hu9FXGDsRcT3lfK5t2cBQnAY/nls1LOjDS6p0Fy7/ezqjWVtm1yT862bYLijqpPVnDZy5pcxUsr3q+nqWo01DR63bDRHiPIj1RNh+giMhE9/rEspl03nj6P+7uZnYRkXKk1Vake6h32x2M7AG1MaP3wJdiqd7U7dqV9Fw7lVKBtbiAkY2nW1GcQ3u8+VOFbW8uWH5ehd7HRfmrUyky2nVCxe1PJBqI8vvba4DR5h4omqepUn3cfYaZnczIHv1G7LPnjNyqlCvdfaKmwkiNDNm4hnKvdff7K2yfSk0FMcp0LKlzNMBPOm287UCqDtOJOWsqcfeZZnYxI+d72IoIrIxX3Tpu3GhmfwfWz61a18zm63DEIMApPdyfxpPU9/gE8IeK5Z5EjEDJz1sy1ryAm5AOyp1UsT7nEqMAUukp67AO1TtRPAx8zt2PrVBG6lj2PFClIwQQowzN7LLEa2wFnDfKpq9OLHua6IBWxW+AQRhpXTTCcpDnVfo7nY8s66aywbQ5CpZPr6keReUUvW43dPU9Ztfozsh7hl6+R5GeUPBERKScbRPLbqhxkuFrGRk8aWfS+DqlRjJo4rfOXV1DGan97yF3/3sNZUPsf3n92v+6ITXSo6yidAltT8Baosyqw9xnUrEh1d3vNrNrGTnabKy0TkW9Rqs2nkE02KTSIW1B58GTOn6X41W3jvGXVdy+KPBS5ndRNAKjlx0QUsfp66qODmxxLSODJ+P9OJ06bkyj+r4EcdzIB08mA5sCF3ZY5kQ+biSZ2ZLAiolV51Udme3uD5rZVYzczzc2szlG6cCwacHySucjd59uZucQk58PmruIQNMJnaQly0kdyy6vcX6waxkZPCk8lmUTgG+QWHV+ybmoRnMWxSPVeikfIJylo1E1veDuZ1E9eDWI6gqQF5WT6gzXa93uBDAI71GkVgqeiMh4dpG7b9ftFzEzI33RXtR7vBOpxpXUkPlSzGw+IgfyBkSPqtWJvKcLEkPtqPUNAAAgAElEQVTDiy7Si9Q1mfBEc5+7pya8bldq0tNu73/LmJkNSS/bqRW2LWos6LjMrKfW84zsaViUm7is2929jjzG1zEyeLKcmS09ysiCVGq/l6hnP72hYHlRA1kZ11fYtu+ynPSvJj739YC1gUWI4/uCwJxtFlnHMX5qlY2zHskvMDLdQ5ngSeoc/UivRhdlE2bnG+phgK8T+i1LrbR2YtXfazrvjHbcuLDDMsf1caNLitK6Fn3+7bqBkQ3rcxPp/Iq+j9S5YSbFo9va8XcGM3iyIrAdMSK+4+CJmc1F+nfZz2PZq0iPJKqjA9GtxKiauuac61RRoLHdc7mUVxR8rauNtGj0RV0ps8ro6nvMrkVTgZJevkeRnlDwRERkbEuQbtRc38yOq+k1lk8sazufv5ltDRwAvJHIIVwXBU86U3lkUha8WyWxaoUa979FE8smEY2W+cmlx5uXKqRogeKUCfmc3Z2Um79Zr5ojuK7GqqIGiVUoHh2QSjl2Sw1zPeDuj5nZvcCyuVVFud7LqGvUYE+Z2UpEHvu9KZfOqqw6jvFVfxMQDUj538Gov4usV3J+3wC4pob6lLU0MF9i+UY1HqdTE3JXmfen35YgPf9mXXMQFJUz4Y4bXVaUbrKf32PqmvquitcCY9Wn3wx4C7Clme3s7p0GO1Yk3Ua0ZY3HstQ17WjHstT3CTUEw7LOLDeRnjOrl4pG0KTOK1KPouvTuubrKAp89TIV20R4jyI9oeCJiMjYUo0yAOtmj26Zb4y0BP9jZksQw/W71Rsu1eNLxlY1dQJEbu3UxenK2aObFmb8B0+eHWflVlFlguUy5YzWwJ5aV8eoq9ay8sfiKg3Hdfw2e8bM5gQ+A/wf3Tke11FmHQ2TnViKdCN8Lxu6i64T1qO7+fQXNLPJ43RC4aLjSV3HjaJyOj1uTHf3QTzu99sgfo/dPB8Vpd2sw7fd/ZOpFdk54BXAGsSow3dk/89bDjjPzLZ097s7qEPRsWxj6pkbq0i73yeMj++0rKL3MreZLVhDKjYZ6YmC5fPXVP4Cbb5uN0yE9yjSE6kbDRERmV3VeQiqGDONj5ktD1xOd9MIKHdpZ+rIDz3Q+58MlLpurotuepINGNnoqNQNVJ03+6k6VRktUVfu9q7L0qicDnyZ7gWyx/MxvqgRoJeB334dp436GkF6rej3W9dx4ynS8wV0etzoV3Bw0HX7e2zrfJRJ/R7rSGkJfQq8u/uL7v6Qu1/i7ocTE8sfSHri56WBU7NzR7v6dSybN0sBlFJUp3H9nebcM8q6cZueccA9VrC8rmwLReUUvW43TIT3KNITGnkiIjK2fubBHbVBy8wWInJ3jzUC4SWiN/l/iRvR54kh4qneqtuRTg8i/TGw+58MnLoaEorKKQqmzUe6Q06djY2psop6vA2bJvC6MZ4zk+i5ek/27wvEcT6VsmFtqs0XM2iK0kbU9XsoQ8fp9hUFfWo5bri7m9lzjEx7M1GOG73S1e9xlHJG+x5Tdapr1NBABNGy0WZHm9lU4DRGnoM3Bg4DPttm0YN4LCvax4bmO3X3h8zsSdLXWWtR75wzEopGp9aVErUotWCdo7LHMhHeo0hPKHgiIjK2QU6H8RWKAyc3AT8HLgZucPdU77QRzOx3KHgySAZ5/5PBUlejR1E5RROaPkM03ucbb+qcdymV97uXjeN9YWYNYPeC1Q8CvwDOA64sm8/fzA5iuIInRbm1ezkiQ8fp9hWN/qrluJGNiEuN1Br640aPdfV7pHjOh9G+x2cYOVqh2/XpC3c/08w+B3w9sfrTZvYbd/9XG0UO4rGs6Nw2bN/p34mUbHkbAKf2uC4TQVFau7pG+hSlwLurpvLHlAXlnmPkuXCpmlJ+9v09ivSKgiciImMrumh/n7v/rKc1aZFNHPzhxKoXgI+6+7EdFj1eU4AMq6L970vu/sVeVkQGXl1p1orKSaZBynp4P8XIxqo6076lyqpjgvKBlTX+HlGw+ofAwe7eyaScw3aML2q8rSstRRlFx+l3uftxPazHeFKUVq2u48b8pEfEDfVxow+6/T0WlTPa9zgNWCa3rK4RR4OYzvRI4I3AFrnlk4GjgB3bKKvoWPYWd/9dB3WrQ9E+Nmzf6dWkgyfb9roiZZjZG4A397seCe9291TKxrw7C5avWlM9VilY3uvAwl3EiONWU4AVgTsqlj0o71Gk6xQ8EREZ2wMFy7s9WfdY3kTcGOUd4O4/r1DuIhW2Ha8GuTHxAcAZmc6g3/ufDJ66frtF5Yw2h8TjjAyeLF5PdQrL6uWcFv2wCXFzm3ecu3+kQrnDdox/kPTIp7rSUpQxqNcJg6yo8buu40ZROcN+3Oi1QfweU+u6XZ++cfeZZvZB4DpGHgd3MLMd3f28ksUN4rGs6Lsetu/0z8AnEsu3NLNF3f3RXldoDOsD7+x3JRLeS3q+q9m4+8Nm9iAjrxWWNbMF3b3qXDj5gAXAY+5+b8Vy2/XPgrqsTfXgSarcWa8pMlQ0YbyIyNj+S3oY+2q9rkjObollN1YMnMDI3nqDpKiXdSeTYrYa2MbErGd56ma23/ufDJ51ulxOUaNK0bq1zGyOqpUxs4WB5ROr7q9a9oBLHeOnA5+uWG5RmoVxKUtJ+d/Eqo17WI27STfW6Dhd7CHSn9l6NZW/fsHyYT9u9FrReaGf32PqeLCymdWRnmndGsqonbv/HfhNweqvtFFUUY/xfh7LUt8n1PBdZJPUr1W1nJpcRDod3RTgLT2uy0RxfWKZARtWKdTMlgaWTqy6oUq5HUq9R4CNaig7dZ31ApqjR4aQgiciImNw9xeBGxOrXmNm/TyOrp5YdnqVAs1sBQa7Ya2oF1DVofuDPsdL6sJ3EzPL9/SXiW3dLNVTVanGqmeAW0fZ5urEsjmpp1FigzZec5ikjvGXufvDFcvdsuL2gyjVILGEma3Yixd39+eBWxKrXlPTb3LouPtzpK+tihrL2zVRjxu9VvR5Fn3+7UqV8xww2jwe1ySWTaKewEdd76sbDiUC7HlbmNlOZQpw98eA/yRW7VClYhX9i/jO8+r4LtYgPTdSz2WdpU4uWP0xnUu64tKC5VVTpRVtf0nFcjvRlfdoZkuRvk79W9l5VkXGEwVPRETKSV3sLMrI/MK9tERi2dSKZdaRV/fFguV1pIosCp6keve0Y5uK23dbav+bAuzc64rIQJsf2KpKAWY2D+nfw7VjTCz5t4LlqdET7XpDm6/Za9065qXSTk2tUmA2V1ZqFM94V9Qg0ct87Kk6LAls2sM6jDep3/AiZlZHgC913JhBumFdOuTuD5EerbCjmc1ZpWwzWwLYLLHq2jEa564qWP76ivWZAuxSpYxucve7gOMLVh/WRlGpY9lKZlaUoqersu86FSDfIbtmqaLo+qJfflKwfC3gbb2syARxbsHyXSuWW7R90et105WkRzRtXbET3iC9R5GuU/BERKScMwuWf7SntchkN6SpVFWpnlnt+GDF7SF9gQY19OzKeqo+kVhVtTdhHQ283XRGwfK+7H8y0KreXO9OeiTXWIGKywuWv71KZbKUGm9NrJpJ3BAOgm4d81LfwyAc4wfRHwqWf6CHvXUH6jphnOjWcWMt0mlX/uHuRRNiS+dS3+PCVG+AfCvpIPQVY2x3DeljZepc0o4dGZz5MYp8lfTok63M7LUlyxjEY1kqoDM/FQNiwD4Vt6+Vu18L/Klg9ZFZGtOuKpvezt2/6u42gI92Rj1cA6TmINnczDqa58fM5gXemFj1IH3o9JN9Hn9MrJoT2LNC0UX3G5WyYIgMKgVPRETKOZ90zt29zayuvM6lZanEUjeGy3VappltTcVe65mihsS60oGlep+9ptPCzGx76ksT0hXufjPp1BjbmNnA9oKUvni7mS1WYfuPFSwvyqUOgLtPJX1TuE7FfXQ/0iPL/uTugzLxc7eOean3V+UYvzAxkerQcfdbSfc2X43Yh3rhL6QbYd5mZnXNRzRsTgeeTSzfz8xSI6/K+kzB8hMrlCnFTipY/slOCzSzuSg+H436Pbr7s8CpiVWrmFmVAMrnKmzbEzWNPjkTSE1O/p5s9GI/FL2nQzpNoWxmr2cwr/8/S3quzWWA47uZMtrMVqW4M8LQcfeZFB+/io4/Y3knsGBi+cljjODuphMKlnf0HrNRaNsnVv3d3VPpOEXGPQVPRERKyC52vpdYZcBJZlZ1zo1OpCbpLJXTOC/rJfOLatUJ7v408Ehi1avqKB+4LrFsg04ap7IRPN+uXqWeKKrnz7KJCUUAFgK+1smGZvY20gHUq7LJaMfyw4LlR3UycXx2XP16weoftFteF00tWF71mJc6xv+/rFGxE8cAi1Soz6A7umD598ys46BTWVnvzu8nVk0irhPm73YdxpssAJpquFqQ4t/+qMxsM+AdiVXPUtN1joxwNnBnYvnW2XmlE58AVkksv7Tk+ehnBcuP7OSa3cz2ZfBTvM5SNPpkazPbcayNszmcjkmsmgM4xczmrli/tmUNsqkOGhsB72u3vCzd13eq1qsbsv37mwWrXw8c240Aipm9kRjRu1rdZQ+4Y4jRzHkfMLO25sTMRu18PrFqJvCjditmZtPNzBOPVdssqugYvYGZ7d1uvYjzc2pUb+oaSGQoKHgiIlLeD0g3kq0N/K6uAIqZLWlmZW4EUmkLtjeztm7uskbN40lP+tap1I3tm7N80VWdU7D8yHYKyVK5HM1gT/7Zqkl69MmywFlZbvDKzGwhM1OamfHtfWbWViqKrBdZ0U1PqhEl5bfAQ4nlaxA3+6XTJ2XHipOBpRKrbyN6+Q+ErAH47sSqqiliUsf4BYGD2i3IzD4DdHKDPJ6cRHoS6VcAZ5vZMlUKL5lb/2jSo1TXBZp1BVDMbCkzG5ZRREVB13eZ2bvbKSj7jpukG3VOdPfH262cjC3rvV10nviRmbWVWjVr4P9iwepSjXPufhFwfWLV8sDp7QShs5HZRXNRDJyaRp98k/T5fFPgxBrmGgHAzJYzs/1LPv27Bcu/Z2alJ7TPri9+y2AHCb5I+pof4N3AmXWl8DKzxc3s58RIwGHuYJHk7ncS5428uYFfZeljyzqaGCGU93t3v62T+tUhO0YX3Scf3c71kZntR6T4zbuX4hEuIuOegiciIiW5+wvEBasnVu8EXG1mG3VavpltaGY/JgI0B5bY5OyC5U0zW6Pkay4BnEX9k+pemli2KjHcvOooifNIN07tamalbgqzm77jgQ9UrEvPuLsT+9/zidUbA9dnKcg6YmZrmtk3ic/2K52WIwPBiBu+Uj1+s+PWn0nfNF9JcSPMbLJjZFEKgHcCx5TpsWpmCxIN4al8+Q58MPs9DJLUMW8HM/ummb2iwzKLAsVfzXqIjsnM5sp+19/osA7jRtY48D7SPUhfBVxqZq9ut1wzW8bMjgJ+X6IOzwHvIX2d8DrgKjPrOGBvZhub2U+J64SPdFrOIHH364EfF6z+acnOJJjZakRQNdVT+GHGQcqlce77pIOXCwF/NrPNyxRiZrsCpxH5+PPOIxq9y3o/6fRH2wPnlum9bWbvIuagmDdb9GIbr99PRaNPtilzrejuT1J8jbwncEWn6QgtbG5mvyR6w5cKBLt7k3THibmAM8zsQ2N10shGEpzN7HMdDtx3ml1PvZH0/Q5E/W82s/077ZhmZotk9023EfcXE9n/kb6/2hb4dZapoFC2Tx9O+nN8geJUkr30M9LH6CWIY/SYARQz253iUX2fzvZbkaFURw9gEZEJw90vMLPPk06LswZwrZmdTfTAu9jdU5ObA/8LXGxE3MS9GWh3YrrfEsNm8+lIls7q8RXgp6meltnNwz7AIcSN7SyPAbdQfe6TXwGHMrL35z5Aw8xuAu4AniZ9Y/sDd78mVbC7zzSzY4DDE6u/aGbrA59395vyKy3mgtgzq1vrfATnA6V7rfWLu//LzD5AfL55ywDnm9mlRCPGX909lT4N+N/8BxsBrwb2Alpvggv3WxloFxE3ehDpNU40szcD33T3EekuzGwVorH5YNLXhC8B78sapUtx91PMbA/Soxw+AGxnZocCZ2UNza31mZ84Fn4ZWKHgJY529wvK1qeHjiM9yfUngY+Z2T+J0SnPkG7cP9Td/9O6wN3vMrPTgD1yz50CnJYF27/t7nfkC8t+37sDX2D21DdONATult9mGLj738zss6SDRSsBF2ef6bHABVl6mhHMbHlgO2JC1B2Jz/yiknU418y+CHwpsXot4Doz+xMRMLg4a6RMspj3o/U6oV/zDXTbJ4HXMjJN02QigLIH0Wv24vzxyMyWBfYn5gmYl7T3u/vDtdZ4fNjJzI6rucxH3H3EXCbu/oKZvYMIuOfTNC4NXGZm3wd+4u635Lc3sw2J4Pv+Ba87DXhXO4Fzd78me83UaL1XA/8wsybR6/wm4H5iH1qWmEtvX2Cz3HaHUzwqZmBk54/jSTfmHgb8tUQZp2fB908lVq8P/NPMTgd+SqRTe7qorKxhdkPiePpmYgRQJz5INADnf+vzEWmR3mdmvyYCbfcS59yliGPvXsS1SesIwNuJ+bJS5+++cvf7s0DX+aSviZYEfgl8OXvPZwDXu/tLRWVm55TtiO/gDcToignP3aea2edIp0jeB1jLzD4FnJ8/BpnZJsT9eFFKvC9ko1v6yt1nZCNWL2HkMfpVRCe8zxKjNGe7NsquiQ4BPkR6ZOef3H3UuRFHkwWptx3zienO/wuXPM+d6e5jdoIRKaLgiYhIm9z9cDNblMjHnPK67DEzCxI8QAQlZhLpQ15BXARXSiGS3ah+jnRD+nxE49HXzOwfRC/V54DFstdeM1Uk0fur8g1EdtN2AumJeicTKUxGS+PwByAZPMl8m6hnqtfbHsAeZvZvojfVNOIzXzZ7zfyF153EzWUq7c7AcffjsyBQ0Rwo22QPN7PbiJvHx4iedbP2v2VJ986V8e104D7iRm+WPYE9zewe4vfwILAwcRxYe4zyDnH3f3ZQjw8TDSup48wawCnA02b2L2L/NGKfXA8YLRXIlUTvwEF0PnAZsHVi3ZzE6LCNR9n+KOA/ieVfIM4n+QYOI25iP5Qd624GniR+30sTn2XqOv/bxGc+lMETAHc/wsxWIPbDlD2yx3PZOfoh4hg5PzH6ag2iJ2aVOnw5u05IjcQy4vPfDZiR1eFBRl4nvJL4Loeeuz9jkWrwAuL6JW/WddUDZnYr0ci9ANEA+ypGz6ZwtLufXnOVx4u1sked7qZgInh3v97MDiY9/9BkIohxUHZtMpWYlHxxImg2WmBwBhE4uaeD+h4CbEJ6vpJ5iFGR7yxZ1plEA/0XC+o4aL5KzP+TPxf8PzN7TcmOCJ8hrhlSo0OMl4+n083sRl4+nsLLx7IViYb+yrL7i/2J64hUI+6G2aOM54gA+QcT6wbi+3T3f2cjJk8jAukpyxPB488Cz2fXBP8BniKu/ecj7v9Wpdy956CN7O2V7xKdB1PZGDYAzgXuN7PriHvLBYj7ytGOXWcA36q5nh1z9yvN7JOk53FdghhV8l0zu4Y4z85J7DfrUXyevZPyx9AiW1coY96S295DiRHEIkUUPBER6YC7H2xm9wJHUHwsnUTc1Nc1UXqqHsdnqRCKGokmU/5G4iB3P83M6up9dQBxsdlWrusy3P1FM3sn0XNuwYKnrZo9RnMfkXLtsTGeN1Dc/Ttm9hCRg7uop60RjYClUrjJ0HgPMYotnyJlOUaOUhvNt9y9o8lU3f0xi5z151B8/Jsf2KKNYi8DXp8frTIo3N2zY+dlzD6qrWq5N5rZe4ATR3lamWMdRGPTZyhOrTY03P0AM5tGBNuK0rjMw+gBrap1ODALWh5O8XVCmc4EE4K7X21muxFB4KJUd0uRngepyA/pYI4g6Zy7f99iPpEjKf7trU75efZeAPbvNACWdTR6PdEpp8qE7xcRnXZSwT2Ieg6UEqNPxgyeZL3s35cdyw6luAF1CtFpouvc/bdmNi/R0Ntpe9bzwN7Z6KTUnBYD8326+3/MbCtiHpoDGD1YPDfV7j0vID3SaOi1XMfNSwTrU5amfOeTc4G3DlqaWXc/2swWIkZ5pyxAjLwr425gh9EyHYgMC815IiLSoaxhcSvgupqLfgw4tY3nH0i6B0lZzwBvd/dUT8GOuftTRAPu0cCzdZadlX8tMUS608DH9cAWqZQ344G7n0D0Qqs7hdHTxHwTMg5lwYUdKZ4TaSwvAR9390o3z+5+L3F8/HWVcoie+EcD22cTsw8sd7+baDw6iXSu+U7L/Q3wLtL5uEsVQTS6vK2dFGzjnbt/jkhLUpQzvl1tN6a5+zeJBtvUxNVVPMoQ9qDMJvnenEijU8VTRMrBjwxaw9VE4O7fItIGPlCxqNuB7dz95Ir1eYI4L36PdNrEUTcnUuztnKWlKgrsPdN5DbuqaO6Tbc1su7KFuPuXiAbVG2uq1ywPEb3z2+LuvyLSGXYyavwO4prirOzv1Hc6UN+nu7/g7h8DNqVEyrUOXAe8wd23z+6vJqRszo7diVEonZ47nEifvGtRatB+c/evENkhClPtlXABsJm7T62lUiIDTsETEZEK3P1qIh3AW4gcop1eaD1C5Fx+C7C0u5ea+Dyrw3R3Pwh4E+3d1EwnGvnWqZKndIy6PefuBxI9dd5FjJT4GzF0dhoVh8Vnn/+6xHwDZW+IHycmjt3c3etqVOsLd7/V3bcHdiF6+Xf6eT5JpKN4J7CUu3+opipKH2QNPLsRc4zc28am5wGbuPtRNdXjKXd/B9Hg0u7N/kyip/Dm7n6guw/chK4p7v6ou7+NGOXzESIf+bXE9/Ak7TfczSr3OGKkTmqy3NFcAmzj7p+eSIGTWdz9j0Taok/SWSPbTOByYh6GN3ZYhyuJ64S9gUupdp1wCpG3fxl3L+o1Oq65+23AlkRv+Vvb3PxZIq3SWu5eNKmt9IC7/4H47X2V9ju53Ef0fl/PE/N1dVifF7Jr5Y2JOQML54XITAfOArZ29w/5yxMhL1Tw/AfbqM5NxJxI+Ue7x/cxuftdxIjU1OsVBYKKyrqY6CCwH5FGs1MPAr8hjqnLZkHmtrn7JcQ+9ikiDdxYpvLyfnVFy/LUd9rO99kz7n6du+9A7Mc/Jc4LnXqISBm6vrtvnP1mJ7zsvvoTxHmo3WvXi4lrro+5e22daLoh64S3JnEP3c419u3ENdEO7v5Q/TUTGUymzjgiIvUxs+WIob6bEfNxrEDkCp6b6LX6ZPb4DzEx+81EMOG6OnpHmpkRk67uTPR2XRZYlEgN8hQRtLiZ6C1yprvfnyhjcUamJXh20C+Qssmv30C895WJPNoLEr3H/gvcQAQYTvdRJrUcz7Lv7nVEI+uriJz5ixL730vEPvAk0ZB7S/a4Erhq0C/yZXZZSqL8Df/H84EPM5uT2Cd2I9L3rUT8Lpxo0LqVaGBvemfzm7RT55WIxpJtiBu25Xj5WPMU8Tu9OavP6dnoFckxsw2InpHbEse6xYgUVM8QDT63EA3+Z7n7iIC6mS1IzO3RaqbnJqwfNtn5cUviHLkZkepsKSJFx0xiH3wCmDWHzJXAuXWno8iuE3bN6rA2cZx+BXGcfp6Xj9Ot1wmXAzcMwiiK7JiSypt/XzeCnGa2BdFBYAtgNSIv+7zEOe0JokH0BuBCYp8fyvP7eJbtM7sQIwU2Jo5bCwNzEfv8I8RogKuJVDcXuHtX55zIjoPbErn8l8nq8iwxWuZG4KLUaEcz2w84Prd4BjBfS4BlQjCzFXn5nmct4p5nIWY/lj1BBK5nHcsuA/5Z97EsO76vTxwnViWuc2Zmr387cZ37j4Jt72Fkus3Pu/vX6qxjN2QpxzYjRvpuSPy2lie+h3mIIODj2eMxYt++KnvcNBE7VbTLzNbm5WvXtYj7y3mJ48UjvLxfn+Hu/+pXPaswsyWJTpjbE+0XyxHpdV8i9p3biblI/whcqP1GJiIFT0RERETGmbLBExEREamHmf2QkfMM3u7uZedwkQFiZssTgeq8N7v70KVGFBGRzihtl4iIiIiIiIhIATObRIxuzrum13WR2uxasHzCzvshIiIjKXgiIiIiIiIiIlJsN2CVxPILe1wPqc9HEsvucvdO5sgSEZEhpeCJiIiIiIiIiEiCmc0NpObAmAGc3uPqSA3M7D3E/IB5p/a6LiIiMtgUPBERERERERGRoZNNJl5l+0nAMcC6idV/cPeHqpQv7an6fWZlbAwcnVjlwC+qli8iIsNFwRMRERERERERGUZXm9l7zWyudjc0s4WBM4H9E6sd+HrFukn7DjazY81sjU42NrO3AhcD8yZWn+buN1eqnYiIDB0FT0RERERERERkGK0JHAvcZ2Y/N7Nds6BIkplNMrP1zewI4G5irpOU49z9yi7UV0Y3N/Be4GYzu8zMDjKzNbMRQklmtqiZ7W1mVwInkQ6cPAsc3J0qi4jIeDal3xUQEREREREREemiRYB3Zw/MbCpwFzCNaDhfMHvOOsArxijrZuBj3aqolGLAVtnju8AzZnYT8AjxnU4hvs9lgTWy54/m/e4+tWu1FRGRcUvBExERERERERGZSFbMHu26FXituz9da22kqvmATTvYzoED3f3EmusjIiJDQmm7RERERERERERGdzKwubvf2++KSC3uB3Z19+/3uyIiIjK4FDwRERERERERkWH0XmLS9xc63N6Bc4Dt3H0fd3+itppJJ04CjgTurFDGfcDngdXd/ZxaaiUiIkPL3L3fdRARERGRNpjZNGCh3OKPu/tR/aiPiIjIIDOzeYDNgC2BVxEpu5Yn5jqZl0hp/jzwOPBf4Ebgb8Af3f3+PlRZxmBmqxJznmwCrEJ8p4sT3+c8wHTgGWKEyR3AdcD5wGXuPrMPVRYRkXFIwRMREREREREREREREZEWStslIiIiIiIiIiIiIiLSQsETERERERERERERERGRFgqeiIiIiIiIiIiIiEqKfdMAACAASURBVIiItFDwREREREREREREREREpIWCJyIiIiIiIiIiIiIiIi0UPBEREREREREREREREWmh4ImIiIiIiIiIiIiIiEgLBU9ERERERERERERERERaTOl3BUS6bDKwWm7ZY4D3oS4iIiIiIiIiIiIikmbAIrlltwMz+lAXBU/a1Wg0VgI2AJYB5gfuB+4GLm82my/1uW4bEYGCZbNF9wK3NZvN6/tXq75bDbi535UQERERERERERERkbatBdzSjxc2d3XAL6PRaOwFfALYsuApjwGnAIc2m81HelivOYCDgfcCqxQ87d/Az4DvtBPgaTQa2wEXVKje3c1mc8UK29dhTRQ8ERERERERERERERmP+hY80ZwnY2g0GvM3Go2TgN9SHDiBGE70IeBfjUZj5x7VbTXgb8DXKQ6cAKwKfAO4otForNqLuomIiIiIiIiIiIiIjFcKnoyi0WhMJkaTvDW36mHgL0RA5Tpmnz9jSeCMRqOxTZfrthRwLrBRbtW/gTOAM4E7cus2Bv7SaDSW6GbdRERERERERERERETGM815MrpvALu2/P0Skbrrp81m88VZCxuNxtpEWqxZI1PmAk5vNBrrNpvN++uuVKPRmAScDryyZfH9wP7NZvMvuefuAvwSWCpbtBJwWqPR2KbZbLabs+17wFFtPH96m+V3w2P5BQ8++CBKVyd1mzx5Mosvvvj//n744YeZMaMvc1mJ1EL7tAwb7dMybLRPy7DRPi3DSPu1DBvt09JtZsaSSy6ZXzyifbdXFDwp0Gg0VgYOzC1+S7PZPCP/3GazeVOj0dgBOJ+XAyiLAocBH+xC9d4ObN7y92PAVs1mc2qibuc0Go2tgGuBhbPFWwF7Aye3+brTUq8x4EZESdydmTNn9qMuMsQmTZp9IJ/2MxnvtE/LsNE+LcNG+7QMG+3TMoy0X8uw0T4t3ZbfxzJ96wWvtF3FDgPmaPn7uFTgZJZms/kcsD/wYsvi92RBmNpkqcS+lFv8idGCGs1m8y5ixEyrr2YjWEREREREREREREREpIUazxMajcY8wF65xUeMtV2z2byNSKc1yxTgbTVWDWAbIvXWLPcCJ5TY7tfZc2dZhRiBIiIiIiIiIiIiIiIiLRQ8SdsZmLfl7yuazeYtJbf9Ze7vPeup0v/skfv7+GazOWZywew5+SBL3XUTERERERERERERERn3FDxJ2yX394VtbHsJs0+UvmGj0Rgxy00FVeqWf+7rKtVERERERERERERERGQIKXiS9qrc31eU3bDZbD4D/DO3eJ3KNQIajcZcwKq5xX9ro4jLc3+v1mg05qxWKxERERERERERERGR4TKl3xUYUGvl/v53m9vfAWzY8vfawF8r1SisAUxu+fuhZrP5ZNmNm83mk41G4xFgsWzRZGB14F8li3hNo9FYD9gAWCLb/jHgASLAdAFwZrPZfKlsnUREREREREREREREBo2CJzmNRmMRYJHc4v+0WUz++at1XqPZ5EedtFuvWdss1vL3apQPnvy/xLJlssdGwAHAPY1G4xvAj5rNpndQPxERERERERERERGRvlLwZKRX5P5+NkvF1Y6Hcn8vVKE+rfJ1y79OGd2q2yzLAT8AXtdoNPZtNpvT6iy80WgsASxe9vlbb731wgceeOBsyyZPnsykScpYJ/WaPHnyqH+LjDfap2XYaJ+WYaN9WoaN9mkZRtqvZdhon5ZuM7N+V2E2Cp6MNH/u7+c6KCO/zQId1iWvX3V7EjgPuAi4kQjAPAcsTKT9ei2wNzB3yza7Aac3Go2dms3mix3Us8iHgcPKPvkf//jHiGWLL1469iLSsUUXXbTfVRCplfZpGTbap2XYaJ+WYaN9WoaR9msZNtqnZdgpeDJSPkDxfAdl5AMU+TI71eu6PQC8Czi52WwWvdbVwImNRuMQ4BfA61rWbQt8A/hEB/UUEREREREREREREekL5S4aWyfzdvRqro+u1q3ZbN7SbDaPGyVw0vrcB4jRJr/NrTqg0Wis1GYdRURERERERERERET6RiNPRno69/c8HZSR3yZfZqcGuW40m01vNBr7A9sAS2eL5wTeA3y+ppf5ESMDNIXWW2+9hYFLWpc9/PDDuGsue6nX5MmTZxuu+uijjzJjxow+1kikGu3TMmy0T8uw0T4tw0b7tAwj7dcybLRPS7eZ2UBNuaDgyUiDHKAY5LoB0Gw2n200GkcDX29ZvAs1BU+azeZDjJz0fjQjfm0zZsxg5syZdVRHpNCMGTOYPn16v6shUhvt0zJstE/LsNE+LcNG+7QMI+3XMmy0T0vdJk0arERZg1WbwfBE7u95G43GfG2WsUTu72kV6tMqX7dOwnDdqlurc3J/r9uF1xARERERERERERER6QoFT3KazeajwOO5xSu0Wcwrc3/f3nmNRi0n/zpldKturabm/p6z0Wgs1IXXERERERERERERERGpnYInaTfn/l61ze1XHqO8Tt0KtCYSXKLRaCxQduNGo7EgsFjLohl0J3jyXGJZJynGRERERERERERERER6TsGTtH/l/t6y7IZZiq/1xiivI81m8wXgjtzi0nUDtsr9fXtWZt0WSyx7tAuvIyIiIiIiIiIiIiJSOwVP0vJzdmzXxravBqa0/H19s9l8sHKNXlalbvnnnl2pJsU2z/39cLPZfKlLryUiIiIiIiIiIiIiUisFT9L+zOypp7ZsNBprltx2/9zfp9VSo+Ly9ms0GpPH2ih7zr5jlFWXt+X+vrBLryMiIiIiIiIiIiIiUjsFTxKazeazwO9yiz8z1naNRmN1YI+WRdOB39RYNYBLgLta/l6OkUGRlH2BZVv+vgO4rMZ6AdBoNLYD9swtPqPu1xERERERERERERER6RYFT4p9EWhNNbV/o9HYvejJjUZjbuCXwJwti3/ebDbzc5Tkt/PcY7vRnt9sNmcAh+U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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Poor answers...\")\n", + "measure(LogisticRegression, {'C': C_best}, '09_poor_'+name, X_new, Y_poor, plot='poor', feature_names=feature_names);" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P=0.77 R=0.25 thresh=0.62\n", + "P=0.78 R=0.23 thresh=0.65\n", + "P=0.79 R=0.21 thresh=0.66\n", + "P=0.80 R=0.13 thresh=0.74\n" + ] + } + ], + "source": [ + "precisions = good_results['med_precisions']\n", + "recalls = good_results['med_recalls']\n", + "thresholds = np.hstack([[0], good_results['med_thresholds']])\n", + "\n", + "for precision in np.arange(0.77, 0.8, 0.01):\n", + " thresh_idx = precisions >= precision\n", + " print(\"P=%.2f R=%.2f thresh=%.2f\" % (precisions[thresh_idx][0], recalls[thresh_idx][0], thresholds[thresh_idx][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "good_thresh = 0.66" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ship it!" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.24937413 0.00777857 0.0097297 0.00061647 0.02354386 -0.03715787\n", + " -0.03406846]]\n" + ] + } + ], + "source": [ + "clf = LogisticRegression(C=C_best)\n", + "clf.fit(X, Y)\n", + "print(clf.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.24937413 0.00777857 0.0097297 0.00061647 0.02354386 -0.03715787\n", + " -0.03406846]]\n" + ] + } + ], + "source": [ + "import pickle\n", + "pickle.dump(clf, open(\"logreg.dat\", \"wb\"))\n", + "clf = pickle.load(open(\"logreg.dat\", \"rb\"))\n", + "print(clf.coef_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the classifier's `predict_proba()` to calculate the probabilities for the classes `poor` and `good`:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.30127876, 0.69872124],\n", + " [ 0.62934963, 0.37065037]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remember that the features are in this order:\n", + "# LinkCount, NumCodeLines, NumTextTokens, AvgSentLen, AvgWordLen, NumAllCaps, NumExclams\n", + "good_post = (2, 1, 100, 5, 4, 1, 0)\n", + "poor_post = (1, 0, 10, 5, 6, 5, 4)\n", + "proba = clf.predict_proba([good_post, poor_post])\n", + "proba" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[False, True],\n", + " [False, False]], dtype=bool)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "proba >= good_thresh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, we manage to detect the first post as good, but cannot say anything about the second, which is why we would show some nice motivating message to improve the post." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch05_3rd/chapter_05.ipynb b/ch05_3rd/chapter_05.ipynb new file mode 100644 index 00000000..a94dbfb6 --- /dev/null +++ b/ch05_3rd/chapter_05.ipynb @@ -0,0 +1,975 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter we are discussing two methods to reduce the feature space: filters and wrappers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities we will need" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "CHART_DIR = \"charts\"\n", + "if not os.path.exists(CHART_DIR):\n", + " os.mkdir(CHART_DIR)\n", + " \n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "\n", + "import numpy as np\n", + "import scipy\n", + "\n", + "DPI = 300\n", + "\n", + "def save_png(name):\n", + " fn = 'B09124_05_%s.png'%name # please ignore, it just helps our publisher :-)\n", + " plt.savefig(os.path.join(CHART_DIR, fn), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Detecting redundant features using filters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.99962228516121843, 0.017498096813278487)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import pearsonr\n", + "pearsonr([1,2,3], [1,2,3.1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.25383654128340477, 0.83661493668227427)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pearsonr([1,2,3], [1,20,6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at sample data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm\n", + "\n", + "def _plot_correlation_func(x, y):\n", + " r, p = pearsonr(x, y)\n", + " plt.scatter(x, y, c='b', s=15)\n", + " plt.title(\"Cor($X_1$, $X_2$) = %.3f\" % r)\n", + " plt.xlabel(\"$X_1$\")\n", + " plt.ylabel(\"$X_2$\")\n", + "\n", + " f1 = scipy.poly1d(scipy.polyfit(x, y, 1))\n", + " plt.plot(x, f1(x), \"r--\", linewidth=2);" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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r6LTTChq+fs8eLevoKFHwM/N5SCpy/X65Pp+cXK4EEc3MS7xSedvLOYlEwUlQuXC4RNEURzmToM6V9LAx5k3W2r1ljAMAqlJra1o33BCZU9/uQCCnlhbOT2Bu6B+PyeJXXy3//ffPuMoj19SkZEeHEu96l9xlhfd9R23q66tXT09Qu3ZN/b6yeXNEbW1pdXay6qtQVOwDME+Y6wEAAACqAIusUYkiEXdhnCNwXTnptJxEQnWDg9Lvfy/F41IspkW//70aY7EpW8M5Y0lOkysuDQ3lfde5YLDgJCi3jAkvntrLOY7cUEhOLFb8gGa7a6/t8Mrwb+w6jtxgUM5g4R0Aku94h9JtbXLDYeWCQTmRiFaXIEavyt2o9eWSHjXGvHG0zDkAIE+RiKu2trR6e72XHGxvp6IG5o7+8ZhseO1apd76VgXvvfeo29yGBiXXr1fiiiuUW7WqDNGhUkWjTerqWjpjok4q5VNvb1A7dgTU3T1Ai7Y8UbEPwDxjrgcAAACocCyyBgo0PHyk7du4ikuZv/gL5VYXlv7RdP/9Wvbud095W2H1eArnKyBJZ6xa/8DPVmtLCWOayZzay5UjCSqRkFxXcgpbjJpvezm3vl5uKKRcKCQ3FJIbDCoXDssNBiduD4WUG902fvvhbeGwXL9f8nn7DMgde6xyxx57+LrjcZxSqYSzjWsk/dAYc5619pFyBwMA1aSzM6kdOwKeKjs0NLjq6PCWkQyMR/94TCV+1VUKfPnLh1ehuD6f0u3til99tYaf97wyR4dKE402acOG/CuCZTLO6P79JELlgYp9AMqAuR4AAACggrHIGgvCoUMjlZLGJy+Naw13uMpSPhWX0lMn/A186lNKv+UtBYXllrP1WTw+6z6Tq/VH5JQtCWou7eXqihxLPpxcTk46LTcQKOi4VFubhl72sqMSlSYkMgWD0qJFBSdYLUSVkATlSloq6VvGmEustTvKHRAA5GssC3rPnnolEo5CIVdr12bV0jI/X/6bm7Pq7h4o6MTxmG3bBmglhKKgf3yNyuXUdP/9Un29Bs85p/DDjztOyYsuUugzn1H63HMVv+YaZZubSxAoql1fX726upZ6Orara6nWrdvH59ksqNgHoAyY6wEAAAAqHIusUXFyuZG/C6wq03T//QrdddfEFnHJpJxMpgRBTuSl/VlZk6BmiXeqav0JVW6805nLv7HrOCOVkoLBmSsuTdp2OIGpsbHg+zx01lk65DliTFbOWex9ko4ZvexKWiTpHmPM8621W+czEGNMk7WWJeQA8jY5C3qyzZsjamtLq7MzWfITsyMVMPpnbSE0pqHB1bZtA2pp4W0PxUH/+Brjulr00EOKbN2qhp//XMOrV2vwjDOkpqaCh0pceaXSb36zMi9+cQkCRa3o6Ql6mmyTRipC9fQEtWXLwSJHVVuo2AdgHjHXAwAAAFQJFlmjKDKZCS3iJlRcGqusFI8fuTy++tJY0tK4/fffd5+GTj21oBB8/f1qfOyxEj3AWe7bQ6WiXJ6tz0phpninq9afU52SCiio+a8Q77Ud3tAppyi3ZEnB7eLcUGikTRzVlqpaOZOgXinp65JO0MjEmCvJJ+kTxpjnS3qPtTZXygCMMWFJl0u6StKqUt4XgNoxVRb0ZKmUT729Qe3YEVB390DJW/W0tg5q3bp96ukJaufOqROzAoGc2tvT6ugofWIWFhb6x9eOxh/+UJEtWyb8YKx75hkF/+//VfLSSwseL7d8uXLLlxczRNSYWMzRrl3+OY2xc6dfmzbFKL8+Ayr2AZhHzPUAAAAAVYRF1phK4yOPaNGPfnSkjdx0yUuJhJxDxa1f4yXppayVlbzEGw6XIJL8TBfvbNX6Y4oUPQkqFwhMTEKaKlFpyRJPY8c2by5qrKguZUuCstbuMcacKikq6dU6MjnmSLpU0vOMMcZaW/SUQmPMUo1Mhl0haXGxxwdQu6bLgp5OJuOM7t9f8kSo5uastmw5qE2bYnrggaCefTaiWEyKRKRVq2J605uSnLxESdA/vvo1/OQnimzdqkWPPDLl7aFbb1XqggvK+mMStSkanTpxtxCplE+7d/u1fv38r0SqFlTsAzBfmOsBAAAAqg+LrKtALjcx8WgsOWmsgtLkRKVx+xz41KeUW1XY+pBF3/++wrfdVqIHMzOfh/ZnuXImFVVbO7xpkqBmq9YfV1jH6M+KK3zUn5giWvkCv172msb8Ky4FAlJdXakeJha4claCkrX2gDHmTEmfl/Q2TZwce4Ok7xpjzrXWPluM+zPGrJJ0jaTLJAVH70ej9wkAM5otC3omXV1LtW7dvnn5cRCJuLrookGtXBk5vG3v3kFls7zVoXToH1+d6n/xC4Vvukn+b3xjxv3q+vsVvOsuJd773nmKDAvFnj3F+TlSrHFqFRX7AMwn5nowE2PMGkknSzpWUkjSnyT9TtIPrbWZcsY2G2ubdMYZWRZvAACAmjR+kfXu3X7t2VOveNxROOxqzZqsWlvTLLKeg7qnnlLds88e3S5ufBu5ye3ixic7pbyvI/ENDBScBOWWsV2bE48XfEw54/V5iHc+2+G5TU0jSUijLeGyL3zhUfvkU63/xfqZhtSoIz+5Jwr8KafHrn2W30uoCGU/WzA6wfF2Y8xvJX1AEyfHTpH0qDHmjdbaX3i9D2PM/5F0naR/lLRIEyfEaOgIIC+zZUHPJJNx1NMT1JYtB4scFVAZ6B9fXer27FH4llvk/8pX5Lj5/SgJ3XmnkpdcInept2RQYCqJRHG+isfjfKWfCRX7AMw35nowmTHmzZLeJ+nUaXbpN8bskPRha+2f5y+y/H3kI2Fdd11QbW1pdXZSAQEAANSmSMRduNW2XVdOOj11otK45KTkZZdJvsIWmoW3bFEgGi1R4DPzlFRUzkpF1VZZyUO8WrRIbkODnMzR60Bcxzm6mlIwOLGy0nSt4yZXXAoGpYaGWcPJp1r/kBbNeDvV+lFJyp4ENcZa+0FjzG8kbZdUpyMr9p4v6YfGmH+w1n63kDH7q6/oAAAgAElEQVSNMc0amWx7u0Ye63SrAQ94jRvAwpBPFvRsdu70a9OmGCcQUbPoH1/5fH/8o8Kf+pQCO3bIyRZ20sYXjyu0fbvimzaVKDosRKFQcT4TWYk4Oyr2ASgH5npgjAlJukvS+bPsukzSuyW1GWMuttZ+s+TBeZBK+dTbG9SOHQF1dw+UvO09AAAAZjE8fFSi0owt4sZXXBprLze6zUkm5eRys95l6sILC066KWeSjqf2cuWsrDRNu7aZlDPe6drLzab/s5+Vu2iRfIsXa9nxx0uRiBQOa18ioWwez8Niolo/ak1FPROttT3GmKcl3aeRktjSyCTWEknfNMb8o7X23tnGMca8WNImSW+W5NPUE2KOpD9L+pSkW4vzCADUqnyyoGdDFjQWAvrHVybf/v0K3XabgnffLefQIc/j+L/yFcWvuUZqbCxidFjI1qwpzntAscapZVTsA1AuzPUsXMaYOkk7JL1x0k37JP1E0kFJL5D0Uh35/1wlKWqMOdNa+/35irVQmYwz+pnaTyIUAABAIVxXGhyU7+DBkYSkoSFl/+IvCh5m2UUXqfEHP5BvcP6/izmJROFJUFWWpFPWykrzFG/O7z9SZSkcnlhxabR93ISKS2Mt5aaouOTFode9TpJUX18vrVx55IZUSprnJCiq9aPWVFQSlCRZa79ljHm1pK9JOnZ0syupUdIXjDHHW2v/eapjjTGvkPRBSW8a3TTdhNifJN0s6Q5rLdkIAGZFFjSQP/rHVw4nFlPozjsVvOsuTyt+xuRCISUvvVSJSy8lAQpF1dqa1g03ROaUaBwI5NTSki5iVLWLin0AyoW5ngVriyYmQGU00hLvX621Q2MbjTHrJH1GR1rlLZL0VWPMi621f5qvYL3o6lqqdev2kSwMAABqWy4nJ5WavuJSPH7kcjI5UllpXPUlXyIhpdNSPC7F41o5rkJ9bskSPfP//l/hMWUyZUmAkrwl6eTC4RJEkh9P7eWqLN7c4sVKXnLJ4dZwExKZJreIG2sTV1dXguirE9X6UWsq8my8tfZxY8wrNTI5dpJGJrZcjUxqfcwYs0bSu6y1OUkyxvydpA9JOmN0iOkmxJ6SdKOkHmut9zIIABYcsqCBwi3o/vFl5qTTCn72swrdfrt8B7x3gnGbmpS85BIl3vMe5ZYVXj0GmE0k4qqtLa3eXu+r4drb07SaLQAV+wCUC3M9C4sxZq2krkmb32KtjU7e11r7c2PMGZIe0pFEqOWSrpf0rpIGOkeZjKOenqC2bDlY7lAAAADmJPLhD6vu6acntokbl9jkuKWZe3ESiZHqUE5h507K3V5uuMBjylkJyhePF3xMsdrLuYsWHamgNFZlaXKi0vjkpFBI2Re8oPA78vt18OMfL0rMCxHV+lFrKjIJSpKstX8wxpymkXLpZ2vi5FiHpOcaY+6QdK2kvx09bPKE2Nj1X2tk9dnd1lpefQAKRhY0gKowNKTAvfcq3N2tur17PQ/j1tcrdcEFil95pXLPeU4RAwSO1tmZ1I4dgbwqE03W0OCqo8N7lbOFiop9AMqFuZ4F5XpJDeOuf36qBKgx1tq0MeYSST/TSIUwSeowxtxorf1N6cKcu507/dq0KUZS9jixmKNodOQ7RiLhKBRytXZtVi0tJK8DADAt15WTTh+ptpRnlSUnmRzZZ/Ty8POep/3WFnz3ix5+WA2//nUJHtjMnGxWGhyU/P6CjitruzYPSUXV1l4ut2SJMi94wcQWcbMkL01VcUkNDbPfGcqOav2oNRWbBCVJ1tqEMeZcSdsldWri5NjZo3+k6SfEnpD0CUlfHFtJCABekAUNoKJls/Lv3Knwv/yL6p9+2vMwruMo3d6u+Pvep+Hjjy9igMD0mpuz6u4e0IYNhVcb27ZtgEpFc0DFPgDlwFxP7TPG+CW9edLmrbMdZ63tM8Z8VZIZ3VQv6QJJHytuhMWVSvm0e7efz1RJfX316ukJateuqatNbt4cUVtbWp2dVJsEANQ415X/vvuOJCqNayE3ftvh7WNJTrkifL31eUtiKHdlpVyBSVC5MsdbqFLG69bXH0lCGl9xaTQpKbNuXcFjDr/gBdr3ve+VIFpUIqr1o9ZUdBKUJFlrhyVdaoz5rUYmPcZPjo2ZPCH2U0kft9bunK84AdQ2sqABVKRcTk1f/7rCN92khl/9ak5Dpd/4RsWvuUbZE04oUnBA/lpbByX1q6traV4VoRoaXG3bNqCWlsHSBwcAKDrmemre2ZIC464/aq39RZ7Hfk5HkqAkqU0VngQlSXv2VPwUa8lFo02zfpdLpXzq7Q1qx46AursHRr8DAgAwzw4dGqmsNFpR6XCVpWkSlbIveIGSl15a2H04jpa8//1yDs1/t2YvVX+k8rZrcxIJ6ZhjCjqm7PEWaHK8uaamo6omFVRxKRw+fFmLFhXcThCYjGr9qCUV/wvdGOPTyKqvt4/b7OjIZNj46/+pkQmxB+YvQgALAVnQACqK62rRd76j8I03qvFnP5vTUIOnn674ddcp85KXFCk4wJvW1kGtW7dPPT1B7dw5dfWAQCCn9va0OjqoHgAA1Yy5npr395OuP1zAsY9IyurInOVLjTGrrLXPFiOwUonHF/ZJp2i0qaCqnpmMM7p/P4lQAID85HLy7ds3daLS+HZx+VRcymQKuutDp51WeBKUpFwwqLoqSoLKhcNFjiR/npKK5jle13EOJyqprq7g44de9So9+6MfHW4Zp/qKP0WPBYZq/aglFfsOa4xpkPQOSRslPX/SzVNlEWQk/TOTYgBKhSxoABUhl9PyCy7QokcemdMwh/76rxXfuFFDr3xlkQID5q65OastWw5q06aYdu/2a8+eesXjjsJhV2vWZNXamlY4TEIxAFQr5noWjBMnXX803wOttUljzM8kvXTc5r+SVNFJUAv5+0lfX726upZ6Orara6nWrdvHCRMAqEXZ7LRVloaPPVaZl7509jHG8e3fr9Uve1mJgp2Z46H1mTSapNPfX+RoZucbHJSy2YKTbMpZWcnnIQkql0e8bmPj4aQjzxWXxq4HAnOqtuT6/Ro+7jjPxwPzgWr9qBUVlwRljPFLukzSNZKeo5lLoY9tcyU1StppjOmy1t4+H7ECWFjIggZQEXw+Zf7yLz0nQQ2deKLiGzfq0GtfS5lkVKxIxNX69alyhwEAKBLmehacF026Xmjf5l9rYhLUOkn/PqeISmzNmoX7e7+nJ+hpsZg0UhGqpyeoLVsOFjkqAEDBXFcaHJy5otL4ikujf/smV1kauz44/Qnx5Pnn62CBSVC5UGiuj9AzJx73dFy527W5S5YUdIxbzn9jD0lQQ696lQ588pNHEpXC4YkJT2Nt4gAUhGr9qAUVkwRljIlIulzSVZKW68jk1+RS6DlJ90m6S9LNkk7SkcmxOkm3GmOOt9ZunKfQASwgZEEDqASJK65Q4N575StgJVrmhS9U/NprNfjGN0q+o3+4AAAAFBtzPQuPMWaZpMkrh54qcJjJ+zd7j6j0AoGcWlrS5Q6jLGIxR7t2+ec0xs6dfm3aFFMksnCraQGAZ7ncSJWiurqRKjUFqO/r09J3vWtiYtPwcIkCncjnJamoqUluXd28xTielypFUnkTt3zJpIYLTILyGq/r801MQBqrqBQOH6645ITDCq5eLYXDUiSiA7mcsn7/4YQlLxWSss3NyjZX9NdEoGpRrR/VruxJUMaY5ZLeK+k9kiKafkIsK+kejZRB7xs99jRJuySdqSOTY46ka4wx/0fSRdbawpr7AsAsyIIGUG655cuV7OxUuLt71n2zz32u4u97n9Lt7fSaBwAA84K5ngVt8tm2lLW20B4yeyddXzyHeEquvT29YBN4otGp50QKkUr5tHu3nyqgABaOoaEJbeIOV1uaoeKSLx6fePtYtaXUyHtn7JprlHjvewuLw3HU8MtfluAB5nHXXtrLOY7cUEjOwfmvHui5HV6VVVbKnHii0i0tR1rDTVFZyQ2HJ7SOc0MhuU1Ns1abr6+vV3DlysPXh/buVTbLeROg0lGtH9WqbGfCjDHPkXStpHdKCmj6CbFDkj4naau19nfjx7DWJowxb9TISsGLNXFyzEh6jjHmPGvtgVI+FgALD1nQAMotcdllCt59t3wHpv6aM7xypeJXXqnUBRdQ+hkAAMwL5nogafLZPi8lkiYfE/YYy2HGmJWSVuS7/6te9aqlXV1ds+7X2Ojq0ksHVb9AFxv87neNRRqnYcH+G1ajurq6Ga8DC1XdL36hwL33StmsFI9L8biWDgxIsdjEVnGHDhX/vlOpgt9HfYvLl2PsSyY9ve+7oZBUpiSo+rq6WRN9jlLEJCjXcSYmHk36kxu7PJqo5KxaVfC/cba9XfH29rz2dTSxl/Vs+OwApsfrA7XAKfQzssTK+etyj6QGTT8hlpL0r5Justb+abpBrLVZSe8wxjwt6UOaODn2akk/MMa8wVpbaOltAJgVWdCYq1jMUTQ6kkiXSDgKhVytXZtVS8vCXU2M/LiLFyuxYYMin/jEhO25JUuUeM97lHzHO+T659aaAgAAoEDM9WDy2T4vveEnJ0EV4wziBknX57vz448/ntd+X/iCo9NOW+41pqpXrI5E2WxQK1cGizMY5t3y5Qv3NYAKNjx8OBFJ8bgUi028Pt22se0f/KDU0lLYfT72mHTnnRM2NRTxIc0kODw8ocpOXpqaShNMHhoHB7Wy0HglafFi6Q9/KH5As3ByOa0MhaRggZ9Vxx4rLV0qRSIjbeDG/sx2fYptTjAox5d/9cU5Z5CXGJ8dwPR4fQBzV84kqEYdmcQa40iKSfq0pFustfvzHcxa+2FjzO8kbZdUpyOTYy+S9J/GmHOstf9drOABAJiLvr569fQEtWvX1O0DNm+OqK0trc5OWirWOicel1xXbiRS8LHJd7xDwc98RnV79yoXDCr5zncqcdllnsYCAAAoAuZ6MJmXlR0VvxqksVH6whckY8odSXmFi3SGlZ8vAKb0+99LP/vZxGSl6S5P3pb2UohwnKefLvyYcr6ZxeOFH1PGVm2KxbwdV6wPnpkEAlMnImU8dGPevn3kDwAAmFeVUmfYkbRfUrekbdZaT9+ArLU9xpg/StohKagjkyarJH3XGPNWa+3XixEwAABeRaNN6upaqkxm+vKQqZRPvb1B7dgRUHf3gFpbvSygRkVLpxW8+26FbrtN6fPPV+xDHyp4CDcQUPyaa1T/q18pcfnlyrFKBB5QkQ4AUCLM9SxMiUnXvZQmnXzM5DHLJhiULrxQuvJK6UUvKnc05dfcXFnjAJhnuZyUTOaXlHTxxYW/2L/2Neld7ypN7LPxkqQzHwk60/ESr8838sGWTBY/ntl4SdqSpv439vmOrqKUb8WlyddDIYn2rAAAVL1yf5o7kp6RdLOk7dbaOfeUstb+mzHmdEkPaGRCTBqZIAtK+qox5gpr7Z3THQ8AQClFo03asGFZ3vtnMs7o/v0kQtWKoSEFvvhFhbdtU90zz0iSgp/7nBKdncqtXl3wcKm3v73YEWKBoCIdAKBEmOtZ2Co1Cep2Sfflu/NJJ520VNIj47fdcENCZ52VVjg8koe3d28RoqpyZ5zhKBBYPuV3yXwFAjm97nX7tXcvyffVoq6ubkKblv3792u4WL0RUXqZjJxEYsIf3/jr8fjI38nkkdsmbTu8Xyolx83vtXvghBM0tHhxQaEuyuVU2BHFk3zmGSULfKP3HTqkY0oUz2yG+vt1wMMH0/JgUHVlSILKHTqkP3uIt+6f/knOxo1yQyG5oZByweBI5SZn+oWmeRsakvr75z4OpsRnBzA9Xh+oBY7jaMWKFeUO47ByJkE9JWmrpM9aaw8Vc2Br7X8bY/5W0r9JOkFHSrHXS7rdGHO8tXZTMe8TAIDZ9PXVq6trqadju7qWat26fSQiVLPhYfm/8hWFb75Z9U89NeEmZ3BQ4W3bdPATnyhTcFhoqEiHYorFHD3wQJOeeWZkQW84LK1e3aRzz01STQxYeJjrwcFJ1wPGmKC1tpAzrCsnXT8wx5hkrd0rqZCzrUfN3r75zWllMhll+Ul2WCAgtbWl1dsb9DxGe3tagQD/rtVseHhYWf4DS8d15QwOHkk+Sibli8eVOflkuf7C8kzDN96ocHd3iQKdmRuLFfw8qSvw8RWTG48XHK+vjPHKQ7yS5Abzf//OBYMjyUfBoHJjSUijf7ujt03YPmnbWNKSGwqN9JX1EG/2hS88eiOJAlWJzw5gerw+UI18Pu8LY0qhnElQL7TWluwVbK39rTHmVEm7JZ2mI5NjjqSNxpjjJV1irfXQyBcAgML19ARnTDiYSSbjqKcnqC1bJp9TwHwruHWY66rp3/5N4ZtuUkNf37TjBu65R4nLLtPw8ceXMHqAinQonpmriUV0/fUhqokBCw9zPQuctXa/MWZA0vjVH/9H0hMFDDP5C/GTcw4MJdPZmdSOHQFPv3UbGlx1dJShDRNQasPDRyopJZOHKyn5xiopjSYyHb48xbYJ+0+R5LH3299WtsC+nG5TU7EeYcGcROFF/dxQqASR5MfnId5cAQlFxeYlXklKdHbKl0hMmajkhsMTEp9UYSc3AQAAplO2JKhSToqNu48DxpgzJfVKerMmTo6dL+k5xph/sNZyRhkAUFKxmKNdu+a2ImznTr82bYpRVaNMCm4d5rpa9N3vKrx1qxoff3zW8Z1sVuFbbtGBMq3KxMJARToUC9XEAEyFuR6MekLS3467/kIVlgS1dorxUKGam7Pq7h4oKMl+zLZtA3y3RFUL3nGHmr797SMJTGPJS6k5d4KdlZekolw4XIJI8uN4aLnmljNeL0lFixbJbWyUMzTk+X7dpqYjiUd5VFTKhcMjlZmWevudn7rkEs+xAgAAVKpyVoKaF9baIUnGGHOLpKs0cXLsNZJ+YIx5g7X26TKGCQCocdHo1IkzhUilfNq926/160s/mYaJ/j979x8eV13n/f91kkwzv2sKbVFxMcX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(0) # to reproduce the data later on\n", + "plt.clf()\n", + "plt.figure(num=None, figsize=(8, 8), dpi=DPI)\n", + "\n", + "x = np.arange(0, 10, 0.2)\n", + "\n", + "plt.subplot(221)\n", + "y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(222)\n", + "y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(223)\n", + "y = 0.5 * x + norm.rvs(1, scale=1, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(224)\n", + "y = norm.rvs(1, scale=10, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "\n", + "save_png(\"01_corr_demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While correlation is always a good start, it has its weaknesses with non-linear data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(num=None, figsize=(8, 8), dpi=DPI)\n", + "\n", + "x = np.arange(-5, 5, 0.2)\n", + "\n", + "plt.subplot(221)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(222)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(223)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.subplot(224)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x))\n", + "_plot_correlation_func(x, y)\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "\n", + "save_png(\"02_corr_demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mutual information" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import entropy\n", + "\n", + "plt.clf()\n", + "plt.figure(num=None, figsize=(5, 4), dpi=DPI)\n", + "\n", + "plt.title(\"Entropy $H(X)$\")\n", + "plt.xlabel(\"$P(X=$coin will show heads up$)$\")\n", + "plt.ylabel(\"$H(X)$\")\n", + "\n", + "plt.xlim(xmin=0, xmax=1.1)\n", + "x = np.arange(0.001, 1, 0.001)\n", + "y = -x * np.log2(x) - (1 - x) * np.log2(1 - x)\n", + "plt.plot(x, y)\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.grid(True)\n", + "plt.ylim((0,1.05))\n", + "plt.xlim((-.05,1.05))\n", + "\n", + "save_png('03_entropy_demo')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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5FpqvjaiVdJ/b5jA3lbRv13lEcxSt5r7b7m2O4byQJEmS/sAcV/XMmYwXn5c5rnFgLqNDFeW4NudeEfHMiHh/RFwUEb+OiLURsS4iroqISyPirIh4U0QcOuq7LA7onkkaAxZBSdIUysxrgfcWdN0XeO2Aw+lZ8932Rdvb/nBQMWTmV4EfFHQ9ClhU0L4KOL6vQamUzwuAywraAthv0IGo1PKS9t/0OnBmbgR+W9D1wF7HHrLnA7sUtK8HvtTmGGVFUDtHxHw7ObVj2Tx9D2pzjOUl7c4LSZIkTR1zXNUzZzJefF6AOa5xsLyk3VxGuSpyXJuzDKjT2D3x0cADgCXAVsCuwF7A44F/oLHj2vUR8e6IuH9F16/aIO6ZpDFgEZQkTa9TKP6L3ldHxI6DDqZHDweWFrQPLEHU9M42j/t/wDMyc0M/g9FmTfvzKkoQARww0ChaRMTHIiJH7NevhnQ7yn4vLkrsdKNonMURcZ+Kxh+oiNiaxirwIv+Zmb9rc6ibgdsK2rcBHtJNbLPM9/PV7p+9zgtJkiRpU+a4qjftOZNxM+3PyxyXOa6JymVUmOOq2r2BE4FfRMQ7mzsvjYQRvmeShsAiKEmaUpl5K8VfkJcCfzfgcHq1d0n7FQONAj4L/Hwzx9wI/Elm3jKAeDS/qX5embkG+H1B16MGHYtKbV/SfmtF45eNU3bdUfcuoGgl2h3A29sdpPmKiQtKuo/sIq7Zjpinr9377ryQJEmSZjHH1RdTnTMZQ1P9vMxxjQVzGZ2pJMfVR/cCTgK+MUK7Qo36PZM0QBZBSdJ0+wCwpqD9pRGxfLCh9KTsFUJXDzKI5ta7p85zyJ3AUzNzc0kJDYDPCyjecrrdV3Kp/8pWqxUl9rpRNs42FY0/MBHxVOBlJd3vzsxfdDjkN0va/7LDce4REQ8CjprnkG3bHMp5IUmSJM1ljqtC5kzGi88LMMc16sxltKkPOa7NuR34PnAW8F80Xo+3CvgxkJs597HARcP+c3YI90zSiLMISpKmWGbeDryloOtewMmDjaYnZe/3Lkp+9duu8/SdkJnnDSwStWPan1dRgmj5oINQqS1L2u+saPw7OrzuSIqIhwArS7p/TPGfc5vzhZL2fSLiaV2MB/APzP/9q9377ryQJEmSWpjj6otpz5mMm2l/Xua4Rpu5jDb0KcfV6k7gq8ArgUcAW2fmfpn5hMx8dmY+KzOPysw/Au4HPBP41jzj3R/4SkTcu4LYOjageyZpzIzMuzolSUPzUeC1wB4t7S+IiFMy80dDiKlTDyho+11mrhtkEBHxZ8DfznPIskHFos0bhecVEbsAj6SxMm07YAa4GbgSuGgAW5QXJVF3iogtM7MsedBv832pHpYbhnTdLUraN1Q0ftk4ZdcdORGxDfBFindRWg88r5u5nJk/jIizgGMKuv8lIi7KzKIEa1mczwBesJnD2r3vzgtJkiSpmDmuioxCzkTtG4XnZY6rkDmuPzCXsRn9ynHN8hsar4g9PTOva+eEzLyJxus2PxsRjwM+DuxUcOjDgNPoYQf1bgzgnkkaUxZBSdKUy8wNEfEm4JMtXQuAtwJPHXxUHSva1va2QQYQEYcCp2/msL+OiHdl5to+xnE/YH/ggFm/dm457EGZ+at+xTAOhvW8ImIJ8CTgCcCRwG7zHJ4RcQHwQaDe3Nq8amU/J/cBru/D9TYrMz8CfGQY1x4jm9uKutdxoqLx+yoi7gV8HnhoySGvzMxLerjE2ykugtoJ+GZEPK6d7bQjogb8Zw9xtMt5IUmSpKlmjqsa5rjGizmue5jjGk/mMhhIjovM3NzvE5s7/2sRsQ9wNvBHBYf8eUT8S2b+oJfrtGsQ90zS+PJ1eJIkgE8DRf9D+JSIOHDQwXRhSUHb7YO6eHPL1c/T2GJ9PvelD6shImLfiPiviPgFjS/1X6GxzeuxzE0OTb1hPa+I+BvgOhrvVn8R8yeHoPEl/Y9pJG/Pi4jdq4pllrKfk6V9uJY6d1dJe1ULGcpWw1W1FXnfRMQCGqvPjig55JQKkjvnAv9c0r078MOI+PuIKFxRGxH7RMRnaPzMz77XZasT17cZmvNCkiRJKmeOqwfmuMaLOa5NmOMabeYySgwix1WVzLyGRvFjUWFhAG8YRBzjdM8kDYdFUJIkMjMp3zL5bYOMpUtDSxBFxPbAl2gkE9pxYkRUvQ3vXkCNxnbTmseQn9d+lCdefgtcBnwPuLag/yDggoh4WEWx3K3s56ToZ0qDV5ao2bKi8cuSpOOwTfQHgWeV9K3MzJMqus6JwMUlfUtoJON/ExGXRMQXI+KTEfG1iLgS+D7wjILzyv5cbff1Fs4LSZIkqYQ5ru6Z4xov5rjmMMc12sxllBtUjqsSzd33ymJ6SkTcZwBhjNU9kzR4FkFJkgDIzC9T/J7yIyLi6EHHU4G+b3U7a8vVhxR03wB8uaB9N+C5/YyrxUC3TB9lI/a81tNY/fYsYKfMXJaZe2fm/pm5M/BIGqtZZtsB+Epzu/GqjMWW0FPs1pL2e1c0/tYdXnckRMQ/AS8v6f4c8JKqrpWZdwJPpPjPx3tCAvamsRLuOcDRlK+C/QDw7yV97W7P77yQJEmS5mGOq4sLjFbOpIw5rqYRe17muNQOcxkFBpnjqth/AFcUtN+LRl6sb8b4nkkaIIugJEmz/U1J+6ivlCvaPWOrAVz3I8AhBe13AE8BXgPMFPSfFBH9+GJ+O3AhjZUQL6KRZNimD9cZV6PwvG6gsVJml8x8XmbWM/O61oMy80eZ+ULgxWz6TvvllK+06UbZz8naCq+h7t1U0r5tReOXjVN23aGLiNcBbyzp/irw3MzcWOU1M/MG4Egav4fkZg4vsxF4Q2a+Erh/yTHXtDmW80KSJEnaPHNcnRmFnMls5rjmNwrPyxyXOmEuo8UwclxVacb1uZLug/p13XG+Z5IGyyIoSdI9MvNbNLZRbrV/RDx90PF0oOjL7OJ+XjAi3gS8oKT7uMw8PzN/AvxvQf8jgGMrDOfrwKOA+2TmQZn5isz892aSoSjhMXVG5Hn9G/DgzDwlM29u54TM/Bjw/pbmP68glruVJYjafS2X+qto23iAnSoaf+eCtjuA31U0fqUi4mXAu0q6zwOe2ty5qXKZeWdm/iWwP8UraudzDrBfZr6z+Xl5yXG/bHM854UkSZK0Gea42jciOZO7mePajBF5Xua41ClzGbMMM8dVobNL2h/aj4tNyD2TNCAWQUmSWv0txTtdvDUiFg46mDbdUtBWtgVuzyLiOcDJJd0nZ+YnZ31+e8lxb6gqnsy8MjMvzcwNVY05SUbleWXm2Zn5+y5OfQeb/kzuGhF/1Gs8TUVbTicjvlX0FPl1SfuuvQ7c/P28KEH0q8zsdrejvomI5wP/UtL9XeBJmXl7v+PIzO9n5p8ADwT+Evgv4AfA1TReAXAnjR2dvgG8BdgzM4/OzEtnDVP28/ujNsNwXkiSJEntMce1GaOSM7mbOa75jcrzMselLpjLaBqVHFcFyhbzle2A3rUJumeSBmTRsAOQJI2WzLw0Ij4NPKel62HAC4GVg49qs64saNs6IpZmZqVbHkfEHwNnUPye+U9m5j/MbsjMiyPi68ARLcceFBErMvO8KuPTpibheWXmbyPiCjZdRfMA2i+YmM8uBW2/HeaqmYj4C+Cxw7p+iRsy87VDuO4vStr3qGDs3YAtCtrb3Y1oYCLiacDHKP45/j/g8Zk50JV9mXkljdcPfKSL0x9V0n5Jm+c7LyRJkqQ2mOOa3yTkTKbJJDwvc1wjwRzXEI1ijqsHc15/2VRUkNi1CbtnkgbEIihJUpG/B57B3C8PJ0fEJzLzjiHENJ+ylST3B66o6iIR8WAaW0lvWdB9PnBcyanvYG7CARorr0wQ9cmEPa/WrcXL3nPfqaKVOWU/T4PyWBrJ6FHya2AYCaIflrQ/ooKxy8You+ZQRMQxwKeAolXaPwWOzsybBhtV95qrEw8q6LoFuKzNYaZ+XkiSJEkdMMdVYMJyJhNvwp6XOa7hMsc1JJOW46K4KKnaC0zePZM0IL4OT5I0R2b+HPhoQdduwMsHHE47ylaS9Lyd7t0iYlvgS8D9Sq7/lLLEWWaeTWNb1lZPjIi9qopRfzCBz6s1kXNjn8aF8p8nDd4PStr3rWDs/Tq85sBFxCHA54F7FXT/GjgyM3872Kh6dhBwn4L2b2TmTJtjTPW8kCRJkjphjmuuCcyZTLQJfF7muKbTVOcyJjTHtUNJeyW7Fk7oPZM0IBZBSZLKvAUoeo/yGyNi60EHsxmXlrQ/rIrBI2IL4HMl491C453TN2xmmHeWtJ/US2yaa9KeV3O1X2uy86cVjLsrxdsTt/tKLvVZZv6G4q2794iIouReJw4taR+JlbsRcQBwJrBVQfc1wFGZedVgo6rEM0raP9PuANM8LyRJkqQumeNqmrScyaSbtOdljmt6TXMuY4JzXLuXtP+m14En+J5JGhCLoCRJhTLzGuADBV07ACcOOJzN+X9A0XufH1nR+B+ieOvoDcAzMvPyNsb4PMXblj8rIpZ3H5oKTNrz+nM23V748sys4p32ZSv+ilYIanjOKWl/QrcDNpP8Bxd0/SQzr+523KpExCOBs4Civ4y4gUai42eDjap3EbEYeEFB1++BL3Q43NTNC0mSJKlb5rg2MWk5k0k3ac/LHNd0m7pcxqTmuJqOLmnvqbBxwu+ZpAGxCEqSNJ930FhV1Oo1EVG0BfNQZGYC3y/o2rPXsSPiDTS+oBc5PjNXtTNO8zVH7yroWgS8psvw1GLSnldzJdQrW5o/VtHwRT8fCXyvovG7kpkvyswYsV/Lh3hL/rek/Tk9jPl0YHFB+//0MGYlImIP4GzgvgXdtwKPy8wfDzaqyryM4v+uj2Zmp1uFT9W8kCRJkipgjmvCciaTbtKelzmukfm1fIi3ZKpyGZOc44qIhcDTSrov7GHcib1nkgbLIihJUqnMvBk4paBra+CNAw5nc84taHtURCzqdsCIeAbwtpLuUzPz9A6H/A+Kt4P984goe4e22jRpzysiAljJptt5Xw18sKJL7F/Q9oPMLFpxquH5KnB9QfsREdFtEvyvS9o/0eV4lYiI3WisCty5oPs24AmZ+YPBRlWNiNgR+LuCrjuA07oYcmrmhSRJklQFc1yTlTOZdJP2vMxxqWlqchmTnONqeiHwkIL2u2gUMXVsCu6ZpAGyCEqStDnvA35b0H480Ov7uqv0lYK2ewP7dTNYRDwa+DibbtF8t/8BXt/pmJl5J8V/2b0V8IpOxxsFEZElv5YPOI5JfF5vAh7X0vayzFzX68DN5NOhBV1FP0caoszcAHy4pLssIVoqIp5J8e+LqzPz/zoc62MlP/8ndxHXjjSSJA8s6L4deHJmXtDpuKMgIhYAZwDbF3S/NzOv7HTMUZ4XkiRJ0ggzxzXXuOZMKmeOq6/McWmkcxnmuNoXEQ+msbtikS9n5k1djDnR90zS4FkEJUmaV/MVPW8t6NoS2GPA4cznO8CNBe2HdTpQRDwQ+AKNRECr7wHPa24l3Y1/A4q+CPxVRNy7oF2bMYnPKyJqwJtbmt+fmV+q6BJ/BBSt9PtyReOrWh8Ail6X9qSIeFG7g0TEzpSvsixLXvRdRGxDYzXgQwu67wSenpnfGGxU1WgWQJ0OPLGg+xcU//naromeF5IkSVLVzHHNMZY5k0k2ic/LHJdaTHQuYxRzXBGxT0TsVdFYuwBfovhnDuCdXYw5cvdM0vizCEqS1I4PA78cdhDzaSYAir48H97JOBFxH+BMYKeC7qtprDroepVSZt4G/HNB132Bl3Q77rSaxOcVEYcA/86mK/6+Dry2wssU/VxcD1xU4TVUkcy8Fnh7Sfe/RcRTNjdGRCwDvgbsWNB9VmZ+tYcQuxYRS2j83v2ogu4NwHMyc2irNyPi2RHxzojYtYtzd6Nxz48r6L6LRvL6tm5jm+R5IUmSJPWROa6GscyZTLJJfF7muNRqknMZI5zj2hu4JCL+OyIObu6e1rGIeDzwA+BhJYd8otPdmkb4nkkac12/Q1qSND0y866IeDONrZhH2SeAP2tpOzwitsnMWzd3ckQsBOrAIwu6bwOOzcw1vYfJ+4HXAEta2l8dER/IzLsnPn3zAAAgAElEQVQquMbEm8TnFRH70Fjxt3hW8/eAp1Q8L4oSCp/KzI0VXkPVOhV4BnOTAvcCPh8R/wq8PTOvmt0ZEYuB5wH/RHEi9VYar34Yln8CDi7puwp4ckQ8ucLrvTYzb+jg+G2Bk4ATI2I18Fng7Mz8RdHBzUTSPsCLgL9k05/l2V6SmRd2EEeZSZ0XkiRJUl+Y4wLGNGcyySbxeZnj0jwmNZcxyjmuoPGz8hTgqoj4DI1Csh9k5nWlJ0XcFzgSeCXw2HnG/znw123GMtso3zNJY8wiKElSuz5B4y+Ci76Mj4pVwDXAsllt9wKeRCP+zfkg8PiC9hnguZl5Sc8RApl5Q0R8hMaXh9l2BZ4PnFHFdabARD2viHgoja1/t5nVfDlwTGb+voprNK+zPXBIQdeoJ4CnWmbe0dxC/gJg+5buAF4OvDQiLgN+SmOnoWXA/sDWJcPOAC/KzGGugt5unr4HNX9V6WSgm2THIuCo5i8i4hbgRzReUfE7Gq/P2JnGNvz3nWecBE7MzI91EcPcwSZ3XkiSJEn9ZI5rzHImU2Cinpc5Ls1ngnMZ45Lj2g04sfmLiFgD/JpGEdktNJ7B9sAuwMPZdCe3ImuAJ2TmLV3EMi73TNKY8XV4kqS2NLfi/tthxzGf5gqfokTQ0zZ3bkScCLyspPs1mfnFXmIr8G4aX+BandTtlrSDFhFlX1Jupc9fJibteUXEg4Bz2PR96r8Eju7D6pQnM7cQ/vLM/F7F11HFMvOnNJKiN5UcsoDGKrpnAs+lsSV8WXJoI/AXmfk/Vcc5JbalsVLtyTQSxc8EVjB/AdRa4PmZ+d4qA3FeSJIkSZ0xxzVeOZNBMMcFmOPSAJnLGCm7AAcBxwDPBp5FYxHgI9h8AdS5wGOaz1OSRoZFUJKktmXmF4AqXt/TTx+isfJjtj9prgwq1NxS9ZSy8ar+C2uAzLwS+FRB18OAP636en1StNIK4AOZeVu/Ljppzysi7k9jheeus5rXAEdl5m96GbtE63b60FhxqDHQTOQ9Gri0h2GuB56Yma7IHZxvAftm5if7MbjzQpIkSeqMOa7qmOPq3qQ9L3Nc6oS5jLG2jkYx8eGtry2UpFFgEZQkqVN/M+wA5pOZv6DxvvnZtqT4SzERsS/wSYr/TPwac7eHrtI7abwaqdXr+3jNKh1a0HYbUHmC5m6T9rwiYkcayaHZW/teDxzZnMuVioiHMPe53Qz8e9XXUv9k5s9pbAH+KqCTJOJa4H3AQzPza/2IbQKdT+P3nLKViZvzTeApmbkiM6+oLqy5nBeSJElSx8xxVcccV4cm7XmZ41I3zGUMxNeBNzX/eXuPY/2axu8Tu2bm25o7K0rSyInMov/PkSRpfEXEITT+4nm2yzPzEcOIZ9giovUP+wdl5q8qGPd7wL4tze/MzDf0OvY0iIj7AquBPWc130JjBc0lfbrmO4GTWprflZnjkpRUi4jYAngc8AQaP4+7A9sAC4HfA1cCPwTOBr6QmbcMKdSxFhELgb1obA++H7AHsJzGa/GWAhto3O+rgJ/Q2PnprH4ketuM13khSZIkTQBzXJsyxzWazHGpCuYy+q95j/+IxqsG96RRtLgbsAy4N7CERoHkLTSKCm8GLgcuaP76sYVPksaBRVCSpIkUEecDf9zSfERmfmMY8QxTPxJEEbENjZ1RZq9WW9cc+7pexp4GEXEfGqvj9p/VfBtwdGb2ZTv+iNiKxmqdHWY1rwd2z8w1/bimJEmSJEnqjTmuPzDHNXrMcUmSpFHj6/AkSZPq7wraXAlUnRXM/f+IfzM5tHkRsRT4Mpsmh24HntSv5FDTcWyaHAL4Z5NDkiRJkiSNNHNc/WWOq0vmuCRJ0iiyCEqSNJGaq+FaV8Q9PiIeNYx4JtChLZ/vAE4ZRiDjJCK2BP4HOHhW853AUzOzdXv7Kq+7EHhNS/NtwDv6dU1JkiRJktQ7c1x9Z46rC+a4JEnSqLIISpI0yd5Y0PaGgUcxmVoTRB/JzGuGEsmYiIhFQB04albzBqCWmV/t8+WfTeMd77O9JzNv6PN1JUmSJElS78xx9Y85rg6Z45IkSaMsMltfoSxJ0uSIiP8Anj+rKYF9M/OSIYXUFxGxH7BdSffZLZ+fD1xbcNztmXl+G9faGrgZWNhsuhPYIzOvajPcqRQRZwAvamk+nUbSqFNrMvPHbV53C+ByYPdZzVcBD8vMdV1cW5IkSZIkDZg5LsAc10gwxyVJkkaZRVCSpIkWETsDVwBbz2r+amYeM6SQ+iIiVjN35Vqnfp2Zy9u41jHAV2Y1nZ6ZL+nx2hMvIn4FPLCi4f49M1/U5nVfAby/pflZmdlNYkqSJEmSJA2BOa6OmOPqI3NckiRplPk6PEnSRMvM3wJvaWl+fEQcMYx4JkFmnpWZMeuXyaER1VzR+HctzatNDkmSJEmSNF7McVXPHNf4MMclSZLatWjYAUiS1G+ZeSpw6rDjkAYtM38P7DTsOCRJkiRJUu/McWlameOSJEnt8nV4kiRJkiRJkiRJkiRJksaar8OTJEmSJEmSJEmSJEmSNNYsgpIkSZIkSZIkSZIkSZI01iyCkiRJkiRJkiRJkiRJkjTWLIKSJEmSJEmSJEmSJEmSNNYsgpIkSZIkSZIkSZIkSZI01iyCkiRJkiRJkiRJkiRJkjTWLIKSJEmSJEmSJEmSJEmSNNYsgpIkSZIkSZIkSZIkSZI01iyCkiRJkiRJkiRJkiRJkjTWLIKSJEmSJEmSJEmSJEmSNNYsgpIkSZIkSZIkSZIkSZI01iyCkiRJkiRJkiRJkiRJkjTWFg07AE2chcBDWtpuAnIIsUiSJEmSNMoCuG9L20+BjUOIRdIfmN+SJEmSJKk9I5XfsghKVXsIcPmwg5AkSZIkaUw9HPjJsIOQppz5LUmSJEmSuje0/Javw5MkSZIkSZIkSZIkSZI01iyCkiRJkiRJkiRJkiRJkjTWLIKSJEmSJEmSJEmSJEmSNNYWDTsATZybWhuuvfZaMnMYsbRl4cKF7LDDDvd8vv7669m4ceMQI5J657zWpHFOa9I4pzVpnNOaRIOY1xHBTjvt1No853u1pIEzvyWNAOe1Jo1zWpPGOa1J5LzWpJnG/JZFUKranGxQZjIzMzOMWNqyYMGmG6KNerxSO5zXmjTOaU0a57QmjXNak2gQ87r1GndfqtKLSOqG+S1pBDivNWmc05o0zmlNIue1Js005rd8HZ4kSZIkSZIkSZIkSZKksWYRlCRJkiRJkiRJkiRJkqSxZhGUJEmSJEmSJEmSJEmSpLFmEZQkSZIkSZIkSZIkSZKksWYRlCRJkiRJkiRJkiRJkqSxtmjYAagztVrtYcDewK7AVsB64DrgZ8Cl9Xp97RDDkyRJkiRJkiRJkiRJkgbOIqgxUKvVtgFeBbwYeOA8h26s1WqXAJ+t1+vvGEhwkiRJkiRJkiRJkiRJ0pBZBDXiarXaM4EPAdu3cfhCYD8au0RZBCVJkiRJkiRJkiRJkqSpYBHUCKvVam8GTi7ouhK4ArgeWAwsA/YElg4sOEmSJEmSJEmSJEmSJGlEWAQ1omq12muYWwD1KeDt9Xr9hwXHLwAOAp4OPL7vAUqSJEmSJEmSJEmSJEkjwiKoEVSr1fZm09fZ3QU8t16vf7bsnHq9PgOcD5xfq9V8rpIkSZIkSZIkSZIkSZoaC4YdgDbVLGBayaYFai+drwCqVb1e31B5YJIkSZIkSZIkSZIkSdKIcseg0fNMYN9Zn1fV6/UzhhWMJEmSJEmSVKVarfYwYG9gV2ArYD1wHfAz4NJ6vb52iOFJkiRJkqQxZRHU6Hlpy+e3DSUKSZIkSZIkqSK1Wm0b4FXAi4EHznPoxlqtdgnw2Xq9/o6BBCdJkiRJkiaCRVAjpFar7QEcOqvpV8A3hhONJEmSJEmS1LtarfZM4EPA9m0cvhDYj8YuURZBSZIkSZKktlkENVoOb/m8ql6v51AikSRJkiRJknpUq9XeDJxc0HUlcAVwPbAYWAbsCSwdWHCSJEmSJGmiWAQ1Wh7d8vkCgFqtFsCRwPOAA4H703h2NwA/Bc4BPl2v1381sEglSZIkSZKkedRqtdcwtwDqU8Db6/X6DwuOXwAcBDwdeHzfA5QkSZIkSRPFIqjRsn/L58trtdpy4KPAEQXHP6D560jgLbVa7XTgdfV6fV1fo5QkSZIkSZLmUavV9mbT19ndBTy3Xq9/tuycer0+A5wPnF+r1cxbSpIkSZKkjiwYdgDaxLKWz0uAiykugGq1BXA88K1ardY6jiRJkiRJkjQQzQKmlWy6APOl8xVAtarX6xsqD0ySJEmSJE00V1SNlm1bPp8B3K/572uBfwW+AlwNLAX2Bo4DHjvrnH2Az9VqtUPr9fpdvQZUq9V2BHZo9/iDDz54uxNOOGGTtoULF7JgwejW2y1cuHDez9I4cl5r0jinNWmc05o0zmlNokHM64iofExpRDwT2HfW51X1ev2MYQUjSZIkSZKmg0VQI6JWq20JbNnSvGvznz8GjqnX61e19H8fOKNWq70GOHVW+0HA64G3VhDa8cCb2z34sssum9O2ww5t11CNhO23337YIUiVc15r0jinNWmc05o0zmlNIue11JGXtnx+21CikCRJkiRJU2V0t+eZPmVLSm+luADqHvV6/d3AaS3Nr67VaveuKjhJkiRJkiRpc2q12h7AobOafgV8YzjRSJIkSZKkaWIR1Iio1+vrgJmCrvfMVwA1y9/TKJi6232BJ1QRmyRJkiRJktSmw1s+r6rX6zmUSCRJkiRJ0lTxdXijZS2wdUvbx9s5sV6vr63Vap8HXjyr+TDgMz3G9C+djLHXXnttB5w3u+36668nc3RzXQsXLtzktQY33ngjGzduHGJEUu+c15o0zmlNGue0Jo1zWpNoEPM6IsbuFfJSGx7d8vkCgFqtFsCRwPOAA4H708hN3gD8FDgH+HS9Xv/VwCKVJEmSJEkTxSKo0XILmxZBXdth4udCNi2CenivAdXr9euA6zo4ZU72duPGjczMFG1yNZo2btzIhg0bhh2GVCnntSaNc1qTxjmtSeOc1iTqx7xesMANujWR9m/5fHmtVlsOfBQ4ouD4BzR/HQm8pVarnQ68rrlruiRJkiRJUtvMto2WK1o+X9Ph+WtaPm9feJQkSZIkSZLUH8taPi8BLqa4AKrVFsDxwLdqtVrrOJIkSZIkSfNyJ6jR8iMaq97udkeH57cev7i3cCRJkiRJkqSObNvy+Qzgfs1/Xwv8K/AV4GpgKbA3cBzw2Fnn7AN8rlarHVqv1+/qNaBarbYjBbuXlzn44IO3O+GEEzZpW7hw4Ujv3rZw4cJ5P0vjyHmtSeOc1qRxTmsSOa81aQYxpyOi8jF7YRHUaLms5XNr0mhzWo+/sYdYJEmSJEmSpLbVarUtgS1bmndt/vPHwDH1ev2qlv7vA2fUarXXAKfOaj8IeD3w1gpCOx54c7sHX3ZZa4oOdtih7RqqkbD99m4Qr8njvNakcU5r0jinNYmc15o00zCnR3f50nT6CpCzPj+4Vqt1spvTI1s+X917SJIkSZIkSVJbypaU3kpxAdQ96vX6u4HTWppfXavV7l1VcJIkSZIkabK5E9QIqdfra2q12gXAHzebtqDxerwvtTnEMS2fz6sqNkmSJEmSRl2uW0tefB5ctwbW3w6Lt4KddiH2X0EsWTrs8KSJV6/X19VqtRnmLrx8z3wFULP8PY1X423T/Hxf4AnAZ6qLUpIkSZKk0TWz9jbWnftVNvzmKmZuX8uCrZay6P4PYMkhj2PBUtcJbY5FUKPnDP5QBAVwIm0UQdVqtRXAo2c1zQBfrjY0SZIkSZJGT665klx1JnnRarhj/dz++kriwMOIo44llu02+ACl6bIW2Lql7ePtnFiv19fWarXPAy+e1XwYvRdB/UsnY+y1117b0bK48PrrryczS84YvoULF27yWoMbb7yRjRs3DjEiqXfOa00a57QmjXNak8h5rWGa+c2VbDznf5m54BuF+a2bT38PCw46nIVH/ykLdnlAW2MOYk5HxEi9Qt4iqNFzBo3Cp4c3Px9Rq9VOrNfr7yk7oVar7dg8b7Z6vV7/eZ9ilCRJkiRpJMx851zyjPfChg3lB92xnjz3LPLb5xDHvZoFB6wYXIDS9LmFTYugrq3X67/q4PwL2bQI6uFlB7arXq9fB1zXwSlzsrcbN25kZmam11AGZuPGjWyY7/dFaQw5rzVpnNOaNM5pTSLntQal3fzWzOqvMPOts7vOb/VjTi9Y0LoZ9HCNVjSiXq9vBE6gsZPT3d5dq9XeV6vVtms9vlarHQWcD+w+q/lm4I19DVSSJEmSpCGbufg88vRT508QzbZhA/nhU5i52LfHS310Rcvnazo8f03L5+0Lj5IkSZIkaQKY36qWO0GNoHq9fnatVjsB+MCs5lcCL6/VahcCvwG2Ah4FPLDl9DuB59Tr9V8OJFhJkiRJkoYg11xJrjytu3NXnkbuutxX40n98SPgyFmf7+jw/NbjF/cWjiRJkiRJo8n8VvXcCWpE1ev1DwLHA+tmNW8BrACeDfwpcwugrgUOr9frXx1IkJIkSZIkDUmuOrP9FXKtNmwgV32x2oAk3e2yls/bdnh+6/E39hCLJEmSJEkjy/xW9SyCGmH1ev1DwF7AfwK/n+fQ3wInAw+t1+vfHkBokiRJkiQNTa5bS160urcxLlxNrltbTUCSZvsKkLM+P7hWq3Wym9MjWz5f3XtIkiRJkiSNFvNb/eHr8EZcvV7/OfCCWq22FXAwsCuwM43X3l0PXFqv11tX2EmSJEmSNLHy4vPgjvW9DXLHevK75xGHHFNNUJIAqNfra2q12gXAHzebtqDxerwvtTlE6w/leVXFJkmSJEnSqDC/1R8WQY2Jer1+O3DOsOOQJEmSJGnorltTzTjXXlPNOJJancEfiqAATqSNIqharbYCePSsphngy9WGJkmSJEnSCDC/1Re+Dk+SJEmSJI2X9bdXNM66asaR1OoM4PJZn4+o1WonzndCrVbbsXnebPXmLumSJEmSJE0W81t9YRGUJEmSJEkaL4u3qmicJdWMI2kT9Xp9I3ACjZ2c7vbuWq32vlqttl3r8bVa7SjgfGD3Wc03A2/sa6CSJEmSJA2L+a2+8HV4kiRJkiRpvOy4SzXj7LSsmnEkzVGv18+u1WonAB+Y1fxK4OW1Wu1C4DfAVsCjgAe2nH4n8Jx6vf7LgQQrSZIkSZpauW4tefF5jdfTrb+9UZy00y7E/iuIJUv7d2HzW31hEZQkSZIkSRorccAK8jMr4Y713Q+y5WJi/xXVBSVpjnq9/sFarbYROBW4e2nqFsB8P3zXAk+r1+vf7nd8kiRJkqTplWuuJFedSV60ujDHlPWVxIGHEUcdSyzbrfLrm9/qD1+HJ0mSJEmSxkosWUoceFhvYzzmsP6u5pMEQL1e/xCwF/CfwO/nOfS3wMnAQy2AkiRJkiT108x3zmXmH19FnntWeRHSHevJc89i5i0nMHPxeZXHYH6rP9wJSpIkSZIkjZ046ljy2+fAhg2dn7xoEXHksdUHJalQvV7/OfCCWq22FXAwsCuwM43X3l0PXFqv1y8bYoiSJEmSpCkxc/F55Omntn/Chg3kh09hBlhwQLW7Lpnfqp5FUJIkSZIkaezEst2I415NfviUzs897sS+bGMuaX71ev124JxhxyFJkiRJmk655kpy5WndnbvyNHLX5ZXmlMxvVc/X4UmSJEmSpLG04IAVxEteB4vaXOO1aBHxkpNYcMBj+xuYJEmSJEmSRk6uOrO7XZegsSPUqi9WGxDmt6rmTlCSJEmSJGlsLThgBbnrcnLVF8kLV8Md6+cetOVi4jGHEUce6wo5SZIkSZKkKZTr1pIXre5tjAtXk097IbFkaTVBNZnfqo5FUJIkSZIkaazFst2I5x9PPu2F5HfPg2uvgfXrYPES2GkZccAhxFZLhh2mJEmSJEmShiQvPq+4uKgTd6wnv3seccgx1QQ1i/mtalgEJUmSJEmSJkIsWdqXJJQkSZIkSZLG3HVrqhnn2muqGaeE+a3eWAQlSZIkSZKGKtetbazGu24NrL8dFm8FO+1C7L+i8u3FJUmSJEmSNIXW317ROOuqGUd9YRGUJEmSJEkailxzJbnqTPKi1YXbkWd9JXHgYcRRxxLLdht8gJIkSZIkSZoMi7eqaBxfSTfKLIKSJEmSJEkDN/Odc8kz3gsbNpQfdMd68tyzyG+fQxz3ahYcsGJwAUqSJEmSJGly7LhLNePstKyw2Z3OR4NFUJIkSZIkaaBmLj6PPP3U9k/YsIH88CnMgIVQkiRJkiRJ6lgcsIL8zMrC3cjbtuViYv9Nc1PudD5aFgw7AEmSJEmSND1yzZXkytO6O3flaeQ1V1UckSRJkiRJkiZdLFlKHHhYb2M85rBNdnWa+c65zPzjq8hzzyovrmrudD7zlhOYufi8nq6vzbMISpIkSZIkDUyuOnP+V+DNZ8MGctUXqw1IkiRJkiRJUyGOOhYWdfnCtEWLiCOPvefjPTudt5vnununcwuh+soiKEmSJEmSNBC5bm1ja/BexrhwNblubTUBSZIkSZIkaWrEst2I417d3bnHnXjP6+zc6Xx0WQQlSZIkSZIGIi8+r3xr8HbdsZ78rivmJEmSJEmS1LkFB6wgXvK69neEWrSIeMlJLDjgsfc0udP56Opyny9JkiRJkqQOXbemmnGuvaaacSRJkiRJkjR1Fhywgtx1Obnqi+SFq4sX7W25mHjMYcSRx96zAxRUuNP5015ILFna0ziayyIoSZIkSZI0GOtvr2icddWMI0mSJEmSpKkUy3Yjnn88+bQXNnYdv/aaRs5p8RLYaRlxwCHEVkvmnFflTudxyDG9jaM5LIKSJEmSJEmDsXirisaZm4CSJEmSJEmSOhVLlnZWjORO5yNtwbADkCRJkiRJU2LHXaoZZ6dl1YwjSZIkSZIkdcKdzkeaRVCSJEmSJGkg4oAVsOXi3gbZcjGx/4pqApIkSZIkSZI64U7nI83X4UmSJEmSpI7lurXkxec1tgBff3sjAbTTLsT+K4glSwvPiSVLiQMPI889q+vrxmMOKx1fkiRJkiRJ6it3Oh9pFkFJkiRJkqS25ZoryVVnkhethjvWz+2vryQOPIw46lhi2W5z+uOoY8lvnwMbNnR+8UWLiCOP7SJqSZIkSZIkqXdxwAryMysL82Jtc6fzvvF1eJIkSZIkqS0z3zmXmX98VWMnp7JEzx3ryXPPYuYtJzBz8XlzumPZbsRxr+7q+nHciYWFVZIkSZIkSdIg3L3TeU9juNN531gEJUmSJEmSNmvm4vPI009tfwenDRvID59SWAi14IAVxEteB4va3KB60SLiJSex4IDHdhCxJEmSJEmSVL046tj281qt3Om8ryyCkiRJkiRJ88o1V5IrT+vu3JWnkddcNad9wQErWPCm9xGHHgNbLi4+ecvFxKHHsOBN77MA6v+zd3+xdafpfdi/75mT8PBwE6y78bCUh/GgQJoYLjaoLUJqdigzkJrIQNg2uiBiQLECBtmgQoC1VNhJXMOb2hdJO5NqDSQBMgNwkl4E6dkLI5DsKpthzYrxWDOUfbFIYaABGluTpSxtdr2pLerQy+HbC+6fGQ2lIXl+JA/JzwcYYM45v/fhQ+AsgX3n+z4vAAAAAEPBpPPhtcdoGgAAAHBS1MVbO58A9bSNjdTFmymXr37kozIxmXL5auqlK6n3lpOHD5L+WtLpJuMTKVPnUka7A3YPAAAAAM1qTU1nM1sHAHe0b9Zup8xfd9BvnwlBAQAAAM9U1x6nvrM0WI27S6mXrqR0x7b9vHTHUs5dHOhnAAAAAMBBak1Np770curizdS7S8l6/6MPjXRSzs6knJ81AeoACEEBAAAAz1RXlrffwNmN9X7qvWVBJwAAAACOFZPOh4sQFAAAAPBsj1abqfPwQTN1AAAAAGDImHQ+HFqH3QAAAAAwxPpPGqqz1kwdAAAAAIBtCEEBAAAAz9YZbaiOsd8AAAAAwP4RggIAAACe7cVTzdQZn2imDgAAAADANoSgAAAAgGcqU9PJSGewIiOdlNPTzTQEAAAAALANISgAAADgmUp3LOXMzGA1zs6kdMeaaQgAAAAAYBtCUAAAAMBzlQuzSbu9t8Xtdsr52WYbAgAAAAB4ihAUAAAA8FxlYjJl/tre1s5fT5mYbLgjAAAAAIAP2+MxTgAAAOAkaU1NZzNJXbiRbGx8/IJ2O2X+elpTr+x7bwAAAAAAQlAAAADAjrSmplNfejl18Wbq3aVkvf/Rh0Y6KWdnUs7PmgAFAAAAABwYISgAAABgx8rEZMrlq6mXrqTeW04ePkj6a0mnm4xPpEydSxntHnabAAAAAMAJIwQFAAAA7FrpjqWcu3jYbQAAAAAAJElah90AAAAAAAAAAADAIEyCAgAAgBOkrj1OXVlOHq0m/SdJZzQZP5VyejqlO3bY7QEAAAAA7IkQFAAAAJwAdfV+6uKt1HeWkvX+Rz/vLaScmUm5MJsyMXnwDQIAAAAADEAICgAAAI65zXfvpL75hWRj49kPrfdT79xOffutlPlraU1NH1yDAAAAAAADEoICAACAY2xzZTn1jdd2vmBjI/X1V7OZCEIBAAAAMHTq2uPUleXk0WrSf5J0RpPxUymnp1O6Y4fdHodICAoAAACOqbp6P3Xhxt7WLtxIfellV+MBAAAAMBTq6v3UxVup7ywl6/2Pft5bSDkzk3Jh1p7WCdU67AYAAACA/VEXbz3/Crzn2dhIXbzZbEMAAAAAsAeb797J5s//ROqd29sGoJIk6/3UO7ez+XOfy+bK8sE2yFAQggIAAIBjqK493joVN0iNu0upa4+baQgAAAAA9mBzZTn1jdd2fthvYyP19VcFoU4gISgAAAA4hurK8rNPxe3Uej/1ns0iAAAAAA5HXb2funBjb47gAA0AACAASURBVGsXbqQ+eK/hjhhm7cNuAAAAANgHj1abqfPwQTN1AAAAADjR6trjrYN7j1aT/pOkM5qMn0o5PZ3SHdt+zeKtnU+AetrGRurizZTLVwfomqNECAoAAACOo/6ThuqsNVMHAAAAgBOprt5PXbyV+s7StpPLa28h5cxMyoXZlInJ776/9nhrzSA/++5S6qUrzwxZcby4Dg8AAACOo85oQ3W6zdQBAAAA4MTZfPdONn/+J1Lv3N42AJUkWe+n3rmdzZ/7XDZXlr/zdl1ZfvaanVrvp95b/vjnOBZMggIAAIDj6MVTzdQZn2imDgAAAAAnyubKcuobr+18wcZG6uuvZjNJa2p669q8Jjx80Ewdhp5JUAAAAHAMlanpZKQzWJGRTsrp6WYaAgAAAODEqKv3Uxdu7G3two3UB+8l/SfNNNNfa6YOQ08ICgAAAI6h0h1LOTMzWI2zMyndsWYaAgAAAODEqIu3ko2NvS3e2EhdvJl0RptpptNtpg5DTwgKAAAAjqlyYTZpt/e2uN1OOT/bbEMAAAAAHHt17XHqO0uD1bi7lHzyU430k/GJZuow9ISgAAAA4JgqE5Mp89f2tnb+esrEZMMdAQAAAHDc1ZXlZL0/WJH1fmpJMtIZrM5IJ+X09GA1ODL2eBwUAAAAOEh17fHWBtKj1aT/ZGsc+PiplNPTz72yrjU1nc0kdeHGzkaQt9sp89fTmnqlueYBAAAAODkerTZSpvzu15MzM6l3bu+9xtmZ5+6dcbwIQQEAAMAQq6v3UxdvbY0Q3+YEXe0tpJyZSbkw+8zJTa2p6dSXXk5dvLk1Sny7k3gjna1NofPPrgMAAAAAH6v/pKE6aykX/pvUt9/a2eG+p7XbKednm+mFI0EICgAAAIbU5rt3Ut/8wvM3edb7qXdup779Vsr8tbSmth/vXSYmUy5fTb10JfXecvLwQdJfSzrdZHwiZepcymh3n34TAAAAAE6MzmhDdbpbe1rz11Jff3XXy8v8dYf9ThghKAAAABhCmyvLqW+8tvMFGxupr7+azeSZQagkKd2xlHMXB28QAAAAALbz4qlm6oxPJNna69pMUhdu7GwiVLudMn89ralXmumDI6N12A0AAAAAH1ZX729t6uxl7cKN1AfvNdwRAAAAAOxMmZpORjqDFRnppJz+7kG/1tR0Wj/7Cyk/cvHZtUc6KT9yMa2f/QUBqBPKJCgAAAAYMnXx1s5OtW1nYyN18WbK5avNNgUAAAAAO1C6YylnZlLv3N57jbMzKd2xD783MZly+WrqpSup95aThw+S/lrS6SbjEylT51JGu4O2zxEmBAUAAABDpK49Tn1nabAad5dSL135yEYRAAAAAByEcmE29e239nbQr91OOT/77NrdsZRzFwfojuPKdXgAAAAwROrKcrLeH6zIen/rNBwAAAAAHIIyMZkyf21va+evp0xMNtwRJ4FJUAAAADBMHq02U+fhg2bqAAAAAMAetKams5mkLtzY2USodjtl/npaU6/se28cT0JQAAAAMEz6Txqqs9ZMHQAAAADYo9bUdOpLL6cu3ky9u7T9BPSRTsrZmZTzsyZAMRAhKAAAABgmndGG6nSbqQMAAAAAAygTkymXr6ZeupJ6b3lrgnl/bWv/anwiZepcyqi9LAYnBAUAAADD5MVTzdQZn2imDgAAAAA0oHTHUs5dPOw2OMZah90AAAAA8F1lajoZ6QxWZKSTcnq6mYYAAAAAAI4AISgAAAAYIqU7lnJmZrAaZ2dSumPNNAQAAAAAcAQIQQEAAMCQKRdmk/Yeb7Bvt1POzzbbEAAAAADAkNvjjioAAACwX8rEZMr8tdTXX9392vnrKROT+9AVAAAAACdRXXucurKcPFpN+k+Szmgyfirl9LRp5AwVISgAAAAYQq2p6WwmqQs3ko2Nj1/QbqfMX09r6pV97w0AAACA46+u3k9dvJX6zlKy3v/o572FlDMzKRdmHcpjKAhBAQAAwD4a5KRca2o69aWXUxdvpt5d2nazKSOdlLMzKedtNgEAAADQjM1376S++YXnH85b76feuZ369lsp89fSmpo+uAZhG0JQAAAAsA+aOilXJiZTLl9NvXQl9d5y8vBB0l9LOt1kfCJl6lzKaHcffxMAAAAATpLNleXUN17b+YKNjdTXX81mIgjFoRKCAgAAgIbtx0m50h1LOXex4U4BAAAA4Lvq6v3UhRt7W7twI/Wll00r59C0DrsBAAAAOE6+c1LueQGoD/r2SbmV5f1tDAAAAAA+Rl28tfN9radtbKQu3my2IdgFk6AAAACgIU7KAQAAADAs6trj1JXl5NFq0n+SdEaT8VMpp6dTumPbP//O0mA/8+5S6qUr29aH/SYEBQAAAA1p4qRcuXy12aYAAAAAOFHq6v3UxVtbgab1/kc/7y2knJlJuTD7oQN5dWV52+d3Zb2fem855dzFwerAHrgODwAAABrQ2Em5tcfNNAQAAADAibP57p1s/vxPpN65/exA03o/9c7tbP7c57K5svzd9x+tNtPEwwfN1IFdEoICAACABjR5Ug4AAAAAdmtzZTn1jdd2Pql8YyP19Ve/G4TqP2mmkf5aM3Vgl4SgAAAAoAlOygEAAABwSOrq/dSFG3tbu3Aj9cF7SWe0mWY63WbqwC4JQQEAAEATnJQDAAAA4JDUxVs7nwD1tI2N1MWbyYunmmlmfKKZOrBLQlAAAADQBCflAAAAADgEde1x6jtLg9W4u5T8Fz+cjHQGa2akk3J6erAasEdCUAAAANAEJ+UAAAAAOAR1ZTlZ7w9WZL2f/N+/nnJmZqAy5exMSndssF5gj4SgAAAAoAFlatpJOQAAAAAO3qPVZuo8fJByYTZpt/e2vt1OOT/bTC+wB3v85gIAAMDxVtceb52ie7Sa9J9sXXc3firl9PS2p9lKdyzlzEzqndt7/plOygEAAACwa/0nDdVZS5mYTJm/lvr6q7teXuavp0xMNtML7IEQFAAAAHxAXb2fungr9Z2lbceI195CypmZlAuzH9nUKRdmU99+K9nY2P0PdlIOAAAAgL3ojDZUp5skaU1NZzNJXbixs32udjtl/npaU6800wfskRAUAAAAfMvmu3dS3/zC8zd31vupd26nvv1Wyvy1tKa+e32dk3IAAAAAHLgXTzVTZ3ziO//amppOfenl1MWbqXeXtj0smJHO1mTz8x89LAiHQQgKAAAAkmyuLKe+8drOF2xspL7+ajaTDwWhnJQDAAAA4CCVqenULy5sH1TaqZFOyunpD71VJiZTLl9NvXQl9d5y8vBB0l/bmhg1PpEydS5ltDtg99AcISgAAABOvLp6fyu0tJe1CzdSX3r5Q6fdnJQDAAAA4KCU7ljKmZnUO7f3XuPsTEp37Nn1z13cc204KEJQAAAAnHh18dbOpjZtZ2MjdfFmyuWrH3rbSTkAAAAADkq5MJv69lt72+Nqt1POzzbfFBwwISgAAABOtLr2OPWdpcFq3F1KvXRl29NyTsoBAAAAsN/KxGTK/LXU11/d/dr56yaVcyy0DrsBAAAAOEx1ZXn76+p2Y72/Ne0JAAAAAA5Ja2o65bM/mbR3OA+n3U757E+lNfXK/jYGB8QkKAAAAE62R6vN1Hn4oJk6AAAAALBHranp1JdeTl28mXp3afvDfyOdlLMzKednTYDiWBGCAgAA4GTrP2mozlozdQAAAABgAGViMuXy1dRLV7amlz98sLV31ekm4xMpU+dSRruH3SY0TggKAACAk60z2lAdG0cAAAAADI/SHUs5d/Gw24AD0zrsBgAAAOBQvXiqmTrjE83UAQAAAABg14SgAAAAONHK1HQy0hmsyEgn5fR0Mw0BAAAAALBrQlAAAACcaKU7lnJmZrAaZ2dSumPNNAQAAAAAwK4JQQEAAHDilQuzSbu9t8Xtdsr52WYbAgAAAABgV4SgAAAAOPHKxGTK/LW9rZ2/njIx2XBHAAAAAADsxh6PuQIAAMDx0pqazmaSunAj2dj4+AXtdsr89bSmXtn33gAAAAAAeD4hKAAAAPiW1tR06ksvpy7eTL27lKz3P/rQSCfl7EzK+VkToAAAAAAAhoQQFAAAAHxAmZhMuXw19dKV1HvLycMHSX8t6XST8YmUqXMpo93DbhMAAAAAgA8QggIAAIBtlO5YyrmLh90GAAAAAAA70DrsBgAAAAAAAAAAAAZhEhQAAAAAAAAAwIDq2uPUleXk0WrSf5J0RpPxUymnp1O6YwdeB04aISgAAAAAAAAAgD2qq/dTF2+lvrOUrPc/+nlvIeXMTMqF2ZSJyX2vAyeVEBQAAAAAAAAAwB5svnsn9c0vJBsbz35ovZ9653bq22+lzF9La2p63+rASSYEBQAAwLFiXDgAAAAAB2FzZTn1jdd2vmBjI/X1V7OZfCjA1FQdOOmEoAAAADgWjAsHAAAA4KDU1fupCzf2tnbhRupLL6dMTDZWB0hah90AAAAADGrz3TvZ/PmfSL1ze9sAVJLvjAvf/LnPZXNl+WAbBAAAAOBYqYu3nn913fNsbKQu3my0DiAEBQAAwBH3nXHhO90s+va4cEEoAAAAAPagrj3emkY+SI27S9n82lcbqVPXHg9UA44LISgAAACOrIHHhT94r+GOAAAAADju6srys6eR79R6P/UX/7dm6txz2A8SISgAAACOMOPCAQAAADhwj1abqfM7X2mmzsMHzdSBI04ICgAAgCOpqbHjxoUDAAAAsCv9J83UGXQK1Lf115qpA0ecEBQAAABHUmNjx40LBwAAAGA3OqPN1BnpNFOn022mDhxxQlAAAAAcTU2NHTcuHAAAAIDdePFUM3X+0+9rps74RDN14IgTggIAAOBoamrsuHHhAAAAAOxCmZoefIrTSCflL/54M3VOTw9WA44JISgAAACOpqbGjhsXDgAAAMAulO5YypmZwWqcnUnrU9/bSJ3SHRuoBhwXQlAAAAAcTU2NHTcuHAAAAIBdKhdmk3Z7b4vb7ZTzs43WAYSgAAAAOKIaGztuXDgAAAAAu1QmJlPmr+1t7fz1lInJRusAQlAAAAAcUU2NHTcuHAAAAIC9aE1Np3z2J3c+yandTvnsT6U19cq+1IGTbo8z1QAAAODwlQuzqW+/lWxs7H6xceEAAAAADKg1NZ360supizdT7y4l6/2PPjTS2TqMd372mZObmqoDJ5kQFAAAAEfWt8eF19df3f1a48IBAAAAaECZmEy5fDX10pXUe8vJwwdJfy3pdJPxiZSpcymj3QOrAyeVEBQAAABDoa49Tl1ZTh6tJv0nSWc0GT+Vcnr6uVfWtaams5mkLtzY2USodjtl/rpx4QAAAAA0qnTHUs5dHJo6cNIIQQEAAHCo6ur91MVbqe8sbTvmu/YWUs7MpFwwLhwAAAAAgO0JQQEAAHBoNt+9k/rmF54/wWm9n3rndurbb6XMX0tranrbx4wLBwAAAAA4uYSgAAAAOBSbK8upb7y28wUbG6mvv5rN5JlBqMS4cAAAAACAk6h12A0AAABw8tTV+6kLN/a2duFG6oP3Gu4IAAAAAICjTAgKAACAA1cXbz3/Crzn2dhIXbzZbEMAAAAAABxpQlAAAAAcqLr2OPWdpcFq3F1KXXvcTEMAAAAAABx5QlAAAAAcqLqynKz3Byuy3k+9t9xMQwAAAAAAHHlCUAAAABysR6vN1Hn4oJk6AAAAAAAceUJQAAAAHKz+k4bqrDVTBwAAAACAI6992A3wXXNzc38nyecHKPFPe73eX2mmGwAAgH3SGW2oTreZOgAAAAAAHHkmQQEAAHCwXjzVTJ3xiWbqAAAAAABw5AlBAQAAcKDK1HQy0hmsyEgn5fR0Mw0BAAAAAHDkuQ5vuP1Ykru7eP7396sRAACAppTuWMqZmdQ7t/de4+xMSneswa4AaMLc3NzfSfL5AUr8016v91ea6QYAAAA4SYSghtvv9Hq93zrsJgAAAJpWLsymvv1WsrGx+8Xtdsr52eabAgAAAADgyHIdHgAAAAeuTEymzF/b29r56ykTkw13BAAAAADAUWYSFAAAAIeiNTWdzSR14cbOJkK12ynz19OaemXfewOgMT+W5O4unv/9/WoEAAAAON6EoAAAANizuvY47//6r+Ybv/eNbD55nNboWN7/o9+T+kN/JqU79rHrW1PTqS+9nLp4M/XuUrLe/+hDI52UszMp52dNgAI4en6n1+v91mE3AQAAHD917XHqynLyaDXpP0k6o8n4qZTT0zvalwKOHyEoAAAAdq2u3k9dvJX6zlI21/v5vacf+OdvpJyZSbnw8cGlMjGZcvlq6qUrqfeWk4cPkv5a0ukm4xMpU+dSRrv79rsAAAAAcHR8cF9quwN1tbew430p4HgRggIAAGBXNt+9k/rmF55/hd16P/XO7dS330qZv5bW1PTH1i3dsZRzFxvsFAAAAIDjZL/2pYDjQQhquP31ubm5n0nyA0k+leSbSb6W5LeT/Oskt3u93vIh9gcAAJwwmyvLqW+8tvMFGxupr7+azcSGEwAAAAB7Zl8K+Ditw26A5/pLSc4nOZVkJMknknx/knNJfjrJnbm5uZW5ubkLh9ciAABwUtTV+6kLN/a2duFG6oP3Gu4IAAAAgJPAvhSwEyZBHX2nk3xpbm7u7yb5mV6vV5ssPjc392KS793p85/5zGe+53Of+9yH3nvhhRfSag1v3u6FF1547ms4inyvOW58pzlufKc5qr75K7/0/FHjz7OxkfzKL6X943+j2aZgnxzE3+pSSuM1AQAA4Diqi7cG2peqizdTLl9ttilg6AhBDaevJPnlJO8m+c0kX0+yma0r8X4oyV9I8uc/8HzJ1mSoVpK/3XAvV5N8fqcPf/nLX/7Ie9/7vTvOUA2FT33qU4fdAjTO95rjxnea48Z3mqNg8/HvZ/Xu0kA16q/9Sv7Yf/9TaY19opmm4AD5Ww179tfn5uZ+JskPZGtv65tJvpbkt5P86yS3e73e8iH2BwAADLm69jj1naXBatxdSr10JaU71kxTwFASghou72Yr3PSvnjPR6e0k/2Bubu50kn+W5E984LO/NTc3d7fX6/2Lfe4TAAA4Ydbu/MvU/pOBatT+k6zd+VI+8aOXGuoKgCPgLz31eiTJJ5J8f5JzSX56bm7uXpK/3ev13jro5gAAgOFXV5aT9f5gRdb7qfeWU85d3P5nrD3O+7/+q/nG730jm08epzU6lvf/6Pek/tCfEZyCI0QIaoj0er1f3sWz9+bm5s4m+bUk//kHPvp7c3Nzt3q93vuNNwgAAJxYG195r5k6q83UAeBYOZ3kS3Nzc383yc8853DgnszNzb2YZMejyj/zmc98z+c+97kPvffCCy+k1Wo12VajXLfMceR7zXHjO81x4zvNQdr46u+kif/43frqw7TbH45IbH7lft5/619k89d+JZvr/fze04v++Rtp/Vd/Ni/81/9tWqf+eANdwME5iL/VpZTGaw5CCOoI6/V6X5+bm/uxJPeydSVekvypJH82SVMn5/5Rki/u9OFPf/rT35PkQyPMv/rVr6bWRveuGvXCCy986FqDr33ta3n/fRkyjjbfa44b32mOG99pjqJvfv1rjdR5/LX/kD949KiRWrCfDuJvdSnlyF0hD7vwlSS/nK3J57+Z5OtJNrN1Jd4PJfkL2ZqI/m0lyU8naSX52w33cjXJ53f68Je//OWPvHfU/rfqCk+OI99rjhvfaY4b32n209dLzeMG6nSymf/kxRe/83rt//qX+dr/+neSjW8+e9F6P5tL/0c2//Vb+dT/8D+le+7PNdAJHI6T8LdaCOqI6/V6vzE3N/elfHjT6GIaCkH1er1HSXbzXyg+siP0/vvvZ3Nzs4l2DsT777+fjY2Nw24DGuV7zXHjO81x4zvNUVBHRhqq0/F950jaj7/VwzxVBgbwbrb2qf7VcyY6vZ3kH8zNzZ1O8s+S/IkPfPa35ubm7vZ6vX+xz30CAABHRGu0mevoWt1PfOff1+58KV/7X/7HnS/e+Ga+9j//dJIIQsEQs9t2PNx+6vWnD6ULAADg+HrxVDN1xieaqQPAUOr1er/c6/W+tJMr7Xq93r0kZ5P8P0999Pfm5ubcpwIAACRJ2t832UydU1t1vnn//83X/v6Oh8V+yNf+/ufzzfv/rpF+gOaZBHU8/NZTr4/WfG4AAGDolanp1C8uJOv9vRcZ6aScnm6uKQCOvF6v9/W5ubkfS3IvW1fiJcmfSvJn09Ck8yT/KMkXd/rwpz/96e9JsvzB97761a+m1o/NdR0a1y1zHPlec9z4TnPc+E5zkOoP/FAy0hl4X+rxD/yXWXv0KN/83998/hV4z7PxzTzqvZk/9ON/Y++9wAE5iL/VpZShukJeCOp4ePLU69FD6QIAADi2Sncs5cxM6p2nB9HuosbZmZRuM+PLATg+er3eb8zNzX0pW9fofdvFNBSC6vV6j5I82sWSj+zevv/++9nc3GyinQPhumWOI99rjhvfaY4b32n21R8eaWRf6v0/PJL6//3HbP7arwzUzubb/2e++d/9ZftcHDn78be61RquC+iGqxv26o899fo/HEoXAADAsVYuzCbtPZ6labdTzs822xAAx8nT/zXj04fSBQAAMJSa2peqK8uDTZRKkvV+6r3lj38OOHBCUMfDmaderx5KFwAAwLFWJiZT5q/tbe389ZSJyYY7AuAY+a2nXg/PLH0AAODQNbYv9aih/5T+8EEzdYBGuQ7viJubm+skufTU20uH0AoAAHCE1LXHWyffHq0m/SdJZzQZP5Vyevq5o7xbU9PZTFIXbiQ7GZ3cbqfMX09r6pXmmgfgOHry1OvRQ+kCAAAYWo3sS/Wf/r8ee9Rfa6YO0CghqKPvbyb5vg+8fj/JLx1SLwAAwJCrq/dTF2+lvrO07ejv2ltIOTOTcmH2mZObWlPTqS+9nLp4M/Xu9nUy0kk5O5Ny/tl1AOAD/thTr//DoXQBAAAMtYH3pToNnbfodJupAzRKCGpIzM3N/eUkX+r1eg93seavJfn8U2//k16v99uNNgcAABwLm+/eSX3zC88/KbfeT71zO/Xtt1Lmr6U1Nb3tY2ViMuXy1dRLV9L6jbcz+nvfyOba76fV/USe/JFPZvOHP5MyajMIgB0789Trhu6oAAAAjpsP7kvVe8tbV9P117aCSeMTKVPnnr0v9eKpZpoYn2imDtAoIajh8VeT/OO5ubkvJuklWer1eo+3e3Bubu50kp9O8hef+ugrSX5mX7sEAACOpM2V5dQ3Xtv5go2N1NdfzWbyzCBUkpTuWF6Y+dF88sUXv/PeHzx6lLqTkeQAkGRubq6T5NJTby8dQisAAMARUrpjKecu7m7N1HTqFxe2nyC1UyOdlNPP3i8DDo8Q1HAZTfLj3/pnc25u7t8m+a0k/zFb19x9KsmfTjK+zdqvJ7nY6/V+52BaBQAAjoq6ej914cbe1i7cSH3pZVfaAbCf/maS7/vA6/eT/NIh9QIAABxjpTuWcmYm9c7tvdc4O5PSHWuwK6AprcNugGdqJfmTSf58krkkP5bkz2X7ANRikj/d6/X+zcG1BwAAHBV18dbzr8B7no2N1MWbzTYEwLE0Nzf3l+fm5rbbu3remr+W5PNPvf1Per3ebzfXGQAAwHeVC7NJe4/zYtrtlPOzzTYENMYkqOHxC9m6zu4zSb5/B88/TvKlJP+w1+st7mdjAADA0VXXHqe+szRYjbtLqZeuOOEGwMf5q0n+8dzc3BeT9JIs9Xq9x9s9ODc3dzrJTyf5i0999JUkP7OvXQIAACdamZhMmb+W+vqru187f93EdBhiQlBDotfr/WKSX0ySubm5Tyb5wSST2Zr81M3WZKhvJPndJL+Z5Mu9Xu/9w+kWAAA4KurKcrLeH6zIej/13nLKuYvNNAXAcTaa5Me/9c/m3Nzcv03yW0n+Y7auuftUkj+d7aedfz3JxV6v9zsH0yoAAHBStaams5mkLtzY2QT1djtl/npaU6/se2/A3glBDaFer/eNJL962H0AAADHwKPVZuo8fNBMHQBOklaSP/mtfz7OYpK/0uv1/v3+tgQAALClNTWd+tLLqYs3U+8ubX+QcKSTcnYm5fysCVBwBAhBAQAAHGf9Jw3VWWumDgDH2S9k6zq7zyT5/h08/zjJl5L8w16vt7ifjQEAAGynTEymXL6aeulKWr/xdkZ/7xvZXPv9tLqfyJM/8sls/vBnUka7h90msENCUAAAAMdZZ7ShOjZ7AHi+Xq/3i0l+MUnm5uY+meQHk0xm6+q7brYmQ30jye8m+c0kX+71eu8fTrcAAADfVbpjeWHmR/PJF1/8znt/8OhR6k6uygOGhhAUAADAcfbiqWbqjE80UweAE6HX630jya8edh8AAADAydE67AYAAADYP2VqOhnpDFZkpJNyerqZhgAAAAAAYB8IQQEAABxjpTuWcmZmsBpnZ1K6Y800BAAAAAAA+0AICgAA4JgrF2aT9h5vQ2+3U87PNtsQAAAAAAA0TAgKAADgmCsTkynz1/a2dv56ysRkwx0BAAAAAECz9ngUGAAAgKOkNTWdzSR14UaysfHxC9rtlPnraU29su+9AQAAAADAoISgAAAAjpC69jh1ZTl5tJr0nySd0WT8VMrp6ZTu2HPXtqamU196OXXxZurdpWS9/9GHRjopZ2dSzs+aAAUAAAAAwJEhBAUAAHAE1NX7qYu3Ut9Z2ja8VHsLKWdmUi48P7xUJiZTLl9NvXQl9d5y8vBB0l9LOt1kfCJl6lzKaHcffxMAAAAAAGieEBQAAMCQ23z3TuqbX3j+NXbr/dQ7t1Pffitl/lpaU9PPrVm6YynnLjbcKQAAAAAAHA4hKAAAgCG2ubKc+sZrO1+wsZH6+qvZTD42CAUAAAAAAMdF67AbAAAAYHt19X7qwo29rV24kfrgvYY7AgAAAACA4SQEBQAAMKTq4q3nX4H3PBsbqYs3m20IAAAAKYmcZwAAIABJREFUAACGlBAUAADAEKprj1PfWRqsxt2l1LXHzTQEAAAAAABDTAgKAABgCNWV5WS9P1iR9X7qveVmGgIAAAAAgCEmBAUAADCMHq02U+fhg2bqAAAAAADAEBOCAgAAGEb9Jw3VWWumDgAAAAAADDEhKAAAgGHUGW2oTreZOgAAAAAAMMSEoAAAAIbRi6eaqTM+0UwdAAAAAAAYYkJQAAAAQ6hMTScjncGKjHRSTk830xAAAAAAAAwxISgAAIAhVLpjKWdmBqtxdialO9ZMQwAAAAAAMMSEoAAAAIZUuTCbtNt7W9xup5yfbbYhAAAAAAAYUnvcTQcAAGA36trj1JXl5NFq0n+SdEaT8VMpp6efOa2pTEymzF9Lff3VXf+8Mn89ZWJy0LYBAAAAOOL2si8FcBQJQQEAAOyjuno/dfFW6jtLyXr/o5/3FlLOzKRcmN02tNSams5mkrpwI9nY+Pgf2G6nzF9Pa+qVwZsHAAAA4MgadF8K4KgRggIAANgnm+/eSX3zC88PL633U+/cTn37rZT5a2lNTX/kkdbUdOpLL6cu3ky9u7TtplVGOilnZ1LO27QCAAAAOOma2pcCOEqEoAAAAPbB5spy6huv7XzBxkbq669mM9l2w6lMTKZcvpp66UrqveXk4YOkv5Z0usn4RMrUuZTRbnO/AAAAAABHUtP7UgBHhRAUAABAw+rq/a3r6/ayduFG6ksvP3OaU+mOpZy7OEh7AAAAABxT+7kv1ZS69jh1ZTl5tJr0nySd0WT8VMrp6ZTu2L7+bOB4E4ICAABoWF289fxR48+zsZG6eDPl8tVmmwIAAADgSNlLWGiY96Xq6v3UxVup7ywl6/2Pft5bSDkzk3Jhdt+DWMDxJAQFAADQoLr2eGsjZ5Aad5dSL11x8g0AAADgBNprWGi/96UGmeC0+e6d1De/8PyA1no/9c7t1LffSpm/5mo+YNeEoAAAABpUV5a33ZzalfV+6r1l194BAAAAnDCDhIX2a19q0AlOmyvLqW+8tvMeNjZSX381m4kgFLArQlAAAABNerTaTJ2HD5qpAwAAAMCRMHBYaB/2pQad4FRX76cu3NhTG3XhRupLL7saD9ix1mE3AAAAcKz0nzRUZ62ZOgAAAAAMvYHDQg/ea3xf6juhrOcFoD7o26GsleXv9rZ4a+frt6u3eHNva4ETSQgKAACgSZ3Rhup0m6kDAAAAwNBrJCzU4L5UE6GsuvZ46wq9AdS7S6lrjweqAZwcQlAAAABNevFUM3XGJ5qpAwAAAMBQayoslE9+qpF+Mj7RSCirriwn6/3Belnvp95b/vjnACIEBQAA0KgyNZ2MdAYrMtJJOT3dTEMAAAAADLXGwkIljexL5Qd/uJkJTl/57cF6+baHD5qpAxx7QlAAAAANKt2xlDMzg9U4O5PSHWumIQAAAACG26PVRsqU3/16I/tS+Te/3kgoK//+3w1W49v6a83UAY49ISgAAICGlQuzSbu9t8Xtdsr52WYbAgAAAGB49Z80VGetmX2phkJZ+YM/aKZOp9tMHeDYE4ICAABoWJmYTJm/tre189dTJiYb7ggAAACAodUZbahOt5l9qaZCWX/oDzdTZ3yimTrAsScEBQAAsA9aU9Mpn/3JnZ+8a7dTPvtTaU29sr+NAQAAADBcXjzVTJ1vhYUG3pdqKpQ1+Z8lI53Baox0Uk5PN9MPcOztcQ4eAADAyVDXHqeuLG+NAe8/2doEGj+Vcno6pTv23LWtqenUl15OXbyZencpWe9/9KGRTsrZmZTzsyZAAQAAAJxAZWo69YsL2+8d7dRTYaGB9qUaCmWVl/54cmYm9c7tvdc4O/Oxe3AA3yYEBQAAsI26ej918VbqO0vbbhLV3kLKmZmUC88PL5WJyZTLV1MvXUm9t5w8fJD015JONxmfSJk6lzLa3cffBAAAAIBhVrpjKfsQFtrrvlSjoaw/8YOpb7+VbGzsvka7nXJ+du89ACeOEBQAAMBTNt+9k/rmF56/ObPeT71zO/Xtt1Lmr6U19fyx3KU7lnLuYsOdAgAAAHAclAuz+xYW2u2+VKOhrO5Yyvy11Ndf3X2N+esmpwO70jrsBgAAAIbJ5spy6huv7XzDaWMj9fVXs7myvL+NAQAAAHBslYnJlPlre1u7D2GhcmE2ae9xpspToazW1HTKZ39y5/Xa7ZTP/lRaU6/s7ecDJ5YQFAAAwLfU1fupCzf2tnbhRuqD9xruCAAAAICTYpjCQk2HslpT02n97C+k/MjFZKSz/cKRTsqPXEzrZ39BAArYE9fhAQAAfEtdvLW3kePJ1kSoxZspl6822xQAAAAAJ0Zrajr1pZdTF2+m3l1K1vsffWiks3Xd3PnZfb0urjU1nc1sHf7b0Z5Zu50yf/2ZAaYyMZly+WrqpSup95aThw+S/lrS6SbjEylT51JGu83+EsCJIgQFAACQpK49Tn1nabAad5dSL11J6Y410xQAAAAAJ84whYX2I5RVumMp5y423yxw4glBAQAAJKkry9tv4uzGej/13rJNHAAAAAAGNixhoWEKZQE8jxAUAABAkjxababOwwfN1AEAAACAITIsoSyAZ2kddgMAAABDof+koTprzdQBAAAAAAB2TAgKAAAgSTqjDdUx+hsAAAAAAA6a6/AAAACS5MVTzdQZn2imDgAAAABHUl17nLqynDxa3Zo+3hlNxk+lnJ5O6Y4ddnsAx5YQFAAAQJIyNZ36xYVkvb/3IiOdlNPTzTUFAAAAwJFRV++nLt5KfWdp2z2m2ltIOTOTcmE2ZWLy4BsEOOZchwcAAJCkdMdSzswMVuPsjNN8AAAAACfQ5rt3svnzP5F65/azD9mt91Pv3M7mz30umyvLB9sgwAkgBAUAAPAt5cJs0t7jwNx2O+X8bLMNAQAAADD0NleWU994LdnY2NmCjY3U118VhAJomBAUAADAt5SJyZT5a3tbO3/dGHMAAACAE6au3k9duLG3tQs3Uh+813BHACeXEBQAAMAHtKamUz77kzufCNVup3z2p9KaemV/GwMAAABg6NTFWzufAPW0jY3UxZvNNgRwgu3xngcAAIDjqzU1nfrSy6mLN1PvLiXr/Y8+NNJJOTuTcn7WBCgAAACAE6iuPU59Z2mwGneXUi9dSemONdMUwAkmBAUAALCNMjGZcvlq6qUrqfeWk4cPkv5a0ukm4xMpU+dSRruH3SYAAAAAh6SuLG9/eG431vup95ZTzl1spimAE0wICgAAOJbq2uOtjahHq0n/SdIZTcZPpZye3tXJutIdswkFAAAAwEc9Wm2mzsMHzdQBOOGEoAAAgGOlrt5PXby1NYp8m5N4tbeQcmYm5YJr7AAAAAAYQP9JQ3XWmqkDcMIJQQEAAMfG5rt3Ut/8QrKx8eyH1vupd26nvv1Wyvy1tKamD65BAAAAAI6PzmhDdbrN1AE44YSgAACAY2FzZTn1jdd2vmBjI/X1V7OZCEIBAAAAsHsvnmqmzvhEM3UATrjWYTcAAAAwqLp6P3Xhxt7WLtxIffBewx0BAAAAcNyVqelkpDNYkZFOymkH9ACaIAQFAAAceXXx1vOvwHuejY3UxZvNNgQAAADAsVe6YylnZgarcXYmpTvWTEMAJ5wQFAAAcKTVtcep7ywNVuPuUura42YaAgAAAODEKBdmk3Z7b4vb7ZTzs802BHCCCUEBAABHWl1ZTtb7gxVZ76feW26mIQAAAABOjDIxmTJ/bW9r56+nTEw23BHAybXHSCoAAMCQeLTaTJ2HD5qpAwAAAMCJ0pqazmaSunAj2dj4+AXtdsr89bSmXtn33gBOEiEoAADgaOs/aajOWjN1AAAAADhxWlPTqS+9nLp4M/Xu0vaTy0c6KWdnUs7PmgAFsA+EoAAAgKOtM9pQnW4zdQAAAAA4kcrEZMrlq6mXrqTeW96aPN5f29p3Gp9ImTqXMmoPCmC/CEEBAABH24unmqkzPtFMHQAAAABOtNIdSzl38bDbADhxWofdAAAAwCDK1HQy0hmsyEgn5fR0Mw0BAAAAAAAHTggKAAA40kp3LOXMzGA1zs6kdMeaaQgAAAAAADhwQlAAAMCRVy7MJu093vbdbqecn222IQAAAAAA4EAJQQEAAEdemZhMmb+2t7Xz11MmJhvuCAAAAAAAOEh7PCoNAAAwXFpT09lMUhduJBsbH7+g3U6Zv57W1Cv73hsAAAAAALC/hKAAAIBjozU1nfrSy6mLN1PvLiXr/Y8+NNJJOTuTcn7WBCgAAAAAADgmhKAAAIChUtcep64sJ49Wk/6TpDOajJ9KOT2d0h372PVlYjLl8tXUS1dS7y0nDx8k/bWk003GJ1KmzqWMdg/gN+H/Z+/+g+vM97uwv5+zSiRLSUgIrCOzhqU/hoYpaUmssUlWvip2kw0Tt2VneGDAYRMBm+J0Zq/d3BumwG24NyUFb/ANhQvsTnRhLpTMcwsE7FInXRVFahZ75aRpZvojKSWJt5VrZ5K5cCP5KDl7vv1D3nu9u7ZXPx7pSEev14xHR4/O93PemnnG4/n48/0+AAAAAACwVwxBAQAA+0JZuZ0yfy3l5sIjT3AqzVyqkzOpzm7uBKdqfCLV6ed3ISkAAAAAALDfGIICAAAGrv/mYsqnP5n0eo9/03o3ZfF6yhuvp5q9mM7U9N4FBAAAAAAA9jVDUAAAwED1l5dSXntl8wt6vZRXL6efGIQCAAAAAACSJJ1BBwAAAA6vsnI7Ze7K9tbOXUm581bLiQAAAAAAgIPIEBQAADAwZf7akx+B9yS9Xsr81XYDAQAAAAAAB5IhKAAAYCDK2mrKzYWd1bixkLK22k4gAAAAAADgwDIEBQAADERZXkrWuzsrst5NubXUTiAAAAAAAODAMgQFAAAMxr2VdurcvdNOHQAAAAAA4MAyBAUAAAxG935LddbaqQMAAAAAABxYhqAAAIDBGDvSUp3xduoAAAAAAAAHliEoAABgMJ4+1k6do5Pt1AEAAAAAAA4sQ1AAAMBAVFPTyejYzoqMjqU6Md1OIAAAAAAA4MAyBAUAAAxENT6R6uTMzmqcmkk1PtFOIAAAAAAA4MAyBAUAAAxMdfZcMjKyvcUjI6nOnGs3EAAAAAAAcCAZggIAAAammjyeavbi9tbOXko1ebzlRAAAAAAAwEG0zS3XAAAA7ehMTaefpMxdSXq9D14wMpJq9lI6U8/tejYAAAAAAOBgMAQFAAAMXGdqOuWZZ1Pmr6bcWEjWu+9/0+hYqlMzqc6ccwIUAAAAAADwLoagAACAfaGaPJ7q/IWUF15MubWU3L2TdNeSsfHk6GSqqdOpjowPOiYAAAAAALAPGYICAAD2lWp8ItXp5wcdAwAAAAAAOEAMQQEAAK0oa6spy0vJvZWkez8ZO5IcPZbqxHSq8YlBxwMAAAAAAIaYISgAAGBHysrtlPlrKTcXkvXu+3/ezKU6OZPq7LlUk8f3PiAAAAAAADD0DEEBAADb1n9zMeXTn0x6vce/ab2bsng95Y3XU81eTGdqeu8CAgAAAAAAh4IhKAAAYFv6y0spr72y+QW9Xsqrl9NPDEIBAAAAAACt6gw6AAAAcPCUldspc1e2t3buSsqdt1pOBAAAAAAAHGZOggIAALaszF978iPwnqTXS5m/mur8hXZDAQAAAHAolbXVlOWl5N5K0r2fjB1Jjh5LdWI61fjEoOMBsEcMQQEAAFtS1lZTbi7srMaNhZQXXtSEAgAAAGDbysrtlPlrG72q9e77f97MpTo5k+rsuVSTx/c+IAB7yuPwAACALSnLS49sKm3Jejfl1lI7gQAAAAA4dPpvLqb/iQ+nLF5/fK9qvZuyeD39j7+c/rJeFMCwcxIUAACwNfdW2qlz9047dQAAAAA4VPrLSymvvbL5Bb1eyquX00/SmZretVwADJaToAAAgK3p3m+pzlo7dQAAAAA4NMrK7ZS5K9tbO3cl5c5bLScCYL8wBAUAAGzN2JGW6oy3UwcAAACAQ6PMX0t6ve0t7vVS5q+2GwiAfcMQFAAAsDVPH2unztHJduoAAAAAcCiUtdWUmws7q3FjIWVttZ1AAOwrhqAAAIAtqaamk9GxnRUZHUt1YrqdQAAAAAAcCmV5KVnv7qzIejfl1lI7gQDYVwxBAQAAW1KNT6Q6ObOzGqdmUo1PtBMIAAAAgMPh3ko7de7eaacOAPvKyKADAAAAB0919lzKG68nvd7WF4+MpDpzrv1QAAAAABwYZW1142SneytJ934ydiQ5eizVienHb57r3m/nw7tr7dQBYF8xBAUAAGxZNXk81ezFlFcvb33t7KVUk8d3IRUAAAAA+11ZuZ0yfy3l5sIjH21XmrlUJ2dSnT33/h7S2JF2QoyNt1MHgH3FEBQAABxi29px90Bnajr9JGXuyuZOhBoZSTV7KZ2p59oJDwAAAMCB0n9zMeXTn3xyL2m9m7J4PeWN11PNXkxnavqLP3v6WDtBjk62UweAfcUQFAAAHEI72nH3kM7UdMozz6bMX0258ehaGR1LdWom1Zkn1wIAAABgePWXl1Jee2XzC3q9lFcvp598YRCqmppO+ezco3tQmzU6lurE9Ae/D4ADxxAUAAAcMjvecfce1eTxVOcvpLzwYsqtpeTunaS7tnGs+NHJVFOnUx1xxDgAAADAYVVWbm+cJr6dtXNXUp55dqMHNT6R6uRMyuL1bWepTs184AnoABxMhqAAAOAQaWPH3eNU4xOpTj+/s4AAAAAADJ0yf+3JG/KepNdLmb+a6vyFJEl19lzKG69vr97ISKoz57aXA4B9rzPoAAAAwN7Y8Y67O2+1nAgAAACAYVfWVlNuLuysxo2FlLXVJA9OJZ+9uK061eylVJPHd5QFgP3LEBQAABwSbey4AwAAAICtKMtLyXp3Z0XWuym3lr7wbWdqOtVLH0lGNvngo5GRVC99NJ2p53aWA4B9zePwAADgEGhtx90LL6Yan2gnFAAAAADD795KO3Xu3nnXt52p6ZRnnk2Zv5pyY+HRg1ajY6lOzaQ6c25TJ0CVtdWNoa17K0n3fjJ2JDl6LNWJaT0xgAPAEBQAABwCbe64q04/304oAAAAAIZf935Lddbed6maPJ7q/IWUF17cOCnq7p2N942NJ0cnU02dTnVk/ANLl5XbKfPXNjYRPqKHVpq5VCdnUp3d3DAVAINhCAoAAA6DXdpxBwAAAABPNHakpTqPH2aqxie2vXGv/+Ziyqc/mfR6j3/Tejdl8XrKG6+nmr2YztT0tj4LgN1lCAoAAA6DXdxxBwAAAACP9fSxduocnWynzkP6y0spr72y+QW9Xsqrl9NPDEIB7EOdQQcAAAD2wB7suAMAAACA96qmppPRsZ0VGR1LdaLdoaOycjtl7sr21s5dSbnzVqt5ANg5Q1AAAHAY7OMddwAAAAAMr2p8ItXJmZ3VODWTanyinUAPlPlrT34E3pP0einzV1vNA8DOGYICAIBDYL/uuAMAAABg+FVnzyUjI9tbPDKS6sy5VvOUtdWUmws7q3FjIWVttZ1AALTCEBQAABwC+3XHHQAAAADDr5o8nmr24vbWzl5KNXm81TxleSlZ7+6syHo35dZSO4EAaIUhKAAAOCT22447AAAAAA6PztR0qpc+svn+1MhIqpc+ms7Uc+2HubfSTp27d9qpA0Artvk/IAAAwCCVtdWNHWv3VpLu/WTsSHL0WKoT0489remdHXfl1ctb/rzd2HEHAAAAwOHSmZpOeebZlPmrKTcWHn0a0+jYxonkZ87tXj+qe7+lOmvt1AGgFYagAADgACkrt1Pmr6XcXHhkk6g0c6lOzqQ6++gmUWdqOv0kZe5K0ut98AeOjKSavbQ7O+4AAAAAOHSqyeOpzl9IeeHFjcfJ3b2zMUw0Np4cnUw1dTrVkfHdDTF2pKU6u5wTgC0xBAUAAAdE/83FlE9/8snDS+vdlMXrKW+8nmr2YjpT0+97y77ZcQcAAADAoVWNT6Q6/fxgPvzpY+3UOTrZTh0AWmEICgAADoD+8lLKa69sfkGvl/Lq5fSTRw5C7YsddwAAAAAwANXUdMpn5x69OXCzRsdSnXh/3w2AwTEEBQAA+1xZub3x+LrtrJ27kvLMs489zWmgO+4AAAAAYACq8YlUJ2dSFq9vv8apmVTjEy2mAmCnOoMOAAAAPFmZv/bkR+A9Sa+XMn+13UAAAAAAcMBVZ88lI9s8M2RkJNWZc+0GAmDHDEEBAMA+VtZWU24u7KzGjYWUtdV2AgEAAADAEKgmj6eavbi9tbOXHnvyOgCDYwgKAAD2sbK8lKx3d1ZkvZtya6mdQAAAAAAwJDpT06le+sjmT4QaGUn10kfTmXpud4MBsC3bPN8PAADYE/dW2qlz9047dQAAAABgiHSmplOeeTZl/mrKjYVHb0gcHUt1aibVmXNOgALYxwxBAQDAfta931KdtXbqAAAAAMCQqSaPpzp/IeWFFzdOVL97Z6OfNjaeHJ1MNXU61ZHxQccE4AMYggIAgP1s7EhLdTRpAID9o67rH0nyh99z+Zeapnl2AHEAACBJUo1PpDr9/KBjALBNnUEHAAAAnuDpY+3UOTrZTh0AgB2q6/o/yvsHoAAAAAB2xElQB5CdcgAAh0c1NZ3y2blkvbv9IqNjqU5MtxcKAGCb6rr+yiR/Y9A5AAAAgOHjJKgDxk45AIDDpRqfSHVyZmc1Ts2kGp9oJxAAwM78YJJ3jrr8/CCDAAAAAMPFENQBYqccAMDhVJ09l4xs8xDXkZFUZ861GwgAYBvquj6bZPbBt70kHxtgHAAAAGDIGII6WOyUAwA4hKrJ46lmL25v7eylVJPHW04EALA1dV1PJHntoUt/JcnPDCgOAAAAMIQMQR0QdsoBABxunanpVC99ZPMnQo2MpHrpo+lMPbe7wQAANucHkjz74PW/TPJ9A0sCAAAADKVtPlODvWSnHADA8Chrq3n7p34yn/v859K/v5rOkYm8/RVflfL135hqfOKJaztT0ynPPJsyfzXlxkKy3n3/m0bHUp2aSXXmnBOgAIB9oa7rb0zy3Q9d+q6mae7XdT2oSAAAAMAQMgR1MDxqp9zJQYUBAGDrysrtlPlrKTcX0l/vvv/Zxj/yWqqTM6nOPnl4qZo8nur8hZQXXky5tZTcvZN015Kx8eToZKqp06mOjO/q7wIAsFl1XY8mmcsXT6T/O03TvD7ASAAAAMCQMgS1z9kpBwBw8PXfXEz59CeTXu/xb1rvpixeT3nj9VSzF9OZmn5izWp8ItXp51tOCgDQuu9L8rsevP7lJP/54KIAAAAAw8wQ1D5mpxwAwMHXX15Kee2VzS/o9VJevZx+8oGDUAAA+1ld11+f5HseuvThpml+ZVB5AAAAgOHW+eC3MEDfFzvlAAAOrLJyO2XuyvbWzl1JufNWy4kAAPZGXdcj2djc984mzOtN0/y3A4wEAAAADDknQe1TdsoBABx8Zf7akx+B9yS9Xsr81VTnL7QbCgBgb/yZJP/eg9erSf70oILUdf10kt+62fd/0zd901e9/PLL77r21FNPpdPZv/tJn3rqqSd+DweR+5ph455m2LinGUbua4bNXtzTVVW1XnMnDEHtQ3bKAQAcfGVtNeXmws5q3FhIeeHFVOMT7YQCANgDdV3/7iR/7qFLf75pml8cUJwkuZDkv9zsm3/2Z3/2fdd+62/d9AzVvvDVX/3Vg44ArXNfM2zc0wwb9zTDyH3NsDkM9/T+3b50uO2bnXIAAGxPWV5K1rs7K7LeTbm11E4gAIA9UNd1J8kPJxl9cOmnkvzVwSUCAAAADgsnQe0z+22nnOPC4WByXzNs3NMcRL1f/v/ydgt1Or98NyMj/tnO/ubvaYbRYTwuHFrycpJTD173kvzJpmna+GcRAAAAwBP535R9ZJ/ulHNcOAwB9zXDxj3NQfCrVclqC3XG0s9vfvrpFirB3vH3NMPIfQ0frK7rfyPJ9z906a80TfMzg8rzkE8l+exm3/x1X/d1X5XkXcdx/vIv/3JKKW3nas1TTz31rr+nfuVXfiVvv232jIPNfc2wcU8zbNzTDCP3NcNmL+7pqqr21UyIIaj9xU45AIAh0Tky0U6d8S9rpQ4AwG6q67pK8lqS8QeX/mWS7xtYoIc0TXMvyb0tLHlf9/btt99Ov99vL9Que/vtt9Pr9QYdA1rlvmbYuKcZNu5phpH7mmGzG/f0fnsq2P5Kc4jt451yAABsw8hvO95OnWPt1AEA2GV/Ksnvf+j772qa5v6gwgAAAACHj5Og9oH9vFMujguHA8l9zbBxT3MQla/9+mR0LFnvbr/I6FhWv/b3Zu3eVg4ugL3n72mG0WE8Lhx26C889PqfJvkXdV0/+wFrvuY93488Ys1K0zS/vsNsAAAAwCFgCGp/2Lc75RwXDsPBfc2wcU9zIHzpaKqTMymL17ddojo1k7e/dDRxv3PA+HuaYXQYjguHHTry0Os/kOQXtlHjtz1i3e9N4rR0AAAA4AMZgtof7JQDABhC1dlzKW+8vr0hppGRVGfOtR8KAAAAgEOprK2mLC8l91aS7v1k7Ehy9FiqE9OpxicGHQ8AdswQ1P5gpxwAwD63nSZRNXk81ezFlFcvb/nzqtlLqSaP7zQ2AAAAAIdcWbmdMn8t5eZCst59/8+buVQnZ1KdPacfBcCBZggKAACeYKdNos7UdPpJytyVzZ0INTKSavZSOlPP7Tw8AMAeaZrmK7e6pq7rmST/7KFLv9Q0zbNtZQIAIOm/uZjy6U8+uS+13k1ZvJ7yxuupZi+mMzW9dwEBoEWGoAAA4DHaahJ1pqZTnnk2Zf5qyo2FRw5TZXQs1amZVGfsuAMAAABg5/rLSymvvbL5Bb1eyquX008MQgFwIBmC2gfslAMA2H/abhJVk8dTnb+Q8sKL6fz0Gzny+c+lv/Zr6Yx/We5/+Vem/w3flOrIeHu/AAAAAACHVlm5vXEy+XbWzl1JeebZx27UK2vuHbSTAAAgAElEQVSrefunfjKf+/zn0r+/ms6Ribz9FV+V8vXfmGp8YiexAWBHDEEBAMB77GaTqBqfyFMz35qvfPrpL1z79Xv3UjbzqDwAAAAA2IQyf+3Jp5s/Sa+XMn811fkL7665cjtl/lrKzYX017v5/HvX/chrqU7OpDrrpHMABqMz6AAAALDftNEkAgAAAIBBKGurKTcXdlbjxkLK2uoXvu+/uZj+Jz6csng9We8+etF6N2Xxevoffzn95aUdfT4AbIchKAAAeMhuNIkAAAAAYK+U5aXHDypt1no35dbGIFN/eSnltVc2v2mw10t59bJBKAD2nCEoAAB4SNtNIgAAAADYU/dW2qlz987GI/DmrmxreZm7knLnrXayAMAmjAw6AAAA7CstNokAAHi8pmkWklSDzgEAMHS691uqs5Yyf23zJ0C9V6+XMn811fkL7eQBgA/gJCgAAHhYi00iAAAAANhzY0daKVOe+pKUmws7q3FjIWVttZU8APBBnAR1QNkpBwCwS1pqEmVsvJ06AAAAALAVTx9rp87a55P17s5qrHdTbi2lOv18O5kA4AmcBAUAAA9rq0l0dLKdOgAAAACwBdXUdDI6trMio2PJ+Je1E+junXbqAMAHMAQFAAAPaatJVJ2YbicQAAAAAGxBNT6R6uTMzmqcmkn1dq+dQN21duoAwAcwBAUAAA9prUk0PtFOIAAAAADYoursuWRkZHuLR0ZSnTmXjB1pJ8zYeDt1AOADGIICAID3aKVJBAAAAAADUk0eTzV7cXtrZy+lmjyePH2snTBHJ9upAwAfwBAUAAC8RytNIgAAAAAYoM7UdKqXPrL5zX4jI6le+mg6U88lSaqp6WR0bGchRsdSnZjeWQ0A2KRtbm8HAIDh1pmaTj9JmbuS9HofvGBkJNXspS80iQAAAABg0DpT0ynPPJsyfzXlxkKy3n3/m0bHUp2aSXXm3Ls291XjE6lOzqQsXt/251enZlKNT2x7PQBshSEoAAB4jJ00iQAAAABgP6gmj6c6fyHlhRdTbi0ld+8k3bVkbDw5Oplq6nSqI+OPXnv2XMobr29uk+B7jYykOnNuh+kBYPMMQQEAMNTK2mrK8lJybyXp3k/GjiRHj6U6Mb2pXWg7aRIBAAAAwH5RjU+kOv381tZMHk81ezHl1ctb/7zZSzYNArCnDEEBADCUysrtlPlrKTcXHnmCU2nmUp2cSXV2cyc4badJBAAAAAAHXWdqOv0kZe7K5k6EGhlJNXspnanndj0bADzMEBQAAEOn/+Ziyqc/+eSmzHo3ZfF6yhuvp5q9mM7U9N4FBAAAAIADpDM1nfLMsynzV1NuLDxy02FGx1Kdmkl1ZnObDgGgbYagAAAYKv3lpZTXXtn8gl4v5dXL6ScGoQAAAADgMarJ46nOX0h54cV0fvqNHPn859Jf+7V0xr8s97/8K9P/hm9KdWR80DEBOMQMQQEAMDTKyu2NY7m3s3buSsozz9qlBgAAAMC+UtZWU5aXknsrSfd+MnYkOXos1YnpVOMTe56nGp/IUzPfmq98+ukvXPv1e/dSNvOoPADYRYagAAAYGmX+2pMfgfckvV7K/NVU5y+0GwoAAAAAtqGs3E6Zv5Zyc+GRj58rzVyqkzOpznr8HAAkSWfQAQAAoA1lbXWjIbSTGjcWUtZW2wkEAAAAANvUf3Mx/U98OGXx+iMHoJIk692Uxevpf/zl9JeX9jYgAOxDhqAAABgKZXnp8Q2hzVrvptzSMAIAAABgcPrLSymvvbL5E897vZRXLxuEAuDQMwQFAMBwuLfSTp27d9qpAwAAAABbVFZup8xd2d7auSspd95qOREAHByGoAAAGA7d+y3VWWunDgAAAABsUZm/tvkToN6r10uZv9puIAA4QAxBAQAwHMaOtFRnvJ06AAAAALAFZW015ebCzmrcWEhZW20nEAAcMIagAAAYDk8fa6fO0cl26gAAAADAFpTlpWS9u7Mi692UW0vtBAKAA8YQFAAAQ6Gamk5Gx3ZWZHQs1YnpdgIBAAAAwFbcW2mnzt077dQBgAPGEBQAAEOhGp9IdXJmZzVOzaQan2gnEAAAAABsRfd+S3XW2qkDAAeMISgAAIZGdfZcMjKyvcUjI6nOnGs3EAAAAABs1tiRluqMt1MHAA4YQ1AAAAyNavJ4qtmL21s7eynV5PGWEwEAAADAJj19rJ06RyfbqQMAB4whKAAAhkpnajrVSx/Z/IlQIyOpXvpoOlPP7W4wAAAAAHiCamo6GR3bWZHRsVQnptsJBAAHzDafFQIAAPtXZ2o65ZlnU+avptxYSNa773/T6FiqUzOpzpxzAhQAAAAAA1eNT6Q6OZOyeH37NU7NpBqfaDEVABwchqAAABhK1eTxVOcvpLzwYsqtpeTunaS7loyNJ0cnU02dTnVkfNAxAQAAAOALqrPnUt54Pen1tr54ZCTVmXPthwKAA8IQFAAAQ60an0h1+vlBxwAAAACAD1RNHk81ezHl1ctbXzt7yYnnABxqhqAAANiXytpqyvJScm8l6d5Pxo4kR4+lOjHtSG8AAAAAhlZnajr9JGXuyuZOhBoZSTV7KZ2p53Y9GwDsZ4agAADYV8rK7ZT5ayk3F5L17vt/3sylOjmT6uw5O9sAAAAAGEqdqemUZ55Nmb+acmPhkX2yjI6lOjWT6ow+GQAkhqAAANhH+m8upnz6k0/e4bbeTVm8nvLG66lmL6YzNb13AQEAAABgj1STx1Odv5Dywospt5aSu3eS7loyNp4cnUw1dTrVkfFBxwSAfcMQFAAA+0J/eSnltVc2v6DXS3n1cvqJQSgAAAAAhlY1PpHq9PODjgEA+15n0AEAAKCs3E6Zu7K9tXNXUu681XIiAAAAAAAADhInQQEAMHBl/tqTH4H3JL1eyvzVVOcvtBsKAAAAAHagrK2mLC8l91aS7v1k7Ehy9FiqE9OpxicGHQ8Aho4hKAAABqqsrabcXNhZjRsLKS+8qHkEAAAAwMCVldsp89c2el7r3ff/vJlLdXIm1dlzqSaP731AABhSHocHAMBAleWlRzaDtmS9m3JrqZ1AAAAAALBN/TcX0//Eh1MWrz++57XeTVm8nv7HX05/WU8LANriJCgAAAbr3ko7de7eaacOAAAAAGxDf3kp5bVXNr+g10t59XL6STpT07uWCwAOCydBAQAwWN37LdVZa6cOAAAAAGxRWbmdMndle2vnrqTceavlRABw+BiCAgBgsMaOtFRnvJ06AAAAALBFZf5a0uttb3GvlzJ/td1AAHAIGYICAGCwnj7WTp2jk+3UAQAAAIAtKGurKTcXdlbjxkLK2mo7gQDgkDIEBQDAQFVT08no2M6KjI6lOjHdTiAAAAAA2IKyvJSsd3dWZL2bcmupnUAAcEgZggIAYKCq8YlUJ2d2VuPUTKrxiXYCAQAAAMBW3Ftpp87dO+3UAYBDyhAUAAADV509l4yMbG/xyEiqM+faDQQAAAAAm9W931KdtXbqAMAhZQgKAICBqyaPp5q9uL21s5dSTR5vOREAAAAAbNLYkZbqjLdTBwAOqW1utwcAgHZ1pqbTT1LmriS93gcvGBlJNXspnanndj0bAAAAADzW08faqXN0sp06AHBIGYICAKBVZW01ZXkpubeycRT42JHk6LFUJ6ZTjU88cW1najrlmWdT5q+m3FhI1rvvf9PoWKpTM6nOnHMCFAAAAAADV01Np3x27tG9rM0aHUt1Yrq9UABwCBmCAgCgFWXldsr8tZSbC49s+JRmLtXJmVRnnzy8VE0eT3X+QsoLL6bcWkru3km6axvHgR+dTDV1OtURR4MDAAAAsD9U4xOpTs6kLF7ffo1TMx+4gRAAeDJDUAAA7Fj/zcWUT3/yyY+xW++mLF5PeeP1VLMX05l68s62anwi1ennW04KAAAAAO2rzp5LeeP1J/fHHmdkJNWZc+2HAoBDpjPoAAAAHGz95aWU117ZfIOn10t59XL6y0u7GwwAAAAA9kg1eTzV7MXtrZ299MST0wGAzTEEBQDAtpWV2ylzV7a3du5Kyp23Wk4EAAAAAIPRmZpO9dJHkpFNPoxnZCTVSx9NZ+q53Q0GAIeEx+EBALBtZf7a9o74TjZOhJq/mur8hXZDAQAAAMCAdKamU555NmX+asqNhWS9+/43jY6lOjWT6sw5J0ABQIsMQQEAsC1lbTXl5sLOatxYSHnhxVTjE+2EAgAAAIABqyaPpzp/IeWFF1NuLSV37yTdtWRsPDk6mWrqdKoj44OOCQBDZ+iGoOq6/i1Jvi3J1yb5iiT3kvzzJPNN0/zGFmtdTPJ7kpSmaf5E21kBAA6ysrz06J1sW7HeTbm1lOr08+2EAgAYAvpbAADDoRqf0PcCgD00NENQdV0/leQTSS4m+dJHvOVuXdd/oWmav7WFst+c5FuSlCSaRAAAD7u30k6du3faqQMAcMDpbwEAAABsX2fQAdpQ1/VIkn+U5HuTjCap3vOWKsnXJPlUXdfzdV0f2+OIAADDp3u/pTpr7dQBADjA9LcAAAAAdmYohqCS/PlsHBGebOxqS5JukrsPfZ9sNIv+gyQ/Xdf1N+xdPACAITR2pKU64+3UAQA42PS3AAD2ibK2mv5PXE//s3Ppf+avb3xdvJ6ytjroaADAExz4Iai6ro8m+TP5YjPo/0zyfJIvb5rmWJIvS/JHk/wvD35ekjyd5H+q63p6j+MCAAyPp1s6fODoZDt1AAAOKP0tAID9oazcTv8zn0r/o9+Z8nc/lfLjP5qy+GMbXx9c73/mUyl33hp0VADgEQ78EFSS70jyJQ9e/1ySb2ya5sebpuknSdM03aZpfiTJVJKPJelno1H05Umu13X9zXsfGQDg4KumppPRsZ0VGR1LdcL/2wEAh953RH8LAGCg+m8upv+JD6csXk/Wu49+03o3ZfF6+h9/Of3lpb0NCAB8oGEYgvrQQ68vNE3zrx71pqZp+k3TfH82jhX/fDYaRUeS/OO6rr/tUWsAAHi8anwi1cmZndU4NZNqfKKdQAAAB5f+FgDAAPWXl1JeeyXp9Ta3oNdLefWyQSgA2GeGYQjq333w9W7TNAsf9OamaX4sG42lu9loFI0m+Qd1Xb+wawkBAIZUdfZcMjKyvcUjI6nOnGs3EADAwaS/BQAwIGXldsrcle2tnbvi0XgAsI8MwxDUb85Gs+fnN7ugaZr/NcnpJP/Pg7VfkuRH6rr+I7uSEABgSFWTx1PNXtze2tlLqSaPt5wIAOBA0t8CABiQMn9t8ydAvVevlzJ/td1AAMC2DcMQ1LaOHmia5l8kmU7yL7PRKBpJ8pm6rv94i9kAAIZeZ2o61Usf2fyJUCMjqV76aDpTz+1uMACAg0N/CwBgAMraasrNhZ3VuLGQsrbaTiAAYEeGYQjqVx98/ZqtLmya5nY2jg7/+Ww0ip5KMlfX9Z9oLx4AwPDrTE2n87EfSvWh55PRsUe/aXQs1YeeT+djP2QACgDg3fS3AAAGoCwvJevdnRVZ76bcWmonEACwI8MwBPVzSaok/2Zd179pq4ubplnJRqPof3twqZPkb9V1/afbiwgAMPyqyePpnL+Qzl/+dKpvv5Dqm/9gqtPfsvH12y+kc/lvp3P+gkfgAQC8n/4WAMAg3Ftpp87dO+3UAQB2ZFtHbe8zP5ONJk8nye9P8o+2WqBpmnt1Xc8keT3Jv/+g1l9L8q/biwkAsH+VtdWNnW/3VpLu/WTsSHL0WKoT06nGJ7ZUqxqfSHX6+V1KCgAwlPS3AAAGoXu/pTpr7dQBAHZkGE6CWnjo9Xdst0jTNL+a5D9I8uaDS1WSr9h2KgCAA6Cs3E7/M59K/6PfmfJ3P5Xy4z+asvhjG18fXO9/5lMpd94adFQAgGG28NDr79huEf0tAIAtGjvSUp3xduoAADsyDENQ/yzJrz94/a11XW/7+SpN0/yrJGeT/M9tBAMA2M/6by6m/4kPpyxeT9a7j37Tejdl8Xr6H385/eWlvQ0IAHB46G8BAAzC08faqXN0sp06AMCOHPghqKZp/nWS/zEbO9tGknxsh/V+Lcm3ZOPo8GrHAQEA9qH+8lLKa68kvd7mFvR6Ka9eNggFALAL9LcAgMOsrK2m/xPX0//sXPqf+esbXxevp6yt7vpnV1PTyejYzoqMjqU6Md1OIABgR0YGHaAl35Pkbz94/Rs7LdY0zf26rr8tyctJnF8JAAyVsnI7Ze7K9tbOXUl55tlUk9s+nAAAgEfT3wIADpWycjtl/lrKzYVHnlJemrlUJ2dSnT236V5UWVtNWV5K7q0k3fsbj7s7eizVielU4xPve381PpHq5MzGSenbVJ2aeWRtAGDvDWwIqq7rqmma0katpml+LsnPtVHroZq/nuRymzUBAPaDMn9t8ydAvVevlzJ/NdX5C+2GAgA4gPS3AAC2p//mYsqnP/nkHtV6N2Xxesobr6eavZjO1ONPW9rJQFV19lzKG69vr182MpLqzLmtrwMAdsUgH4f3j+u6PjLAzwcAOHTK2upGM2gnNW4s7Mlx5AAAB4D+FgDAFvWXl1Jee2XzQ0e9Xsqrl9NfXnp0vTcX0//EhzdOc3rEAFSSLwxU9T/+8vvqVJPHU81e3Mqv8MW1s5ecmA4A+8ggh6C+LclCXddPDzADAMChUpaXHt8M2qz1bsqtRzedAAAOGf0tAIAtKCu3U+aubG/t3JWUO2+961pbA1WdqelUL30kGdnkQ3RGRlK99NF0pp7b3PsBgD0xyCGoJDmR5J/Xdf27BpwDAOBwuLfSTp27d9qpAwBw8OlvAQBsUpm/tr3HziUbA0zzV79Yq+WBqs7UdDof+6FUH3o+GR179MLRsVQfej6dj/2QASgA2IcGPQSVJL8zyRt1XT/+Qb4AALSje7+lOmvt1AEAGA76WwAAH6CsrabcXNhZjRsLKWurG69bHKh6RzV5PJ3zF9L5y59O9e0XUn3zH0x1+ls2vn77hXQu/+10zl/wCDwA2Kf2wxBUSfJVSX68rus/POgwAABDbexIS3XG26kDADAc9LcAAD5AWV5K1rs7K7LeTbm11PpA1XtV4xPpnH4+nT/0nel8+3dvfD39fKojemIAsJ8Ncgjqlx96XZKMJvl7dV1/714Hqev6MWdaAgDsb2VtNf2fuJ7+Z+fS/8xf3/i6eP2xDZw8faydDz462U4dAICDTX8LAGCz7q20U+funVYHqgCA4TEywM8+leSfJvld2WgSlWwMZf3Fuq6fTfLdTdP0dzNAXddfnuQ/S/LhJEd387MAANpUVm6nzF/b2PH2iIZPaeZSnZxJdfbcu47nrqamUz47t7Mm0ehYqhOe9AIAEP0tAIDN695vqc5aqwNVAMDwGNhJUE3T/EKS35dkKUn14HJ58PqlJP+krutdOVOyruuvquv6LyT5pSTfn+S37MbnAADshv6bi+l/4sMpi9cfP8y03k1ZvJ7+x19Of/mLO9qq8YlUJ2d29PnVqZlU4xM7qgEAMAz0twAAtmDsSEt1xtsdqAIAhsYgH4eXpmk+l+Rskr+f9zeKvjXJT9R13doOtrquj9Z1fTkbzaE/l+QrH/pcAIB9r7+8lPLaK0mvt7kFvV7Kq5ffPQh19lwyss0DQUdGUp05t721AABDSH8LAGCTnj7WTp2jk+0OVAEAQ2OgQ1BJ0jTNbzRN88eS/EDe3yj6hiT/vK7rf2cnn1HX9W+v6/qvJfmFJJeSfNmD+mUndQEA9lJZuZ0yd2V7a+eupNx5K0lSTR5PNXtxW3Wq2UvverweAAD6WwAAm1FNTSejYzsrMjqW6sR0uwNVAMDQGPgQ1DuapvmzSf5UkrcfXCoP/jyb5I26rj+01Zp1Xf/bdV3PJfm/kvzpJGP5YnPo4QbR57afHABgb5T5a5s/Aeq9er2U+atf+LYzNZ3qpY9s/kSokZFUL300nanntvf5AACHgP4WAMDjVeMTqU7O7KzGqZmNOm0OVAEAQ2PfDEElSdM0P5zk25L82kOXSzaO9f6xuq7/6Gbq1HX9e+q6/vtJ/vckLyb5kry/OVQl+ZVsHBv+bBv5AQB2S1lbTbm5sLMaNxZS1la/8H1najqdj/1Qqg89//im0ehYqg89n87HfsgAFADAJuhvAQA8XnX23OY35b3XyEiqM+c26rQ4UAUADI9t/itj9zRN8+N1XU8n+e+TvHOWZUnypUk+U9f172ia5gcetbau66kkfzbJuQeXHj5+PA9du5PkB5P8zaZp1lr+FQAAWleWl5L17s6KrHdTbi2lOv38Fy5Vk8dTnb+Q8sKLKbeWkrt3ku5aMjaeHJ1MNXU61ZHxHaYHADhc9LcAAB6tmjyeavZiyquXt7529lKqyeNf/P7suZQ3Xt/eyekPDVQBAMNj3w1BJUnTND9b1/WpbDSKvi5f3OFWJfn+uq5/Z5L/tGmafpLUdX06Gzvezjwo8bjm0O0kfznJDzdNs77rvwgAQFvurbRT5+6dR16uxifeNRwFAMDO6G8BADxaZ2o6/SRl7srmBphGRlLNXnrfKeVtDlQBAMNhXw5BJUnTNP9vXdfPJflskm/JuxtFfyLJM3Vd/80kH0nyjQ+Wvbc59M73/3eS/zrJ32maZhvj4AAAA9a931IdhwQAAOwV/S0AgEfrTE2nPPNsyvzVlBsLjz4BfXRs45F1Z849dmCprYEqAGA47NshqCRpmubX6rr+tiR/I8mfzLsbRd/y4E/y+ObQ/5HkLyb5++/sqgMAOJDGjrRUx6PtAAD2kv4WAMCjVZPHU52/kPLCiym3ljZOMO+ubfSvjk6mmjqd6sgH97LaGqgCAA6+fT0ElSRN07yd5KW6rn8xyffn3Y2id7y3OfQzSf6rpmn+wV7lBADYVU8fa6fO0cl26gAAsGn6WwAAj1eNT6Q6/fzOarQ0UAUAHGz7fgiqrutOkj+a5I89dLnKFxtDD39/MxvNoWt7lxAAYPdVU9Mpn5179E62zRodS3Viur1QAABsiv4WAMDeaGOgCgA4uPbtEFRd11+S5DuTfG+SZ9/z4/K+BclvJPkBDSIAYBhV4xOpTs6kLF7ffo1TM6nGJ1pMBQDAk+hvAQD7UVlbTVleSu6tJN37ydiR5OixVCem9Y4AgANt3w1B1XV9JMl3JfmeJJN58rHg71wrSb40yT+o6/rlpmk+tRdZAQD2UnX2XMobrye93tYXj4ykOnOu/VAAALyP/hYAsB+Vldsp89dSbi488rTx0sylOjmT6uy5VJPH9z4gAMAOdQYd4B11XX9FXdf/RZJfSvKDSY7li8eAv/Pnne+bJP9hkp/NFxtGJclTSf6buq7/0t6mBwDYfdXk8VSzF7e3dvaS5hUAwC7T3wIA9qv+m4vpf+LDG6eMP2IAKkmy3k1ZvJ7+x19Of3lpbwMCALRg4CdB1XX91UkuJvnuJF+Rdzd93lEl6SX5e9k4EvznH6x9Lsk/THI2724kfU9d1789yR9vmuY39uL3AADYC52p6fSTlLkrmzsRamQk1eyldKae2/VsAACHlf4WALCf9ZeXUl57ZfMLer2UVy+nn41eFADAQTGwIai6rieTfCTJn0oynsc3h9aTfDrJX2qa5pcertE0za/Vdf0HkryW5MW8u1FUJ5ms6/o/aZrmc7v5uwAA7KXO1HTKM8+mzF9NubHw6N17o2OpTs2kOuP4cgCA3aK/BQDsd2Xl9sZmuu2snbuS8syzeksAwIExyJOgfiHJl+TxzaG1JK8mudw0zZ3HFWmappfkO+u6fivJn8u7G0XTSX6yrutvbZrmdvu/AgDAYFSTx1Odv5Dywospt5aSu3eS7loyNp4cnUw1dTrVkfFBxwQAGHb6WwDAvlbmr23uNPFH6fVS5q+mOn+h3VAAALukM8DP/tIHX99p6iQbjZ3PJ/mBJL+jaZpLT2oQPaxpmo9lY9fd2w/VrZJ8bZKbdV1/fVvBAQD2i2p8Ip3Tz6fzh74znW//7o2vp583AAUAsDf0twCAfausrabcXNhZjRsLKWur7QQCANhlgxyCeliV5FeTfCzJb2+a5s82TfMrWy3SNM0PJ/mPk7zzr7F3GlBHk/zEg6PFAQAAAKBt+lsAwL5SlpeS9e7Oiqx3N04hBwA4AAY9BFUluZvkI9nYGff9TdP8650UbJrmf0gy86DuO0qSiSQ/Wtf1d+2kPgAAAAA8RH8LANif7q20U+fupg61BAAYuEEOQd1O8t1JfmfTND/YNM1aW4WbpvnpJN+Y5Oez0YhKNhpFI0k+Vdf1X2zrswAAAAA4tPS3AID9q3u/pTqt/RMHAGBXjQzws/+tpml6u1W8aZpfrOv69yX5J0meyxePDq+SfG9d178jyXc0TfMbu5UBAOBxytrqxpHk91Y2GlJjR5Kjx1KdmE41PjHoeAAAbI7+FgCwf40daanOeDt1AAB22cBOgtrNBtFDn/G5JGeT/Hd59465KskfSfJjdV3/pt3OAQDwjrJyO/3PfCr9j35nyt/9VMqP/2jK4o9tfH1wvf+ZT6XceWvQUQEA+AD6WwDAvvb0sXbqHJ1spw4AwC4b5OPw9kTTNL/eNE2d5JN5f6PoQ0l+sq7r44PKBwAcHv03F9P/xIdTFq8n691Hv2m9m7J4Pf2Pv5z+8tLeBgQAYF/S3wIAtqOamk5Gx3ZWZHQs1YnpdgIBAOyyoR+CekfTNJeSfDgbDaLki42i353kxqByAQCHQ395KeW1V5LeJg8L6PVSXr1sEAoAgC/Q3wIAtqIan0h1cmZnNU7NpBqfaCcQAMAuOzRDUEnSNM1fTVInWX9w6Z2G0dcMJhEAcBiUldspc1e2t3buikfjAQDwBfpbAMBWVGfPJSMj21s8MpLqzLl2AwEA7KJDNQSVJE3T/MMkZ5P86qCzAACHQ5m/tvkToN6r10uZv9puIAAADjT9LQBgs6rJ46lmL25v7eylVJOeuAsAHByHbggqSZqmeSPJNyX5xQFHAQCGXFlbTbm5sHWehpMAACAASURBVLMaNxZS1lbbCQQAwFDQ3wIANqszNZ3qpY9s/kSokZFUL300nanndjcYAEDLDuUQVJI0TfPzSU4l+alBZwEAhldZXkrWuzsrst5NubXUTiAAAIaG/hYAsFmdqel0PvZD/z97dx9bV3rfB/57ruiKL35d16NSkWIlTYImLdIiFSGhHspMpG0VJCxS7ebsFp1itkwzBSbtTqSFDSyCOAunwCKYKTRu47aYKeh2++acyXZRS94IyTBhxXRWGip1102BAtukWbmhKnmdGHFEkTbFZ//gzESjoSiS9/Dt8vMBBPiee57f/Q58oAGe+f2ek+qjZ5OD/avfdLA/1UfPpvOJT2mAAgD2pE2+BLg3NE3z5bqux5L8853OAgD0qDtz7dS5faudOgAA9BT7WwDAelXDR1M99WzKuadXBu5u30oW5pP+weTQcKqRU6kGBnc6JgDApu3rJqgkaZrmbl3XP7TTOQCAHrVwr6U68+3UAQCg59jfAgA2ohocSnXq7E7HAABo3b59Hd6DmqZZ3ukMAECP6h9oqY4pPAAAHs3+FgAAALDfaYICANhKTxxup86h4XbqAAAAAAAAQA/SBAUAsIWqkdHkYH93RQ72pzo+2k4gAAAAAAAA6EF9Ox0AAKCXVYNDqU6MpVy9svkaJ8dSDQ61mAoAAAAAulPm76bMziR35pKFe0n/QHLocKrjo/ayAIAdoQkKAGADNrO5U50ZT3nt1WRpaeM/2NeX6vR4l6kBAAAAoB1l7mbK1OWU69PJ4sI7v28mU50YS3VmPNXw0e0PCADsW5qgAADWoZvNnWr4aKqJ8ykvPb/h360mLtgsAgAAAGBXWH79aspnXlx72G9xIeXqlZTXXk01cT6dkdHtCwgA7GuaoAAAHqONzZ3OyGiWk5TJi+s7EaqvL9XEhXRGnuwuPAAAAAC0YHl2JuXlF9a/YGkp5aXns5xohAIAtoUmKACANbS5udMZGU05cixl6lLKtelVT5TKwf5UJ8dSnXZcOAAAAAC7Q5m7uTLct5m1kxdTjhyz1wUAbDlNUAAAj7AVmzvV8NFUTz2bcu7plBszye1bycJ80j+YHBpONXIq1cBgG/EBAAAAoBVl6vL6TjdfzdJSytSlVE89224oAICHaIICAHiErdzcqQaHUp0620U6AAAAANh6Zf5uyvXp7mpcm04593SqwaF2QgEArKKz0wEAAHaj1jZ35u+2EwgAAAAAdkCZnUkWF7orsriwcio6AMAW0gQFALAKmzsAAAAAkOTOXDt1bt9qpw4AwCNoggIAWI3NHQAAAABIFu61VGe+nToAAI+gCQoAYDU2dwAAAAAg6R9oqc5gO3UAAB6hb6cDAADsSjZ3AAAAANhFyvzdlNmZlRPMF+6t7F8dOpzq+GiqwaGt++EnDrdT59BwO3UAAB5BExQAwGps7gAAAACwC5S5mylTl1OuTyeLC+/8vplMdWIs1ZnxVMNHW//9amQ05ZXJVX973Q72pzo+2l4oAIBVeB0eAMAqqpHR5GB/d0Vs7gAAAADQheXXr2b5p3885eqVRzchLS6kXL2S5U8+l+XZmdYzVINDqU6MdVfj5NjWnlYFABBNUAAAq7K5AwAAAMBOWp6dSXn5hWRpaX0LlpZSXnp+axqhzownfZt8wUxfX6rT4+0GAgBYhSYoAIBHsLkDAAAAwE4oczdTJi9ubu3kxZRbX2o1TzV8NNXE+c2tnbiwJa/pAwB4mCYoAIBHsLkDAAAAwE4oU5fXfwLUw5aWUqYutRsoSWdkNNUzH1v/0GBfX6pnPp7OyJOtZwEAWM0mjzYAANgfOiOjWc7KBN26Np76+lJNXLC5AwAAAECSpMzfzf1f+9f56te+muV7d9MZGMr9934g5Xv+TKrBoVXvL9enu/vNa9Mp555etX43OiOjKUeOpUxdSrk2nSwuvPOmg/2pTo6lOj1uSBAA2FaaoAAAHsPmDgAAAAAbVeZupkxdTrk+neXFhXzt4Rs++3KqE2Opzrx9P6nMzqy+/7QRiwspN2ZSnTrbXZ1VVMNHUz31bMq5p1NuzCS3byUL80n/YHJoONXIqVQDg63/LgDA42iCAgD2hTJ/d2UD6c5csnAv6R9IDh1OdXx0XRNxNncAAAAAWK/l16+mfObFtU8WX1xIuXol5bVXU02cT2dkdOX6nbl2Qty+1U6dR6gGh7akyQoAYLM0QQEAPe3BibvVJuhKM7nqxN2j2NwBAAAAYC3LszMpL7+w/gVLSykvPZ/lrJxInoV77QRZmG+nDgDAHqEJCgDoWV1N3AEAAADABpW5mymTFze3dvJiypFjKyeYt6HfqeUAwP6iCQoA6EldT9wBAABdqet6IMkfS/LhJIeTvCfJu5L8XpKvJPn1JP++aZo1phYAYG8pU5fXHshby9JSytSl5Oi3thPm0HA7dQAA9ghNUABAz2lj4m49r8YDAADerq7rv5Lk+5KcSPJHk3Qes+T367pukvydpmn+7VbnA4CtVObvplyf7q7GtelU3//DycH+ZHFh84UO9qc6btAPANhfHrcJAQCw57QycQcAAGzGTyd5Ksm3Z317j+9OMpHkRl3XF+u6NrQJwJ5VZme6a1xKVtb/+19LdWKsqzLVybFUg0PdZQEA2GM0QQEAPaWtibsyf7edQAAAsL/NJ/l3ST6f5J8n+bkkv5Tkvzx034EkP57klbquD2xrQgBoy525durcvpXqzHjSt8ne4L6+VKfH28kCALCHmKwCAHpKWxN35cZMqlNn2wkFAAD7x90kn0vyC0leS/LrTdMsr3ZjXdcnk/zNJKcfuPxDSS4keX6LcwJA+xbutVRnPtXw0VQT51Ne2vi/EquJC6mGj7aTBQBgD9EEBQD0lhYn7gAAgA37E03TfGM9NzZNc62u6z+b5B9l5RV6b/qJuq7/dtM0i1uSEAC2Sv9AS3UGkySdkdEsJymTF5Olpcev6+tLNXEhnZEn28kBALDHeB0eANBbWpy4AwAANma9DVAP3L+c5MeycoLUm96X5HvbzAUA2+KJw+3UOTT81v/sjIym84lPpfro2eRg/+r3H+xP9dGz6XziUxqgAIB9zUlQAEBvaXniDgAA2FpN0/xeXde/muTPPXD523YqDwBsVjUymvLKZLK4sPkiB/tTHR99e93ho6meejbl3NMpN2ZWTjBfmF/Zvzo0nGrkVKoBe1kAAJqgdqm6rgeS/LEkH05yOMl7krwrye8l+UqSX0/y75umWcf5pwCwj2zBxB0AALDlfuehz+/ZkRQA0IVqcCjVibGUq1c2X+PkWKrBoUfXP3V207UBAHqdJqhdpK7rv5Lk+5KcSPJH8/jXFf5+XddNkr/TNM2/3ep8ALAXbNXEHQAAsKU+/NDnuR1JAQBdqs6Mp7z2arK0iRn2vr5Up8fbDwUAsE88rsmG7fXTSZ5K8u1Z3/83704ykeRGXdcX67rW1AbAvvfmxF1XNdaYuAMAANpV1/V3ZGUo8E0lyb/aoTgA0JVq+GiqifObWztxIdXw0ZYTAQDsH5qgdrf5JP8uyeeT/PMkP5fkl5L8l4fuO5Dkx5O8Utf1gW1NCAC7UHVmPOnbZG+wiTsAANg2dV0PJ3klK/tbb/r5pml+a2cSAUD3OiOjqZ752Pr3p/r6Uj3z8XRGntzaYAAAPc7JQbvL3SSfS/ILSV5L8utN0yyvdmNd1yeT/M0kpx+4/ENJLiR5fotzAsCu9ubEXXlp4/9KNHEHAABb542TzD+Q5DuT/GCSv5bkvQ/c8ptJ/voORAOAVnVGRlOOHEuZupRybTpZXHjnTQf7V04kPz1uPwoAoAWaoHaXP9E0zTfWc2PTNNfquv6zSf5RVl6h96afqOv6bzdNs7glCQFgj+iMjGY5SZm8mCwtPX5BX1+qiQsm7gAAoEV1Xb+Y5Ll13v4rSf5y0zR3tjASAGybavhoqqeeTTn3dDr/5rUMfO2rWZ7//XQG351773l/lv/0R1INDO50TACAnqEJahdZbwPUA/cv13X9Y0n+QpKhNy6/L8n3JrnScjwA2HNM3AEAwJ7wuSSfbprmF7eieF3XTyT50Hrv/8hHPvKB5557e9/WgQMH0ul02o7WmgMHDqz5GfYizzU95b3vy4HTP5j3f/CDb126/5Wv5P79+zsYCrrj72l6keeaXrMdz3RVVa3X7IYmqD2uaZrfq+v6V5P8uQcuf9tO5QGA3ebBibtyYya5fStZmE/6B5NDw6lGTpm4AwCAnfX9SQ7Udb3QNM3VLaj/bJKfWu/NX/ziF99x7UMfWncP1a7wwQf+Izv0Cs81vcYzTa/xTNOLPNf0mv3wTGuC6g2/89Dn9+xICgDYxarBoVSnzu50DAAA2G8+meTFBz4PJPlgkj+VldPNvy/Ju5L8QJIfqOv600mea5rG0RgAAADAhmiC6g0ffujz3I6kAAAAAIAHNE3zO3nnAF+S/GqSn63r+skk/yR/sL/1Y1lplPqR7UkIAAAA9ApNUHtcXdffkeTEA5dKkn+1Q3EAAAAAYN2apvnVuq6/N8lsVk6ISpKJuq4/1zTNv2zpZ/5uklfWe/N3f/d3fyDJzIPXvvzlL6eU0lKc9h04cOBtrzX4yle+kvv3HabF3ua5ptd4puk1nml6keeaXrMdz3RVVbvqFfKaoPawuq6Hs7KBc+CByz/fNM1v7UwiAAAAANiYpmn+U13Xn0zyqQcufzxJK01QTdPcSXJnA0vesXt7//79LC8vtxFnW9y/fz9LS0s7HQNa5bmm13im6TWeaXqR55pesxXPdKfTabVetzRB7SF1Xfcl+UCS70zyg0n+WpL3PnDLbyb56zsQDQC2TJm/m/u/9q/z1a99Ncv37qYzMJT77/1Ayvf8mVSDQzsdDwAAaMdn8/YmqJN1Xb+/aZqv7lQgAAAAYG/RBLWL1XX9YpLn1nn7ryT5y29MtgHAnlfmbqZMXU65Pp3lxYV87eEbPvtyqhNjqc6Mpxo+uhMRAQCAljRNc6eu69/NygBgknSSfEuSL+xcKgAAAGAv0QS1930uyaebpvnFrShe1/UTWeUI8Ef5yEc+8oHnnnt739aBAwd23RFoDzpw4MCan2Ev8lyz192//q9y/x/8rWStIzkXF1KuXkl57dX0/dX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def normalized_mutual_info(x, y, bins=10):\n", + " counts_xy, bins_x, bins_y = np.histogram2d(x, y, bins=(bins, bins))\n", + " counts_x, bins = np.histogram(x, bins=bins)\n", + " counts_y, bins = np.histogram(y, bins=bins)\n", + "\n", + " counts_xy += 1\n", + " counts_x += 1\n", + " counts_y += 1\n", + " P_xy = counts_xy / np.sum(counts_xy)\n", + " P_x = counts_x / np.sum(counts_x)\n", + " P_y = counts_y / np.sum(counts_y)\n", + "\n", + " I_xy = np.sum(P_xy * np.log2(P_xy / (P_x.reshape(-1, 1) * P_y)))\n", + "\n", + " return I_xy / (entropy(counts_x) + entropy(counts_y))\n", + "\n", + "def _plot_mi_func(x, y):\n", + " mi = normalized_mutual_info(x, y)\n", + " plt.scatter(x, y, s=15)\n", + " plt.title(\"NI($X_1$, $X_2$) = %.3f\" % mi)\n", + " plt.xlabel(\"$X_1$\")\n", + " plt.ylabel(\"$X_2$\")\n", + " \n", + "\n", + "np.random.seed(0) # to reproduce the data later on\n", + "plt.clf()\n", + "plt.figure(num=None, figsize=(8, 8), dpi=DPI)\n", + "\n", + "x = np.arange(0, 10, 0.2)\n", + "\n", + "plt.subplot(221)\n", + "y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(222)\n", + "y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(223)\n", + "y = 0.5 * x + norm.rvs(1, scale=1, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(224)\n", + "y = norm.rvs(1, scale=10, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "\n", + "save_png('04_mi_demo_1')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.figure(num=None, figsize=(8, 8), dpi=DPI)\n", + "\n", + "x = np.arange(-5, 5, 0.2)\n", + "\n", + "plt.subplot(221)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(222)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(223)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.subplot(224)\n", + "y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x))\n", + "_plot_mi_func(x, y)\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "\n", + "save_png('05_mi_demo_2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Asking the model about the features using wrappers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recursive feature elimination" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False True False False False True True False False]\n", + "[5 4 1 2 6 7 1 1 8 3]\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from sklearn.datasets import make_classification\n", + "\n", + "X, y = make_classification(\n", + " n_samples=100, n_features=10, n_informative=3, random_state=0)\n", + "\n", + "clf = LogisticRegression()\n", + "clf.fit(X, y)\n", + "\n", + "selector = RFE(clf, n_features_to_select=3)\n", + "selector = selector.fit(X, y)\n", + "print(selector.support_)\n", + "print(selector.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\t[False False False False False False False True False False]\t[ 7 6 3 4 8 9 2 1 10 5]\n", + "2\t[False False False False False False True True False False]\t[6 5 2 3 7 8 1 1 9 4]\n", + "3\t[False False True False False False True True False False]\t[5 4 1 2 6 7 1 1 8 3]\n", + "4\t[False False True True False False True True False False]\t[4 3 1 1 5 6 1 1 7 2]\n", + "5\t[False False True True False False True True False True]\t[3 2 1 1 4 5 1 1 6 1]\n", + "6\t[False True True True False False True True False True]\t[2 1 1 1 3 4 1 1 5 1]\n", + "7\t[ True True True True False False True True False True]\t[1 1 1 1 2 3 1 1 4 1]\n", + "8\t[ True True True True True False True True False True]\t[1 1 1 1 1 2 1 1 3 1]\n", + "9\t[ True True True True True True True True False True]\t[1 1 1 1 1 1 1 1 2 1]\n", + "10\t[ True True True True True True True True True True]\t[1 1 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "source": [ + "for i in range(1, 11):\n", + " selector = RFE(clf, i)\n", + " selector = selector.fit(X, y)\n", + " print(\"%i\\t%s\\t%s\" % (i, selector.support_, selector.ranking_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Princial Component Analysis (PCA)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.96393127]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import decomposition\n", + "\n", + "np.random.seed(3)\n", + "\n", + "x1 = np.arange(0, 10, .2)\n", + "x2 = x1 + np.random.normal(scale=1, size=len(x1))\n", + "\n", + "plt.clf()\n", + "fig = plt.figure(num=None, figsize=(10, 4), dpi=DPI)\n", + "plt.subplot(121)\n", + "\n", + "plt.title(\"Original feature space\")\n", + "plt.xlabel(\"$X_1$\")\n", + "plt.ylabel(\"$X_2$\")\n", + "\n", + "x1 = np.arange(0, 10, .2)\n", + "x2 = x1 + np.random.normal(scale=1, size=len(x1))\n", + "\n", + "good = (x1 > 5) | (x2 > 5)\n", + "bad = ~good\n", + "\n", + "plt.scatter(x1[good], x2[good], edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(x1[bad], x2[bad], edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(122)\n", + "\n", + "X = np.c_[(x1, x2)]\n", + "\n", + "pca = decomposition.PCA(n_components=1)\n", + "Xtrans = pca.fit_transform(X)\n", + "\n", + "Xg = Xtrans[good]\n", + "Xb = Xtrans[bad]\n", + "\n", + "plt.scatter(Xg[:, 0], np.zeros(len(Xg)), edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(Xb[:, 0], np.zeros(len(Xb)), edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "plt.title(\"Transformed feature space\")\n", + "plt.xlabel(\"$X'$\")\n", + "fig.axes[1].get_yaxis().set_visible(False)\n", + "\n", + "print(pca.explained_variance_ratio_)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.autoscale(tight=True)\n", + "save_png(\"06_pca_demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Limitations of PCA and how Linear Discriminant Analysis (LDA) can help" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first consider a data set where the labels aren't distributed according to the axis with the highest variance: " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.98318496]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "fig = plt.figure(num=None, figsize=(10, 4), dpi=DPI)\n", + "plt.subplot(121)\n", + "\n", + "plt.title(\"Original feature space\")\n", + "plt.xlabel(\"$X_1$\")\n", + "plt.ylabel(\"$X_2$\")\n", + "\n", + "x1 = np.arange(0, 10, .2)\n", + "x2 = x1 + np.random.normal(scale=1, size=len(x1))\n", + "\n", + "good = x1 > x2\n", + "bad = ~good\n", + "\n", + "plt.scatter(x1[good], x2[good], edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(x1[bad], x2[bad], edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(122)\n", + "\n", + "X = np.c_[(x1, x2)]\n", + "\n", + "pca = decomposition.PCA(n_components=1)\n", + "Xtrans = pca.fit_transform(X)\n", + "\n", + "Xg = Xtrans[good]\n", + "Xb = Xtrans[bad]\n", + "\n", + "plt.scatter(Xg[:, 0], np.zeros(len(Xg)), edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(Xb[:, 0], np.zeros(len(Xb)), edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "plt.title(\"Transformed feature space\")\n", + "plt.xlabel(\"$X'$\")\n", + "fig.axes[1].get_yaxis().set_visible(False)\n", + "\n", + "print(pca.explained_variance_ratio_)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.autoscale(tight=True)\n", + "save_png(\"07_pca_demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that PCA doesn't work at all. LDA will still work fine here, because it takes the instance labels as additional input and is then able to minimize within-class distances in the reduced feature space while it maximizes samples that are in different classes." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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vPT/RC2Vd330ysFXBOkyscf3OMEzOI1r43V3gN2iZnEcE3P8f1c8DFwPzd+M4beM7bUC0VK9rXzqszfU2Tc4jep78Y4n1v0YEsK3Cb3IyrZPc253eJhoOzFTjNhsD/A/RMrOOOhZ+KUTcd6SCxEWnU6lwHdSkSZMmTZo0aerERB8k5xG9s/2hwH1VMjmP/otjTMgpd3y2vJcxnzmA39f4W03KWc/4GtfR1n5J+bjh4OlN4IfA6A5t+zrirU0TuYhe7V4o8d1fBLbNWedKlE8caOv5uc3fd0eqNbjz7POVG7Rm9dmb6LGrTD3OIEv2aLVNOznRo+S8ftiWRAPOOq8dZ9BmxwnZ+pWcF2U0Tc6j2DW+reQ84lz9r4q/9XPArt04Ttv4PjMDv6C+ON9rBdbdNDkP+Agx0kXROtwMLFfhN1kz20Z1HVsPUXPj5Gw/vL+m+t1Qsg67ECOBVFn3Y8DmvT4ONGlyd0YhIiLD1Y5E1/SNXgHO6uSK3f1BonVrys5VyzezDbLyl6xaVl2y4TRvptz3mwX4oZmdmnVf3dfMbFGiG/HNShYxBrjAzD5Q8vNLE0M71uVjwLVmtkiNZQ5bWffltwC71VDcbsB1ZrZAic+OIYYmqct8wCVmtl+NZY5oZrY8ca54b41lLkEMyfkVqp8HPgjckp2/e8bMNiWGgVqml/VolA2Rei0RBClqVuKF0R+yYbDKWI70fUwZBhwIXGhmc1QuLPaZm4CvEoHeOhR6NjezVYFbgU/VsO49gGvMbL4ayhIREREZFrLhA/9CDANZ1ZCJY/Qy5pMNR3oJ8PGS6+5bZrYc5eOGg40mGkJdUsezzWDdiLea2SeIIWXzhsdsZixwrpmt11DmakS9Vy1Zre+Z2ZdLfnagDmZmRwN/AharUlb2+UvN7HMV63QYcBIxTHAZnwDON7O6nnmHhD7blnVfOz4BXJ3Fe6QGZnYCkbxa23YyswOJ0SuWq1jUvMBZ2bmgZ7K44B+J3t366t2XmX2FaIQxf4mPrwvcamablFz9/MQ2qssywA1mtl0dhZnZocQw3SvWUR7F455mZscT+07V+90lgD+b2e4VyxGpbETdVImIjDDb5Mz/k7u/0IX1/wZI3ZhuaWaj3f2tkuUuDpxPtF4ezIF/ED1pTSZubhenvpvHXNkD7ZVEq9sq9iBa8lxWsZxOmoMYxnTZhvkO3Mf033+e7G/yfv9ZgFPNbBV3n1pj/aYA/8z+nUK0xpqXCCyuRP5DwFrAmWa2hbu/XWN9hpUsGeRKoqv3PK8Qw4Y+S7RqXoB4OMwLKKwBXGZmG7r76zVV9W2iy/+niGFYXiSCuPMRiWLz5HxuJuBEM/uXu/+lprqMVGOJYVMWSix7kBiG+L9Et/QD5+qmAWMzW5LovaFZEtvrRE99zxDbfl6iJ9lVcv5+aeBKM1vb3Z9rtv5OMLP5iRcTzV6qPEWc1yYTx9cYYG7it12JOJ/WXa/ZiGtt2ZcbAz4CuJl9zN29es3e8QyxH00lzvWjid9kOZrvH9sCJwKfLbviLOn0r7RODn6M2M+fAd4gzj+LEEMKzVx2/VkdViPOxc2Chy8z/Vz8EnEuXpYZr98D1iZeGmzs7m9UqZ+IiIjIMHEqMfxao/8Q9+f/IZ4hlyDusco2dOinOEavYz4/IR1LHPAK8Vs9mv3fieeAebK6lHm53nFmtjjxgnupJn/2AvEbP0E83yxOxC9mzfn79wMXmdkH3P3NGqrZ8Xirma1P9IjZuE8/S3z3p4lnpSWJ/Tu1789GJJus7O6vZgmqlxLPO4O9RjwPPZ39fxFgHfKfvY82s4vd/aGi3yvzS+DzLf7mX8Qz4mTiGFmY6DlpTOJvZwJ+bWZT3P2copXJksG+1+LP/pHVZwqwKLBC9u9gWwPHEMfbSNFX27KJKcAD2b9TmX7tWIo4PvOuHesCZ5jZVoqBV2NmXyKGHG70EnAvcZ/wOpGkOZ64X2hV5uHAd1r82SNEL2mTiWS3hYHViRhryvfMbKq7/7TV+jvkCOBDTZa/xvRj6mViyNu5iGv78sT3q52ZfZQYEaNKwuDcRLL8Zu5+Wz01A6KX3AeI2PkU4hw8F/FOZjVmvFYPGEtcI9d29wfKrtzMfgwc0OLPXieu3c8Q++JY4hy0Iun3AUXWb8SIKnu2+NOB/eZZ4n5pEeJcnHrHMBr43+xcfH6V+olUoeQ8EZFhKLt5SQUQIb9Hu7rlrWcuIrh0R8lyf867g23PAd8HznT3Jxv/2MzmIVqFddLp5CfmPUd0GX8O8dD0DHGTugTRa9NuvDtp6RvEjW2/OoF394L1DPH7/8Hdn27846xl8HdJb4PFsmVfqVCfF4CLiADiLe7+cN4fmtmcxIPgl4D1E3+yGXBYVqeh5FJiGJ7BNmbGYPp/sr9t5v68BWa2IDEMaCox721iH/8pcGMqMGxmywJfzKbGZKK1gB/R+qGvmTuIJKcrgLvd/eXUH2Xnx/cQD3d7Ew+O7/oT4DQzW93dn2m2Qnffg0iqHSh7ItDYAutqd5/Q7pcYRr7Pu89trwDHAaelgt1mNjvRA1gyKShbfiH5iVeXEkPdXu3uryU+vwTwOeBgZgzGjwd+RT09YhR1FOkXSFOI7/O7rDfapKwF+0rAB4jz28bU01r3SGC9hnl3ENe7y4kXRq8SL2VWI87xO5J+vvwo0dvmsRXq8wxxnr8QuC11vR+QJTzuSgRIV078yR5m9hd3P6NoJbKe5f6P/MS854jhc89z9/tyypiNOOftSPQIUqhXiqxBwMWk95tpRIvSE4Gbcs7FyxPXwX2ZMUlwXeLYPbhInURERESGoZ2I+9wBDvyOeOa9pbHhiZnNRCRLzRAXSejnOEbPYj5mti75jWjOI377a5slopnZYsAGxG+2Hc2T9V4iGhY3SvWqcj3ROKiZ5PIsBnEq+Yl5fyeevy5qfJbNYpofIxI1UskJE4DDgW+3qFs7Oh1vHUM8Tw6OB12WreP6xiShLAZ1COle88cBB5rZD4ghkAcnlN1PJIJc3BgXyp7FdifiT42NNmcDfkwMuVtI1itVXjLXJOIZ8Rx3fyrx2TFEI7LvErGqdy0mkrpucvfHC9RnGeJ4SZlGJMH+ojHWkO2rE4BDifPZgK8SDSV75UlmPFbnJN0L5SXEeauZ/+Qt6Ldt2WAq7752TMr7w+za8WHi2tEY2wHYgji+ftBshdk+8k7iUnbO/1fiTzdx9+ta1H+4GUckrg52AxHnuzLVAN3MVqdJgl7Wq1deYt4zRJzwzNR9g5nNTGzXI0nfLxxrZle7+9156++E7Hx0UM7iK4jz7pWpWO6gMhYk9uMPAdszYxJxGYsTx8DgxLyXiBHH/kAkxj1NXCuWzNa7G+m49BzAeWb2ngqdorxN3GucQ7xXvTev4Wx2z/k+4r3GbswYi52DaLyxXpnGt1nPjc3e0VxM9Mp6ubsnE7fNbBxxPfkEsGWijq0cQX5i3sNMPxen7k3HEPeA32XGuPAo4BQzu6VZbFmko3o9rq4mTZo0aap/IhIiPGdau0t1MCLgmKrDnm18fkKT7zAwXQbMX6GOe6TKLVjGZ5rU7w/Agi0+PzPx0PXWoM+9nlPe+AL1uirx+YkFPt/O7/8nYM42y/tqThlTgdkL/uYnE61fPwHMUnLf3Cfnd34ZmK9geeNzvtuETh9nTeo0MVGfqyqWeVHO93wYWKdAOasSrdwby3kb2LJAOUsQyUE/B5Yr+Z3GEQ+8qe/1o3743TtdNhG0aSx3UoHP5+3/g6fbgSUr1vNXOWU/A2xVoJyliNarqbI+U8e2KlCXWbJzYGM9HqTA+T7x/X4MfLnNv98r57d4c9D/XwL2A6xFWWsTL5ZS5b0CLFvwu/wVuJoIbo8q8VvMTARiUvWZBMxUosw/NtnPfw3MW7A8I4KLtwDbt/mZS3PW/xCwVoF1r0b0fpE6F0/o5rGgSZMmTZo0adKUmkg/AzmwR83rmdTieeY5YNOK6+i3OMaEFt/Z6XLMh2hgkirjsyV/81mIZL8LC36u1n2OaKCY9xv/FJi1jTIWaPIc8BawXs3bvmq89agW5b9Jm8/fwKezZ5TGMp7KjonB834CjG6jzNWy4zr1LFQodgFsRCS8pb7nccDMbZYzE9GDU6qcKwvWKW9feRpYv43PG5GYkfrdB08Plt1Hqk7kv/vYuEKZ/bgtJxINJT9G+WvHF4mGsI11eQmYu9e/e6fLJpKCUuV+ukAZv21xLEwD9qtYz+WJOHeq/N9Q4P0J0flD6vi9p8x+VPF7HZbznQ4pWd5MRIPcqwt85unE+huPiSuAcS3KmZVI7s47T/ys4Hd5P3Et+h6wcMnfYy0ikTBVnz1KlLcO744JD54ep8C7m0FlLkacE9s6/wGb5uy/bwNH08Z1ftC+8tOc7/Lnbh4HmjQNnnpeAU2aNGnSVP9EvOhN3XS83s0bcOKlevKBto3PTmjywOPAn2kjgNViHXukyi7w+bmJrqVT9ft1wbp8tsX3dforOe8MCiY2EK3MU2XtVrCccTXtnzuTfpg6uGA543O+14Q66lnyu01M1OeqCuV9NOc73kfBlwBZeUsSLVUby7u+QBmzUfLBtaGc2YmWaY11eRaYrZe/ezfKpvPJeXdRMGEpsY4NST+UP1HmfEC0evxnorx/UyIJrML3en/Ob7ZRF+uQl5w3ML0ObFGgvHmJYF/yul2wboW3bU45B+XUZ6eC5eTdWzlwRA31bHm+IV7mptZ/T5njjEhQTt3HtB3k1KRJkyZNmjRp6tSU8wzkdDc5byqwRg3rGFdTXeuKY0xo8RzQ9ZhPznY4tQf7XW37HBE3fDGnzJMKljUGuC6nrNtq3PZ1xFtbJecVfRbLOxcMbuh8fMEy98wp85sFypiJiIulytm75G93bE55m7f5+Q/nfP4lCjbYJ3qVarYdh01yXj9uy6yMcTX9XruSjqsd0MvfvRtl053kvMqNfYnksFTZR5Ys7ys55RV6D1PD97oxUYdLu1yHVHLe4OlC2kz4ysprTAwfmN6mjQToQeXMD4yt4fstRsS0G+vT9r1BVo4Bd+Z8t39WPR8BY9r4m9HkJxuWbazx45zyNunmfqhJ08CUN+a8iIgMbXndYz/sJboyriBviMzc7rvbNBXY3RPdg3fZp4nWq42uI27S2+bup1JtyL9uehD4vLtPK/i5gVZbjbYqUoi7P1JwvXnlnEt6CJO96yh/uDCzUaSHyJkCbOfuzxUt090fY9BwsINsaGbrtFnGa+6eOxRFgbq8Qgxr0nhunJ/0EBnSvreIoM/zFcs5incPMwDRim+HMucDd59CtDp+q2HR0kQCVrekhkKY5O7Xd7EOrezr7le2+8fZtt6GuE432iobwqPdsuo61x8HXJNYVPRcf1TO/JPdPW/YkbZ5kyE84J1hK1Ln4ueJc3Hh4yz7jVPDRGxqZmsULU9ERERkGDrE3e+sWsgQi2N0PeaT3esumVj024J16Dd7AGMT8+8G9i1SkLu/CuxCjFLSaC0z27Bw7WbUjXjrz939TwU/kzds88Bwt7cTiWRFTCRGdWi0dYEyPsWMQ+ZBJAr+umB9BnwTuDkxv63hoYle71MOcvfbilTE3X9E9KI4EvTjtqzz2vEH0udTxcCrO8PdT6tSgJltQQxJ2+gsdz+yTJnu/hPggsSitve/mqRin6d3uQ7N3Afs6u6NMeJc7v5LYojhRgZ8rUA5k939pXb/vkk5T5J+F7mWma1VoKhdgVTc9mVg26rno+w+ppXPACsk5h+dvT8t4xtA6vrX7WNBBEDJeSIiw9RiOfOndLUW+etbvGK5R7n7UxXLqMPnEvPeJoYTLBrEhAg2PVmtSl1xSJkHB3d/nBimsFGRh4S6fT8xb3kzW7jrNelf2wIrJeb/wN0fLluou19CDCnbKC+Q2DHu/iBwdmLRxt2uyzDzK3e/u0oBZrYasHli0S/c/fay5br7HaS3eTf3v4US8x7r4vpbuREoHPhw9yfIf3ny+Uo1Ku+HiXkbmllj0meSmW0OvDex6BHgwCoVK2B7oiV5o++5e+qlUlvc/ULgb4lFXT8Xi4iIiPSZe4Ff9roSCZ2OY/Qi5rMg6XdV/fR8VEZeAt7+ZeKGWQPFvGetLxUtL6HT8dZXiCH+z45yAAAgAElEQVQOC3H3fxMJeHm+4e5vFizzbeDcxKK2G5QRwzk3ego4okhdBssSRL6dWLSDmTVt7G5mSxE99Df6O1A2wexAZmzYOBz11bbskB8k5q1sZvN3vSbDxxvUExM6IDHv5Zz5RRxK9BA22Npm9r6K5bYla/Cf2r/66dr+lTaTxhodTowK1OjDZpaK93aUu/+ZdAJakXcbX86Zf7C7P1S8VqWkzsWPA6UbRWf3B4cnFu1oZouWLVekLCXniYgMT3PmzE/1ZNNJeevLq1873iTdSrirzOy9wJqJRRdnSR+FZcHPVKubfvIIcF6Fz6cCtSuZ2awVyiwte7D4V2JRVx5Sh4jdE/OmACfUUPbPE/NSLQW74f8S89brei2Gl7LB38H2SMybRvqFVFGp/W9jM5u5hrLbkXp50PUAThOHu3tjEK9dPwWeScz/VI/O938FGoNtc5NOPE7ZLWf+Me7+YulaFZM6F08GflZD2f10LhYRERHpFydXuB/umA7HMXoV88lLrOqn56NCst5qVkwsusndUz17t+vnxFC5jXau+KzVjXjr7yr0rH9jzvwH3P3ykmWmGinNY2bjWn0wa0iYigsf5+4vl6wP8E6SxYMNs0cRQxI38wnS73xPKNmIHHe/j2Hee16fbsvaufv9QKqRtWLg5V1QdVSXLJFr28Sik6omS7v730mPJNGVeE+WBJ069/TLtf2astePLBZ4TGLRzETvb71Q+t2GmS0LpHrgfRw4qUql2pXdN6UaRh9bMoFysEuASQ3zZgI2q1iuSGFKzhMRGZ7G5Mzvl+S8vPq14wp3/2+Fz9dlk5z5Z1Qs90xmbNHUTy4pG9DJ3JOYNxPp4YG7pTFIArB212vRh8xsNOkhPc6raZiTVOB+nJnl9f7ZSan9YLUuJmoNN/e7+101lLN9Yt5f3T2V+FXUTUTL/cHGkA7KdkIqgLeimW3QpfU38xRwRdkPZ60Sz0osmpse9Jaana8eTyxqea7Petf7YGLRS3RpKA4zm4X0cGB/cvfGIbnLSA1dvGwvWvuKiIiI9JHU/Wy/6FQco1cxn+dJJ+jtUaEuvZb3XFdpqN7sBXWqx7dZqfYs2414aypxoF3/yJl/SYUy78uZ305POqlYhZPuob+MqxLzWg1dnFr+FnBOxbpUjXX3u37clp2iGHi9zqyhjG2YPkT3YHXdg6Ri793c/1Lx2z26uP5mKl2Pgd8To2g1So0A0w1Vju/tcuafVPG+sIjUufht4I9VC84a21yVWNSrc7GMYErOExEZnlI39BBdbXdT3vry6teOWyp8tk7rJuY51QJNuPtjwJ1Vyuiw6yt+Pq/F19wVy63i2cQ8JSSENYG5EvP/XEfhWevCJxKLepGclNoPRgPzdrsiw0Tlc3XWtfzyiUV17X9vkn551K39L6/l/x/MrNfB0fOzFq5V5L0A6FWr7LLn+uWB1BBhf+5ir3lrA2NTdaijcHd/knSyaD8kioqIiIj0wmPu/nSvK9FEp+IYPYn5ZM8eNycW7Wlmh2RD4w016+fMv6iGsi8ouM52dCPemuqprl15iYN5z9VVymwnRpnqbecfWVy3DqnhCVs9n6Vi1Te7++SKdakU6x4C+nFbdopi4PWq47yZ2v+mkr4mltHr/S91jt7OzI7LGqL2UirRvW1Zz4Y3JBalzsXdUOX4zhv+tnJiXAGpY+HeLGZZh14fCyJAvHAUEZHh57Wc+d1OgMpbX5VuiEsNGdsBqZvsh939hRrKvovu9dxU1KSKn89LZKi0b5rZGGBL4ndbDVgBmIcYQnlOit/zzFOlPsNI3n6Y17q4jGeBxRvmLVG2MDNbmejZcjWiK/RFiX1gLmD2EkXOQ7qVnzRXx7m6W/tfo9L7XxHufr+Z3Qesklj/zWZ2LnAycGWWSNhNqYBFUXcQSevWML90cp6ZzUP0ILc6cYwvy/Tje06KNz5r51y/Rs78ugKl7ejWsdCYhNiVY0FERESkD3U89tSncYxJFT9fJeZzLrBRwzwDfgh8xsx+BpxdUy/q3bBOYt4Ud59UQ9l5jXqrJAR0ep9/reLwjy/lzJ/UgTJTjVQbpZ7ReharyEagSPX4d3fVirj7FDN7DFiyall9qq+2ZStmNjszXjvmRjHwbnvO3R+toZzU/vePrKevOqT2vwXMbNaaRsVp5Vxgl8T8rxHDsf8COLPGZNh2PVJD4jLA7cyY2LagmS3t7qlhpFsys3WJZPvVgFWBBZke+5ytYHFzmZm1sT+lYp8vAPcXXF8VQ+pcLFKWkvNERIanxmH6BgyH5LwqgZw6pQISqR6YyqgcOOmg5yt+Pi9xdNYyhWUPK/sDOxIPKXVRYCKkei0DOMTM6nqATwUPC/VWl73U2BfYjfxEmrK0L5RTx7k6b//7vJl9tIbyYcbEOOhub4nfJT0MxijgI9k0xcyuJLrfvw64uwtDClTuwdXdXzCzScDSDYuWLVqWmW0NfIEYXrbU9SJHO8d3Y/0H3FpjPVrJOxYONbO6EjdTrWnVc6iIiIiMVB2LPfV5HKOXMZ9fAQcDiySWrQycCJxgZjcTw/RdDdzk7lPKVLQLUrGOuuKGDxOJkI37T+q3a1en461V9628GFSVcvPKbLq/mtncRLJEo1XMbGKF+gy2WGJes2O8sdHrgLr2uXsYhsl5fbotk8xsPeBLxLUj1bN+WYp7llPXOTMV71m0xv0vb/vOC3Sjh+CzgG8DKyWWLQ0cAxxjZrcz/dr+N3dPJVLVqa6Rq+7Kmb8sca1ui5nND3wF+CQl4qZNzETcK+R2KGJmBoxLLLq9xiTRprLvn4pBvrfGYyGViKe4p3SdkvNERIanKt3y1ynv5r/Kw0sdPdNVkg2nkQqg1tV6N2/79YOXe10BADObD/gRsBcz9spUhzEdKHMoygvufbzD6237wcjMtgd+CozvUF20L5RTx7k6b//7UA1lN9O1B3N3P8vM3k+cy/LMA+ycTQAvm9l1wBXApe5eV7B9sLqGDHiSGZPb2g78mtlSwAnAh2uqT6N2ju/USzXobmOBvGPhkx1er4JUIiIiMlLVHnsaInGMnsV83P1lM/sYcDkwc86fjSJ6k1kf+CbgZnYv8Wx0BfCXLvUE1FT2ortjcUN3dzN7NrGOKvfvnY635jUk79dym8l7PluFdAPAuow2szndPdVDZd5zdl0JLp1OlOmVftyW75IlrRwDfJb+vXaMRJXPmWY2F+lEy3HA7lXLb6EryXnuPi1rYP03mieVrpVNXwMwsweYfm2/zN3rvj+pM+6ZUiT2uTfRS/D8tdRoRmNovr/OD6SGGO6HuOd7sqlTZjWzMe5epTMZkUKKDvsjIiJDw+M58/Nucjol1TIM4IkKZeYNOdBNc5F+GG75QN2mnicg9jMzWxi4FtibzgQl6GC5Q023E3oHtDOECGb2ZeBCOpeYB9oXyqrjXN3X+1+NvkAEWts1B7B19pm7zewuMzsgG9qkLnVdh6Ym5rUVoMqGqL6JziXmQXvHd17wsJs9dIyUY0FERESkX9Qae1Icoz3ufg3wftpPYjPgvcBXidjAU2b2y+xZopfmJv3urc54X+lnrRz9EG8dKnr1fAb5z2h5216x6ub6cVu+w8wWBa4H9kTXjn4zlOOe0MV4j7vfSwz9OqnAx1YE9gPOAZ42s9+YWWq4+LI6GfeE9mOfJwIn0bnEPGh9jI/kuCco9ildpp7zRESGp4dy5s9vZuPc/ZEu1WPtnPl59Rsq8m5Y62qt2Re90/WjLPnkr8SQJs28BTwFPEYMcfFaNr2V+NuNqbe78OFkth6tt2VgyMz2BH7SRllTiYTlJ4lj9DVi6JLGbtnHArsUq6Z0WN/uf3Vy97eBb5jZuURLyc0LFrEa8GPg62b2LXf/36pVor7rUOpFQDsB6CWIc/3CLf70deLYfowIrA2c61PD/m5LeujWVlKtR6G+lxztGBHHgoiIiMhwpDhGMe5+jZmtBBwK7Es0TmrXvETjp73N7HTgAHevOpxqGXlxwzrjfamy6hwmWfL16vkM8p/R8o4Txaqb68dtGQvNxgJXASu0KOdNpl87phDXjVdJx0U2AZYpWlHpmL7d/+rm7neZ2apEz3gHUCyZfCzwGeAzWex0f3ev2vNdXTG9vHLaiX0eDXyxjXU8S7zbeJrp94apXoIXJxo4FDWS456g2Kd0mZLzRESGp3uIB7CZEsvWATqenJcN1zEuZ/GdnV5/h+UFJOrqtahI4HGkOYz8gPY/gVOAq4Hb3f3Ndgo0s4kM06B2DVKBnJ4zswWB43MWvwGcDZwHXO/uT7VZ5niUnNdv+nL/6xR3vwnYInsR9RlgG2B12u/tfBHgFDObAHzW3cv+fkYEZuoYDioVXHmtjc/9mPzEvFuBiUTPI/dmyY0tZUMBl0nOy/sdxpLfQrZuI+pYEBERERlmFMcoKEuoO9jMvgvsCuxMJJW0m3w2ihgScDMz287d7+tMTXPl9ahUZ2/nqdhhN1+kj2T9+HymWHU5/bgtBxxOfmLeA0y/dtxR4NrxW5Sc10/6ef+rXTY07XfN7Bjiur4LMAGYr0AxOwMbmdlO7v63CtWpKxksr5ymsU8zW4NsCN+EF4HTgEuAG9ptZGBm21AuOa9Z3LNbRtSxICObkvNERIYhd3/FzO4jhnZotC7RHXSnrZsz34Hbu7D+TppKfI/GVhV1tVBVV8oJZrYIcGBi0VvEw8xP3b2xN7R2dPNBY6hJBfemunuVoVLqcBjpFna3Ah8p2Tuo9oP+kxdcntPdh+2QO+5+P9FLxKFZovsmwEbApkSPtK2e4XYjrlP7V6jGXMB/K3x+cDmNmg6LYGbrAh9NLHoR2NPd/1iyLmWP8bx9bR7giZJlFpU6Fia7+wJdWr+IiIiIlKA4RjXu/iKRgHKKmY0mnoc2zqZNaD0M3Hjg/8zsfe7e7lC5dZgKvM2MDa3qjPelyupFL4EjUV6sYn93P7GrNZku7zlbserm+nFbYmaLE8N1N3qT6HXs57p2DAt5+9/R7n5IV2vSRe7+GnAGcIaZjSIaJg+OfS7SooiFgQuza/u/S1ajrnNaXjmthoQ9mnTHKhcCn3H3MkPKdiLu2S15x8K+7v7LLtZDpOPa7YVBRESGnr/kzP+ImXWjq97Ui3WA29x9chfW3zFZLz2p1qgL1rSKusoZbnYAZk3MP8jdTygZlIBirbNGmqcT8+bOEoZ6KdXD3SPA+ysM293r79Qr/RyYS+1/MIJa+br7c+5+vrt/3d3XJwIjOwAn07zXti9lSW5l1XU8pMppFWD6SM78T1ZIzMurSzvyet9sNeRunVLHwvxmNlxfkIiIiIgMF4pj1MTd33L3m9z9eHffiYidrQF8mxhBJM844LvdqOOAbLu+kFhUZ7wvVVaZl/lSXD/GKvK2fV0NuoZrw7B+3JYAHwJmTsw/wN1/pmtHIf0c93yW9ND1vd7/usbd33b3O7J7oo+5+6JEb8NfB25u8tH5iVE3yupk3BOaXI/NbF5gi8Si64GdSibmNatLK8+R7j2v13FPGEHHgowcSs4TERm+LsqZvyzRAqVjzGx2YtiJlIs7ue4ueiwxL9VTYRl1lTPcbJeYNwk4oWK5i1f8/HD2cM785btai0Gybt9T2+xId68yxGS/7weph+TUS56i+jkw13f7X6+5+8vufpG7703ssweR37rwoAqrek+FzwJgZjMBKyYW5QVcBqTO9Ve4e959TTt1mZlyQ9oC5LXCXbtkeWXoWBAREREZmhTH6BAPd7n7Ue6+GjE03p05f76nmbXqZa9uqeeeWuJ9ZrY06R7R8hoWSb2eIh2j6eXzWV6v7opVN9eP2xLS144H3f1nFcvt52tH3tCaVWOffRv3dPdppN8x9Xr/6yl3v9/dj3X39YB1gGtz/vTDZpY39HMrleOeLcppFvvcmvSoKAdl+0RZpY7vLNl3UmLRWl3q5AXiGpYanntEHwsyPCk5T0Rk+LoaeDRn2Z4dXvdHSQeJHDitw+vullsT85apqRebNWooYzhKPWydX6G1IFkPcLrJz3dHzvwtu1qLd8t76D6vYrkbVPx8p6Va3dcxPMm4GsrolH7c//pGlqh3PLAV6QDGVtnwEGWsXr5m71gRmC0x/7YWn0udk6se32tSPqCb94JvvZLllaFjQURERGRoUhyjS9z9amBD4G+JxTOT7qWmk25JzJvPzJaqoey8uGFqnVKzbESVuxKLNs0ahnWduz8JPJlYtFrVss1sHmDJquX0o37clpnktaNKgWa2EP3dC1Uq7gnVY5/9HPeEdLxnNTMbrr1VFuLutxHX77wOP7YuWfQqZpZKkCsqFT99Hbi3yWdSx/fT7n5jxbpUebeRin3OTbrRde3c/S3SvSBvljX+Fhk2lJwnIjJMZQ+Xp+Qs/qSZ1fHifQZmNgY4Imfx5e6e1wPNUJPqVtuAD1Yp1MyWQMl5eVJdaU+qWOZmxHaTtJtIJ/1s3+2KDJLaD6ZW6PJ9wGYVPw/wRmJeHQ/6kA5SLVqlwCwI0c0Eo6IeAP6bmJ9qQTxiufvfSCe+zwssUbLYD5SvUcsycoelyIZ2mCWxaFLFulQ5vh8i3QPFB8ysW8Oj3Eh6qJNenotFREREpDXFMbrI3V8FDsxZ3O2ev/JestfxPLtDwXVK/VI9Oc0FbNLtigySSs5cN0vorWLbip/vd/24LTt17agqFfeEemKfLxIdOzSqFPsENq74+U5L7X+jqPiOaTjJErf2z1lc9to+KxWPCTObJaeMu9w971iB9PH9SMW6zEq1GH9e74S7VCizjjrMC2zUxTqIdJyS80REhrefAqlhHkcDJ1XoUaeZbwNL5yz7fgfW1yt5N6yfqFjux1GQNU+qpdyrFcvcp+LnhzV3fwG4KrFoAzNbp8vVGVD7fmBmG1BDa2IikNRoTA3lQjo5aD4zW6xCmZsDc1T4fEdlSeapoUyXMrMPd7s+fe7ynPllW9qub2ZVW1R/KjHPiaTfPHktoksf49kQDJ8v+/msV5NUC905SX/H2rn786TvOzbOhvoWERERkf6kOEb33QS8lJjf7V6IbsiZX+kZwsxmI/2y/DXye9yW+l2QMz8vgaQb8nqNrJpc8cmKn+93/bgtUw0B++HakYp7Qg2xzywG+ExiUdXE6n5v1NiP+1/fcfeHicazjapc26ue27YlPWxyq0T5TtwbfpJIKi4rr2fCvTv0DjlFx4KMCErOExEZxrKXucfmLH4fcECd6zOzNYGDchZflg0xMSy4+z2ku3vevmyvhGY2B/DVShUb3lI9o5XtFYosoWGr8tUZMSbmzD+qm5UYJLUfLJC1ECvr6xU+O1gqSLV4TWXnDa25eYUya70GdMjEnPnf7WJwYChIvXyCdM+X7fpK2Q+a2YZAKoH3CndPBVsH5PWAWfpcD+wMLFfh85DumRDgG13sPW9iYp7Ru3OxiIiIiLSmOEaXZY1rXk4sqvJsVKYedwL/SCzaKGsgWNY+pF/A/7FFTz1Sr2uBBxPzdzSzdbtdmczvgbcT8/cvOyygma0MbFOpVv2vH7dlqrOFKteOtalnaO+85LxOxj5Lxz2z2NT7ylen87JRplKNMdcxsx27XZ8+l4p9Vrm272pmVXpmzIubntHic3XfG46mYozf3ScB1yQWjQP2qlJ2AVeR7iF0l+y9s8iwoBdaIiLD37HAfTnLjq6r9yEzGwdcSLTIa/QKsF8d6+kzJyfmjQJOKJk08m3qe5gdjp5OzCs19GLW7fhvUC+F7TgbeDQxf2sz+3K3K0N6PxhNySCTmX0cqCvYMSkxb2EzW7CGsu8hHXD4dJnCzGw7hsDQJO5+DXBrYtFqwI+6XJ1+lpd89mSFMvczs1WKfijrqe4nOYtPavbZrLfOVGvRsuf6BYATy3x2MHe/lnQvFEsDx1Utv01nAU8k5m9nZsPxHktERERkOFAco8vMbG4g9Qxe5dmorJ/nzC8VN8xiC0fkLK783CPty5JA/1/O4t+a2TzdrA+Auz8K/CWx6L3A50oWezz1DFnat/pxW1LvtWM24tpRWTa8aCousWod5QO3J+atlCUXFpIlLP1P9Sp1xfE5839pZkt2tSZ9Ktue4xOLqlzbx1IytmxmO5NOHL3b3ZuNGALp43s5M8sbjayVb1O9h0mAE3LmH1+hbm3Les9MnYsNOCO7vxMZ8pScJyIyzGWtJncjhjdoNBPwRzPbo8o6zOy9wHXkJ5YdmLUCGm5+CzybmL8p8LMiBZnZZ6iv967hKjU8w/vMrFALzqy16CnUM4zpsOfubwKH5iz+sZntWte6zOz9ZrZ+iz9L7QcAhxUNbpvZ+4BfFvlMC3m9232sasHu/jrpIYa3MbNCrUjNbFng1Kp16qK8HlkPNrPSvbs1MrP1zKxrvVCY2dfMrPJ5yMxmJh1of9TdJ1coejRwZolA9LGke817Cji/jc+njvFdzWylIpXIerQ7G1ikyOea+FbO/C+YWd45sm1ZwDxXdg7Iq8NPzKzqUEWD6/KB7PwoIiIiItUojtEGM9vczD6dvXyv6guk33v1YsjX00j3NLUO+clASdnzwh+B1PPZLW0kA0j9TgL+lZi/AnBuXUldZraAmX2hzT/PSwg9Lut5s8h6D2IINOqsSb9ty9S1Y0Mze3/B9Y0G/hd4T5HPtZCKfe5UcUSTAZfmzD+6RFnH0+e95g1w9/NJ9563MHBhxd7d3mFmc3azob2ZrWVmX2gV72rTJ4FUclbVa/tnir4bNbPlyX+f8Is2ish7t3F4kXpkddmZ/Pc2RZ0L3JaYPxa4pGqiqJm1M/z1L0gPX7wScI6ZVRm6d3BdFjKzz9dRlkhRSs4TERkB3P12YO+cxaOBU83sdDMr9ALbzGYxs68BN5Pf9fLJ7v6rIuUOFe4+FfhazuJ9zOz3Wa89ucxsZjM7nAiyDrR+1jAUaZfkzP9tliDakpnNRwQzS/U2NlK5+++AixKLZgLOMrPjzWz2MmWb2dgsCH8b0cK3aQKOuz8F3JVYtCHws3aH6sh6Db2C9IN9WX8Hnk/MP8rMdi07jMggeQl1v80CAy2Z2XrADaR7EuhL2ZDoedeR/2dmp5nZvGXKNrNZzWwnM7sauBFYr2w9S9gJuMvMLjGz7bIku0Ky3jNOJd1K+ayqFSRaXv6fmS3WRl1Gm9n3yb8ufrXNYZZS5/rRwAVm1lbvsma2FHE+mdDO37fD3S8B/pCz+Ptm9ssyrTizbX8z0DLI7u6/IR2sHk00tjimwrl4TjPbzczuAC4jXkKIiIiISDWKY7RnHHA68KCZHVA0PjnAzD4EfDex6L/AlRXqV0rWM/jBOYv3N7Nj23kONLP5iZfmmyYWv8XwHK2k72WNWT8LTEss3hy4NYvBlGJmq5vZicRoFnn7UWOdLiD9zDgnkVzR1jCtZrY/cEy7dR3q+nBb5l07zjCzthLtBp03PtFeLdt2XWLeUsDv2o3ZNHEt6cScLc3sB+0UkMX5TgZ6MdpLFXsRo081Wh24w8y2Lluwma1gZj8CHqO7x/VCRBLbJDP7VharK8zMNiXdq9trwHkV6jfg1+0m6GX3bn8mHVe/g/RIW42uJ524v4eZHdhmPczMvkjEKWvp3TTrRXRv4r6i0YrA9UU7CAAws0XM7DjS75Ya6/AGsCfpIdq3JM7FqQbh7dZlDTP7OTH6UVu/tUjdlJwnIjJCuPtvga82+ZNPE0GwX5jZhs2SSMxsOTP7BvAAMZxbXuuXs4F9ytZ5KHD300gPGQDwceB+M/uJmW1mZktkCY0LmdnaZnYYkczzHabfRDvww87XfEi6kHQrxvmBm8zs23nJkGa2eJZI+i/ePYTpi8DVtdd0eNqD9O8P8TDzqJl9x8xWM7PcYXayBJ73mtmeZnYeESQ/HVirQF1+nDN/H+BqM9syVYds3VuY2cXEw/vYQYsvLLD+pCyYd0Zi0dxEotQzZnalmZ1lZhMTU6vhMf9Eurv+xYAbzeyLZjZH48LsgX1tM/sNkZi3ULbobdK98fWjA4C8ngB2I/a/481s3RbXr1FmtpKZfcrMziD2v7yXHN2yDRGgeNrMTjazXSyGis9lZvOY2e7A3cCnEn/yEgV7cB1kGhEoGrABcK+ZfSV1jrVIMt+O2LfyWmte5O55iW2NTgGmJuYvD9yd7edjE8sHAo5HAfcDg3vhfIQYGrqqfcg/D36BuI86xMxWzCsguw9Y18x+YGYPE9u+rRckmc+QDlZDBPkfMbMjsvNsq3Pxamb2OTM7H3iG6NmjUG8KIiIiItKU4hjFjCOe95/Inp2/lD3LzpL3gewZbyMz+z3xnJ/qvem4bDjGrssaLOf1BnUQcItFg7EZ6p099+0N3Et+D2bfd/db66mtFOXu15M/EsqyRKzmMjPb0Vr0vmZmC5rZ1mb2QzP7F9FD2ReBdnobGmw/4NXE/EWAGyySQpdNrN+y+PVlRCLMwPOkA38tWIchp8+25XlAahSiBYGbLRKd5s9Z95JmdjBx7dhh0KKpwDVtrr+Z00knMe5CxObuMbPzLDqCSMU+N8grOEsOyotlfdPMLrScxHYzm8/MPgf8k3ePLnFFe1+rt9z9n0RSkicWLwxcamY3mNknzGyhxN+8I7t2bG5mh5vZ3cQ7vG9QbwP1IhYGjiKS9G4wswPNbH1r0qNedj5ax8x+TX7j+l9kSfBlDL6PGui85PzsfiL1TmF8Fm+8lfTwutOAvdu518gS0PKGoj/ezM41s+R7EjMbY9HhwM1ZGYNj4HW827iD/BFslgSuNLMLLBoa557PsnvYT5nZRURS8teAGd5Z5NThGuCbOYuXJ+6bLjWzD7VxLl4oOxf/yMweJBIo96X4dVWkNrVk04qIyNDg7j8xs1eIh5xUy8w5iBfP+wAvmdk/iBY1LxMJeAsQvVq105X2ycC+7p56WBtudiN6XBqfWDY/0VKr3dZaPyKSZY5ILBsJv2Uud59mZt8kWow3GkO0jj7SzO4BHvbgOA8AACAASURBVCZam81PPDisklPsfkRvRZvVX+Phxd0nWwy9cwX5+/rh2fR8dv54jgj+zE4MvTIf0dKqanf2vyOStVJJJBsBlwOTzex2YDJxvluEGMYh9dB2NxGk2CGxrKj/AXbn3Yl/A+YjWtzmeYT8B2Dc/TUz25f00KDzEQ/lx5vZLcDTRPLdQsDKRCCk0RHE88CEJnXqC+7+qpltT2zb1RN/MpZIEj2QuH79ndj2zxP727zZtDzpbdMP5iMCiJ8DMLNniSSs54ljaRQwF7AM8T2a9cR4sLs/UqEunyMCTgO/1bzEsEvHZYG9x4kWqosR59f5mpT1BJG41hZ3f96iRXRq2JKB/fzHZnYncY/yBhGgXpoImDd6g7hOV058z+r2QeI6nWoRvkC2nh+a2aPEMf1MVod5ifPQKkDuy8U26vDfQefiVKvjBYAjs+k5M7uf6efiOZh+Ll6B6udiEREREWlCcYzSRhHPzgPPz2+Y2QPAs8S97avEve1ixPNus2HObiaGN+ylPYlh7FKNsFYnGoxNzZ5jnySe0xcnhjFuNlTkFUTCg/SQu/84S5TKa6z2gWx6O4uVPU3sx9OI57N5iBFp8kalKVqfhy16vkv14DSaiDsdlO1vjxLPigsT8bpUr/n/jzg3Fe4xaajpl23p7m+Z2aHAmYnFsxPH/XfM7F7av3bsA2xfpV5Z3Z4ws/8lPUrTKGJkh9ToDgMuJ39YT4CfEjGcNRPLtge2N7N/E40ypxC/+WLEyA+NcbLHiAaOTzRZX99w97OyhP2fMj05drANsgkz+yfxvZ4DXmf6/rcY6bh9PzAGfQfgrex7PEPEPl8m9u+FiWt7s1jjv4DDKtTlUiIB9rOD5n0om57I6vUfIilwKeK4ym0ACxzu7qkhYfMcR9wbpOL1OxFDRT9BvLN4nrjnWYS4L0glll1IDGte+d1G9g55KfJ7ltshm14zs/uI7Tc5q+NAvLFUD8iD6nBMdi7OS5jeOpsGn4snE+9CBo6FJUnHbkV6Ssl5IiIjjLv/OrtpOp14kZ1nLNGTS5HeXCBuog9y91+WrOKQ4+7/MbMtiS6tU4kB7ToV+BawRc7y1yuUPSy4+zlmdiz5QwCMIgKbqeSdRt9y99+aWcuhBCW4+7/NbCPgHN7dK1WjeYlhZjtVj2lmtiNwC/nDs84PbNVGcQ8DHySdsFymbg9ZtGw/nQ7ca7v7BWZ2EvD5nD+ZFdi4jaJ+5u5HmdmRtVWuw9z9WTPbDPg9+b0GQFy/ujk8bacskE1FHVb1GuzuD5jZx4neGgcfG6OJXi7b7enyGWBbd0/1+NjMscD7iJbXKbNky9/XopxpwB7ufm2i4Wsp7v5gth9eSAQL8yxFOnmurjpsRLzEa3afNh8dPBeLiIiISGuKY9RiFiLxoqhbgA/2uuGwuz+VxQ0vBZbL+bO5KXbv/hdgp171CCjv5u7fMrOniETQvMZYo4hGo20NS1qxPqeY2WKkh3ke0E5dLiWSI46sqWp9r1+2ZZaotR7RODllJtq/dnzD3c/MGr3W4QAiLrR2TeW9I0tM3J3o5S+vV6xlsqmZZ4i48DM1Vq/j3P1nZvZfYlSJZo2LV8imoWw0kfSWl1Ca5wFgK3dPDQNcxL7ENXmThvmLUyyp60R3b2vY5QHu/pyZ7UT0SpqXhN9uPW4khq9u/B6lufvXzOwFomF/XkB1NoqNhFS0Dt8wsyeJGHHee5uuXVdF6qJhbUVERqCsm/ZVgWOIllW1FEu8xF91JCXmDXD3fxPJIOeV+PgbwCHuvmfWfXveg+fLZes3zBxC9DCY6ua9Ha8BexV9aJKQJdlsAnyb+s4fA24mWoS1U49HiETWByus73pgfXevtQWlu59JJMjdXme5g+xDtIYrYxrxQudLNdana9x9KrAdMRzI8zUXfw/NW+/W7Y2ay3sG+KS7f7+Owtz9YqIlZGqI2XbcDWzs7oWHk82uhbsBvy25boj9Y3t3/32FMpLc/SEiMfBXlL8WNSq0P7j740RPoUeQHrKoihupZxhgEREREQmKYzRX97ORE4kNW7r75JrLLiV7hliPaGBTxVtEb93burvihH3E3U8kGrLeVHPRzwNnl6jP94he7Ms29v498OGRmADaR9vyICIxpaxXgc+6+zEVyphBdu7ZiEhgrP08lMWRNgf+W7KIe4D13P2B+mrVPe7+B6LnwL/UXPTLxGg03fJmB8o8G9jE3R+rWpC7vw5sA1xQsoi3iHd6+5dc/9+I3iCfLbl+gD8AW3TifsDdv0M0jp9UU5GFr0Xu/hOip8VbaqrDgOdJ92ot0nFKzhMRGaHc/RV3/wbRzfV3iG6cy5hCdJO/hrvv7O6T6qnh0OPuk919J6J7+ytoHXR9BZgIvNfdBw/fN3fib19W0C24+9vu/k2it7M7C3x0GtHj26rufkpHKjdCuPtb7n4UMbTm0ZRvhejENvwhsLK7r+fubSe0ufu9wDpEd/9FklMeJYYC2tTdO9KC0t1vcve1iWDK4UQA/n6iS/xKSY0evgjsTHTj367rgQ2G+gud7Pv/nGjdeBgxTEVZ/yCGaFnH3Vdz9yvqqGObtiG64D+RGL62rCeIY2iFuhPR3P0yovXhmbQ/tPoLxD6/nrsX2T8b1/2qu+9GDBNd5B7lNeDXwErufmnZ9bfi7i+5+z5ES/GzKBd0fB44CVjT3f9cog5vuvt3iXPxMZQPXDtwB/AD4nfbwN3vKlmWiIiIiDRQHKM5dz+D6JX660RvSWWT9d4gGg6v7+57ufuLNVWxFu7+nLvvQrzsvr7gxwf2hbXc/dBe9wYoae5+B5FIsDPRI1LZhNzniMSBjwOLZuePMvU5iejZqMiz8b+BT7j7J9297sTZIaMftmV27fg60Ui1yDP6NCKJaVV3n1jgc21z99fd/SBgUSJu8wvgBiJGN4VIXKpS/p3EEJ6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "\n", + "plt.clf()\n", + "fig = plt.figure(num=None, figsize=(10, 4), dpi=DPI)\n", + "plt.subplot(121)\n", + "\n", + "plt.title(\"Original feature space\")\n", + "plt.xlabel(\"$X_1$\")\n", + "plt.ylabel(\"$X_2$\")\n", + "\n", + "good = x1 > x2\n", + "bad = ~good\n", + "\n", + "plt.scatter(x1[good], x2[good], edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(x1[bad], x2[bad], edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(122)\n", + "\n", + "X = np.c_[(x1, x2)]\n", + "\n", + "lda_inst = LinearDiscriminantAnalysis(n_components=1)\n", + "Xtrans = lda_inst.fit_transform(X, good)\n", + "\n", + "Xg = Xtrans[good]\n", + "Xb = Xtrans[bad]\n", + "\n", + "plt.scatter(Xg[:, 0], np.zeros(len(Xg)), edgecolor=\"blue\", facecolor=\"blue\", s=15)\n", + "plt.scatter(Xb[:, 0], np.zeros(len(Xb)), edgecolor=\"red\", facecolor=\"white\", s=15)\n", + "plt.title(\"Transformed feature space\")\n", + "plt.xlabel(\"$X'$\")\n", + "fig.axes[1].get_yaxis().set_visible(False)\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.autoscale(tight=True)\n", + "save_png(\"08_lda_demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MDS" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1. 1. 1. 1. 1.]\n", + " [ 2. 2. 2. 2. 2.]\n", + " [ 10. 10. 10. 10. 10.]]\n" + ] + } + ], + "source": [ + "X = np.c_[np.ones(5), 2 * np.ones(5), 10 * np.ones(5)].T\n", + "print(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import manifold, decomposition, datasets\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "np.random.seed(3)\n", + "\n", + "# all examples will have three classes in this file\n", + "colors = ['r', 'g', 'b']\n", + "markers = ['o', 6, '*']\n", + "\n", + "\n", + "X = np.c_[np.ones(5), 2 * np.ones(5), 10 * np.ones(5)].T\n", + "y = np.array([0, 1, 2])\n", + "\n", + "fig = plt.figure(figsize=(10, 4), dpi=DPI)\n", + "\n", + "ax = fig.add_subplot(121, projection='3d')\n", + "ax.set_facecolor('white')\n", + "\n", + "mds = manifold.MDS(n_components=3)\n", + "Xtrans = mds.fit_transform(X)\n", + "\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(\n", + " Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"MDS on example data set in 3 dimensions\")\n", + "ax.view_init(10, -15)\n", + "\n", + "mds = manifold.MDS(n_components=2)\n", + "Xtrans = mds.fit_transform(X)\n", + "\n", + "ax = fig.add_subplot(122)\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(\n", + " Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"MDS on example data set in 2 dimensions\")\n", + "\n", + "save_png(\"09_mds_demo_1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KYOO3dEt7p9WS+xURz6M8vWNver9WrQN8AvhJk3y9SIiI7SnVvf+e3hLFoVRDPrl5QkWtz3WBH1OO+14SxaHcz74MfKmpDj2wplL3b4DPM7tEcYDlKMm1v4+IvbuIZ6qIWDIiPkl5osBsxkiPBY6PiPcMuP93UD6fl9NfojiU+/JOlCcx9fpddyoiHksZO3+W2SWKQ6mcfgBwbkTs2XVs94qIxwO/o0wG7SVRHMr5+BbgV81k0373PRHjE2ncFov/M0GSJEmSJEmSJEmiJIXP1i6Ux49PdSdw+GDhjEaTSFhL8LiEkqS5sBOb9qn2nqSExIU1SYFfAj4NrDhgdzsCv4yIHQaP7IEi4jOUipX9fo6voyQQzWkR8SjKY963moBY3kz5zGvXgEVOU3n/Z8DT+uxideDYiNito5CWB9bvqC8ok36Oi4j9O+yza4+kv3tRmzdTvpNBJjJ0onmCxUnAtmOOYxXgWEp14+UG7G474PQmkVR9iIiHUq7pUyuX92p54LsdXncmySNb2q8caRQVTWLwdyjX/X7sREmknvj7Z0Q8hXLtWrePzZcC/jsiXjalz/WBk5nd5MCF7UtJuh9IM5HyJGDzAbtaFTgsIt43aEwLi4ilgP8B3jRAN+9tKlr3s/+PUu4V/SaJT4SIeD5wCiWBfhBrAF+PiA8NHtUDNU+g+CnwsD672JgyqXeDPvY9EeMTaRIsNe4AJEmSJEmSJEmSpBFZs6U9gWtblj23pf3EzGzbZtLsRr2S5+GZ+YDqlZmZEXE48PYp667X9HPccEIcyH8Dr51meVKq2F0C3Eip+LcZpfplzZrADyJi58w8s4sAm6qHtQTWKyiVb6+gJJGvS6lA3VbVcp+I+H5mfquLuCbQSpQkz1o18fOBi4CrKMnb61O+x0GTQasi4qnAx6dZ5R5gAXABcANwGyX+VYENKEmAE1O8rUkmPgbYZJrVbqCcK3+lfK7rUxKPFs4rWIZS6X7YCTf3UL7zy5q4bqJ8vmtQJhKs1rLdksCnI+K8zDx+yDF27SrKe74BuI7yua9GSZBqSyqFcm3+HPDqYQfYJiKWpiT8TVdR9RrK9e5K4BbKsbQq5Zr7aHqvMjpdHKsAP6JUbW9zN3AO5di6jvuvJ1tTP2fXAH4YEdtn5nmDxriYWRH4Lg8+fpNSxfkvlONitWadzVr6WQb4SkQ8JjNvGFKsIxURW1O/jt1CeeLC2ETEM4GvMP3ktj83r6spT6l4JKUi/8K2Ar5GuTZMpKZS8Xd48BNpbgLOpozP7qaMg7ejPj4LyvF5SmZe2jyV6BgeXMX7bkoF60ub/tcGHk97Qv6bIuLbmXnyLN9WCSri34D/mGG1S4DzKOchlLHx1pRrc827I+LGzPzPfmKqOATYvdJ+MWV8dSUlkXsDStXxtmPy/0XEsZn5y153HBGvplS4bnNXE8OfKd/XHZT7xaqUyt39Pg2lU80TX77N9BXxLwP+BFxOif/hwGOmWf/tEXF3Zr67oxi3oJxnU+/zd1DOiSson/FalOtG28SNDSgV8p8/i31PxPhEmhQmi0uSJEmSJEmSJGlxsU1L+3WZeXfLsh1b2n/SQTyjMq+l/bCW9kN5cLL4vf1MVLJ4ROwF7Ney+G+USt7zM/MB1dIjYgng6ZT3+czKtisDR0TENpn5twHD3IkHVnK+h/IZfwr4dWbmlNhWplSUfC/1RLJPRcQxHcQ1iT7AA5OZbwX+Czg0My+YunKTkLUXJdmka59tab+Aknx1dGZe1bZxRKxISbh6LiURatyV0j88TQx/AN4DfC8zb194QUQ8hPJkggO5v8L6qpRJGl37NSWh58fA2Zl5S22liAhgC8okkddTksgfsApwaERsnZnTVujNzHksdI2MiPnAPlNWOzkzd+71TczCVZTkqe8Bv8rMv7atGBFrAHsA/0h571PtHRHHZ+ahQ4izF/9ESaia6jZKMuCXM/Psto2ba/IjgWdQzpdnMMuJIE0fR9CeKP4LyvXkR5l5Y2X7NSnXk3dTkjgXthpweETskJl31TrPzJNYqGp8U0X1xMqqG2XmguneyxzySR543bmScp0/MjMvn7pyRGwC/DuwZ6Wv9Zplbx5CnOOwV0v7z6dO5BulZsLFV2nPJzsU+Hhm/rqy7ROAA3jg9/cCJjvR8quUCSH3OoNyvzshMx8wtmjGZ/tT7pdT39NKlGN7X8r4buuFll0KHAR8KzOvm9LnUsCLgI9REmGn+kxEbDV1rDiTiHgl7YniV1Oqlh/RMrZailIR/UDgKZXtPxARJ2bmGbOJqeLVlGv9ve6gPCXoi5n5h0pca1IqkL+NB98flqCMS9p+603ta0WgLeH9LOAjwDG1e8VCfaxKSfZ/PuW+Nd2krqFoPpOv0Z4o/l3gYOBnld8bG1GeWvSvLdu/MyJOyswTBgxzWeBIHviUnrMp1/MfTB3rNWO8p1Lu19tV+nteRLw4M/+3x/2PfXwiTZKJmcksSZIkSZIkSZIkDdluLe3n1BojYjnqf1wGOK2TiIYsIlajXrHvV5n5+9o2TYJGLQHkhU1/EyEi1qckldT8DtgmM983NVEcIDPvycwfU46JN1OqnE61KSVZZFA7c//fZS8HnpSZ+2bmmbXkn8y8KTM/CTy5WX+qdYGXdBDXJHrsQv/9a2DzzDywlswEkJm3ZuYXMvPWLoNoEt62rCw6Fti62WdrongT2y2ZeUpmvjszHwtsDxxFqVQ5UhGxPfDGlsVfArbNzG9NTRQHyMwrM/OjlO9m4Ur7u3QU3m2UqtiPysxtM/P9mXlqW6J4E1Nm5m8z8wDK9/R/ldXWpSQtTqL/oySXPTQzX5+Z350uURwgM6/NzM9TEuEOon7N+kCT5DcO+1bargV2zMx/ni4RC+67Jp+XmYdk5vMoSZMHUqqN9uot1J8GciuwT2Y+qTnOq8l/mXlNc+3dlHqS93aUJFH1buFE0KOBTTLzk7VEcYDMPD8zX0V7td95zSShRVpErE6Z6FLz7VHGUvEh6k+j+Rvwwszcp5YoDpCZZzTf38uBhe8nXd0vhmHhyXzvBrbPzGOnJorDfeOz/6BcZ2oT9l4VEXvwwKfd/C+waTNuuG7qBpl5V/O0mCcCF1b63GJKjDNqqqV/sWXxEZQJK++fZmx1V2aekJlPpZyLU+83S1MmzwyaMLvw9eGPwBaZ+ZZaongT1zWZeRCwK3BzZZWtI6Jtku9Uz6H+hKEvAdtl5hHTJYo38dyQmT/JzAMyc5Mmrh9Svz8Py+eoV+G+E9g3M1+YmT9t+b1xYWa+izI+rR1791bMH/S337o8sIr5u4HHZea3a2O9Zoz3U2AHoO1JSrWnNbWZhPGJNDFMFpckSZIkSZIkSdKcFxGPoZ5EBnB8S/umtFdWrCZaT6A9qVc/a6sqPt3yZYFXDhxRdz5MvfL2RcBumfmnmTpoEhI+Cfxzyyr/EBGPbVk2W1cAO2Tm6b2snJnnAq9pWfzalva54mxgl1qi/4jUHm9/PbDXdEnM08nM0zNzj8ysJTYP26ep5wYcCbw+M2+bqYPMvIiSXPXnDuO6GtgwM/fPzPP76aCJ61nAKZXFr+sgma1r+2XmUzPzmGx/okWrJonvvdQT4TcAXjxwhLPUJCbWqp0fkJlnVtpn1CQF/ntmvq3HGB5OeRrDVLcAO+UsKq5n5vXAs4GTK4sPmKRJU4uQI4CXZeZNvaycmR8Hvl5ZtArw0i4DG5ODqI9frqN8VmMREVsBb6gsuht4ZWZ+t5d+muTnVzPapNlBvTUzP1BLqp0qM08GPlhZtAwPPG7/F3h5L+OGzLyU9icBtY0F23wGWLHS/h+Z+arMrCVat8X1ceqTzTalfMdd+AMlcbencUAzjmqbDNbr+Lg2zjsPeGO2PD2ih7hOyMxnN+OSoYuIXSgTMx4UCiVRfH4v/WTmbyiTZ2tPYtkA+H/9xlixf3Oezfj0hOZ7eDXliT5T7RIRj5ipj0kYn0iTxmRxSZIkSZIkSZIkzWkRsSzwZdr/NnZcS/vDWtpvyRmqCk+QeZW2u5g5GekI6hWQa/2NXESsDexRWZTAHk3STc+ahPFv1nYF/OPsI6zGNS8za5X7povreOBHlUVPa47ruegu4NW1CpwjtHGl7ftNEukiJSK2o1REnuoSyjHZczJfZl5JmTDSSQJgZt6WmQNXZsxSWX4fYGol2DWZsCr8XSWRNQl8P6ksaqtWPEy18+Uu6tfUYXk3sHylfe/MrD0pY1pNVeG9KMm7C1sReN3sw1usnQ/8XR+TI94G1BIKdx08pPGJiB1oH1cc3GtC/ZC8gfpY+eO9JorfKzOPAj7bSVTD98PM/K9ZbvNR6tWtl2z+vZQyOajn476pplybpPKsXvtojq/a5NijgXf02s+UuA6hTC6b6s399DfF7ZSJCNfMcrsvUqqRT9Xr9aF23/pmZt45yzjGqe068tnMPHw2HTWJ+vu0LH5tR090+EZmfm42GzSTGWvJ6kFvTyyYhPGJNFFMFpckSZIkSZIkSdKcFRFrUBIkntiyyg+nqSy2Xkv75QMHNgIRsTnl0eJT/bBJ+mzVJMP/oLLoiU2/4/Y6SgXHqeZn5i/77POtwK2V9r06qCR7TGbWPs9e1Kq8LwVsNUA8k+yQmR4JPwIPqbSNq8r5oPZraX9HZv5ttp01lfG/NlhI3WsSnY6qLHrKqGMZoQ9V2p4cEUtW2oepdr5c1UvF+i409/la5d1jM/PofvvNzL9SqvJPtX+/fS6m3j6bSsb3ysy/ACdWFm07eEjj0YwlDqeeq/Vn4ODRRnS/5ikMr6osuhp4X5/dHsiDJ1xMorfMdoNmktKx06zyvj4nvdXuY+tHxFo9bv8vlbbbgDfPZnJYxbt48OSNLSLiaQP0CfCFfsZ8zXupjUUe1uNntUiP8yLiYcALKouuoc9K4M3vlO9XFq0G7N1Pnwu5k/Zq8DM5GqhNounlXjDW8Yk0iUwWlyRJkiRJkiRJ0pwTEWtFxAHAOcCzW1ZL4J3TdLNyS/uikPgCsG9Ley35uObQlvZ5sw+lc3u2tH+k3w4z8xLqFddXAF7Yb7+Njw+wbS1ZDmCbAfqcZF8YdwCUpJapagknEy0ilqBUAp/qcmZ+usB0PjrAtsNUS9xrmyg0F5wM3DKlbSXgMSOOo3a+rBYRS49o/68Eak9aeG8HfdcqI28UERt20Pfi4CJKol+/ave/zRfFJ2s01+PDgA0rixN47ZgTGJ9HSQqd6iuZeUM/HTbVotvGkpPixMz8XZ/bntbSfiO9j7WnOrWlfeuZNmwmI+xeWfSVzLy4z3iA+yZkHV9Z9IxB+gU+McC2g4yPF/Vx3su5v4r9wr444JN5/qOlve13V6++mZmX9bNhM7HxF5VF/X7PoxyfSBPHZHFJkiRJkiRJkiQtKjaPiPnTvA6PiGMj4vfAlZQKjW3VwQH+ZZqq4gDLtbTf3u8bGJWmsmytCtyNwHd67Oa7wPWV9lePoXLtfSJiZWCLyqJfZea5A3bfVjH5yQP0eTNwUr8bN9Vtr60sWqffPifYuZn5m3EHAVxRaXthBxXmR20LYNVK+1GZObVCaM8y8yzg931HNTznV9oeO1eTgjLzTqCWAPj4EYdSO1+WB14xov0/v9K2YICnTNwnMy8HaveVHQbtezFxXGbePcD251TalgR6rbI8ST5I/VgF+EhmnjzKYCrajulvDNjv1wfcftimqw4+kz+0tP80M6dO5OnVuZTJA1M9tIdtd6P+1J1v9hnLVLXk7EGuhec2Sej9ql0foLfxce2+tdciNF5o+10y6JNffg5cWGnfbsDffrWK5bNR+677/Z5HOT6RJo7J4pIkSZIkSZIkSVpUrAPsM83rVcBzgEcDMUNfH8jMmarZtf1RvO8kyxHajXqi/FG9Vq7MzNuBoyqL1gOeOUBsg9qe+t85B01EgFKpt1ZB80kD9PmLAZPloFSCnqqWBLyoO33cATRq1ULXAr4TEb0kbE2K7Vraj+ug7y766NrVlbalgNVHHcgI1d7zqKujngncUWn/TES0PdmjE0215qdWFv2ow938qtI2yASixcnPB9y+rRLtInX/i4jXAW9rWfxT4N0jDKdN7X5x5QyTKmfUTNq4ZpA+hqytkncvrmppb6s4PqPhB7nbAAAgAElEQVTMvJUHPzECejvmd6q0/Q34Wb/xTFG7Fj4xImb6zdVmoOtDZt4I3FpZ1MtnVfuONge+HhGLwvWl9rvkosz87SCdZmZS/z21IrDVAF0P417Qy/c0tvGJNKlMFpckSZIkSZIkSdLi5DLg+ZnZS2JOW1J1rWrfpJnX0n7oLPtpW7+t/1F4Qkv7WYN23CRJnF1ZtGVEtFWan8mC/iO6z02VtkUhmWW2fj3uABrHUH+CwNOAP0bEJyNi1NWb+9F2rtSO8dnqoo+qiHh0RPxdRHw6Ik6OiD9FxGURcUtEZNsLuKCly4muCB8Rq0bEyyLifRHxnYj4bURcHBHXR8RdM7znWqL0SN9vk9z4w8qiVYDjIuL45v2tMITdbwKsVGnvsvJ9LSF/gw77n8sWDLh97d4Hi9D9LyJ2Bz7fsvhc4CWZedcIQ3qQJtl328qirq7zQ7tfdGDBANvePIQ+2/pdpYftHldp++MgTxKZonYtXIXeYqtZ0H8o9+l3fPw/Le0vA86PiA9ExOb9hzU8EbEO9XvQwL+DZuinbQLiTO7KzL/0G0yjr+95zOMTaSItNe4AJEmSJEmSJEmSpBH4I/BF4IuZeX2P2/ytpX3FbkIajohYDdi9sugiZl9d8OfAn4GNp7S/KCJWzcxaFe5ha6vs3GVS09QEzCWBtYFL+ujvuoEjqk9cWLaDfidN7XHxI5eZl0XEF4B/rCxeGXgT8KaIuBj4MXAScEpmnj+6KHuyfqXtusz8awd9n9NBH/eJiOWBNwKvBrbpsm8mNFk8InYF3gA8j27P53G83/dT3ketYOEzm9etEXEy5Xz5GXBm8wSLQTyqpf1FEVFLnOxHrZ+5XK2+S4Pe/9om7S0S97+I2An4JvUn1VwM7JqZk1B1ezXqY9uurvPnAE/vqK+uDXKMtl2/Bj3ua/32cszXrodrRcT8wcK5z8ot7atTfyrPTMY2Ps7MMyPi+8DzK4vXAt4JvDMi/sT947xTM7Of3wFdG8XvoJp1++yv19/d06l9z71O3h7X+ESaSCaLS5IkSZIkSZIkaa64C7iRkrBwLfBb4AzgtMw8o4/+rmxpX6e/8EZmT6BWBftrTeXsnmVmRsTXgPdMWbQc8ErgkP5CHEhbImTb9zVbbf2sTn/J4rcMEMvi5sZxB7CQfwN2pJ4oeq+HA/s2LyLiSuBESmLRsR0lZQ+idq7UKoP2o6t+iIjnA58CNuyqzymWH1K/fYmIhwGfAF48pF2M/P1m5i8j4l3Ah6ZZbQXgOc0L4I6IOI1yvvwoM0/rY9e1CREAO/fR12yYLN6bxfb+1zx94rvUx2NXAs/soNpuV9rGVRN3v+haU3m4a8Poc1oRsSwlyXmqDYB9hrz71emvSvi4rw/7AadTxnJtNm1ebwSIiEuAn3D/OG8ckz3G+TuoH2P9nsc4PpEmUm3WhCRJkiRJkiRJkjSJTs7MmOa1dGaumZkbZ+YTMnNeZn66z0RxgLYknrUiopb8MynmtbQf1md/bdu17WfYakkS92TmzR3131YdcSKrE88xXX2HA8vMvwHPAH4wi80eArwC+G/g4ubx9ntERAwjxh7UjtmbOuq7k8T+iPgn4HsML1EcYFyf/4NExGbALxheojiM6f1m5oeBf6JM3OrFMsDTgPcCp0bEBRFxUESsOYvdrjrLMLuyypj2q0VARDyacu+oHSfXAbtl5nmjjWpabeObibpfaFrjvCYtktfDzLySMilwNr8TH0ZJvj8UuCwijo6IZw0jvmm0na9dnWdz7nfQmMYn0kQyWVySJEmSJEmSJEmqO7+lPYDNRhlIryJic2D7yqJfZuYf++kzM88HTq0selJEbNpPnwNaqdLWZRXHtgp4K3e4Dy0CMvN64LmUKvp/muXmS1Aebf9N4KyIeGrH4fVixUpbV+fKwJUiI+K1lArbM7kB+B1wPPAdymd6KPDVKa9vDxrTMEXEesBJwENnWPV24ELgZ8D3gW8BX+PB7/erwBVDCrcvmfkpYEvgf4B7Zrn5xsCBwJ8j4h0R0Us+y7gmbk3MBARNlojYGDiBepXnm4HnZuZvRhvVjGr3Cpig+4VmNM5JrIvs9bCp7v9kYH9gtk+DWRp4IfCDiPhpRGzZdXwtar+DoLvzbE7+DhrD+ESaSEuNOwBJkiRJkiRJkiRpEmXmlRFxGfXEvm2ASUv2Adi3pX2FiJg/QL9tiQnzgHcO0G8/atWnl++w/7akqa4qbGoRkpkJfDMijqRUGXwVsCuw0Sy6eSzwk4h4S2Z+cghhtqkl/KzQUd9t50lPImJt4OCWxXcARwFHAz/PzMt67HND4KWDxDVk/wWs27LsTOArlATxczKzp0SmiDgJWKeT6DrSTEx6aURsALwGeB6wHSWxrherAB8EdomIF2fmdNfeuwcKVupQRKxPSRRfr7L4NmD3zDxttFH1pC05dCLuF+qJ18I+ZeZdwOci4r+BZ1OeELML9fO4zVOBX0TEPpn5rSGEubC2p/AM+3xd5H8HjXh8Ik0kk8UlSZIkSZIkSZKkdqcDu1fad6JUdZ0YEbEksHfL4i2bV9deHRHv7jWxsSPXV9qWjIgVM7OLqnqrtLRf10HfWkQ1SeMnNy8i4hGU5PEdKdeDzWfoYingExFxRWZ+c5ixLqR2rnRVGbLtPOnVu4HVKu1nAC/LzIv66LNtUsvYRcTjgD0ri24GXjfAMTGx77mp2PpB4IMRsRKwA/AUSlLdk5i5Eu4uwBERsfs095i2a/7jMvOsPsKW+hIRDwF+TH0i0Z2U69qJo42qZ7V7BUzO/UIza7sWfiIz/3mkkSyiMvNu4JjmRURsRrlfPYUyzttwhi5WAA5vxnk/G2KobedrV+fZnP8dNKLxiTSRLIsvSZIkSZIkSZIktfthS/tzJvAR1Lsxuyp4XdgAeOaI99mWrLB2R/239dOWnKHFUGZelJmHZeYbMvPRlOrO8yjVsO+cZtNPR0RXCXgzqR2za3XU96D91CqAXwQ8s89EcYA1Bohn2F7W0v7qAScPTPJ7vk9m3pyZP8rM92Tm04FVgWcAnwCmqxz/PGCPaZZf3tK+cX+RSrMXEasDPwI2qyy+G9grM48ZbVSz0ja+mZT7hWaQmTcAf6ss8lrYp8z8Y2Z+MTPnZeZGwMOB/YHjgWzZbBngkCH/RvR3UIeGOD6RJtKk/R9YkiRJkiRJkiRJ0iQ5hnpCwLqUPyRPknmLyX7bkgMf21H/W1fa7gau6qh/zUGZeWVmfjUzXww8Avgc9WvHWozunPlrpW31iFi/g7636nfDiNgGqMVwUJPw1q8u3tewPK/SdnJmHt1vh83TJNbpP6Txycw7MvPEpuLtIyjnxJUtq791mq4ubGl/1ADhST1rqtIeR33skJQnBxw12qhm7Xrqlan7vs4PqR9Nb0GlzWthRzLzksz8XGbuRvlc287rR1O/53elLYF5mL+DptvvnNLh+ESaSCaLS5IkSZIkSZIkSS2aCrcntSyemMe6R8RqwO5j2v2LImLVEe7v9Jb2bQbtOCKCerLFOZl526D9a/GQmZdl5v7AAS2rPHtEoZzR0t5FQtEgfWza0t534nTjyQNuP0y1hL1B3+/WwAoD9jF2mXlnZn4V2AG4trLK4yLiIS2bnwPcVWnfpav4pDYRsRzwPeCJLau8KTPnjy6i/mRmAmdWFnWVfGqy+Gj8utK2eUcTxLSQzLwgM/egVJ+uGdo4LzOvBC6pLBr4d9AM/bT9/pqzBhyfSBPJZHFJkiRJkiRJkiRpeoe0tD83Ip400kja7QksV2l/f2ZGVy/gXyr7WJ7RPob7l8A9lfYuqvg9FVit0n5aB31r8fMJ4A+V9lElzv2ypb2LJKbnDLBtrRr2DZl5/QB9Auw04PYAd1Talhqkw4hYmXpS94JB+qWb9zsxMvMC4D8riwLYsmWbvwG/qizaqfncx6l2LMGAx5MmQ0QsDXwb2Llllbdl5mdGF9HAaveLh0TE4wbpNCK2ozxRQ8P3s5b2YVa5Xty9g1KZf6phj/Nqv0s2iohHd9B37Xi5BfhtB30vkvoZn0iTymRxSZIkSZIkSZIkaXpHAb+vtAfwmSZhaKgiYsUZVpnX0n5Ex6F8k3qidtv+O5eZN1OqyU61fURsMmD3e7e0nzpgv1oMNdVaf1JZNKrEud8DN1TaXx4RfecKRMTWwGP6jgpqSbx/G6A/IuLJdFMF96ZK2/ID9tmWtNz3e26egvB3/W4/wU5oaZ/unPlupW0Zxv/51I4lGPx40pg118+vAc9tWeXfM/MjIwypC23jnFcO2O+rBtxevfsekJX2f2zuGepYM2Hp55VFwx7nndLSvtcgnUbEDsAjK4t+mZl3D9L3HNDP+ESaOCaLS5IkSZIkSZIkSdPIzHuAd7Ys3hY4eJj7b6qXz59m+ebA9pVFZ2dmLcm9b5l5GXByZdEOEfGoLvc1g7Yk+H/tt8OIWJ96UtMtwHf67VeLvZsrbXeOYsfNtesblUUPBV4xQNe1JwzMRq0K51oRsewAff7bANsurJbgu/6AfV7X0r7BAH3uDmw+wPaTqna+wPTnzGFALYntbWOuLt6WLD7o8aQxapJu/5v2J6ocnJkHjjCkrhxD/dq8b0Ss0k+HEbEG8JqBolLPMvOv1BNat2Kwe76mN45x3lHAXZX210fEqgP02zaWOnyAPueKfsYn0sQxWVySJEmSJEmSJEmaQWZ+B/hWy+I3RcS7hrHfiHgjcCKw9jSr7dvSXksS7UJbv/OGtL+aLwG3V9pfHxGP67PPjwC1Cu6HZWatOrPUi1q1+0tHuP8vtrR/OCKWm21nEfF4Bk/+u7zSthTwjH46i4hXAi8aKKL7Lai0rRMR012Dp9VUHq0lDu/WT39NAuZn+41nwrU9HaL1nMnMSyiJc1OtDcwfY0XdvwJ3VNq3HHUg6tTBwH4tyz6fmX1PWhunzLyNekLo2sC7++z2IGCNfmNSX9omsH46IjYeaSSLj5GP85qJAbWJrA8B+pqsEhG7Ai+sLLoW+Ho/fc4xsx6fSJPIZHFJkiRJkiRJkiSpN28ELmpZ9v6IOKSf5MuaiNgoIr5PSQhs7TMilgT2blk8rGTxb1GvovbqiBjJ3x8z82rq728J4MiIeMhs+ouI/alXFU/g07OPUIuyiPhwRDy0g34eDjyvsujXg/bdq8w8Azi9sujhwJdnk0jbJEx/Axg0+fbUlvZ3z/YaEhHbA58fMJ6FndXSPmhV1tp7fmlEbDGbTiJiBeBIYL0B4+lMROwXEU/rqLs3VtpuB/4ww3bvoj6B6CXAx7q6N0XE5hHxsl7Wzcw7qce9xxgT2DWAiDiQ9icrHArsP8JwhuHzwD2V9gMionYvaxURLwH+oZOo1LPM/CH16uJrAt+LiId1sZ+IWCki/rmLvkYtItaJiHdExGod9LU98PjKolGM89p+n7y51/vUvZqJBIe1LP5SM+ltkTMh4xNpopgsLkmSJEmSJEmSJPWgSVB+AfUKsQB/B5wdEbv3mwgWEQ+LiI8Dv6eeZDrVbtSTBk/LzAv7iWEmmXkt8KPKoofRZ2XgPr2DUu1uqk2AH/ZaQbFJFP9Uy+JPZObv+oxPi663ARdGxBciYrt+OmiSzf+X+mSPYU3kaPMm6gmAewKfj4hlZ+qgSTA7lvbKij3LzMuA31QW7QB8ppkEM6OIeCHwY2DVQWNayO+A6yrt74+IPXqNreK4StuSwHd6Td5r1jse2KXPGIblqcDJEXFqRLyySWiflSg+ADy3svjYzLxxuu0z88+0Vz9+M+WesMFs42piWzIinhkRR1Puzc+fxeb/V2nbDvhsRKzVTzwaj4j4J0ql7JqjgNdmZo4uou5l5m+Bz1UWLUmZiNfTsd8kih+OOWnj8nfUf6s8Bjiz1++xJiIeGRHvBy4GPtZvP2O2PPBB4OKIODgiHtNPJxGxGfDNlsVDH+dl5kkt+1kC+Frz1JUZRcRWlN9161QWXwy8v98YJ8DYxyfSpFlq3AFIkiRJkiRJkiRJi4rMPCcinkVJ/KslKD6K8ljwP0TEYcAxwG8zs5aoCUBEbAg8HdgD2JWSlNOreS3tw05S+Ab1ZPZ51Csadi4zL4uIfwCOqCzehpK4/wHg0OZx7fdpqszuREk437VlF+c2y7V4WhZ4HfC6iLiQUs35ROCMzLymbaOmmvhewFsolTynOpt64vDQZOYvIuJz1Ku8/h2wY0S8BzgmMx9QnblJaH01cCAPvOb9mMGSlj8KfLXS/gZgq6aC70+mJl9GxFLA0yif79TEne9RJvT0LTPvjIiv8+DPalVKUti1EfEb4CqgVmnz6sz810r7V4D3AKtPaX8k8Jvm/c7PzAcl+EXEJsBrKO954USnS4BrKNe7SfCk5nVL82SM71Gq2p/XlkTbJG49izJB44mVVe4B/rPH/R/c7P+llWXPBC6IiK9RjrtfTD3Wp8S1EbAt8GzgRUC/id3zqZ93bwD2i4jfAguAm6lP6PhwZp7b577VkYjYBvh4y+K7KU9b+VKHBeOPzsyju+pslt4JvJgHT0RcgVKZej7w8cx80ISfiNgWOIByD1zYoPcLzUJmXhgRrwG+zYMT9teifI+nA58ETsjMy9v6iohVgccBT6FcWyflftOFlSnH6wER8TvKOO+nwJnTJQBHxObAPpSJeCtWVjkuM88eQrw1/0D5PTP1aTjLAkdExMuBgzPzlKkbNr8/9wP+DVim0ncC8+ZIMvS4xyfSxDBZXJIkSZIkSZIkSZqFzDw1InamVA3esGW1R1Oq1n0QuCkizgf+QkkIuxtYCXgIsCm9JaE96A/ZzePTd6+sew8l4WGYjqYkSi4/pf0lEbHKqBILMvMbEfF0SsLrVCtSPv/3Nwl5l1AqLT4U2AxYd5qubwT2zMzbOg5Zi6aNKMkibwOIiIso5/N1wPWUpJzVKefzw6fp505g38y8c6jR1r2dkmS9VWXZFpSksuubhKlLKYlDGwCPBZaesv4NlHPuggHiORz4F+qJZztSJp1cExFnUhKil6acs1sAq1W2OZvy/QyULN74GCURbKXKsjUok3vaXAQ8KFk8M29oqrEeXNlmdUrS3sERcRblWnU7sDblHlOr5n4nJYH8oGliGZcVgVc0L4AbIuI8ypMgrqPcA1emvLdNKedPm49l5qm97DQzMyL2pnxvz6qssgzw2uZ1e3NfuLqJaynKcbUa5fOemtTfl8w8IyKOA55TWbw0JQnzcdN0MZ8ycUnjtRrQlgm+JPCqjve3gDLOGrnMvDEi9qFMaqrllM0D5kXEBcCfKefQWpTzZqPK+t8D/geTxUcqM4+OiL8HDqFe4X074DCA5jfKJZRr4e2UyVGrUcbLG9F+7M8lWwDvbf47m8/kcspnchPlKTFrUn7fTff74Xrqv0mGIjOvjYi9KOdr7V76Espvs78CfwKuAFahjFW3YPrv9n2ZeWLHIY/bWMYn0iQxWVySJEmSJEmSJEmapcw8q6mg+HlKRfDprMzMCWHT+TYl0XOqPSnJC1OdlJmX9bmvnmTmzU1ltpdPWbQ85fP44jD3P8UbKcla+7UsX4KS8PrYHvu7BnhBZp7VQWyamx7RvGbjVuClmXnmEOKZUXPOPo9SHf2RLautRknUns4dwMsz88+DVNHNzLsj4kWUyo5rt6y2Ju2V/xd2IaXK+NSk9n5juyAiXk9JpOsyp+JjwPbcn6Q01dKUBL7tZujnHmC/zDypw0rGw7Qq8IQ+tvsipeJpzzLztoh4AfA52u8JUBLAHt9HTP2YB5wKbDyi/UkDycwTIuK1lCcitD3t5pG030vudTalynit2r+GLDO/GBFXUyad1J6GdK9NqE9KWlwF5UlRj5rldlcDz87Mv3QfUrvMPDEiXkKZKFyrdA6wfvPq1Ycy88CBg5t8IxufSJOiNntIkiRJkiRJkiRJ0gwy87rMfAWlgumvu+4e+Anw5Mx8WWaeX1lnXsu23+g4ljZt+5k3ov0DkJn3ZObrKI9iv2XA7n4ObGeluMVe15W/zwZ2zswfdNzvrGTmJcBTgP/rs4vrgOdm5vEdxXMR8Aygdn3r1c+BJ2XmX7uI6V6Z+Q3KZ9VZcn9mJuX6OH+Abq4Hds/Mw7qIqSN3dNzfzcBbMvP1mXnPbDfOzDube8LLKNVyu7QAmNXxn5lXUiaLfZlStVeaeM015kWU634/Tqbc927qLirNVmYeTXmCx3Edd/034Osd9zkqd1MmXXXpZGCHzPxVx/32JDOPBXagjDcHcS3lyUrvHDyqiTBR4xNpEpgsLkmSJEmSJEmSJA0gM3+UmdsCuwBHUB5X3q8FwL8Dm2TmLpl5Wm2liNicUqF2qjsplchH4Rjgxkr7jhEx8gqFmflZyuPhD6EksczGH4DXAE/NzAu7jk2LnIdQKvd/HbhqgH7OBvYHts3M07sIbFCZeTmwM/DPlCqYvbgbOBzYIjN/3HE8v6VUdfwUsztvL6Z8tk9rknE7l5m/yMzHUxJ93wP8D3AucAWlUnw/fd6WmfsCezO7JPnbgS8Bm2fmMf3se4jeQEms/w/gHMpkp35cC3yW8h4/OmhQmfltYDPgTZRrfL8WAF+gTGzYODMP7yOWGzNzP2A94O8pVUlPB/4K3EA5x6SJkpnfpzyV5XB6T669Engz8IzM7DfRXB3KzAWZ+Vzg6cB3gLv67OoW4Fjg9cC6mblXRyGOVDNx7qGUp08cTbkG9+sUSnL1zpl5Xhfx9Sszz6aMp/anPHFlNm4CPkq5/45q0vEoTOT4RBqnKJNXJUmSJEmSJEmSJHUhIpajVHd7MrA1sBHl0d8rA8tTkv6ua15XA78Bfgmc3muiQUQ8CXh2ZdFfM/MLg76HXkXEXtQf035sZv5yVHFMFRErA7tTkmIfC2xIedT40pQkzyuA84DTKLFORCKvJlNEPIpyTm8NPLJ5rQWsBCxHOaZuoJzP51CeNPDjJnFnYjXXqpcAzwW2BR4GrEB5P1cBvwNOAr7VVAEfdjwPoVSD3pnyWa9FOW/vvWaeR6n0/UPK53v3lO2XAjaodH15Zt42vMj7ExFLUj77XSnH10OBNYGgJG5dDPye8pSJ72bmg5L7I2JdyjG4sJtr645KRKxOuf9tR7k/bML998AVKYmKN1KqpJ9P+U5/AfwoM7uuArpwXFtQPuvtgU0px/sqwDKUY/6mJqY/UyYF/B742bgTAKVJEBEbAq+kJBxvQbk+L0k5by4CzqIkEn9vEq+3ul9zjX4O5Tq9FWWMvCblN8pdlO/0RuBSyrXwXMrkllMzs+snr4xdRATlmN4B2JJyz9oYWIMyzluGUlH6BspkiN9Qxnk/yMwLxhHzTJr39BTK06+eSHlPa1O+4zso97oLKeftT4BjMnO2E20XOZM6PpFGyWRxSZIkSZIkSZIkSZIkSZIkSZqDlhh3AJIkSZIkSZIkSZIkSZIkSZKk7pksLkmSJEmSJEmSJEmSJEmSJElzkMnikiRJkiRJkiRJkiRJkiRJkjQHmSwuSZIkSZIkSZIkSZIkSZIkSXOQyeKSJEmSJEmSJEmSJEmSJEmSNAeZLC5JkiRJkiRJkiRJkiRJkiRJc5DJ4pIkSZIkSZIkSZIkSZIkSZI0B5ksLkmSJEmSJEmSJEmSJEmSJElzkMnikiRJkiRJkiRJkiRJkiRJkjQHmSwuSZIkSZIkSZIkSZIkSZIkSXOQyeKSJEmSJEmSJEmSJEmSJEmSNAeZLC5JkiRJkiRJkiRJkiRJkiRJc5DJ4pIkSZIkSZIkSZIkSZIkSZI0By017gAkSZIkSZIkSRq3PfbYY2NgO+AJzb/bAisvtMpFRx555IYjjOckYKcButj3yCOPnN9NNJIkSZIkSZKkRZXJ4pIkSZIkSZKkxdIee+yxM/AOSoL4GuONRpIkSZIkSZKk7pksLkmSJEmSJElaXG0D7DbuICRJkiRJkiRJGhaTxSVJkiRJkiRJeqDbgb8Ajxx3IAvZaJbrXz2UKCRJkiRJkiRJixSTxSVJkiRJkiRJi7M7gd8BZwCnN/+eA+wInDjGuB7gyCOPXDDuGCRJkiRJkiRJix6TxSVJkiRJkiRJi6uvAp8/8sgjb5u6YI899hhDOJIkSZL0/9m787Co6v0P4O9Z2EVREVFTUFHUxH0vBHdFUyrFTFPcMnO9trl1Xa62+Quz7nUpC0hNmyyXq6koKhEuaO4LLgnuqIAsMsCsvz9ouMCcwVmZId+v5+FJvuec7/kMnJk5E+/zOURERERE1sWwOBERERERERERERE9k2Qy2WN710BEREREREREREREZEsMixMRERERERERERERkS1IADQrN5YFQGuHWoiIiIiIiIiIiIgqiwhArXJj1wCo7VALw+JERERERERERERERGQTzQBctncRRERERERERERERA6gJYAUe+xYbI+dEhEREREREREREREREREREREREREREZFtOVJncd52koiIiIiIiIiIiJ5FInsXQI4vIiJiFYDuAPwBeAF4AiATxZ1oEgFsl8lkV+1WIBERERERERERERE5JHYWJyIiIiIiIiIiIiJyfDMBdAZQB4ATgJoAAgAMAfApgMsRERG/RERENLVfiURERERERERERETkaBypszgREREREREREREREZlHDOBlAH0iIiImyGSyn629g4iICB8Uh9WN0rNnz1rTp08vM5aRkQGtljcatSeJRIJatWqVfJ+VlQW1Wm3HioiMw2OXqiIet1RV8dilqojHLVVVPHapKuJxS1VVZR67IpEI3t7e5YezbLIzIzAsTkRERERERERERETkuM4D2APgDIDrALIBuADwAdAdwEgAQaXWrw7gx4iIiKEymexXK9fyNoBFxq58+vRpvTGBP5CQnZX+AxlRVcJjl6oiHrdUVfHYpaqIxy1VVTx2qSricUtVlR2OXbt10WBYnIiIiIiIiIiIiIjI8fwAYJpMJrtYwToHASyPiIgYDWANAM+/xiUoDnRA4wUAACAASURBVIy3kMlkd21cJxERERERERERERE5MLG9CyAiIiIiIiIiIiIiorJkMtnXTwmKl153E4A+AOSlhqvBhC7gRERERERERERERPT3xM7iRERERERUZWm1xXdpEolEdq6EiIiIiMi+ZDLZiYiIiIUAokoNj4uIiPiHTCbLt9JuVgP4ydiV27RpUxNAYumxR48elZzHk31IJBLUrl275PvMzEyo1Wo7VkRkHB67VBXxuKWqiscuVUU8bqmq4rFLVRGPW6qqKvPYFYlEqFOnjk3mNgfD4kRE9Mw6fPgwevXqpTeempoKf3//yi/ob0govBkdHY3IyMjKL4aIzBYaGoqEhIQyY+PGjUNMTIxd6tFqtdBoNNBoNFAqlXBycoKTk5NdaiHLxcTEYPz48XrjpgSI/P39cfPmzTJjixYtwuLFiy0tj8guIiMjERsbW2YsJCQEhw8ftk9BRERUlawGsBhA9b++dwbQC8Aua0wuk8keAnhowiZ6fw1Rq9XQaDTWKIesRK1WQ6VS2bsMIpPx2KWqiMctVVU8dqkq4nFLVRWPXaqKeNxSVWXLY1csFttkXnMxLE5kZ+np6Thz5gxSU1Nx584dPHnyBHK5HC4uLqhZsyZq1qyJ2rVrIygoCEFBQQxCERER0TNJq9VCq9WWBEt0/y4sLIRKpYJGo4Gzs7PDdRhXqVS4du0a0tLScPv2bdy9exd5eXmQy+XQaDSoUaMGvLy8ULduXbRv3x5BQUFwdna2d9lEREREVEXJZLKiiIiIQwCGlRpuAyuFxYmIiIiIiIiIiIio6mFYnKqstLQ0NG7c2OTtpFJpSSinZs2aaN26NTp37oyuXbuiY8eONqi0LLVajYMHD2Lr1q2Ii4tDWlqa0du6urqiXbt26N27N8aMGYOWLVvarlAA2dnZqFevHgoLC/WWubi44N69e6hVq5ZNa6CqyVCHUHb4JCIiU+gC4hqNBmq1uuT78h2nFQpFSXjc1dXVblfoarVapKSkICkpCUeOHMGZM2dw6dIlFBUVGT2Hs7MzgoODMXHiRLz88stwdXW1YcVERERE9DeVVu57x7nXKRERERERERERERFVOobF6ZmjUqmQmZmJzMxMAMDJkycRExMDAGjRogWmTJmC8ePHo0aNGlbdb1FREb755htERUUhNTXVrDkKCwtx7NgxHDt2DB999BE6deqECRMmYOLEiTbpQLl582bBoDhQ/Hg2b96MadOmWX2/RESObPv27Thz5kyZMX9/f0RGRtqnIKqy0tLSSs5BSps9eza8vLwqvyAHotFoynyVDoiLRKKSMLhGo4FEIgGAku7iusC4Pe7G8tprr0Emk1k0h0KhQHx8POLj4+Ht7Y3PPvtM8AIsIiIiIqIKFJT73s0uVRARERERERERERGRQ2BYnKiUlJQU/OMf/8CKFSvw3XffYcCAAVaZd8+ePZgxYwb+/PNPq8ync/LkSZw8eRKfffYZli9fjlGjRkEkElltfqEAW/nlDIsT0bNm+/btiI2NLTMWEhLCsDiZLC0tDUuWLNEbj4yMfCbD4qU7iFcUEBc61xGLxRCLxVCpVFAqlSVdxl1cXKx6bvQ0pnQQN0ZGRgYmTJiADRs2YOvWrbyjCxEREREZy7vc9xl2qYKIiIiIiIiIiIiIHIJ97s9O5ODu3buHgQMHYvHixRbNo1QqMWfOHISFhVk9KF5aWloaRo8ejdGjR1ttzpSUFCQnJ1e4zsmTJ3Hx4kWr7ZOIiIieLbpQt1KphEqlKukOrguL60LiYrEYIpGowuC3SCSCVCqFSCSCUqlEYWEh5HI51Gp1JT4i2zh06BB69+6NjAxmfIiIiIjIKF3LfX/PLlUQERERERERERERkUNgZ3H626lbty4GDhxocLlSqURWVhbS0tKQkpJS4VxLlixBnTp1zOqeLZfLER4ejv3791e4XkBAAAYPHowePXqgefPmaNiwITw8PCCVSvHkyRM8ePAA165dw4kTJ3Do0CEkJSVBo9EIznXvnvX+7hMdHW3UejExMVixYoXV9ktERER/b7qO4aU7iAOAq6tryTqFhYVmdQTXBcY1Gg1UKlVJt3JnZ2c4OztXapdxAHBxcUGnTp3QrVs3tGzZEoGBgWjUqBGqV6+OatWqobCwEDk5Obhy5Qr++OMP/Pjjj/jjjz8E5zp79izCw8ORmJhY6Y+DiIiIiKqOiIiIIABB5YYP26EUIiIiIiIiIiIiInIQDIvT306LFi0QExNj1LqPHj1CTEwMoqKikJ6eLrjOzJkz8cILL6Bdu3ZG11BUVIRBgwbht99+M7jO0KFDMXfuXHTv3t3gOl5eXvDy8kJgYCCGDBmCJUuW4N69e4iNjcWqVavw4MEDo2syhVqtxsaNG41ad+PGjfjkk08gkUhsUguRLYWGhpaEFImIyLDDhw9btL0uIK7rGq4Lieteg8ufR1gahhaLxXBycoJarYZCoYBGo4FarYarqyvEYtvdXEkkEiEwMBBDhw7FSy+9hC5dusDFxcXg+tWqVUO1atXQoEED9O7dG++99x4SEhLw1ltvCV7UmJSUhHXr1uGtt96y2WOoqtLS0uxdApFVxcTEGP25loiISCciIkICYGW54esymeySPeohIiIiIiIiIiIiIsdgu6QEURVQp04dvPfee7hw4QL69esnuI5Go8G8efNMmnfixIkGg+INGjTAvn37sGPHjgqD4obUr18f8+bNQ1paGj766CO4u7ubPMfTxMXFCXYpb968ud5Yeno69u3bZ/UaiIiIqOrTarVQqVRQKpVQKBRQKpVQqVQld0kRi8UQi8UWhcMNXfQjEokgkUggkUigUqmgUCiQn58PpVJp9r6e5rvvvkNKSgo+++wzBAcHVxgUNyQkJARHjx5Fly5dBJcvXbrU4F1miIiIiMhxRUREaMt9hT5l/RkRERGuFa1Tbn1nAN8A6FNu0RLTqyUiIiIiIiIiIiKivxN2FicCULt2bezcuRPBwcE4efKk3vK9e/fi1KlT6NChw1PnWrNmDTZt2iS4rGPHjti1axd8fX0trtnV1RXz5s3D66+/jtdffx1HjhyxeE4doQ52EokEmzdvRpcuXaBWq/XWDwsLs9r+iYiIqOrSdRBXq9V6HcRFIlFJZ+/SAXFzwuKl5zVEFxgXi8UlIXWtVgu1Wg0XFxeLO5iXV7NmTavM4+XlBZlMhlatWkEul5dZdv/+fSQlJSE4ONgq+yIiIiIiICIi4jkI/7/y8v8TTxoREeFvYJonMpksw4plfQlgfkRExEYAWwH8IZPJVOVXioiIkAIYDGAxgPK3RjwAQPh/VBIRERERERERERHRM4NhcaK/uLq6IiYmBkFBQYLBoz179jw1LJ6Wlob3339fcFmrVq0QFxeHWrVqWaVeHT8/PyQkJODdd9/FmTNnLJ4vOzsbO3fu1Bvv168fOnTogL59++p1Et+5cyceP35stYAUERERVS26gHjpr9LnUyKRqOTLHkQiEaRSKdRqNZRKZUmY3dXVFRKJxC41PY2fnx/eeOMNrFu3Tm9ZXFwcw+JERERE1vU7AD8j1msAINXAslgAkdYq6C++AN7966soIiLiIoD7AHIAOAHwAdARQDWBbU8CeEUmk1V8hSURERERERERERER/e2J7V0AkSN5/vnnMXjwYMFl+/fvf+r28+fPx5MnT/TGq1Wrhl9++cXqQXEdqVSKL774AkuWWH5X2c2bN6OwsFBv/I033gAAjB07Vm9ZUVERNm/ebPG+iYiIqOrQBcSVSiUUCgWUSiVUKhVUKhW0Wm1JF3FdZ297BcV1dIFxXWhcoVBALpdDoVA8tUO5vRi6c8udO3cquRIiIiIicgAuADqguIv46wBGAAiBflBci+Ku5MEymSyvUiskIiIiIiIiIiIiIofEzuJE5QwYMAC7du3SGz9//nyF2128eBE//vij4LKlS5ciMDDQKvVVJCQkxOI5YmJi9MY8PT0RHh4OAAgPD4enpyfy8sr+rSk2NhZvv/22xfuvTHl5edi1axcOHz6Mc+fOITU1Fbm5uVAoFPDw8EDdunXRrFkzdOvWDYMGDUKnTp0qvcYzZ87gl19+QVJSEq5cuYKsrCxotVrUqVMHPj4+6N69OwYNGoS+ffvC2dm50utzFEVFRdi7dy/i4uJw9uxZ3LhxAzk5OVAqlfD29kbr1q2xfft2uLu727yW/Px87Nq1C8ePH8fp06dx8+ZN5ObmIjc3F2KxGO7u7vDw8ED9+vXh7++PZs2aoUuXLujWrRt8fHxsXp+pioqKsG3bNvz66684deoUbt++jfz8fLi7u8PHxwetWrVCr1698Oqrr6JRo0aVUlNqaioOHz6M8+fPIyUlBdevX0d2djby8vKg1WpRq1Yt1KpVC/Xr10ePHj0QEhKC7t27w9XV1az9rV+/Hr///nvJ96X/rZOSkoLIyEij5vP29sb//d//Gb3/vLw8JCYm4sSJE0hJScGVK1fw8OFD5ObmoqCgANWrV0etWrVQu3ZttG/fHj179kRISAjq169v9D7s6ejRozh48CBOnTqFS5cuITs7Gzk5OVAoFHB3d4e7uzu8vb3h5+eHxo0bo3379ujWrRtatWpl1fBxQUEB9u7di4SEBJw+fRqpqal4/PgxCgoK4OLigtq1a6Np06bo2rUrBg0ahODgYIjFpl13mZKSgk8++aTk+/T0dMH13n33XVSrJtQYUN/cuXPRokULk+qwhFar1esirhsDigPZEonEot9NUVERduzYgT179uDs2bMlr6PVqlWDt7c3GjZsiD59+mDgwIFmnV+JxWI4OTlBpVJBoVBAo9FApVLB1dXV5N+prfn5CTe3NHTs2NPNmzfx448/4uDBg7h06RIePXoEtVoNT09P+Pv7o23btggLC8OQIUPMfj12FOnp6fj5558RHx+PCxcu4MGDB8jPz4enpyeee+45dOnSBSNGjED//v2NPqYUCgX27t2L//73vzh16hRu3bqFnJwceHh4wNfXF61atUJ4eDiGDRuG6tWr2/TxnTx5EnFxcTh27BiuX7+O+/fvIz8/HyKRCNWrV0ejRo3Qtm1bhIaGYtiwYahRo4ZN6ykvKSkJ27dvx/Hjx3H16lVkZ2dDIpHAx8cHvr6+CA4ORlhYGHr27Gn15/Tjx4+xY8cOnDx5EqdPn8bdu3eRm5uLvLw8SKVSuLu7o1q1anjuuefg7++PwMBAdO3aFV27doWXl5dVa7GEVqtFUlIS4uLicPz4cVy/fh2PHj2CXC6Hs7MzvLy8St5ze/XqhbCwMLi5uVVqjZmZmdi2bRv27duH8+fP4/79+5DL5ahZsybq1KmDwMBADBo0CIMHD7bJOY+jnJ8Q0TPvPQC9AHQFUNuI9R8BkAH4t0wmS7FlYURERERERERERERUtYgcqIuewxRCVUNaWhoaN26sNx4SEoLDhw+bPW9iYiJ69uypNy4SiaBUKiGRSAS3mzp1KtauXas33rhxY1y9ehVSqeNfm5GSkoKWLVvqjUdGRiI6Orrk+/HjxwuGyi9duiS4vaO5ffs2li9fju+//x4FBQVGb9eyZUvMmzcPY8aMsSgA4O/vj5s3b5YZW7RoERYvXlzy/alTp/DOO+8YfSw3btwYn376KUaMGGF2XdYUExOD8ePH642Xf5wVMfQcP3ToEEJDQwEUB6tWrlyJqKgoPHz4sML5Hj9+rBfSOXz4MHr16qW3bmpqKvz9/Y2qU+fevXv45z//iR9//FHwDgPG6NSpE0aOHInJkydXevCqPLVajTVr1mDp0qV49OjRU9eXSCQYNWoUVqxYAV9f35JxoedKdHS00cFqANBoNEhMTMTmzZuxZ88e3Lp1y+htdby9vTF79mxMnz7d5J9tZGQkYmNjTd6nIX5+fkhLS6twnZs3b2LLli3Ytm0bTp48CbVabdI+xGIxXn31VSxYsABt27a1oFrb0B1fX375Ja5du2bWHD4+Phg+fDgmTZqE9u3bm13LrVu38Omnn+L777836bnr7++PefPmYeLEiQbPDcoz9JpjidKvibYUGhqKhISEMmOjR4/G119/DZFIVPJcN/T+mJCQgAEDBuiNp6SkwN/fv6QD+dq1a7F8+XLcu3fPqLoGDx6MxYsXo3nz5ia/N+tC72q1GhKJBBKJBG5ubg51znb58mW0atVKb3zo0KHYsWOHHSrSd+/ePXzwwQfYvHmzUa9VdevWxfz58zF9+vSSIK+h8wZTPqsac35VEWNqePz4MebNm4fo6GgoFIqnzhkUFIS1a9eiR48eBtfRaDRYu3Ytli1bhvv37z91Tm9vb3z22WeIjIy0aiBVoVAgOjoaUVFRuHr1qtHbubq6Yvz48Vi4cKFFgV1jzhcOHDiA999/H6dPnzZqzjZt2uDzzz9H3759za5L58qVK/jwww+xc+dOFBUVmby9WCxGcHAwRo4ciQkTJsDFxeWp2widf1j6WVepVGL9+vVYsWIFUlNTjd7O09MTkydPxty5c1GnTh2z97948WK9u2GVPy/Ky8vDokWLsG7dOsjl8qfO6ezsjBkzZmDhwoUWB/Id6fyEnlm80oAMioiIeA5AIIDnUBwcdwOgBvAYQAaAMzKZ7E/7VVihOgDK/E+b9PR0aDQaO5VDQPGdKks3LXj48CFUKpUdKyIyDo9dqop43FJVxWOXqiIet1RV8dilqojHLVVVlXnsisXiMhmqv/iguPFHpXOs1nlEDsDb21twXKvVIisrS3CZXC7HDz/8ILjsH//4h0OFjipSOhBe2htvvFHh9zpCAXJH85///ActW7bEunXrTAqKA8WBrbFjxyI4OBg3btywUYXAJ598gi5dupgUBElNTUVERAQmT578zPyxLTU1FZ06dcLcuXOfGhS3tejoaLRs2RLffvut2UFxoLiT53vvvWd0CMpW0tPT0bt3b8yYMcOooDhQHK7ZuHEjWrVqhfj4eKvVkpmZiUaNGiE0NBTr1q0zKygOABkZGVi4cCGaNGmC/fv3W60+W5gyZQoaN26MuXPn4vjx4yYHxYHi8OFPP/2Edu3aYfbs2WbNYStXr15Fly5dMGPGDLODWEDxCfvq1auxatUqs7bXaDT4+OOPERgYiNWrV5v83E1LS8OUKVPQvXt3m74n2JNWq4VarYZSqRR8bxGJRBCLxRCLxWUC4+bIyMhA7969MW3aNKOD4gCwe/du9OzZ06zXHV0XdCcnJ2g0GiiVSsjlchQWFpoUUrYlQz8LgQ+UdrF79260bt0aGzduNPp15sGDB5g1axZ69+6Nx48f27hC60lOTkarVq2wbt06o4LiQPGdiUJCQrB+/XrB5enp6ejTpw+mTZtmVFAcKH6uTJgwARMnTrTacZqUlIS2bdvirbfeMikoDgCFhYVYs2YNWrVqhY0bN1qlnvLUajVmzpyJfv36mXSOdO7cOfTr1w9Lly61aP8ff/wx2rZti59++smsoDhQ/J6TkJCAt99+2+jftbWdO3cOnTp1wttvv21SUBwoDnBHRUWhRYsW2Lx5s40qBP744w88//zzWLlypVFBcaD4QofPP/8cHTt2fOrFeBVxlPMTIiJDZDLZHZlMFi+TyWJlMlmUTCZbLpPJPpHJZOtkMtnPDhwUJyIiIiIiIiIiIiIHwLA4UTnmhC7i4+ORm5urN+7k5ITRo0dboyyb0wU9y2vYsKFex9JevXqhYcOGeuuaEhSqbBqNBhMnTsT06dORn59v0VxJSUno0qULjhw5YqXq/mfatGmYN2+e2T/H9evXY8KECVauyvFcu3YN3bp1w/nz5+1dClatWoUJEyYIvgZURbdv30ZwcDB+++03s7Z//PgxwsLCEBcXZ5V6CgoKcPfuXavMBQBZWVkYNGgQVq9ebbU5re3PP/+0alB11apVCAsLs+hCBmtJSUlBaGgoTp06Zdc6cnNzERYWhvnz56OwsNCiuU6cOIHOnTsjOTnZStXZV+mAuEKhgFKphFKpNLi+NTob379/H8HBwXqdy41VUFCA1157DYcOHTJre5FIBKlUWnIXmaKiIsjlcoc4pzlw4IDgeNeuXSu5En0bN27EsGHDzA58JyQkICQkpEq8f/7+++8IDQ1Fenq6yduqVCq8+eab2Lp1a5nxu3fvWtQlOjo6GrNmzTJr29L+85//IDQ0FCkpKRbNk5OTgzfeeAMffvihxTWVplKp8Morr+Crr74yew5TOsyXN2fOHMyfP9/skLij2LVrF3r06IFz585ZNE9WVhZef/11zJs3z0qV/c/hw4fRs2dP3L5926ztb9y4geDgYNy5c8fkbR3l/ISIiIiIiIiIiIiIiIjIVqpGu2OiSpSZmSk4LhKJUKtWLcFlv/76q+B4r169DG7jaOLi4gS7V44ePRpicdnrSkQiEUaPHo1PPvmkzPi9e/cQFxeHQYMG2bRWc7z55pv47rvvDC4XiUR4/vnn0bBhQ1SvXh3379/HlStX8ODBA8H1MzMzMXDgQBw+fBgdOnSwSo1Lly4VDLDWrVsXgYGBqFu3LtRqNdLT03Hy5EmDXS1jY2MxZMgQDB8+3Cp1OZonT54gLCxMsJt4QEAA/Pz8UKdOHeTm5uLu3bu4cuWKxWFQQxITEzF79myDy8ViMfz9/dG0aVPUqFEDrq6uePLkCXJycnDnzh38+eefDtUJ/smTJxg8eDCuX79ucJ0aNWrg+eefR4MGDVBYWIi7d+/i3LlzZW7JolAoEBERYfPAjVgsRkBAAOrVq4caNWrA09MTT548QVZWFs6fP4/s7GzB7dRqNaZPn45mzZqhX79+Nq3R2urUqYOAgADUqFEDNWvWhEqlQnZ2Nm7cuIE//zTcSC4uLg5Tp07Fhg0bKrHaspRKJV555ZUKO6rWrl0bgYGB8PHxgYeHBxQKBXJycpCZmYnLly8b3WW0Irm5uejfvz+OHz9ucB2JRIKgoCDUq1cPNWvWLHk9OXv2rOBzNisrCwMGDEBycjKaNWtmcY2VTavVQqvVQqPRQK1Wl3yvu2jBGoFwQ+RyOYYPH653/IpEIrRs2RINGjRArVq1kJubixs3buDKlSuC8ygUCkydOhWnT5+Gm5ubyXXoAuMajQYqlQoajQYajQYuLi5wcnKy6c/AkOzsbMFzF2dnZ4SFhVV6PaUdOHAA48ePrzBQ36RJEzRp0gTe3t549OgR/vzzT72uv+fPn8eYMWPwyiuv2Lhi86WlpWHYsGF6d6Tx9PREmzZtULduXUgkEty7dw8nTpwQPD/TarUYP348evTogfr160Mul2Pw4MF6XbwlEgnatm2L+vXrw9PTE48ePcIff/xhMJD/1Vdf4dVXX0VISIhZj+2zzz7DBx98UOE6DRs2RLNmzVC7dm0AxZ3hz549i5ycHMH1ly1bhurVq+O9994zq6bypkyZgp07d+qNN2rUCE2bNoWPjw+Kiopw584dnD592uAx+a9//QthYWHo0qWL0fvesGEDVq5caXC5VCpF06ZN0aRJE3h6esLZ2Rm5ubnIyclBamqq2XdDsbb4+Hi8+uqrFXbEr1evHpo3bw5fX1/k5OTg1q1buHTpksH1P/nkE0gkEixbtswqNV68eBHDhg3Te593dnZG27ZtUbduXXh6eiIjIwPnz583eOHGnTt38NZbb2HXrl1G79tRzk+IiIiIiIiIiIiIiIiIbIlhcaJyzpw5Izhes2ZNSCQSwWVJSUmC471797ZaXbYWExMjOP7GG28Ijo8dO1YvLK6bx9HC4ps2bcK3334ruMzNzQ3z5s1DZGSkXrd0jUaDQ4cO4ZNPPhHs7JmXl4dRo0bhzJkzZgXTSktISCjTyVksFmPs2LGYMWMG2rdvrxdSy8vLQ3R0NBYtWiQYiJ0xYwYGDx5scV2OaMGCBWXCzO7u7nj33XcxduxYNG3aVG99uVyOTZs2wdnZ2eq1vP3224LjTZs2xQcffIDw8HDUqVPH4Pb5+fk4e/Ysfv31V+zcudPundLnzp1rsIaWLVti6dKleOmll+Di4lJm2cOHD7Fx40YsWbKkpENsTk4O3nzzTavX2L59e7zyyivo06cP2rRpAw8PD8H1tFotLl68iO+++w7ffPONXldtrVaLsWPH4uzZs/Dx8alwnzExMWVeIyMjIxEbG1tmHUu6w1akTp06GDZsGF566SV07NgRDRo0MLhuVlYWZDIZ/v3vf+PixYt6yzdu3Ih+/fph7NixVq/TGF9++SUuX76sN+7q6oopU6ZgwoQJaNOmjcHtNRoN/vzzTxw8eBA7d+7EwYMHTb4QRKPRYNSoUQaD4l27dsW7776L/v37o3r16nrLMzMzsWnTJixbtgyPHj0qsyw7OxujR4/GkSNHIJUKn2KHhoaW6Rp/+PBh9OrVS2+91NRU+Pv7m/DIzKMLROu+ygfExWKxzUPSc+bMwYULF0q+9/Hxwfz58xEeHo769etDIpGUuWjt+vXr+Oc//4nNmzfrzXXv3j0sX74cUVFRKCgoMKtDv1gshpOTE1QqFRQKRUmA3sXFRe/iOVtSKpUYP3684IVRY8aMga+vb6XVUl5ubi7GjRtX5iKh0saOHYvZs2ejffv2estOnjyJqKioMr+///73vw4dtBw3bhyysrJKvu/UqROWLFmCvn376p1b5OXlYfXq1Vi6dKneY3ry5AkWLFiA6OhozJgxA2fPni1ZVr9+fSxevBjDhw9HzZo1y2ynUqmwfft2/OMf/xDsmDxt2jScP3/e5Ofqli1bDAbFvb29MWvWLIwaNUrw3EqlUuHw4cNYsmQJfv/9d73lCxYsQK9evdCpUyeTaipvw4YNOHjwYMn3zs7OmD59OiZNmoSWLVvqrZ+ZmYmvvvoKn376qd77g0ajwZtvvmnws155+fn5BgPv7dq1w/vvv4/BgwcLvlfo5OTk4I8//sCuXbuwc+fOCi/qspXMzEyMGTPGYFB86NCheOeddxAcHKx3DKWmpmL9+vX4v//7P8Ht36CC6AAAIABJREFUP/roI4SGhqJv374W1VhUVISIiIgydxlo06YN/vnPf2LgwIF653parRaJiYl49913ceLECb35du/ejW3btuHll182av+OcH5CREREREREREREREREZGuVl3ggqiLi4uIEx4OCggTHCwsLBf+4DADdunWzWl22lJ2dLdixr2PHjmjVqpXgNi1bthQMgOzYscNgN197uHv3LqZPny647Pnnn8eZM2fw4Ycf6gXFgeLAWJ8+fRAXF4dVq1YJhnCuXr2K999/3+I6Dx8+XNKt1tfXF8eOHUN0dDQ6dOgguF9PT0/MnDkTR48eFQyMpaen45dffrG4Lkd07ty5kn+3b98eKSkpWLJkiWCYCSgOk0+ePBnu7u5WrePkyZNlAo46YWFhOHv2LCZPnlxhUBwAPDw80KNHDyxbtgznzp1DcnIyRowYYTBoakvJyclYs2aN4LKJEyfi1KlTGD58uF5QHCgOd86ZMwfnzp0r02k/Pj7eKrW5urpi6tSpuHbtGk6dOoWFCxeie/fuBoPiQHHQtXXr1oiKisKFCxfw4osv6q2Tnp6OqKgoq9RobS+++CJ27dqF+/fv45tvvsHQoUMrDIoDQK1atfDWW2/hzJkzWLx4seBrx4IFCwwGPG0tOjpab6xWrVpISkrCF198UWEQCyh+TW7WrBmmTJmC3bt3486dO1iyZAnq1q1rdA2ff/654N1A3N3dERsbi2PHjmH48OEGw3+1a9fGzJkzcfXqVcGQ94kTJ7B06VKj67EHrVYLtVoNhUIBhUIBpVJZ0kkbKP45674qo5t26YsswsPDcf36dcycORONGjWCk5OTXkA7ICAAmzZtwueffy44X0xMDIqKiuDh4WH2a6muy7hEIoFKpUJRURHkcnmlPXdSUlLQt29fbN++XW+Zr68vPvvss0qpw5B58+YJ3o3Gzc0NO3bsQGxsrGBQHCgOWv/www/46aefyryfWOv9whZKX8y3bNkyJCcnIywsTPAiNE9PT3zwwQf49ddfBS/Y++GHHyCTycp0jH/55Zdx9epVTJ48WS8oDhR3rx4+fDiOHz+Oxo0b6y2/ePFimRqNkZaWhkmTJgkuGzVqFFJTU7Fw4UKD51ZSqRR9+/ZFYmIiVq5cqfdaoVQqMXr0aIsDs6WD4oGBgbh48SI+//xzwaA4UPwavXjxYuzfvx/VqlXTW3727FmDF/mWt2fPHsE7DE2cOBEnTpzAqFGjKgyKA8V3Y+nduzeioqJw/fp17N+/HwMGDKjUOxVMnTpVsAu3k5MToqOjsWPHDvTs2VOwpsaNG2P58uVITk4WPPZ0HfMt/eyXnp5epov5smXLcPr0abz66quC53oikQg9e/bEkSNHDN5JSehuTYY4wvkJERERERERERERERERka0xLE5UyqVLlwRDZADQr18/wfGrV68aDA8ZClo7ms2bNwuGOQx1Fa9oeVFREbZs2WK12iw1d+5cwQCDn58f4uLi0Lx586fOIRKJMHPmTHzxxReCy//zn/+UCTBbom7dujhy5Ag6d+5s1PotWrTA999/L7isdBDp76hNmzaIj48XDPpXBqHb23t5eWHTpk0Vhpgr0rlzZ8hkMsFgs61Nnz69JCxaWkREBL755hu4uro+dQ4/Pz/s2bMHTZo0sVpd3t7eSEtLw+rVqxEQEGDWHH5+fti3bx969Oiht2z9+vUO1/3x22+/RWJiIgYPHmzwjhYVkUqlWLRokWAQ/s6dO9i2bZs1yjRJWlqaYLfzqKioMhcYmKJ27dr45z//iU8//dSo9W/duoVFixbpjXt4eCAhIcGkjuteXl7Yu3cvQkJC9JZFRUU51EVTwP8C4kqlsiQkrguIa7VavYB4ZQYZdV577TVs3boVnp6egjWUfwzTpk3DyJEj9ebJzc3Fzz//DJFIBDc3N6Neu4SIRCJIJBJIpdKScL1cLkdRUZFZHcsNKSwsxKNHj5CcnIx169Zh4MCBaN26tWD418vLC7t370bt2rWttn9TnT9/HmvXrtUbl0gk2LJlC4YOHWrUPMOHD8eGDRvscqyZa8WKFViwYIFRNYeEhGD+/Pl64wqFAq+//nrJ9y+//DJ++ukno84b6tevb/BOQIbOBQ2ZNm0a8vPz9cY/+OAD/PDDD4JBa0Nmz54teLHZ1atXsWHDBpPqMqRly5ZISkoy+jzgxRdfNHgxmLHnx0Lnec2aNcOaNWvMvhClb9++2Lt3L/z8/Mza3lTx8fH46aef9MZFIhGio6MRGRlp1Dxt27ZFXFyc4J1Y7ty5g3/961+Wllpi9erVWLBggVF3cpBKpdiwYYPgRQ3x8fG4efPmU+dwhPMTIiIiIiIiIiIiIiIiosrAsDjRX4qKijBhwgTBsCQADBo0SHD89u3bguMeHh5P7SrsKISCJ1KpFKNGjapwu1GjRgmGJQwFWSrbo0ePIJPJ9MZFIhFkMhnq169v0nwzZ84UDKZptVr8+9//NrvO0nXFxMQIdu6rSL9+/dC/f3+98d9++w1FRUUW1+WIdOEQoQ6cleXGjRt6Y0OGDIGXl5cdqrHMiRMncOLECb3xhg0bIiYmxqQwn4+PD7Zs2WK1AKCrq6tVOjPqOkeX7wSbmZnpcF34rRUimz17Nnr37q03/s0331hlflMIPV+kUqnga6qtLFu2DAUFBXrjGzduFLxTxtM4Oztj06ZNeq9D+fn5WL9+vdl1WotWq4VGoykJVyuVypIu4lqtFiKRCGKxGBKJxG4BcZ2AgAB88803ghdHlH4MpTugA8W/U6FA4f79+0v+7eTkBA8PD6OCh0LEYjGcnJwgEomgVCpRWFgIuVxu8HzRkC+++KLk51z6y83NDT4+PujatSveeust7Nu3D2q1Wm/7tm3bIikpyezworWsXbtW8LHPnj3b6KC4zogRI/D2229bqzSbGjBgAN59912TtpkzZ45g6Fr3+61fvz6+/fZbky4K6tmzp+BFKvv27TN6jiNHjgheHBseHo6PP/7Y6HlKmzJlCiIiIvTGV61aZdZ8pbm4uGDLli0mXyQxadIkBAYG6o2Xfn2oiND71siRI+Hk5GRSHfZk6PPJ22+/jdGjR5s0V0BAAGJjYwWXfffdd5DL5SbXV95rr72GqVOnmrSNq6urYFhdq9UadccCRzg/ISIiIiIiIiIiIiIiIqoMDIsTAcjKykJ4eDiOHz8uuHzAgAEGwzn37t0THPf19bVafbaUkpKC5ORkvfEBAwYIdo8rrU6dOhg4cKDe+PHjx5GSkmK1Gs21fv16KBQKvfHIyEh06dLFrDlXrFgBd3d3vfFNmzZZ3El28ODBgj9PYwh1eVepVDh//rxFNTmqKVOmPPWW8Lb28OFDvTF7dTm31Lfffis4/vHHH8PNzc3k+Tp37owxY8ZYWpbVBQQEYMSIEXrjv//+ux2qqRzz5s3TGzt69KhgGNWWhJ4vderUMbvrs6mysrIEO++GhYUhPDzc7HkbNGiA6dOn642vXr3a7DktoQuIq1SqMh3E1Wo1NBpNSbdsiURS0kXcEXzyySdlQrVarRYqlQpFRUVQKpUGg9nPPfccevbsqTf+xx9/lOn+LRaL4e7urnexiLFEIhGkUimkUmnJzzY/Px9KpdKqXcaFNGnSBGvWrEFycrLd71hTWFiIH374QW/c29sbH374oVlzLlmyxK4Xfhnr888/N3kbd3d3hIWFGVz+4YcfmvXYhd7H7t69i4yMDKO2X7lypd6Yq6srVq1aZdFrwvLly/Uuyrh48aJgl3xTTJ482axzPpFIJHgucvv2baN+VlX9PO/27dv473//qzdeu3ZtszuBDxw4EEOGDNEbz87OxsaNG82aU8fJyclgN/inCQ8Ph6enp974qVOnnrqtvc9PiIiIiIiIiIiIiIiIiCoLw+L0TMvIyEBUVBSCgoKwd+9ewXVEIhE++ugjg3Pk5eUJjleF4AsAREdHC44LhY+FjB07VnDcEbqLb968WXD8/fffN3vOhg0bCnZcl8vl2LFjh9nzAsVdOc3Vq1cvwfEzZ86YPacjmzx5sr1LEOwsKRQ4cXQajQZbtmzRG/f19X3q3QUqMmfOHEvKshmh4J6hC4X+DkJCQuDh4VFm7MmTJ7h06VKl1iH0fMnOzoZSqayU/W/ZskXwTguLFi2yeG6hzsipqalIS0uzeG5j6QLipbuI6wLiQHFYWvdlj4C4WCwWvBMJUNxJPzw8vEzoWqvVGn1Bg1BY/MqVK8jKyoJKpSoZE4lEcHFxgZubm9k/A12Xca1WC6VSiYKCAhQWFtokMC6VSjFnzhwkJyfjrbfeMjvobk27d+8WvDBu/PjxqFGjhllz1q5d2+C5pKPo1asXnn/+ebO27datm+B49erVjT7XLq979+6C42fPnn3qttnZ2di5c6fe+Pjx49GoUSOz6tEJCAhAv3799MYPHjxo0byzZs0ye1tLzo+r+nneTz/9JPg6OmnSJIs+p37wwQeC44Y+dxlr5MiRqFevnlnburm5oWvXrnrj5v6eK/P8hIiIiIiIiIiIiIiIiKiyMCxOfzspKSmIjIw0+DV69GiEhYWhVatW8PHxwTvvvGOwOzhQ3H3PUFdxoLjLohAXFxeLH4utqdVqwS5w1atXx7Bhw4yaY+jQofDy8tIb37BhQ6V3ri0tLy8PFy9e1Bvv2LEjWrRoYdHchjomHz161Ow5q1WrhtDQULO3b9CgAWrVqqU3/uDBA7PndFQtWrRA27Zt7V0G6tatqze2Y8cOizvMV7aLFy8iJydHb3zEiBF6HUJN0a5dO7t3wRUSEBCgN3bu3Lm/bSjIyclJMAD4xx9/VGodQs+XgoIC/Pjjj5Wy/127dumN+fv7m32XidJ8fX0F31eOHDli8dwV0QWqdR3ElUolVCqVwYB4ZYfEdQFxZ2dnODk5GXw9GThwINRqtdnnDEKvM2q1GhkZGSgoKEBRUVGZMLdUKoW7uzskEolZ+9N1GReLxVAqlSgqKkJ+fn6ZYLo1qFQqREVFwdfXF2PGjBE8p6lsho7p1157zaJ5X3/9dYu2t7WKuoM/TcuWLQXHe/bsqXchj7FatGgh+Hy+f//+U7eNi4sTvOvOyJEjzaqlPKFwtiWvhS1atBB83zZWUFCQ4Lgx58dC71ubNm2qMucLhj6XWHrnlxdeeAGNGzfWGz9x4oRFn/2EOpabQuh3be7vuTLPT4iIiIiIiIiIiIiIiIgqi3CLP6Iq7MGDB4iNjbXKXAsWLHhqNztDfxS3JGRZWeLi4gSD8iNGjDD61tsuLi4YMWIEvvnmmzLj9+7dw4EDBzBgwACr1Gqq5OTkksBcaZYGEYDiTr01atTQC9geO3bM7Dm7du1qdnBNx9fXF1lZWWXGhELAVV3nzp3tXQKA4m6h3377bZmxjIwMDBs2DFu2bDG7O2JlO3HihOD4oEGDLJ570KBBld7B+mm8vb31xlQqFR4/fgwfHx87VGR7Qo+5srujdujQAc7OznohxWnTpsHb2xsDBw602b41Gg0SExP1xvv372+1fXTs2BEpKSllxo4ePWr1EKxWq4VGoynzVToIrQuF26N7uG7/EonEpA7mXbt2tagzt6+vr+B4bm4u6tevD4VCAZVKBTc3t5JzM7FYDHd3dygUCsGO80+je5wikagkoK/VauHs7AxnZ2fBx96qVSuMGzeuzJhWq0VeXh6ys7Nx5coVwXMylUqFTZs2QSaTYdGiRZg3b57dzjGF3i98fHwqvKjSGF26dEHt2rWRmZlp0Ty2YqiTtzHq1KkjOG6o47gx3N3d4eHhgSdPnpQZN+acLyEhQW/Mzc0NwcHBZtdTWseOHfXGjh8/Dq1Wa9br0gsvvGBRPdWrV4e7uzvkcnmZcWN+Vt26dUN8fHyZsZSUFLz++utYv3692d30K4vQ5xI/Pz+0bt3aonlFIhGGDBmCr776qsx4fn4+zp8/j3bt2pk1r6W/a6HzbmN+z/Y8PyEiIiIiIiIiIiIiIiKqTI6fZiWyg3r16mHXrl1YtmzZU9c1FKoW6trnaGJiYgTHx44da9I8htY3NH9lOHnypOC4uQGG0kQiEdq0aaM3fuHCBYOd5p/G39/fwqoAT09PvbG/Y1i8ffv29i4BADB48GDBOwj89ttvCAwMxMyZMyu9e7M5DD1XhI5xU1ljDkMuX76Mr7/+GtOnT0dISAiaN2+OevXqwcPDo0xgtvxX06ZNBedz9I7wOTk52Lp1Kz788EMMGzYMrVu3RqNGjeDl5QWpVFrhYxYKSlf243V3dxe8eCg3NxeDBg1Cv379sHXrVr1AnzVcv35dL1QJCHekNpdQIP/OnTtWmVsXEFcqlSUdxHVdxHUBTLFYbHJI21p0wWldUFoXoi5P6AIuoDi8aIlq1aoJjpd+/9NoNMjPz9frCOzs7Ax3d3ezw9disRhOTk4QiURQKBQoLCxEQUGB4GPt378/YmJiynzFxsbil19+wcGDB3H37l3cuXMHq1atEuxUr1QqsXDhQowbN87gz9KWtFotTp06pTdurdd5W75fWMqSczRDx6el531C8+bm5j51u9OnT+uNBQYGWu0CBKHXwtzcXKNqE2LP8+NXXnlFcHzr1q0ICAjAggUL9C4SchQPHjwQfA+yxuegiuYxdAHi00ilUjz33HOWlGT279me5ydERERERERERERERERElYmdxYlKCQwMxKRJkzBp0iR4eXkZtY2bm5vgeH5+vjVLs7rs7Gzs3LlTb9zPz8/k7oIvvPACmjRpghs3bpQZ3759O3JycuzSee/+/fuC49YMNZUPYKrVajx69AgNGzY0eb6aNWtaXJPQhQvmdEx1dEK3i7eHevXqYfLkyfj3v/+ttywvLw9fffUVvvrqKzRq1Ah9+vRBaGgoevTogYCAADtUa9jdu3f1xmrWrIkGDRpYPHdQUJDFc5RWUFCANWvWYMOGDThz5oxV53bUsPj+/fuxdu1a7N6926rPZ3s83oULF2L37t2CQdcDBw7gwIEDcHd3R0hICEJDQxEcHIwOHToIXpRhimvXrgmOb9++XTA4aQ6heR4/fmz2fIY6iOs6cOsC4vbqIK4jFovh7OxscLlWq4VarYZGo4FKpRJcR/f+Z85j0Wq1Bo8PoYv2CgsLoVKp4OrqWrI/iUQCd3d3FBUV6YXJjSESiSCVSqFWq0u6jKvVari6usLJycmkuRo0aICZM2di2rRpWLFiBRYtWqT3ODZu3AhfX1+sWLHC5FotkZ2dLXhua63X+aCgIBw6dMgqc1mbJedoho5PS8/7hOY15j1C6PUwIyMDkZGRFtWjk5eXJzj++PFjsz4P2PP8uEOHDhgyZAh27dqltywjIwMfffQRPvroIzRv3rzkPK979+5mfQ6wtsr4HCQkPT3drPmM/dxdEaHfs7EXb9vr/ISIiIiIiIiIiIiIiIioMjEsTs8cqVSK6tWro0aNGqhVqxZat26NTp06oVu3bujUqZPJ8/n4+AiOP3jwwNJSbWrz5s2CXbDHjBljcmBLJBJhzJgxWLp0aZnxwsJCbNmyBVOmTLGoVnMYCkIa+n2ZytA8jx8/Nisk4uHhYWlJz4zq1avbu4QSn332GZKSkioMnN66dQvR0dGIjo4GUHzs9OrVC3369EFYWJhVQtmWEHquCHUGNYe15gGAXbt2YcaMGUhLS7PanKUVFBTYZF5z3b59G7NmzcK2bdtsMr89Hm+XLl2wfPlyzJs3z+A6crkce/bswZ49ewAUd37u1q0b+vTpg/79+6Nbt24m71fogggAOHz4sMlzmcKSsLiui7hQQFz3b0cgVIcu6K5Wq0tqr4i7u7stSjNIpVIhPz8fbm5ukEgkAIofh6urK6RSKQoLC42quzxdZ3eVSgWlUgmtVqsXTDdlrrlz56JVq1Z49dVX9YL2UVFRGDZsGF588UWT6zSXofMqR3y/sDZbHKOVfdwDxQHpjIwMvfE7d+4gNjbWpvt+/PixWV3C7X1+/O2336Jz5864deuWwXWuXr2Kq1evYs2aNQCAhg0bonfv3iXnebVr166sckvY83OQOez9e7bX+QkREREREREREREREdne3r17MXPmTHz11VeCdxslepZY537TRA4kJCSkJFwl9KVUKpGZmYkbN27g5MmTiImJwfTp080KigMweMvsjIwMwTC2o4iJiREcf+ONN8yaz9B2hvZja0IhCbFYjGrVqlllfkPdER21O/HfibV+h9bg5uaGgwcPYuDAgUZv8/DhQ/z4449488030ahRI/Tr1w8ymcyscKI1CB2znp6eVpnbWsH+L7/8Ei+99JLNguIA7PbzF3LlyhV07drVZkFxwH6Pd+7cufjyyy8hlRp3vaJCocBvv/2GRYsWoXv37mjatCkWL16MzMxMo/eZk5NjbrkWyc3NNXtbXTduoPi9S/clEokqPSiu65ytC6oL0XUQ14XcVSqVQz2nytNqtZDL5SgqKipTp1Qqhbu7e0mI3FSlf1ZKpRJFRUWQy+VQq9VmzTd06FC9C/EAQKPRYPHixWbNaS5D5zeO9n5BhlnymlSV920JHx8fJCUlmfQ58fbt24iNjcXYsWNRr149hIeHY9++fTasUp+h56u1nmd/x89B9jg/ISIiIiIiIiIiIiIi21u3bh3y8/Oxbt06e5dCZHcMixNZKCAgQHBcq9XiypUrlVyNcVJSUpCcnKw33qVLFwQGBpo1Z0BAALp37643fuzYMVy9etWsOS3x5MkTvTFrdnE01AEvLy/PavugqsHLywu//vortmzZgubNm5u0rUajwYEDBzBy5Ei0a9cOiYmJNqrSsPz8fL0xaz1XrNEp8rvvvsOsWbOeul6NGjXw/PPPo1+/fhg2bBhGjhyJsWPHYty4cWW+Xn31VYtrsqV79+4hNDQU9+/fr3A9FxcXNG7cGMHBwRgyZAiGDx+OMWPG6D3ecePGoW7dupVUvXFmzJiBCxcu4JVXXqkwgCzkxo0bWLJkCZo0aYKPP/64JFBdEXtduFVRWFoXri7fMVpH10XcngFxiUQCZ2dnODs7GwxP6y7C0wXEjfl9GKOyguYKhQIFBQVl6haLxXB3d4eLi4tZc+p+dlKpFGq1uiQwXj6Ybqw5c+YI3rEkPj4e165dM6tGcwi9VwCO9X5BFbPnRayOfPHI0zz33HM4evQoVq9ebfLdYJRKJXbs2IGBAweiZ8+euHDhgo2qLEvocxBgvefZ3/VzUGWfnxARERERERERERERkW09fPgQJ06cAAAkJyfj4cOHdq6IyL6Ma5tERAb5+PigXr16gsG+M2fOoG3btnaoqmLR0dGC43K5HJGRkWbPayiYEBMTg48++sjsec0h1H26oKDAavMbCk1Zq8MmVS0ikQgjR45EREQEfvvtN/zwww/Yv38/UlNTjZ7j3Llz6N27Nz7//HPMnDnThtWWJRT4kcvlVpnb0PPEWI8ePcI777wjuMzZ2RkjRoxAeHg4XnjhBdSrV8+oOdPS0vDzzz9bVJctvfvuu0hPTxdc1qFDB4wfPx7BwcEICgoyOsgUGhqKBw8eWLNMiwUGBuLnn3/GnTt38P3332P37t04ceIElEqlUdvn5uZi/vz5iI+Px7Zt2yp87TW3S7S16e5wousarvu3EHsExIHin5UupG4MjUZT5QNxarUacrkcLi4ucHJyKhnXheQLCgrMCrqKxWI4OzvDw8OjZPvCwkK4uLiYFEJ0cXHB8OHDsXLlSr1lhw4dQrNmzUyuzRyGwqGO8n5BT+cor4VVkVQqxdSpU/Hmm29i7969+PHHHxEfH4979+4ZPUdiYiK6du2K2NhYDB8+3IbVGr4Lj62fr3+Hz0GVeX5CRERERERERERERES2tWfPnpK/1Wq1Wuzduxdjx461c1VE9sOwOJEVdO7cGTt37tQbT0hIwLhx4+xQkWFqtRobN24UXHbhwgWbdLzbsGEDli1bZnKHNkt4eXnpjanVauTn51ulq15ubq7geM2aNS2em6oukUiEkJAQhISEAABu3ryJ3377DUlJSUhISEBKSkqF26tUKsyaNQt169bFyJEjK6NkweeKtTpDGnqeGGvZsmXIzs7WG+/UqRO2bt0KPz8/k+c0dFGLIzh9+jQ2b96sN16tWjWsX7/e7GPCkR/zc889h/nz52P+/Pl48uQJjhw5gt9//x2JiYk4duzYUzvhxsfHY9SoUdi5c6fB9xhDr/mnT59Gu3btLH4MFdEFxHWhal1I3JG67IrF4pKQuCGOVrO1abVaFBYWQq1Ww8XFpSSoL5FI4OHhgcLCQoNd4Cui6xCvo1QqoVKp4OrqWiaY/jS9evUSDIufPHkSb775psl1mUPovQJwnPcLejpDr4WzZs3CF198UcnVVE0SiQSDBw/G4MGDAQBXrlxBYmIifv/9dyQkJCAtLa3C7eVyOUaPHo26desiODjYZnUaer5a63n2LHwOqozzEyIiIiIiIiIiIiIisq3du3f/9S9/AGnYvXs3w+L0TONfrYisYMCAAYLje/bscbiOm3FxcSZ1wbOGO3fu4MCBA5W6T0NhhUePHlllfkPzGApn0LPJz88Pb7zxBtauXYvLly/jwYMHiImJQXh4eIVBwenTp1stgPc0QsdsRkaGVea2dB6hDuB+fn44cOCAWUFxAMjKyrKoJlvaunWr4PiGDRssunjAkR9zadWqVUP//v2xdOlSHDp0CDk5OTh48CBmzZpVYef43bt3QyaTGVzu6+srOH7jxg2LazZEq9VCpVJBqVRCoVCUhIR15wS67t326B6u279UKoWzszOcnJwEg2y6zucKhQIKheJvHRbXUSqVkMvlZTq+i0QiuLm5wdXV1eL5tVotlEolCgoKUFhYaPTPtH79+oLj1jqnMYah8xtHeb+gp6tRowbc3Nz0xm35Wvh3FxgYiEmTJiEmJgapqam4desWVq9ejX79+hl8fVcoFJgyZYpNPyPyc5B12er8hIiIiIiIiIiIiIiR6UwHAAAgAElEQVSIbCcrKwvHjh3767uvAQBHjx6tMvkJIltgWJzICgYPHiwYCEhPT8fBgwftUJFhMTExz8R+DYUDz507Z5X5z549qzcmkUhQp04dq8xPf08+Pj4YN24ctm3bhps3b2Lq1KmCrx0ZGRmV9pxp0KCB3tjjx49x9+5di+c+f/682dueOXNGsIbFixejRo0aZs9rjcdlK/+7qvV/QkJCEB4ebvacarUaDx48sKQsu3F2dkavXr3wxRdf4ObNm4iJiYGPj4/guitWrDA4T+PGjQXHr127ZpU6dXThaqVSiaKiIoMBcV1IvLKD4iKRqExAXCKR6NVQ+jEoFAqoVKpnIiRemkajgVwuh0KhKDPu5OQEDw8PSCQSo+cq//PV/cyVSiUKCwv1gumGeHp6Co5X1kVFQHEIVKgztSWv87aYhyrm7++vN2bt18JnWcOGDTF16lTExcXh2rVrGDFihOB6ly9fFnzPtxZDAWZbfg6qaL9/N9Y6PyEiIiIiIiIiIiIiItvZt2/fX3+LbQegH4C2UKvViIuLs3NlRPbDsDiRFfj5+SE0NFRwmSPd1j07Oxs7d+60y763b9+OnJycSttf586dBcfPnDlj8dxarVYwbBEUFGSVzqP0bKhXrx5Wr16NqKgoweV79+6tlDo6deokOG6NQJElc1y9elVw3JLgNFB8paijEgrsWfp4z549C7lcbtEcjsDJyQnjxo3DkSNHUKtWLb3lp0+fxsOHDwW3DQoKglQq1RuPj4+3uK7y4WqlUgmlUgmNRgOtVguRSGT3gLhEIoGzszOcnZ0FA+JAcUC6dEDc0e6KYg9FRUWQy+VlfhZisRhubm5wdnY2e16pVAqpVFrStV0XTK8olG+oi6+7u7vZdZhKJBKhQ4cOeuPWCp8yLF452rdvrzeWkpLi0BdSVVVNmzaFTCbDrFmzBJfb8jzPx8cHDRs21Bu3xuegiuYx9Pnr78yS8xMiIiIiIiIiIiIiIrKdX3/99a9/vVrmv7Zs6EPk6BgWJ7KSKVOmCI7/+uuvpW5rYV+bN29GYWGh3vjChQuh1Wqt9rVy5Uq9fRQUFFTqbbi7dOkCsVj/Jc4ab/qJiYnIzs7WG+/WrZvFc9OzZ9asWWjZsqXeeGUF57p06SI4bo0Q0549e8zeVqgbdo0aNeDl5WVJSUhISLBoewCCQVGVSmXRnHl5eYKhbqEusKawxuN1JE2bNsV7772nN67VanHhwgXBbdzc3NCxY0e98YSEBLM6M2u1Wmg0GqhUqpKAuEqlglqthkajKQmISySSkpC4IYZCx8Z0m66IRCKBk5MTnJ2dIZVKBWvQarVlHoO9A+KVHaQ3hlqthlwuL/P8FolEcHFxgZub21NrNrRcLBbDyckJWq0WCoUCBQUFKCgoMPg7SEtLExyv7C6+Qu8XDx8+xOnTpy2a98SJE8jIyLBoDjJOcHCw4Dj/p5jtfPzxx4LnLrY+zxP6XJKamorLly9bPLfQ8eLh4YHWrVtbPHdVZc75CREREZGp1Go1CgoKLP5/BkRERERERERVmVKpxO7du/H9998b/IqJiUFiYuJfWwwv89/ExETExMRUuP3u3buhVCrt8viIbEm/zSIRmWXEiBFYunQpLl26VGZcq9Vi2rRpOHbsGJycnGxaQ35+Pjw8PAwuj4mJERwfNWqUVesYOXIk3nnnHb3QU0xMDCZPnmzVfRlSrVo1BAUF6d0mPTk5GdevX0dAQIDZc2/cuFFwvHv37mbPSc8ukUiE3r1764V3Kis416pVK9SoUUOv8/9PP/2ElStXCl50YYyzZ8/qvR6aQijE6+bmZvZ8QHFXcWt0wfX09NQbKygosGhOQ6FlSx6zVqvF119/bfb2jqpv376YN2+e3nhFz5mhQ4fi+PHjZcYUCgW+/vprvPPOO0btV6PRlPnSXSAFoCQgbmrgWehYAsw7nnQB9Yo6mOuC7mq1usJO1vQ/Wq0WBQUFcHJygouLS8nP9v/Zu/c4qerC/+PvM2fO3HYBWc0bAXmpL15ARCHRHl8VDVPjq4maYH5/atnFLFMTy8xbFwMr81vpNy+BFoEFVGKkksjDFP2CgUmkKSaxkQaKgsztXOb8/tg948zO7G12Zmd2eT0fj3nscmbmM5+Zc2Z2hnmf9wmHw2pqalImk6loZxHDMBQOh/M7HgQt9fF4vKQJv7Mdb8rtaFRLnb3PWbhwYdnG6p76xS9+UfF10TvTpk3TpZdeWvL8/9GPfqRLLrmkIXfaGOji8biOO+64koB1rd/nHXvssfrVr35Vsnz+/Pn65je/WfG4q1at0iuvvFKyfNKkSTJNs+JxB4NK3p8AAAB0Zfv27Vq8eLFWr16t9evXq7W1NX/eyJEjNXbsWE2aNEnTp08ve5QTAAAAAAAGo+XLl+vTn/50Dy99qKQx7b8fIukQOc4L+trXvtbtNe+55x6deuqpFc4SaEw0iwNVEgqF9O1vf7vseWvXru1xGK1SzzzzjC688MJOz3/xxRe1evXqkuXjxo3ToYceWtW57Lfffjr++ONLlq9atUovv/xyVW+rK52F4L/73e9WPOaWLVvKhpqampp0xhlnVDwudm/Nzc0ly2q9c0kgFArpvPPOK1n+2muv6YEHHqh43HJHGOiNci2cb7zxhrLZbMVjzpkzpy9TyisX8N2yZUufxhw+fHjZ5f/85z8rHvPBBx/Uiy++WPH1G1W554vU9XPmggsuKBtimz17dpft4oXt24Ut4sHOUKFQKH+qJGTZ2X3517/+1aPrh0IhhcNhRSIRWZZVdh5BCNlxHNm2nQ8mDxb9dV8cx1EqlSraEc4wDMXjcUWj0R6N0XGuhmHkW+BzuVz+NjKZTP6y27Zt09KlS8uO11lLdK2cfvrpZV+b586dq507d1Y05vbt23X//ff3dWrooREjRujkk08uWb5+/fo+/c1H1+rxPu+cc84p2fFEku6+++6SHQR7o7P3Uueff37FYw4Wlbw/AQAAKGfLli26/PLLdfTRR+vGG2/UsmXLioLiktTa2qply5bpxhtv1NFHH63LL7+8z/83BQAAAADAQDB58uQOpVohSadJ+liH09mS7uhw7Tvbl3e87GkqjNEecsghZY/iCgx0hMWBKjrjjDN09tlnlz3vhz/8ob71rW/V5HbvvPNOnXjiidq2bVunl5k7d27Z5eVCotXQ2bidtZvXwic/+cmyAa67775b69atq2jMWbNmKZlMliy/4IILNGzYsIrGBDZu3FiybP/99++32//Upz5VdvlXvvIVZTKZXo/3pz/9qc/hv3333bdkmeu6WrFiRUXjLVy4UL/5zW/6NKfA+973vpJl//73v7t8De5OPB4vG0J/9NFHKxpv+/btuvTSSyueTyMr93yRun7OjBw5Uuecc07J8m3btunCCy8sCvF2DFcXBsR93y8JiPeliXfEiBGKRCIlyzds2NDpdYJG6iAgHrSJFwoaxAsD4h2P9tHIGjXMnsvllEwmSw45FolE1NTUVHIkhp5uG8E6NQxDjuMom80qlUrJ8zx95StfKRvEPvDAAzVhwoTK70wFYrFY2UDotm3bKm4qvvHGG7V9+/a+Tg290NkOrJdddpn+/ve/9/Nsdg/1eJ83YsSIsjuybt26VTfddFNFYy5fvly//e1vS5a3tLRo5syZFY05mFTy/gQAAKCQ7/uaP3++pkyZokWLFimbzWqCpNmSVkh6XdLO9p8r2pdPkJTNZrVo0SJNmTJF8+fPb9jP1AAAAAAAVMPw4cP10EMP6eKLL25fklPbp+XvSFpScPqVpI5Fp8e3Ly+83HckvdY+TlvW7KGHHuq08A8YyAiLA1V25513avTo0WXPu+666/SZz3ymovBlOa+++qo++tGP6tJLL+1yTM/z9POf/7zsebUKi5999tllW9R+9rOf9Vtgba+99ip7/3K5nM4991xt3bq1V+PdcccdZVvFDcPQZZddVvE8MTB95Stf0WuvvdbncTZv3qzf/e53JcuPPPLIPo/dU0cffbQmTpxYsnzz5s26+OKLe/Ul07Zt23Teeef1+YupyZMnl13+zW9+s9evIatXr9ZnP/vZPs2n0Pjx48su72sra7n7vHjx4i6Dw+WkUimde+65PW6n7g/33nuvnnjiiaqMdeedd5Ysi0ajHfYeLvWtb32r7A5ES5Ys0Ze+9CW5rlsUEA9C4r7vyzAMhUKhfDC7qxDw3/72Ny1ZsqRH98WyLI0ZM6Zk+eLFi0ueQ0EDdSQSKRsQl9r+vgVN6I7jDKiA+ECSyWSUTqeL1lEoFNK9995b8WtfEBgPh8P5dXjzzTfrpz/9adnLf/7zn+/TjgqV+uxnP1sSipek73//+2X/lnVlyZIl+vGPf1ytqaGHTjnllLLt4m+++aamTZtW0lhYqV27dukHP/hBVcbqb//+9791yy236O233+7zWKtXr9af/vSnkuX98T6vs88nt99+uxYtWtSrsf7+97/rggsuKHveJz/5ScXj8V7PrxE0wvsTAAAAqe3/76+++mrNmjVLu3bt0rGSnpH0rKRZkk6UtI+kIe0/T2xf/mz75Y5V23vwWbNm6eqrr5bneXW5HwAAAAAA9IdYLKZvfOMbmjt3bnuoe63adqm+T1JPv6/1Jc1rv946DR8+XHPnztXNN9+sWCxWk3kD9UZYHKiyvfbaS0uXLi3bECtJd911l8aNG6cHH3yw4kBRa2urvvSlL+nQQw/tUTDn0UcfLRsaPOaYY3TAAQdUNIfutLS0aOrUqSXLW1tbK24GrsQtt9yilpaWkuUbN27UKaec0uMGxTvuuENf+MIXyp53+eWX67DDDuvTPDHwzJ49WwcccIAuueQSrVmzpqIxXnvtNX3sYx8ru7NHrXbk6MwPf/jDsgHABQsW6LOf/ayy2Wy3Y7S2tuq0007rtFmxN/bbbz8dccQRJctXrVqlz3/+8z3+0uu3v/2tTjrpJO3YsaPPcwocdthhZfcive666/TLX/6y4i/kTj311JJlnufpjDPO6HF4r7W1VR/+8If12GOPVTSHWvnjH/+o448/XpMnT9bChQuVSqV6PYbv+/ra176mZcuWlZx32mmnaejQoV1e/8ADD+y0/fh//ud/dMopp2jTpk3yPE+5XE6GYcg0TZmmmW8R74zneXrsscd0zjnnaPz48WXn2Jljjz22ZNmzzz6rL37xi9q+fbssy1I0GlU4HC77HPV9vyggzhfC/cN1XSWTSbmum192+eWXa+zYsbr//vtL2sB7+p4vFArp7bff1qc//WndfPPNZS9z0EEH1e3IAYcffrg+97nPlSz3PE/nnnuuHnrooR6Ns2TJEp1//vns0FAnd911V9nPKn/96181YcKEHq/Hcl555RVdd911GjVqlK644oq+TLNu0um0rr32Wo0aNUpXXXWV/vrXv1Y0zt/+9jd9/OMfL3tef7zPO+GEEzrdcfYTn/iEFi5c2KNx1q9fr6lTp+rf//53yXmjRo3Sdddd1+e51ksjvD8BAADwfV/XXHONFixYoJCk70p6QtIHJXW3i7DRfrkn2q8XUtv/5V1zzTU0jAMAAAAABr2pU6dq+fLl7cV8SUkXSvqE2o7N1ZUdks6XdJGkpI499lgtX768bM4NGEwIiwM1MHbsWD3yyCMaNmxY2fNffvllnXHGGTrssMN0yy236Pnnn+82LLNp0ybNnTtXp556qg444ADdfvvtPW4onzdvXtnltQ4pdDZ+Z/Ophf3226/T1srnnntO48aN0y233KItW7aUnJ/L5fT4449r6tSp+vznP192HY0ZM0a33HJL1eeNgSGbzeqee+7RpEmTdOCBB+orX/mKHnnkEb355ptdXm/z5s265ZZbNHbsWK1du7bk/HHjxpUNDtfSBz/4wbIBQKktVHbUUUdpyZIlZUPjb7zxhm677TaNHTtWzz77bH75SSed1Kc5XXnllWWX/+///q+OP/54PfbYY2W/+HJdVytWrNDpp5+uM888U7t27cqfN23atD7NSWprg545c2bJ8h07dujjH/+49t57b02ZMkUf//jHdeGFF5acvvzlL5cd96KLLiobQn/llVd0xBFH6Ic//KHeeeedstfduHGjrr/+eo0ZM0arVq3KLx85cmSnTej18Mwzz2jGjBnae++9dd5552n+/Pl66aWXuvwCM5VK6de//rUmT56sb3/72yXnh0IhXX311T26/auuukpnnXVW2fNWrFihww8/XJdeeqlWrVolx3G6DIi/+uqr+vWvf63Pfe5zGj16tE4//XQtXbq011/GdtbUevfdd2v06NE6+uijddZZZ+m///u/i7aj//f//p/++7//W+vXr5fneXwJXAe+7yudTiubzeYf/82bN+uyyy7TgQceqHPPPVfz5s3TX/7yl6JQeTmpVEpPPPGELrvsMh155JGdtv5Go1H97Gc/q+ve7N/+9re1//77lyxPpVKaNm2aLrroIv35z38ue921a9fqE5/4hKZPn170Pravfy/QOwcccIDuv//+sjugvPHGG5o2bZomTZqkn//853r99de7HGvHjh1auXKlvvnNb+rII4/UwQcfrG9961t66623ajX9fvPOO+/o+9//vg477DAdfvjhuvnmm7Vy5cqSnUE6evHFF/XVr35VRx11lDZt2lRy/qmnnqpx48bVaNbFfvzjH2u//fYrWZ7NZjVjxgxNnz696H1DoU2bNunrX/+6jj76aL3yyisl5xuGoXnz5g2KMHS9358AAIDd2y9+8Yt8UPwBSVdJMns5htl+vYV6NzC+YMGC6k4UAAAAAIAGtN9+++mBBx7QrFmzZJqmpF9IKj3KbrEPS1og0zR1zTXXaOHChWW/TwEGm3C9JwAMVpMnT9bKlSv1sY99rGxIQJJeeOEFXXvttbr22ms1ZMgQHXzwwXrve9+r5uZmmaapXbt2aevWrXrppZf0xhtvdHub5UJtb7/9th588MGS5aFQSOeee26v71dvnHnmmYrH40qn00XLlyxZop07d/ZbsOC8887T448/rrvuuqvkvGQyqWuvvVbXXXedDj/8cI0cOVJDhgzRa6+9pr/97W9dBmSGDh2qBQsWcPgRSGoLjs6ePVuzZ8+WJI0ePVrvfe97NXz4cO2xxx7KZrN666239NJLL2nz5s2djmNZlubOnSvLsvpr6nnf+c539MQTT2j9+vUl523YsEHTp0/XHnvsocMOO0z777+/bNvWP//5Tz3//PNyHKfo8sOGDdNdd92lgw46qOL5nH/++brtttv03HPPlZz31FNP6eSTT9aee+6pCRMmaM8995TjOHr99de1YcMGvf322yXXGTdunGbPnq2lS5dWPKfAFVdcofvuu68oiB7Yvn27Hn/88U6vO3r0aH33u98tWT5s2DBdd911uuqqq0rOe+utt/TFL35RV111lcaPH6+RI0cqGo1q27Zt2rRpU9k2d8uydP/99+vGG2/s3Z3rB8lkUg888IAeeOABSW33/f3vf79aWlo0fPhwmaapd955R5s2bdJLL73UZbP9FVdc0b6ncOd831cul5PneZo3b5527dqlRx99tORytm3rvvvu03333adoNKrDDjtMe+65p4YPHy7P8/T2229rx44deuWVV6oWhDzqqKN0yimn6JFHHik5z3EcrVu3TuvWrev0+p/4xCf0H//xH1WZCypj23ZJo7tt23rooYfyDc2xWEwHH3ywhg8frmHDhqmpqUmpVErvvPOO/vWvf2njxo3d7jgY/H045phjanZfemLo0KG67777dOqpp5YNwc+bN0/z5s3TQQcdpAMPPFB77bWX3njjDW3cuFGvvvpqyeWnTZums846q+GOiDDYnXnmmfrJT36iz3zmM2W3vTVr1uR3Zjn44IM1cuRItbS0KBqNaseOHXr77bf12muv6dVXX90tdlbZsGGDbrjhBkltn7kOPvhg7bvvvmppadGQIUOUyWT05ptv6oUXXujy88Mee+xR9jNJrbS0tGj+/Pk69dRTy/4tXbJkiZYsWaIRI0boAx/4gPbZZx/t3LlTmzdv1oYNG7pct1//+td14okn1nL6/a6/358AAABs2bIlf1SpOZLO7uN450j6h6SrJd100006/vjjNWLEiD6OCgAAAABAYzNNU5dffrmam5t1/fXXS9rWzTXazr/xxht18cUX13x+QKMgLA7U0Pjx47V27Vp99rOf1S9/+csuL/vOO+90GwjryvTp0/Wd73ynZPmCBQvKNpCfcMIJNd8rqrm5WR/96Ef1q1/9qmh5Op3WL3/5S33qU5+q6e0XuvPOO+V5nu69996y5+dyOT3//PN6/vnnezTennvuqaVLlzZUYy8ayz/+8Q/94x//6NV1EomEFi9erAkTJtRoVl1rbm7W7373O5144ollGySlth1QnnrqqS7HiUQi+tWvfqUDDzywT/MxTVO/+c1vNHHiRG3bVv7N/Jtvvqnly5d3O9YBBxygZcuWlYTaK3XQQQfp7rvv1gUXXNBtY3BvXHHFFVq9enU+pNSR4zhas2aN1qxZ0+U4oVBI9957r0444YSqza2WduzYUdRK31Of+tSnNGfOnLLnBQHxwpPv+xoyZIgeeughfe5zn+v0b4LU1rparvm/WgzDUCgUkmmauv/++zV58mT9/e9/r9ntobY6hsU7ymQy+stf/lLx+Pvvv79+9KMfacqUKUqlUorH42VbofvLySefrJ/+9Ke66KKLOr3vr7zySqd/SwLjxo3T/PnztXjx4lpME9341Kc+pb322ksXXnihduzY0enlNm7cWHanpN2V7/t6+eWX9fLLL/fqenvttZcefvhhvfe9763RzMo78cQTtWTJEp177rlKJpNlL7Nly5ayR1rqzFe/+lXddNNN1Zpiw6rF+xMAAIBCc+bM0a5du3ScpC9VacwrJP1a0qpduzRnzhzdfvvtVRoZAAAAAIDG9m4R4ZndXPIMSbeXLS4EBrP6JQyA3cTw4cP1wAMP6JFHHtGRRx5Z1bENw9CUKVP09NNPa9GiRTr44INLLjNv3ryy1z3vvPOqOpfOdHY7nc2rVkKhkO655x79+Mc/VlNTU5/GOu6447RmzRqa4nZz1W7+HjdunFauXKmPfOQjVR23t0aOHKknn3xSH/rQhyq6/vDhw7Vs2TJ9+MMfrsp8Ro8erRUrVpR9feup4447Ts8880zVm5TOO+88Pfnkk1UN9xuGoXnz5unCCy+seIw99thDDz74YL4RthFEIpGqjtfc3Kzvfe97uvvuu4sCs0FA3HEc2bYtx3HkOI5c15Xv+/kjcFiWpXvuuUcLFy6semhv9OjROumkkzo93zRNWZalSCSicDgswzC09957a926dbr44osVjUarOp+BotzRUbrj+/6gbzQOhUK65JJLtGbNGk2ZMkWu68q2bSWTSTmOU9f7f8EFF+g3v/mNhg8fXtH1jz/+eK1cuVJDhgyp8szQG2eeeaaee+45nXrqqVUdNx6Pa+bMmVUds7+Ypln1nTGOP/54rVq1SkcddVRVx+2p0047TatWrdK4ceP6NE5LS4sWLFigb3/721WaWX311/sTAACAcrZv354/Iuj3JJlVGteU9P3235cuXart27dXaWQAAAAAABqXbdv6wx/+0P6v6QXnLFNbeHxZwbK285cvXy7btvtngkAD4NsroJ9MnTpVa9eu1WOPPaYZM2b0KRjzvve9T9dff702btyoxx57TMccc0zZy7344otavXp1yXLLsjR9+vQy16i+008/XUOHDi1Z/tRTT9WlofDSSy/VCy+8oM985jOKx+O9uu4hhxyi+++/X3/84x91wAEH1GiGGCi2bt2qBQsWaObMmXrPe95T8Tjjxo3THXfcobVr12rixIlVnGHl9t13X61cuVI/+MEPtNdee/XoOqZp6vzzz9eGDRu6DMpW4vDDD9ezzz6rL3zhC7163o4aNUp33HGHnnjiCe29995VnVPggx/8oP70pz9p3bp1uvnmm3XWWWdpzJgx2meffZRIJCoaMxaLae7cufr5z3/eq5B8NBrVJz/5Sb344os6/fTTK7rtWvnf//1fPfnkk7rmmms0duzYioLBUltI7dJLL9WLL76oK6+8UtK7AXHXdfMhcdd15XmecrmcDMOQaZplw39nnXWW1q9fr9tuu01jxoyp+P6NHj1aF198sR5++GG9+OKLmjFjRtH5oVBIlmUpGo0qHA6XDZANGTJEP/nJT7Rp0yb96Ec/0kUXXaSjjz5aI0aM0LBhw2Sa1fraGLXy/PPP67vf/a6mTp2q5ubmPo01cuRIzZo1S+vWrdP3vvc9DRkyJL8d+b4v27aVTqeVyWTqGhj/6Ec/queff17nn39+j4ORe++9t26//XatWLGi4qA5qut973ufli1bpscff1xnnHGGwuHKDkDW1NSk0047TXfffbdef/11zZ8/v8oz7R8jR47Ua6+9pnvvvVdnnnmmhg0bVvFYxx57rBYsWKCVK1fq/e9/fxVn2Xvjxo3Ts88+qzvuuKPXn2WGDBmiK6+8Ui+++GK/7XTcH2r5/gQAAKA7ixcvlm3bOkrSpCqPPUnSBLUdNY0jOQEAAAAAdgdPPvmkdu7cKWk/ScdKyqrtOF6nS/pt+88vtS8/VtK+2rlzp5566qk6zRjof0YDtfE1zESA/pDJZLRq1So9/fTT+vOf/6xXX31VW7Zs0TvvvKN0Oq1oNKrhw4dr+PDh2muvvXTEEUdo0qRJmjhxYo+DBs8884wefvjhkuUjRozQJZdcUu271Kn58+eXPUz7aaedpkmTqv1f4T33zjvv6MEHH9TKlSv1/PPPa9OmTdqxY4ccx1EikdA+++yj97///TrmmGN02mmnNUyQF43p5Zdf1qpVq/TnP/9Zr7zyil555RW98cYb2rVrlzKZjBKJhIYNG6a99tpLY8eO1ZFHHqmTTjqpzw2PtZbJZLRkyRItW7ZMa9euVbAR6YsAACAASURBVGtrq1KplBKJhN7znvfosMMO0wknnKCzzz5bo0ePrvl8tm7dqkWLFmnlypX685//rDfeeEM7duzIv2a+//3v14QJE3TKKafopJNOKgnYuq6rf/7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "# MDS\n", + "\n", + "fig = plt.figure(figsize=(10, 4), dpi=DPI)\n", + "\n", + "ax = fig.add_subplot(121, projection='3d')\n", + "ax.set_facecolor('white')\n", + "\n", + "mds = manifold.MDS(n_components=3)\n", + "Xtrans = mds.fit_transform(X)\n", + "\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(\n", + " Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"MDS on Iris data set in 3 dimensions\")\n", + "ax.view_init(10, -15)\n", + "\n", + "mds = manifold.MDS(n_components=2)\n", + "Xtrans = mds.fit_transform(X)\n", + "\n", + "ax = fig.add_subplot(122)\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(\n", + " Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"MDS on Iris data set in 2 dimensions\")\n", + "\n", + "save_png(\"10_mds_demo_iris.png\")\n", + "\n", + "# PCA\n", + "\n", + "fig = plt.figure(figsize=(10, 4), dpi=DPI)\n", + "\n", + "ax = fig.add_subplot(121, projection='3d')\n", + "ax.set_facecolor('white')\n", + "\n", + "pca = decomposition.PCA(n_components=3)\n", + "Xtrans = pca.fit(X).transform(X)\n", + "\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(\n", + " Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"PCA on Iris data set in 3 dimensions\")\n", + "ax.view_init(50, -35)\n", + "\n", + "pca = decomposition.PCA(n_components=2)\n", + "Xtrans = pca.fit_transform(X)\n", + "\n", + "ax = fig.add_subplot(122)\n", + "for cl, color, marker in zip(np.unique(y), colors, markers):\n", + " ax.scatter(Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')\n", + "plt.title(\"PCA on Iris data set in 2 dimensions\")\n", + "plt.tight_layout()\n", + "\n", + "save_png(\"11_pca_demo_iris\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch06_3rd/chapter_06.ipynb b/ch06_3rd/chapter_06.ipynb new file mode 100644 index 00000000..e24cb71c --- /dev/null +++ b/ch06_3rd/chapter_06.ipynb @@ -0,0 +1,1342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.5 |Anaconda custom (64-bit)| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter we are discussing two methods to reduce the feature space: filters and wrappers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities we will need" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "\n", + "CHART_DIR = Path(\"charts\")\n", + "if not CHART_DIR.exists():\n", + " CHART_DIR.mkdir()\n", + " \n", + "DATA_DIR = Path(\"data\")\n", + "if not DATA_DIR.exists():\n", + " raise Exception(\"Data directory %s not found\" % CHART_DIR.absolute())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "\n", + "import numpy as np\n", + "import scipy\n", + "\n", + "DPI = 300\n", + "\n", + "def save_png(name):\n", + " fn = 'B09124_06_%s.png'%name # please ignore, it just helps our publisher :-)\n", + " plt.savefig(str(CHART_DIR / fn), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Converting raw text into a bag of words" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n", + " dtype=, encoding='utf-8', input='content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 1), preprocessor=None, stop_words=None,\n", + " strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " tokenizer=None, vocabulary=None)\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "vectorizer = CountVectorizer(min_df=1)\n", + "print(vectorizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['disk', 'format', 'hard', 'how', 'my', 'problems', 'to']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "content = [\"How to format my hard disk\", \n", + " \" Hard disk format problems \"]\n", + "X = vectorizer.fit_transform(content)\n", + "vectorizer.get_feature_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 1 1 1 1 0 1]\n", + " [1 1 1 0 0 1 0]]\n" + ] + } + ], + "source": [ + "print(X.toarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 1]\n", + " [1 1]\n", + " [1 1]\n", + " [1 0]\n", + " [1 0]\n", + " [0 1]\n", + " [1 0]]\n" + ] + } + ], + "source": [ + "print(X.toarray().transpose()) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Counting words" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "TOY_DIR = DATA_DIR / \"toy\"\n", + "posts = [p.read_text() for p in TOY_DIR.iterdir()]\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "vectorizer = CountVectorizer(min_df=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#samples: 5, #features: 25\n" + ] + } + ], + "source": [ + "X_train = vectorizer.fit_transform(posts)\n", + "num_samples, num_features = X_train.shape\n", + "print(\"#samples: %d, #features: %d\" % (num_samples, num_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['about', 'actually', 'capabilities', 'contains', 'data', 'databases', 'images', 'imaging', 'interesting', 'is', 'it', 'learning', 'machine', 'most', 'much', 'not', 'permanently', 'post', 'provide', 'save', 'storage', 'store', 'stuff', 'this', 'toy']\n" + ] + } + ], + "source": [ + "print(vectorizer.get_feature_names())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 5)\t1\n", + " (0, 7)\t1\n" + ] + } + ], + "source": [ + "new_post = \"imaging databases\"\n", + "new_post_vec = vectorizer.transform([new_post]) \n", + "print(new_post_vec)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n" + ] + } + ], + "source": [ + "print(new_post_vec.toarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def dist_raw(v1, v2): \n", + " delta = v1-v2\n", + " return scipy.linalg.norm(delta.toarray()) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Post 0 with dist=4.00:\n", + " 'This is a toy post about machine learning. Actually, it contains not much interesting stuff.'\n", + "=== Post 1 with dist=1.73:\n", + " 'Imaging databases provide storage capabilities.'\n", + "=== Post 2 with dist=2.00:\n", + " 'Most imaging databases save images permanently.\n", + "'\n", + "=== Post 3 with dist=1.41:\n", + " 'Imaging databases store data.'\n", + "=== Post 4 with dist=5.10:\n", + " 'Imaging databases store data. Imaging databases store data. Imaging databases store data.'\n", + "\n", + "==> Best post is 3 with dist=1.41\n" + ] + } + ], + "source": [ + "def best_post(X, new_vec, dist_func):\n", + " best_doc = None\n", + " best_dist = float('inf')\n", + " best_i = None\n", + " for i, post in enumerate(posts):\n", + " if post == new_post: \n", + " continue \n", + " post_vec = X.getrow(i) \n", + " d = dist_func(post_vec, new_vec) \n", + " print(\"=== Post %i with dist=%.2f:\\n '%s'\" % \\\n", + " (i, d, post)) \n", + " if d < best_dist: \n", + " best_dist = d \n", + " best_i = i\n", + " print(\"\\n==> Best post is %i with dist=%.2f\" % \\\n", + " (best_i, best_dist))\n", + " \n", + "best_post(X_train, new_post_vec, dist_raw)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]]\n" + ] + } + ], + "source": [ + "print(X_train.getrow(3).toarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0 0 3 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0]]\n" + ] + } + ], + "source": [ + "print(X_train.getrow(4).toarray())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalizing word count vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Post 0 with dist=1.41:\n", + " 'This is a toy post about machine learning. Actually, it contains not much interesting stuff.'\n", + "=== Post 1 with dist=0.86:\n", + " 'Imaging databases provide storage capabilities.'\n", + "=== Post 2 with dist=0.92:\n", + " 'Most imaging databases save images permanently.\n", + "'\n", + "=== Post 3 with dist=0.77:\n", + " 'Imaging databases store data.'\n", + "=== Post 4 with dist=0.77:\n", + " 'Imaging databases store data. Imaging databases store data. Imaging databases store data.'\n", + "\n", + "==> Best post is 3 with dist=0.77\n" + ] + } + ], + "source": [ + "def dist_norm(v1, v2): \n", + " v1_normalized = v1 / scipy.linalg.norm(v1.toarray()) \n", + " v2_normalized = v2 / scipy.linalg.norm(v2.toarray()) \n", + " delta = v1_normalized - v2_normalized \n", + " return scipy.linalg.norm(delta.toarray()) \n", + "\n", + "best_post(X_train, new_post_vec, dist_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removing less important words" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'about', 'above', 'across', 'after', 'afterwards', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst']\n" + ] + } + ], + "source": [ + "vect_engl = CountVectorizer(min_df=1, stop_words='english')\n", + "print(sorted(vect_engl.get_stop_words())[0:20])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#samples: 5, #features: 18\n", + "['actually', 'capabilities', 'contains', 'data', 'databases', 'images', 'imaging', 'interesting', 'learning', 'machine', 'permanently', 'post', 'provide', 'save', 'storage', 'store', 'stuff', 'toy']\n" + ] + } + ], + "source": [ + "X_train_engl = vect_engl.fit_transform(posts)\n", + "num_samples_engl, num_features_engl = X_train_engl.shape\n", + "print(\"#samples: %d, #features: %d\" % (num_samples_engl, num_features_engl))\n", + "print(vect_engl.get_feature_names())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 4)\t1\n", + " (0, 6)\t1\n" + ] + } + ], + "source": [ + "new_post_vec_engl = vect_engl.transform([new_post]) \n", + "print(new_post_vec_engl)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Post 0 with dist=1.41:\n", + " 'This is a toy post about machine learning. Actually, it contains not much interesting stuff.'\n", + "=== Post 1 with dist=0.86:\n", + " 'Imaging databases provide storage capabilities.'\n", + "=== Post 2 with dist=0.86:\n", + " 'Most imaging databases save images permanently.\n", + "'\n", + "=== Post 3 with dist=0.77:\n", + " 'Imaging databases store data.'\n", + "=== Post 4 with dist=0.77:\n", + " 'Imaging databases store data. Imaging databases store data. Imaging databases store data.'\n", + "\n", + "==> Best post is 3 with dist=0.77\n" + ] + } + ], + "source": [ + "best_post(X_train_engl, new_post_vec_engl, dist_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stemming" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "graphic\n", + "imag\n", + "imag\n", + "imagin\n", + "imagin\n", + "buy\n", + "buy\n", + "bought\n" + ] + } + ], + "source": [ + "import nltk.stem\n", + "\n", + "s = nltk.stem.SnowballStemmer('english')\n", + "print(s.stem(\"graphics\"))\n", + "print(s.stem(\"imaging\"))\n", + "print(s.stem(\"image\"))\n", + "print(s.stem(\"imagination\"))\n", + "print(s.stem(\"imagine\"))\n", + "print(s.stem(\"buys\"))\n", + "print(s.stem(\"buying\"))\n", + "print(s.stem(\"bought\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StemmedCountVectorizer(analyzer='word', binary=False, decode_error='strict',\n", + " dtype=, encoding='utf-8', input='content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 1), preprocessor=None, stop_words='english',\n", + " strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " tokenizer=None, vocabulary=None)\n" + ] + } + ], + "source": [ + "english_stemmer = nltk.stem.SnowballStemmer('english')\n", + "class StemmedCountVectorizer(CountVectorizer): \n", + " def build_analyzer(self): \n", + " analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n", + " return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))\n", + "\n", + "vect_engl_stem = StemmedCountVectorizer(min_df=1, stop_words='english') \n", + "print(vect_engl_stem)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#samples: 5, #features: 17\n", + "['actual', 'capabl', 'contain', 'data', 'databas', 'imag', 'interest', 'learn', 'machin', 'perman', 'post', 'provid', 'save', 'storag', 'store', 'stuff', 'toy']\n" + ] + } + ], + "source": [ + "X_train_engl_stem = vect_engl_stem.fit_transform(posts)\n", + "num_samples_engl_stem, num_features_engl_stem = X_train_engl_stem.shape\n", + "print(\"#samples: %d, #features: %d\" % (num_samples_engl_stem, num_features_engl_stem))\n", + "print(vect_engl_stem.get_feature_names())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 4)\t1\n", + " (0, 5)\t1\n", + "=== Post 0 with dist=1.41:\n", + " 'This is a toy post about machine learning. Actually, it contains not much interesting stuff.'\n", + "=== Post 1 with dist=0.86:\n", + " 'Imaging databases provide storage capabilities.'\n", + "=== Post 2 with dist=0.63:\n", + " 'Most imaging databases save images permanently.\n", + "'\n", + "=== Post 3 with dist=0.77:\n", + " 'Imaging databases store data.'\n", + "=== Post 4 with dist=0.77:\n", + " 'Imaging databases store data. Imaging databases store data. Imaging databases store data.'\n", + "\n", + "==> Best post is 2 with dist=0.63\n" + ] + } + ], + "source": [ + "new_post_vec_engl_stem = vect_engl_stem.transform([new_post]) \n", + "print(new_post_vec_engl_stem)\n", + "\n", + "best_post(X_train_engl_stem, new_post_vec_engl_stem, dist_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stop words on steroids using TFIDF" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "term='a' doc=['a'] tf=1.00 idf=0.00\n", + "=> tfidf=0.00\n", + "term='a' doc=['a', 'b', 'b'] tf=0.33 idf=0.00\n", + "=> tfidf=0.00\n", + "term='a' doc=['a', 'b', 'c'] tf=0.33 idf=0.00\n", + "=> tfidf=0.00\n", + "term='b' doc=['a', 'b', 'b'] tf=0.67 idf=0.41\n", + "=> tfidf=0.27\n", + "term='b' doc=['a', 'b', 'c'] tf=0.33 idf=0.41\n", + "=> tfidf=0.14\n", + "term='c' doc=['a', 'b', 'c'] tf=0.33 idf=1.10\n", + "=> tfidf=0.37\n" + ] + } + ], + "source": [ + "def tfidf(term, doc, corpus):\n", + " tf = doc.count(term) / len(doc)\n", + " idf = np.log(float(len(corpus)) / (len([d for d in corpus if term in d])))\n", + " tf_idf = tf * idf\n", + " print(\"term='%s' doc=%-17s tf=%.2f idf=%.2f\"%\\\n", + " (term, doc, tf, idf))\n", + " return tf_idf\n", + "\n", + "# defining some documents\n", + "a, abb, abc = [\"a\"], [\"a\", \"b\", \"b\"], [\"a\", \"b\", \"c\"]\n", + "\n", + "# defining some copora\n", + "D = [a, abb, abc]\n", + "\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"a\", a, D))\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"a\", abb, D))\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"a\", abc, D))\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"b\", abb, D))\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"b\", abc, D))\n", + "print(\"=> tfidf=%.2f\" % tfidf(\"c\", abc, D))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StemmedTfidfVectorizer(analyzer='word', binary=False, decode_error='ignore',\n", + " dtype=, encoding='utf-8', input='content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 1), norm='l2', preprocessor=None,\n", + " smooth_idf=True, stop_words='english', strip_accents=None,\n", + " sublinear_tf=False, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " tokenizer=None, use_idf=True, vocabulary=None)\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "class StemmedTfidfVectorizer(TfidfVectorizer):\n", + "\n", + " def build_analyzer(self):\n", + " analyzer = super(TfidfVectorizer, self).build_analyzer()\n", + " return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))\n", + "\n", + "vect_tfidf = StemmedTfidfVectorizer(#min_df=10, max_df=0.5,\n", + " stop_words='english', decode_error='ignore')\n", + "print(vect_tfidf)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#samples: 5, #features: 17\n", + "['actual', 'capabl', 'contain', 'data', 'databas', 'imag', 'interest', 'learn', 'machin', 'perman', 'post', 'provid', 'save', 'storag', 'store', 'stuff', 'toy']\n" + ] + } + ], + "source": [ + "X_train_tfidf = vect_tfidf.fit_transform(posts)\n", + "num_samples_tfidf, num_features_tfidf = X_train_tfidf.shape\n", + "print(\"#samples: %d, #features: %d\" % (num_samples_tfidf, num_features_tfidf))\n", + "print(vect_tfidf.get_feature_names())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K-means Clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "seed = 2\n", + "np.random.seed(seed) # to reproduce the data later on\n", + "\n", + "num_clusters = 3\n", + "\n", + "\n", + "def plot_clustering(x, y, title, mx=None, ymax=None, xmin=None, km=None):\n", + " plt.figure(num=None, figsize=(8, 6), dpi=DPI)\n", + " if km:\n", + " plt.scatter(x, y, s=30, c=km.predict(list(zip(x, y))))\n", + " else:\n", + " plt.scatter(x, y, s=30)\n", + "\n", + " plt.title(title)\n", + " plt.xlabel(\"Occurrence word 1\")\n", + " plt.ylabel(\"Occurrence word 2\")\n", + "\n", + " plt.autoscale(tight=True)\n", + " plt.ylim(ymin=0, ymax=1)\n", + " plt.xlim(xmin=0, xmax=1)\n", + " plt.grid(True, linestyle='-', color='0.75')\n", + "\n", + " return plt\n", + "\n", + "\n", + "xw1 = scipy.stats.norm(loc=0.3, scale=.15).rvs(20)\n", + "yw1 = scipy.stats.norm(loc=0.3, scale=.15).rvs(20)\n", + "\n", + "xw2 = scipy.stats.norm(loc=0.7, scale=.15).rvs(20)\n", + "yw2 = scipy.stats.norm(loc=0.7, scale=.15).rvs(20)\n", + "\n", + "xw3 = scipy.stats.norm(loc=0.2, scale=.15).rvs(20)\n", + "yw3 = scipy.stats.norm(loc=0.8, scale=.15).rvs(20)\n", + "\n", + "x = np.append(np.append(xw1, xw2), xw3)\n", + "y = np.append(np.append(yw1, yw2), yw3)\n", + "\n", + "plot_clustering(x, y, \"Vectors\")\n", + "save_png(\"01_clustering_data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering after 1st iteration" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialization complete\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 0, inertia 3.746456379702266\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mx, my = np.meshgrid(np.arange(0, 1, 0.001), np.arange(0, 1, 0.001))\n", + "\n", + "km = KMeans(init='random', n_clusters=num_clusters, verbose=1,\n", + " n_init=1, max_iter=1,\n", + " random_state=seed)\n", + "km.fit(np.array(list(zip(x, y))))\n", + "\n", + "Z = km.predict(np.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)\n", + "\n", + "plot_clustering(x, y, \"Clustering iteration 1\", km=km)\n", + "plt.imshow(Z, interpolation='nearest',\n", + " extent=(mx.min(), mx.max(), my.min(), my.max()),\n", + " cmap=plt.cm.Blues,\n", + " aspect='auto', origin='lower')\n", + "\n", + "c1a, c1b, c1c = km.cluster_centers_\n", + "plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],\n", + " marker='x', linewidth=2, s=100, color='black')\n", + "save_png(\"02_clustering_iteration_1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering after 2nd iteration" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialization complete\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 0, inertia 3.746456379702266\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 1, inertia 2.835476341923971\n" + ] + }, + { + "data": { + "image/png": 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Z/CtgTmZ+qKwAAiALF2fmqymWhCgrQjkmIpp1ixir4dfUQmCPzDw4My9tnHCv57UoM8/OzCtHiHkVxaTwapn5xsysjVQAUY/7SGaeQFH09y6K4qFGR0ZEq10o+tk3Ke+a8Azwlsx8TWb+psnrcXNmfgzYkfJijwC+GxErtZnjGizZdeLjwDaZeWpjAUQ9r8zM31C8nj9qEvPwNnPqC/UlQpot+3RqF1ORJEmSxsQiCEmSJEn9oFmr68coWuur88padt8AvKvVzgaZ+cvM3Dczb2kvtfZFxHoU3RQaPQbsnpnfH2uszHwQ2Bc4v2TzByuYiHvO94BDM/PxiuJ1wjXArpl541h2zszfAh9ssvmwsZ60Pvn+5Sabj8nMocy8d5RcnsnMTwJvHzY8Upt+Le2zwFol4ycB+2Tm3WMNlJk1io4NjZPgqwLvbznDJT0M7FmfuG7Hv2Xmi+vFYWWFDCOqT55/CziIYrmM4WYCb20zv75SXwbjdSWbkqIA4rixxKkXpexD0dWl0TrAJ1rNscThmfnvY1mapP4deTBFx5lGe0XE+hXm1XX1zijNPm9/mZnXdzMfSZIkaSwsgpAkSZLUD9ZtMj6/7K5QdcRGJWM/zMxnup5JZ3wcWK5k/E2Zedl4g9UnPt9IcVf5cMtTtG1v13yKSbh+fv8/BbyhWYeKERxD0T2iUbOuDmXeRDE53ui3wDvHk0y9i0azpWDURH0JhreXbLqUYmJ71MnjRpl5JuWvxTsionEZlVZ8JDOvaDdIVYVdmfljimKnRmW/14nsPU3Gv1HvjDFm9YKrxmWTnnNYRMwcV2blTs7Mb47ngMx8kvIijAD2qiCnXvoQsG2TbWXFhZIkSVLPWQQhSZIkqR+U3UkMY1uPXdUoaze/oOtZdEC9tfybSzadkZmntxo3M28H5pZsqqL9+b/0eQcIgP/JzKvGe1C9sOP4kk3rRkRZYUOZsjvlFwPvy8xFJdtG82ngjhaOm8zezdLLYCTw3swsW9ZirD4LPNIwtiow1EZMgD8B32ozRif8e8nYnIgoW55owomIdYF/KNl0Py12bsjMs4D/K9m0EkWBVDueoXm3mtGcztLvXWheQND3ImJH4N+abD6x3t1HkiRJ6jsWQUiSJEnqBys0GW+8y16dU9bxoawwYiJ6AzCjZLyKO1i/UTK2Yf0u+VbdC/ykjeO75b/aOPbcJuNbj3ZgRGwFbFOy6eeZ+YdWksnMR4H/bOXYySgiplC0/290VmZe2k7szHwIOLFk057txKVYJqXvOqtk5k0USw812rHbuXTI64CpJePHZGY73/FfaDJ+UBsxoeiAdGcrB2bmE8AlJZtG/VzrRxGxGlBj6WIngLupbpkaSZIkqXIWQUiSJEnqB83anLdzN7HG5+6SsTdGRNnkx0SzX8nY/HYnawEy8y7g2pJNu7QR9tQJsAzJtfW29K36Y5Pxsdz9/tIm42UT5+NxMkUnA41ue8pfqx9WFL+sSKadawqqy60Tyq6l7bqeRWfs3GS8rBvMeFwI3FwyvkNElBVdjFVZh4nxKPtsm3BdPSJiOnAqsH7J5gQOy8z7upuVJEmSNHYWQUiSJEnqB80mLMa9prxadnHJ2ObAiRHxvG4nU5X6Hetlk+bnVHiay0vGmk38jcW8No7tlgvbOTgzHwbKlvsYy3tth7KQwBlt5rQAuKKdGJPI7k3Gq7quyq6pORGxSovxFtQLlvpV2WTyoHTi2alk7JbM/FM7QetdPcoKFpYHtmojdFufbUBZF4mJ+B16DM0Lzj6XmW193kqSJEmdZhGEJEmSpH7wZJPx6V3NYnL7cZPx1wI3RsS/R8Tm3UyoIpsAs0rG/1zhOcomMNdpI15LSzp02fwKYjxSMtZqEcTN9cKKdl1ZQYzJoGw5kgdbXUagRLM7zFu9rjp+TUXEchGxX0R8IiJOiYgrI+KWiHggIp6JiGz2oHxpkZU6nXOnRcTqlL9mVRUbNYtT9hkxFs9m5m2tJlPX6uda34iIL1L+noSio8rHu5iOJEmS1JJpvU5AkiRJkoAnmowv39UsJrHM/H1E/B/lS0esCnwU+GhEXA/8CjgP+F397vl+NqfJ+P4RUTaR24qyOCu3Ea9saZJ+s7CCGGXFTzPGcNy6JWPNltcYr6sqijPoSq+riDiuovjRZLzV66pj11RE7AC8F9gfWKHC0BO+CAJYs8l4VddZszhrtBjvwVYTGabsc23CFHRGxIeBf2my+WzgzfUuHJIkSVJfswhCkiRJUj+4p8n4hFtHe4J7K8VSDOuNsM+m9ce7ACJiAfBrisKIMzLz/k4nOU5rNxnfo8PnbacIooqOBp32WC9OWl/epGyiudlnyHjdW1GcQVd2Xa0EHNLh87Z6XVV+TUXEbODzwNtoXrTRjuU6ELPbmhVyVHW9NovT6vukJ59r/SIi3g58ocnmC4EDM/PpLqYkSZIktczlMCRJkiT1g2btp9tZUkDjlJn3ALsCl43jsHUpJj6/D9wZEadHxCs6kV+LetWGfMVWD8zMR6tMZMCsSPmEc1kL+lZMhAKUfjDRrqtKr6n6Mg8XAG+nMwUQdDBuNzUrgqjqOntonOdVExHxeuBbTTb/Afj7zHy8iylJkiRJbbEThCRJkqR+cGOT8TUj4nmZ2WyiQxXLzNsiYmeKyb2P0byTQpllgNcAr4mIC4DDM/NPHUhzPJbt0XkHYQKzH81qMl7V5NykvhN8HCbtdRURM4FzgS1G2fVZ4E5gAcXyMU/WH8+W7LsbsHGFafaLZtdrVddZszhVLksy8CJiP+AHlN8sdw3wCv8cJkmSpInGIghJkiRJ/eCPwCJgasm2rYHzu5vO5JaZzwLfjIjvAPsCrwf2AtYaR5iXApdExCGZ+aMOpDlWi3p4blWv2aTnzIriL19RnEG3iMnbXfTjNC+AuB44luI76/eZ+cxYAkbEcQxmEUSzDhydvl6r6gwz8CJiT+AUiiLGRn8BXp6ZLhMkSZKkCcciCEmSJEk9l5lPRMTVwItKNu+ORRA9kZmLgJ/XH0TEZhTFDbtRvC4bjBJiJnBCRNydmRd0MNWRNJs03yYzr+hqJqrCQ0CydEeAqu78bnkZk0nmMZZecuAnmbl/L5LplohYA/hgyaZngSOAr2dmthC6WceEie7BJuNVXWfN4iysKP5Aq3d9+inlnV1upyiAuKO7WUmSJEnVmKxV+5IkSZL6z9lNxvfrahYTW0cn0jLzusw8JjMPzcwNgfWAw4FfUExMl5kOfDsievX3z7uajG/U1SxUicxcTPld3qtVdIqq4gy6sutqMlxT/wDMKBn/UGZ+rcUCCIDZbeTUz5oVI3T6em1WfKG6iNgGOJPybhr3UhRA3NzdrCRJkqTqWAQhSZIkqV/8rMn4DhGxSVcz6bynygYjomxybTy6OpGWmQsy85uZuQ8wh6KldpktgL/vXmZLaDaJM6erWahKC0rGtqoodlVxBl3ZdbVRD4uduqXsc2w+8LU2467d5vH96s4m42Vdn1rx4nGeV0BEbAGcAzyvZPNCYO/MvLa7WUmSJEnVGvS/nEqSJEmaOH5Lsf50mQ90M5EueLjJeLst/ddv8/iWZeZNmTkE/FeTXfbtZj7D/JGiVX2jvbqdiCpzWcnYRhFRRYv9rSuIMRn8oWRseWCnbifSZZuWjP2kjQ4QRMRsBrQoKzPvobxoqarrrFmceRXFHzgRsRHwS2DVks2PAq/MzCu7m5UkSZJUPYsgJEmSJPWF+iTSd5psfmtE9GyCvwOaFUGs2Wbc3do8vgpHUd6KfCx32D9dMjatnWQy8wng8pJNu0dEu0Un6o1LS8YCeFU7QSNiHQavCKLsmoI2ryvggibjg7580eolY/PbjLk7xfu3HZ16natwccnYhvVuBO0q68zxGPCnCmIPnIhYm6IAYq2SzU8A+2XmJd3NSpIkSeoMiyAkSZIk9ZNvAPeVjC8LfL0bCURE2frYVWvWqrvlVvwRsRKwa6vHV6VedHBhyaayu04bPVIytlx7GQHw05Kx6cA7Koit7ms2AX9Qm3HfQPuT0f2m7JqC9q+rc5vEPiwiZrYZu5+VFU490WbMd7Z5PHTuda7CRU3G39hO0IjYBdi4ZNOlmbmondiDKCJWA34BbFiy+WngwMw8v7tZSZIkSZ1jEYQkSZKkvpGZjwCfabL5HyLig508f0S8GvhiJ89RdxPlk1Z/10bMd1C0o+8Hj5aMPTOG48p+J2u3mQvAD4CySbEj7QYx8WTmH4ErSjbtFxEvbiVmvfhp0JbdgeJaLFuqoa3rKjOfAk4u2bQ68L52Yve5si4367QaLCK2BvZuPZ2/6sjrXJFTKF+S6O0R8bw24n64yfgJbcQcSPXf89lAWfeNZ4E3ZOZZ3c1KkiRJ6iyLICRJkiT1m7mUt88G+FJEHFz1CSNiakQcDZxGFwoJ6kt/lE3i/mNEjPvO3Xob/yPbTqw6m5SM3TGG4+aXjK1ev4O1ZZm5gGIirtFqwHERMWh3/08Gx5SMTQG+FhGt/FvHJ+iPCeNK1T9rbi3Z9MIKwv8nsLhk/JMRsWMF8fvRXSVj+7QSKCKmA9+jgu4jHX6d25KZtwM/Kdn0fOCTrcSMiL2B15RsegA4sZWYg6remeXnwDYlmxcDh2Tmad3NSpIkSeo8iyAkSZIk9ZV6G+s3U37H7RTgexFxdERMreJ89TvHL6CYjOnm35HK7rpcGfjIeILUJzhOAma3mkhErB4RR9WX1GhLffJzu5JNfxjD4WWFIQCvbz2jv/oY8FTJ+IHAV1ucOF9KRGweEa+tIpZGdDzlS+e8DPjv8QSKiDfT/K7yQVB2Xb0mIpZtJ2hm/hn435JNywKnR0QlE/ARMT0i3hIRY1lSp9N+VzK2Y0TsO54g9e+vY4EXVZJVoSOvc0XmNhl//3g/LyNiI4ruPmWOrS/JJP5aaHMa5UtlJfBPmWnRiCRJkgaSRRCSJEmS+k5m3gC8jvIW2kFRsHBxRLys1XPUJ6uPAy4Hdm41Thu+R/ld1EdFxP5jCRARawC/BnZrM5flgM8Ct0bElyPiBa0EiYjNgB822VzWOr/R1cDCkvHPRMRQO4UvmfkX4ONNNr8fOLveUWPc6p1EXh4RpwN/BvZrMU2NUWY+BBzRZPM7I+Kk0SbNI2KZiPhXisno5+7Gf7rCNPvFb0vG1gFObPU9P8y/ALeVjK8JXBIRh7baaSUi1oqIDwM3UxRbzGo9zcqc2WT8+IjYaiwBImI28CPgTZVlVejk69yWzDyP8u+AKRS/uzeMJU79d3wOxbIrjW6l+XJak079+/JEmncq+UBmlnXUkSRJkgbCtF4nIEmSJEllMvOXEfE6ikn16SW7bA+cHxHzKO4KPyszrx8pZkRsDuwJvIGicKBnyyBk5u31SfMDGzYtA5wSEXOBL2bmnY3HRsQGwCHAh1hyYvBXwF5tpLUC8EHggxFxNVADfgP8PjMfbnZQ/fd6CPBeypcTOTMzrxrt5Jn5TEScCLy7YdPzKN4HD0TElcC9QNndvvdl5odGOMWXgZ2AfyzZ9nLgpog4nqJA5ZLMLOscAUBEbAhsC+wL7A/0w13qk0pmfj8i3gTsXbL5DcDeEXEC8GPgJuAeYCVgXeCVFB1n5gwPCXyOFlv097HjKX6uxn8DOoCiU8A1wI3AI8CikuOPycyyCXYy88H65/SvKYqphpsJfBc4MiL+k+Iz+pZmSUbE8hSdEXamuEZ3poef0U38DLiBJd83AKtQFH18DvhmZi7VpSQi1qZ4X36UJTv3PAL8Hti9zdw69jpX5N0UP+OaDeMzgJPq76MvZ+ZFjQfWv/PeStGxpezPAwkcOtL31CT0Hsq/66D4Dt22Xghalc9n5rUVxpMkSZLaYhGEJEmSpL6VmadHxKso7iBtNsm8Q/1BRCykmOS5A3iMYmJkFrAWxaTVWJZ7yDbTHo/3U0zgrtAwPg34AEWr8CuBW4DHKX4HG7D0BBwUd8d+gfaKIIbbEvhU/f8zIm4E7qJYc/0Ripb3qwBbAGuMEOdB4B3jOO9XKQoqyu76ng383QjH3kJRGFIqM7M+aT4LeEXJLtOBw+qPpyLiTxRLLjxA8ZqsVH9sQrF0iXrvYOBiiuui0SrA++qPsfg8cB7lRRBlk8Yyz7kzAAAgAElEQVQTQmbeFRH/A7yrZPMUimt9yxFCnEd5l4Hn4l9cX9LgRyxdCAGwOfAtgIi4FZgP3E/xmbYixTX1fIrPtb7uWJqZiyLiKIqftdFywKeBoyPijxQdLB6neB+uCzTrsHM4RRFWW0UQnX6d25WZD0TEGym6acwo2eVA4MCIuB24Hrib4v2xHkXeIxXE/FtmnltxyhPdSN9Rq1F8z1bpOMAiCEmSJPUNiyAkSZIk9bXM/FVEbEvxD+x7jrL7ytQLIlqwiKLl+r+2ePy4ZeZtEfEu4PuUT/4FsHX9MZJLKO743L7aDJfIYw7lxRcjuQ/YNzPL2uWXysybIuLtFGu+V/531sx8MiL+AfgmxZ3FzcwAtqv6/KpWZt4dEXtRFAFt3Eao7wIfo/lnTNOuIBPEERSfD61+Po4oM8+IiL2BU1j6Tv/h1qs/JqzMPDUivkSxFEiZKcCL64/RfCwzj4+Il1eUXkdf53Zl5rkRcSBFl6GyrkEAa9cfY/W5zBy07i2SJEmS2tTXFfaSJEmSBJCZCzJzL+Agik4PVVoEnAZslZnvyMy7Ko4/osw8geJu9mdbDHEqsGdmPtpGGouAxW0cX+Z8YJfMvHy8B2bmyRTLlfy+4pyei/9MZr4NeC2woOLw84FfVBxTI8jMvwAvAU5v4fCngY9k5mGZmTTvFvNYq/n1g8x8guKa+hLQzmfFSOe4kGI5i+OptqPOM8BPgYUVxmzXRyg6h7T6cz4JvC0zP1tdSt15nduVmWcAuwCjLpE0igeAgzLzo+1nJUmSJGnQWAQhSZIkacKoT45vRrHG+U9p7+7sqynWF183Mw/MzGsqSLElmXkixd27vxrHYTcDb8rM12bm422efwHF3dtvpZhIfqiNcBdRTEztkZk3tJHTJZm5HbANRXeOH1O02r6bosV82zLzVIr303uBdl7/+cD/UHQR2Khe2KIuysz7M/MAYB+K62i0yenHKbrLbJWZXxg2/rySfR/LzAldBAGQmU9n5ocprvU3A98ALqQoBHqQ1guxhp/jvsw8mKKLyvEUk/2teAo4F/hnYO3MfE1mtvO5VKnMXJyZRwGvAq4Yx6GLKArXXpiZx3Yot46/zu3KzKsovvMOp/guG49HgK8Am9f/TCBJkiRJS4niRgdJkiRJmngiYgWKu153Bl4IbAisRdFme1ngCYq7hxcC9wB/AC4FLs3MW3uR82giYjtgP4oJ9XUo1u5eDngY+AtwOfAz4KzMXNShHIJiDfZdKH6vmwAbAbOBWcB0iruMH6L4vV5J8bs9KzNv6kRO3RARWwJ7AzsCmwLrUqxJP51i0vwRiknEv1AUZPwZuKCdYg91RkSsDuwBvAB4PrAMxXt2AcUd6Bdk5lIT9BHxBYriqOGuzcwtOprwgIqI5SkKU3alWNZnA4rXYzmK7jOP1B93AddRXFe/p3h9nuhByi2JiD2BfYGXUnxur0KxnM+jwO0URVbnAz+tF501Hr8qxWfrcE92uzNRL9S/b3YDXkHR0WUT/va99zTFZ+7NFMUmvwZ+PpHeG5IkSZJ6wyIISZIkSZIkICLOB17WMHxaZh7Yi3wkSZIkSdL4uRyGJEmSJEma9CJiFYouII0u63YukiRJkiSpdRZBSJIkSZIkwTsoltFpdF6X85AkSZIkSW1wOQxJkiRJkjSpRcTawBXAqg2b7gDWzczF3c9KkiRJkiS1wk4QkiRJkiRpQoqIqCDGTOBkli6AAPiuBRCSJEmSJE0sFkFIkiRJkqSJav2IuDAi9m+lICIiNgIuBHYr2fwo8LV2E5QkSZIkSd1lEYQkSZIkSZrIdgFOA26JiK9GxMsiYvlmO0fEMhGxS0T8L3AtsHWTXT+Zmfd0IF9JkiRJktRBkZm9zkGSJEmSJGncImID4OaSTYuB64HbgAeBp4GVKJa82ApYbpTQZwD7pf9oIkmSJEnShGMRhNoyNDS0LTAHWLs+dDtwfa1W+0PvspIkSZIkTQYjFEG041fAqzPz8YrjSpIkSZKkLrAIok8NDQ1tBOwAbF//77bACsN2uaVWq23Qg9QYGhpaBjgCeBuwcZPdbgSOAb5Sq9We6VZukiRJkqTJo+IiiEXAl4GPZeazFcWUJEmSJEldZhFEHxkaGtoDOIqi8GH2KLv3pAhiaGhoDnAyRVHGWFwOvKFWq93YuawkSZIkSZNRRMwA3gq8DngZMKWFME8DpwCfy8yrK0xPkiRJkiT1wLReJ6AlbA3s0+skmhkaGloD+AWwfsOmG4GrgQC2ZMnuENsB5wwNDe1Uq9Xu6UqikiRJkqRJITOfAr4BfCMiVgJ2BnYCNgc2oFi6cRawPMXfWZ8E7gVuBa4CLgTOzMyHup68JEmSJEnqCIsgJoangNtovvRExw0NDU0BTmfJAog7gUNrtdo5DfvuC3wXWKM+tCFw2tDQ0G61Ws3WI5IkSZKkymXmg8CZ9YckSZIkSZqkWmkTqc56BrgCOAb4J4pOCisAb+tlUsAbgZcMe/4AsEtjAQRArVY7C9gFWDhseBfg9R3NUJIkSZIkSZIkSZI0qUWmN+b3i6GhoZWBJ2q12pMl2/YAzh02dEutVtugS3lNBW6g6OjwnENrtdr3RjnuUIqOEM+5Cdi0VqstrjxJSZIkSZIkSZIkSdKk53IYfaRWqy0cfa+e2I0lCyBuB44fw3E/AD5DsQYrFMt57AL8ttLsJEmSJEmSJEmSJEnC5TA0Ngc0PP9+rVZbNNpB9X0aiyUOrCwrSZIkSZIkSZIkSZKGsQhCY7Fvw/PzxnFs476vbCsTSZIkSZIkSZIkSZKasAhCIxoaGpoBbNIwfPE4QlzU8HzO0NDQ9PaykiRJkiRJkiRJkiRpaRZBaDSbAVOHPb+nVqs9PNaD6/veN2xoKrBpRblJkiRJkiRJkiRJkvRX03qdgPpeYxeIW1uIcSuw6rDnc4A/tZxR66bWzz3cA0D2IBdJkiRJkiRJkiRJqlIAsxvGbgAW9SCXnrEIQqNZqeH5PS3EaDzmeS3msoShoaHnA6uNdf+DDjpowwMOOOBnVZxbkiRJkiRJkiRJkiaALYBre51EN1kEodHManj+RAsxGo9ZocVcGh0OfHKsO5933nkccMABFZ1akiRJkiRJkiRJktRvpvQ6AfW9xiKIJ1uI0VgE0RhTkiRJkiRJkiRJkqS2WQSh8couHSNJkiRJkiRJkiRJ0ri4HIZG82jD8+VaiNF4TGPMVn0DOGWsO8+ZM2c28JvhYwd/+FvccOv9FaUjSeqW1WfP5MNv3PGvz794wqXc/cDjPcxIktQKP88laTD4eb6kM7/zgV6nIEkteeqJJ7npxr8tGb/xJpszY7lle5iRJGm8ImClFZZvHH6gF7n0kkUQGk3fFkHUarV7gHvGcchqjQM33Ho/V153ZxXpSJK6aL01VuSZZ5756/Nrb76XW+96uIcZSZJa4ee5JA0GP8+XtNzMpf7RWZImhEyW+DyfvuyyfqZJ0mCYdF37XQ5Do3mo4flShQRj8PyG5w+2mIskSZIkSZIkSZIkSU3ZCUKjuaHh+fotxGg8pjGmJEmSJEmSNKEtnDe31ylIkiRJwk4QGt11wKJhz58/NDS0wlgPHhoaWhFYddjQIiyCkCRJkiRJkiRJkiR1gEUQGlGtVnsKuKlheOdxhNil4fkN9ZiSJEmSJEmSJEmSJFXKIgiNxVkNz/cYx7GN+57ZViaSJEmSJElSn3EpDEmSJKl/WAShsTit4fnBQ0NDU0c7qL7Pm0aJJUmSJEmSJEmSJElSJSyC0FhcANw87Pk6LF3cUOZNwNrDnt8EXFhhXpIkSZIkSZIkSZIk/dW0Xieg7hsaGsqGob+r1WrnNdu/VqstGhoa+iTw/WHDXxkaGjq/VqvNb3K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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "km = KMeans(init='random', n_clusters=num_clusters, verbose=1,\n", + " n_init=1, max_iter=2,\n", + " random_state=seed)\n", + "km.fit(np.array(list(zip(x, y))))\n", + "\n", + "Z = km.predict(np.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)\n", + "\n", + "plot_clustering(x, y, \"Clustering iteration 2\", km=km)\n", + "plt.imshow(Z, interpolation='nearest',\n", + " extent=(mx.min(), mx.max(), my.min(), my.max()),\n", + " cmap=plt.cm.Blues,\n", + " aspect='auto', origin='lower')\n", + "\n", + "c2a, c2b, c2c = km.cluster_centers_\n", + "plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],\n", + " marker='x', linewidth=2, s=100, color='black')\n", + "\n", + "plt.gca().add_patch(plt.Arrow(c1a[0], c1a[1], c2a[0] - c1a[0], c2a[1] - c1a[1], width=0.1, color='red'))\n", + "plt.gca().add_patch(plt.Arrow(c1b[0], c1b[1], c2b[0] - c1b[0], c2b[1] - c1b[1], width=0.1, color='red'))\n", + "plt.gca().add_patch(plt.Arrow(c1c[0], c1c[1], c2c[0] - c1c[0], c2c[1] - c1c[1], width=0.1, color='red'))\n", + "\n", + "save_png(\"03_clustering_iteration_2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final clustering\n", + "We iterate at max 10 iterations, but will converge earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialization complete\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 0, inertia 3.746456379702266\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 1, inertia 2.835476341923971\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 2, inertia 2.536046256119067\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 3, inertia 2.447223016211801\n", + "start iteration\n", + "done sorting\n", + "end inner loop\n", + "Iteration 4, inertia 2.447223016211801\n", + "center shift 0.000000e+00 within tolerance 7.366762e-06\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 10 iterations ####################\n", + "km = KMeans(init='random', n_clusters=num_clusters, verbose=1,\n", + " n_init=1, max_iter=10,\n", + " random_state=seed)\n", + "km.fit(np.array(list(zip(x, y))))\n", + "\n", + "Z = km.predict(np.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)\n", + "\n", + "plot_clustering(x, y, \"Clustering iteration 10\", km=km)\n", + "plt.imshow(Z, interpolation='nearest',\n", + " extent=(mx.min(), mx.max(), my.min(), my.max()),\n", + " cmap=plt.cm.Blues,\n", + " aspect='auto', origin='lower')\n", + "\n", + "plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],\n", + " marker='x', linewidth=2, s=100, color='black')\n", + "save_png(\"04_clustering_iteration_final\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing our idea on real data" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18846\n", + "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n" + ] + } + ], + "source": [ + "import sklearn.datasets\n", + "all_data = sklearn.datasets.fetch_20newsgroups(subset='all') \n", + "print(len(all_data.filenames))\n", + "print(all_data.target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11314\n", + "7532\n" + ] + } + ], + "source": [ + "train_data = sklearn.datasets.fetch_20newsgroups(subset=\"train\")\n", + "print(len(train_data.filenames))\n", + "\n", + "test_data = sklearn.datasets.fetch_20newsgroups(subset='test')\n", + "print(len(test_data.filenames))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3529\n", + "2349\n" + ] + } + ], + "source": [ + "groups = ['comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware',\n", + " 'comp.sys.mac.hardware', 'comp.windows.x', 'sci.space']\n", + "\n", + "train_data = sklearn.datasets.fetch_20newsgroups(subset=\"train\", categories=groups)\n", + "print(len(train_data.filenames))\n", + "\n", + "test_data = sklearn.datasets.fetch_20newsgroups(subset='test', categories=groups)\n", + "print(len(test_data.filenames))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clustering posts" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#samples: 3529, #features: 4712\n" + ] + } + ], + "source": [ + "vectorizer = StemmedTfidfVectorizer(min_df=10, max_df=0.5,\n", + " stop_words='english', decode_error='ignore')\n", + "vectorized = vectorizer.fit_transform(train_data.data)\n", + "\n", + "num_samples, num_features = vectorized.shape\n", + "print(\"#samples: %d, #features: %d\" % (num_samples, num_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialization complete\n", + "Iteration 0, inertia 5686.053\n", + "Iteration 1, inertia 3164.888\n", + "Iteration 2, inertia 3132.208\n", + "Iteration 3, inertia 3111.713\n", + "Iteration 4, inertia 3098.584\n", + "Iteration 5, inertia 3092.191\n", + "Iteration 6, inertia 3087.277\n", + "Iteration 7, inertia 3084.100\n", + "Iteration 8, inertia 3082.800\n", + "Iteration 9, inertia 3082.234\n", + "Iteration 10, inertia 3081.949\n", + "Iteration 11, inertia 3081.843\n", + "Iteration 12, inertia 3081.791\n", + "Iteration 13, inertia 3081.752\n", + "Iteration 14, inertia 3081.660\n", + "Iteration 15, inertia 3081.617\n", + "Iteration 16, inertia 3081.589\n", + "Iteration 17, inertia 3081.571\n", + "Converged at iteration 17: center shift 0.000000e+00 within tolerance 2.069005e-08\n" + ] + } + ], + "source": [ + "num_clusters = 50 # np.unique(labels).shape[0]\n", + "\n", + "km = KMeans(n_clusters=num_clusters, n_init=1, verbose=1, random_state=3)\n", + "clustered = km.fit(vectorized)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "km.labels_=[48 23 31 ... 6 2 22]\n", + "km.labels_.shape=3529\n" + ] + } + ], + "source": [ + "print(\"km.labels_=%s\" % km.labels_)\n", + "print(\"km.labels_.shape=%s\" % km.labels_.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "new_post = '''\\\n", + "Disk drive problems. Hi, I have a problem with my hard disk.\n", + "After 1 year it is working only sporadically now.\n", + "I tried to format it, but now it doesn't boot any more.\n", + "Any ideas? Thanks.\n", + "'''\n", + "\n", + "new_post_vec = vectorizer.transform([new_post])\n", + "new_post_label = km.predict(new_post_vec)[0]\n", + "\n", + "similar_indices = (km.labels_ == new_post_label).nonzero()[0]\n", + "\n", + "similar = []\n", + "for i in similar_indices:\n", + " dist = scipy.linalg.norm((new_post_vec - vectorized[i]).toarray())\n", + " similar.append((dist, train_data.data[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count similar: 56\n", + "=== #1 ===\n", + "(1.0378441731334074, \"From: Thomas Dachsel \\nSubject: BOOT PROBLEM with IDE controller\\nNntp-Posting-Host: sdcmvs.mvs.sas.com\\nOrganization: SAS Institute Inc.\\nLines: 25\\n\\nHi,\\nI've got a Multi I/O card (IDE controller + serial/parallel\\ninterface) and two floppy drives (5 1/4, 3 1/2) and a\\nQuantum ProDrive 80AT connected to it.\\nI was able to format the hard disk, but I could not boot from\\nit. I can boot from drive A: (which disk drive does not matter)\\nbut if I remove the disk from drive A and press the reset switch,\\nthe LED of drive A: continues to glow, and the hard disk is\\nnot accessed at all.\\nI guess this must be a problem of either the Multi I/o card\\nor floppy disk drive settings (jumper configuration?)\\nDoes someone have any hint what could be the reason for it.\\nPlease reply by email to GERTHD@MVS.SAS.COM\\nThanks,\\nThomas\\n+-------------------------------------------------------------------+\\n| Thomas Dachsel |\\n| Internet: GERTHD@MVS.SAS.COM |\\n| Fidonet: Thomas_Dachsel@camel.fido.de (2:247/40) |\\n| Subnet: dachsel@rnivh.rni.sub.org (UUCP in Germany, now active) |\\n| Phone: +49 6221 4150 (work), +49 6203 12274 (home) |\\n| Fax: +49 6221 415101 |\\n| Snail: SAS Institute GmbH, P.O.Box 105307, D-W-6900 Heidelberg |\\n| Tagline: One bad sector can ruin a whole day... |\\n+-------------------------------------------------------------------+\\n\")\n", + "\n", + "=== #2 ===\n", + "(1.1503043264096682, 'From: rpao@mts.mivj.ca.us (Roger C. Pao)\\nSubject: Re: Booting from B drive\\nOrganization: MicroTech Software\\nLines: 34\\n\\nglang@slee01.srl.ford.com (Gordon Lang) writes:\\n\\n>David Weisberger (djweisbe@unix.amherst.edu) wrote:\\n>: I have a 5 1/4\" drive as drive A. How can I make the system boot from\\n>: my 3 1/2\" B drive? (Optimally, the computer would be able to boot\\n>: from either A or B, checking them in order for a bootable disk. But\\n>: if I have to switch cables around and simply switch the drives so that\\n>: it can\\'t boot 5 1/4\" disks, that\\'s OK. Also, boot_b won\\'t do the trick\\n>: for me.)\\n>: \\n>: Thanks,\\n>: Davebo\\n>We had the same issue plague us for months on our Gateway. I finally\\n>got tired of it so I permanently interchanged the drives. The only\\n>reason I didn\\'t do it in the first place was because I had several\\n>bootable 5-1/4\\'s and some 5-1/4 based install disks which expected\\n>the A drive. I order all new software (and upgrades) to be 3-1/2 and\\n>the number of \"stupid\" install programs that can\\'t handle an alternate\\n>drive are declining with time - the ones I had are now upgraded. And\\n>as for the bootable 5-1/4\\'s I just cut 3-1/2 replacements.\\n\\n>If switching the drives is not an option, you might be able to wire up\\n>a drive switch to your computer chasis. I haven\\'t tried it but I think\\n>it would work as long as it is wired carefully.\\n\\nI did this. I use a relay (Radio Shack 4PDT) instead of a huge\\nswitch. This way, if the relay breaks, my drives will still work.\\n\\nIt works fine, but you may still need to change the CMOS before the\\ndrive switch will work correctly for some programs.\\n\\nrp93\\n-- \\nRoger C. Pao {gordius,bagdad}!mts!rpao, rpao@mts.mivj.ca.us\\n')\n", + "\n", + "=== #3 ===\n", + "(1.2793959084781283, 'From: vg@volkmar.Stollmann.DE (Volkmar Grote)\\nSubject: IBM PS/1 vs TEAC FD\\nDistribution: world\\nOrganization: Me? Organized?\\nLines: 21\\n\\nHello,\\n\\nI already tried our national news group without success.\\n\\nI tried to replace a friend\\'s original IBM floppy disk in his PS/1-PC\\nwith a normal TEAC drive.\\nI already identified the power supply on pins 3 (5V) and 6 (12V), shorted\\npin 6 (5.25\"/3.5\" switch) and inserted pullup resistors (2K2) on pins\\n8, 26, 28, 30, and 34.\\nThe computer doesn\\'t complain about a missing FD, but the FD\\'s light\\nstays on all the time. The drive spins up o.k. when I insert a disk,\\nbut I can\\'t access it.\\nThe TEAC works fine in a normal PC.\\n\\nAre there any points I missed?\\n\\nThank you.\\n\\tVolkmar\\n\\n---\\nVolkmar.Grote@Stollmann.DE\\n')\n" + ] + } + ], + "source": [ + "similar = sorted(similar)\n", + "print(\"Count similar: %i\" % len(similar))\n", + "\n", + "show_at_1 = similar[0]\n", + "show_at_2 = similar[len(similar) // 10]\n", + "show_at_3 = similar[len(similar) // 2]\n", + "\n", + "print(\"=== #1 ===\")\n", + "print(show_at_1)\n", + "print()\n", + "\n", + "print(\"=== #2 ===\")\n", + "print(show_at_2)\n", + "print()\n", + "\n", + "print(\"=== #3 ===\")\n", + "print(show_at_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(245, 'From: SITUNAYA@IBM3090.BHAM.AC.UK\\nSubject: test....(sorry)\\nOrganization: The University of Birmingham, United Kingdom\\nLines: 1\\nNNTP-Posting-Host: ibm3090.bham.ac.uk\\n\\n==============================================================================\\n', 'comp.graphics')\n" + ] + } + ], + "source": [ + "post_group = zip(train_data.data, train_data.target)\n", + "\n", + "# Create a list of tuples that can be sorted by\n", + "# the length of the posts\n", + "all = [(len(post[0]), post[0], train_data.target_names[post[1]])\n", + " for post in post_group]\n", + "graphics = sorted([post for post in all if post[2] == 'comp.graphics'])\n", + "print(graphics[5])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['situnaya', 'ibm3090', 'bham', 'ac', 'uk', 'subject', 'test', 'sorri', 'organ', 'univers', 'birmingham', 'unit', 'kingdom', 'line', 'nntp', 'post', 'host', 'ibm3090', 'bham', 'ac', 'uk']\n" + ] + } + ], + "source": [ + "noise_post = graphics[5][1]\n", + "\n", + "analyzer = vectorizer.build_analyzer()\n", + "print(list(analyzer(noise_post)))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ac', 'birmingham', 'host', 'kingdom', 'nntp', 'sorri', 'test', 'uk', 'unit', 'univers']\n" + ] + } + ], + "source": [ + "useful = set(analyzer(noise_post)).intersection(vectorizer.get_feature_names())\n", + "print(sorted(useful))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IDF(ac ) = 3.51\n", + "IDF(birmingham) = 6.77\n", + "IDF(host ) = 1.74\n", + "IDF(kingdom ) = 6.68\n", + "IDF(nntp ) = 1.77\n", + "IDF(sorri ) = 4.14\n", + "IDF(test ) = 3.83\n", + "IDF(uk ) = 3.70\n", + "IDF(unit ) = 4.42\n", + "IDF(univers ) = 1.91\n" + ] + } + ], + "source": [ + "for term in sorted(useful):\n", + " print('IDF(%-10s) = %.2f' % (term, vectorizer._tfidf.idf_[vectorizer.vocabulary_[term]]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch06_3rd/data/toy/01.txt b/ch06_3rd/data/toy/01.txt new file mode 100644 index 00000000..97ebb966 --- /dev/null +++ b/ch06_3rd/data/toy/01.txt @@ -0,0 +1 @@ +This is a toy post about machine learning. Actually, it contains not much interesting stuff. \ No newline at end of file diff --git a/ch06_3rd/data/toy/02.txt b/ch06_3rd/data/toy/02.txt new file mode 100644 index 00000000..f7c82b00 --- /dev/null +++ b/ch06_3rd/data/toy/02.txt @@ -0,0 +1 @@ +Imaging databases provide storage capabilities. \ No newline at end of file diff --git a/ch06_3rd/data/toy/03.txt b/ch06_3rd/data/toy/03.txt new file mode 100644 index 00000000..c5e03e90 --- /dev/null +++ b/ch06_3rd/data/toy/03.txt @@ -0,0 +1 @@ +Most imaging databases save images permanently. diff --git a/ch06_3rd/data/toy/04.txt b/ch06_3rd/data/toy/04.txt new file mode 100644 index 00000000..ebd172f5 --- /dev/null +++ b/ch06_3rd/data/toy/04.txt @@ -0,0 +1 @@ +Imaging databases store data. \ No newline at end of file diff --git a/ch06_3rd/data/toy/05.txt b/ch06_3rd/data/toy/05.txt new file mode 100644 index 00000000..f9a26973 --- /dev/null +++ b/ch06_3rd/data/toy/05.txt @@ -0,0 +1 @@ +Imaging databases store data. Imaging databases store data. Imaging databases store data. \ No newline at end of file diff --git a/ch07/README.rst b/ch07/README.rst deleted file mode 100644 index 12a7b051..00000000 --- a/ch07/README.rst +++ /dev/null @@ -1,42 +0,0 @@ -========= -Chapter 7 -========= - -Support code for *Chapter 7: Regression* - - -Boston data analysis --------------------- - -This dataset is shipped with sklearn. Thus, no extra download is required. - - -boston1.py - Fit a linear regression model to the Boston house price data -boston1numpy.py - Version of above script using numpy operations for linear regression -boston_cv_penalized.py - Test different penalized (and OLS) regression schemes on the Boston dataset -figure1_2.py - Show the regression line for Boston data -figure3.py - Show the regression line for Boston data with OLS and Lasso -figure4.py - Scatter plot of predicted-vs-actual for multidimensional regression - -10K data analysis ------------------ - -lr10k.py - Linear regression on 10K dataset, evaluation by cross-validation -predict10k_en.py - Elastic nets (including with inner cross-validation for parameter - settings). Produces scatter plot. - - -MovieLens data analysis ------------------------ - -In this chapter, we only consider a very simple approach, which is implemented -in the ``usermodel.py`` script. - diff --git a/ch07/boston1.py b/ch07/boston1.py deleted file mode 100644 index d0b30447..00000000 --- a/ch07/boston1.py +++ /dev/null @@ -1,39 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# This script shows an example of simple (ordinary) linear regression - -# The first edition of the book NumPy functions only for this operation. See -# the file boston1numpy.py for that version. - -import numpy as np -from sklearn.datasets import load_boston -from sklearn.linear_model import LinearRegression -from matplotlib import pyplot as plt - -boston = load_boston() -x = boston.data -y = boston.target - -# Fitting a model is trivial: call the ``fit`` method in LinearRegression: -lr = LinearRegression() -lr.fit(x, y) - -# The instance member `residues_` contains the sum of the squared residues -rmse = np.sqrt(lr.residues_/len(x)) -print('RMSE: {}'.format(rmse)) - -fig, ax = plt.subplots() -# Plot a diagonal (for reference): -ax.plot([0, 50], [0, 50], '-', color=(.9,.3,.3), lw=4) - -# Plot the prediction versus real: -ax.scatter(lr.predict(x), boston.target) - -ax.set_xlabel('predicted') -ax.set_ylabel('real') -fig.savefig('Figure_07_08.png') diff --git a/ch07/boston1numpy.py b/ch07/boston1numpy.py deleted file mode 100644 index 0074f927..00000000 --- a/ch07/boston1numpy.py +++ /dev/null @@ -1,31 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# This script shows an example of simple (ordinary) linear regression - -import numpy as np -from sklearn.datasets import load_boston -import pylab as plt - -boston = load_boston() -x = np.array([np.concatenate((v, [1])) for v in boston.data]) -y = boston.target - -# np.linal.lstsq implements least-squares linear regression -s, total_error, _, _ = np.linalg.lstsq(x, y) - -rmse = np.sqrt(total_error[0] / len(x)) -print('Residual: {}'.format(rmse)) - -# Plot the prediction versus real: -plt.plot(np.dot(x, s), boston.target, 'ro') - -# Plot a diagonal (for reference): -plt.plot([0, 50], [0, 50], 'g-') -plt.xlabel('predicted') -plt.ylabel('real') -plt.show() diff --git a/ch07/boston_cv_penalized.py b/ch07/boston_cv_penalized.py deleted file mode 100644 index c894c4fa..00000000 --- a/ch07/boston_cv_penalized.py +++ /dev/null @@ -1,46 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -# This script fits several forms of penalized regression - -from __future__ import print_function -import numpy as np -from sklearn.cross_validation import KFold -from sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge -from sklearn.metrics import r2_score -from sklearn.datasets import load_boston -boston = load_boston() -x = boston.data -y = boston.target - -for name, met in [ - ('linear regression', LinearRegression()), - ('lasso()', Lasso()), - ('elastic-net(.5)', ElasticNet(alpha=0.5)), - ('lasso(.5)', Lasso(alpha=0.5)), - ('ridge(.5)', Ridge(alpha=0.5)), -]: - # Fit on the whole data: - met.fit(x, y) - - # Predict on the whole data: - p = met.predict(x) - r2_train = r2_score(y, p) - - # Now, we use 10 fold cross-validation to estimate generalization error - kf = KFold(len(x), n_folds=5) - p = np.zeros_like(y) - for train, test in kf: - met.fit(x[train], y[train]) - p[test] = met.predict(x[test]) - - r2_cv = r2_score(y, p) - print('Method: {}'.format(name)) - print('R2 on training: {}'.format(r2_train)) - print('R2 on 5-fold CV: {}'.format(r2_cv)) - print() - print() diff --git a/ch07/data/download.sh b/ch07/data/download.sh deleted file mode 100755 index 74753364..00000000 --- a/ch07/data/download.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash -curl -O http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.train.bz2 -bunzip2 E2006.train.bz2 diff --git a/ch07/figure1_2.py b/ch07/figure1_2.py deleted file mode 100644 index 3f11a0c7..00000000 --- a/ch07/figure1_2.py +++ /dev/null @@ -1,63 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -from sklearn.datasets import load_boston -from sklearn.linear_model import LinearRegression -from sklearn.metrics import mean_squared_error, r2_score -from matplotlib import pyplot as plt - -boston = load_boston() - -# Index number five in the number of rooms -fig,ax = plt.subplots() -ax.scatter(boston.data[:, 5], boston.target) -ax.set_xlabel("Average number of rooms (RM)") -ax.set_ylabel("House Price") - -x = boston.data[:, 5] -# fit (used below) takes a two-dimensional array as input. We use np.atleast_2d -# to convert from one to two dimensional, then transpose to make sure that the -# format matches: -x = np.transpose(np.atleast_2d(x)) - -y = boston.target - -lr = LinearRegression(fit_intercept=False) -lr.fit(x, y) - -ax.plot([0, boston.data[:, 5].max() + 1], - [0, lr.predict(boston.data[:, 5].max() + 1)], '-', lw=4) -fig.savefig('Figure1.png') - -mse = mean_squared_error(y, lr.predict(x)) -rmse = np.sqrt(mse) -print('RMSE (no intercept): {}'.format(rmse)) - -# Repeat, but fitting an intercept this time: -lr = LinearRegression(fit_intercept=True) - -lr.fit(x, y) - -fig,ax = plt.subplots() -ax.set_xlabel("Average number of rooms (RM)") -ax.set_ylabel("House Price") -ax.scatter(boston.data[:, 5], boston.target) -xmin = x.min() -xmax = x.max() -ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]) , '-', lw=4) -fig.savefig('Figure2.png') - -mse = mean_squared_error(y, lr.predict(x)) -print("Mean squared error (of training data): {:.3}".format(mse)) - -rmse = np.sqrt(mse) -print("Root mean squared error (of training data): {:.3}".format(rmse)) - -cod = r2_score(y, lr.predict(x)) -print('COD (on training data): {:.2}'.format(cod)) - diff --git a/ch07/figure3.py b/ch07/figure3.py deleted file mode 100644 index 7543c1ec..00000000 --- a/ch07/figure3.py +++ /dev/null @@ -1,33 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from sklearn.linear_model import LinearRegression, Lasso -import numpy as np -from sklearn.datasets import load_boston -from matplotlib import pyplot as plt - -boston = load_boston() -fig, ax = plt.subplots() -ax.scatter(boston.data[:, 5], boston.target) -ax.set_xlabel("Number of rooms (RM)") -ax.set_ylabel("House Price") - - -x = boston.data[:, 5] -xmin = x.min() -xmax = x.max() -x = np.transpose(np.atleast_2d(x)) -y = boston.target - -lr = LinearRegression() -lr.fit(x, y) -ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]), ':', lw=4, label='OLS model') - -las = Lasso() -las.fit(x, y) -ax.plot([xmin, xmax], las.predict([ [xmin], [xmax] ]), '-', lw=4, label='Lasso model') -fig.savefig('Figure3.png') diff --git a/ch07/figure4.py b/ch07/figure4.py deleted file mode 100644 index a24d48be..00000000 --- a/ch07/figure4.py +++ /dev/null @@ -1,33 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - - -# This script plots prediction-vs-actual on training set for the Boston dataset -# using OLS regression -import numpy as np -from sklearn.linear_model import LinearRegression -from sklearn.datasets import load_boston -from sklearn.metrics import mean_squared_error -from matplotlib import pyplot as plt - -boston = load_boston() - -x = boston.data -y = boston.target - -lr = LinearRegression() -lr.fit(x, y) -p = lr.predict(x) -print("RMSE: {:.2}.".format(np.sqrt(mean_squared_error(y, p)))) -print("R2: {:.2}.".format(lr.score(x, y))) -fig,ax = plt.subplots() -ax.scatter(p, y) -ax.set_xlabel('Predicted price') -ax.set_ylabel('Actual price') -ax.plot([y.min(), y.max()], [y.min(), y.max()], lw=4) - -fig.savefig('Figure4.png') diff --git a/ch07/lasso_path_plot.py b/ch07/lasso_path_plot.py deleted file mode 100644 index eab64c26..00000000 --- a/ch07/lasso_path_plot.py +++ /dev/null @@ -1,29 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from sklearn.linear_model import Lasso -from sklearn.datasets import load_boston -from matplotlib import pyplot as plt -import numpy as np - -boston = load_boston() -x = boston.data -y = boston.target - -las = Lasso(normalize=1) -alphas = np.logspace(-5, 2, 1000) -alphas, coefs, _= las.path(x, y, alphas=alphas) - -fig,ax = plt.subplots() -ax.plot(alphas, coefs.T) -ax.set_xscale('log') -ax.set_xlim(alphas.max(), alphas.min()) -ax.set_xlabel('Lasso coefficient path as a function of alpha') -ax.set_xlabel('Alpha') -ax.set_ylabel('Coefficient weight') -fig.savefig('Figure_LassoPath.png') - diff --git a/ch07/lr10k.py b/ch07/lr10k.py deleted file mode 100644 index 831706a1..00000000 --- a/ch07/lr10k.py +++ /dev/null @@ -1,38 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -from sklearn.metrics import mean_squared_error, r2_score -from sklearn.datasets import load_svmlight_file -from sklearn.linear_model import LinearRegression -from sklearn.cross_validation import KFold - -# Whether to use Elastic nets (otherwise, ordinary linear regression is used) - -# Load data: -data, target = load_svmlight_file('data/E2006.train') - -lr = LinearRegression() - -# Compute error on training data to demonstrate that we can obtain near perfect -# scores: - -lr.fit(data, target) -pred = lr.predict(data) - -print('RMSE on training, {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('R2 on training, {:.2}'.format(r2_score(target, pred))) -print('') - -pred = np.zeros_like(target) -kf = KFold(len(target), n_folds=5) -for train, test in kf: - lr.fit(data[train], target[train]) - pred[test] = lr.predict(data[test]) - -print('RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred))) diff --git a/ch07/predict10k_en.py b/ch07/predict10k_en.py deleted file mode 100644 index a7dd960a..00000000 --- a/ch07/predict10k_en.py +++ /dev/null @@ -1,73 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -from sklearn.datasets import load_svmlight_file -from sklearn.cross_validation import KFold -from sklearn.linear_model import ElasticNetCV, ElasticNet -from sklearn.metrics import mean_squared_error, r2_score -from matplotlib import pyplot as plt - -data, target = load_svmlight_file('data/E2006.train') - -# Edit the lines below if you want to switch method: -# from sklearn.linear_model import Lasso -# met = Lasso(alpha=0.1) -met = ElasticNet(alpha=0.1) - -kf = KFold(len(target), n_folds=5) -pred = np.zeros_like(target) -for train, test in kf: - met.fit(data[train], target[train]) - pred[test] = met.predict(data[test]) - -print('[EN 0.1] RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('[EN 0.1] R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred))) -print('') - -# Construct an ElasticNetCV object (use all available CPUs) -met = ElasticNetCV(n_jobs=-1) - -kf = KFold(len(target), n_folds=5) -pred = np.zeros_like(target) -for train, test in kf: - met.fit(data[train], target[train]) - pred[test] = met.predict(data[test]) - -print('[EN CV] RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('[EN CV] R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred))) -print('') - -met.fit(data, target) -pred = met.predict(data) -print('[EN CV] RMSE on training, {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('[EN CV] R2 on training, {:.2}'.format(r2_score(target, pred))) - - -# Construct an ElasticNetCV object (use all available CPUs) -met = ElasticNetCV(n_jobs=-1, l1_ratio=[.01, .05, .25, .5, .75, .95, .99]) - -kf = KFold(len(target), n_folds=5) -pred = np.zeros_like(target) -for train, test in kf: - met.fit(data[train], target[train]) - pred[test] = met.predict(data[test]) - - -print('[EN CV l1_ratio] RMSE on testing (5 fold), {:.2}'.format(np.sqrt(mean_squared_error(target, pred)))) -print('[EN CV l1_ratio] R2 on testing (5 fold), {:.2}'.format(r2_score(target, pred))) -print('') - - -fig, ax = plt.subplots() -y = target -ax.scatter(y, pred, c='k') -ax.plot([-5,-1], [-5,-1], 'r-', lw=2) -ax.set_xlabel('Actual value') -ax.set_ylabel('Predicted value') -fig.savefig('Figure_10k_scatter_EN_l1_ratio.png') - diff --git a/ch08/Recommendations.ipynb b/ch08/Recommendations.ipynb new file mode 100644 index 00000000..4c767c05 --- /dev/null +++ b/ch08/Recommendations.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recommendations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make plots inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def load():\n", + " from scipy import sparse\n", + "\n", + " data = np.loadtxt('data/ml-100k/u.data')\n", + " ij = data[:, :2]\n", + " ij -= 1 # original data is in 1-based system\n", + " values = data[:, 2]\n", + " reviews = sparse.csc_matrix((values, ij.T)).astype(float)\n", + " return reviews.toarray()\n", + "reviews = load()\n", + "U,M = np.where(reviews)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Split the data into training/testing:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_train_test(reviews):\n", + " import random\n", + " test_idxs = np.array(random.sample(range(len(U)), len(U)//10))\n", + "\n", + " train = reviews.copy()\n", + " train[U[test_idxs], M[test_idxs]] = 0\n", + "\n", + " test = np.zeros_like(reviews)\n", + " test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]]\n", + " return train, test\n", + "\n", + "train, test = get_train_test(reviews)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For normalization, we make a class that _follows the scikit-learn API_:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class NormalizePositive(object):\n", + " def __init__(self, axis=0):\n", + " self.axis = axis\n", + "\n", + " def fit(self, features, y=None):\n", + " if self.axis == 1:\n", + " features = features.T\n", + " # count features that are greater than zero in axis 0:\n", + " binary = (features > 0)\n", + "\n", + " count0 = binary.sum(axis=0)\n", + "\n", + " # to avoid division by zero, set zero counts to one:\n", + " count0[count0 == 0] = 1.\n", + "\n", + " # computing the mean is easy:\n", + " self.mean = features.sum(axis=0)/count0\n", + "\n", + " # only consider differences where binary is True:\n", + " diff = (features - self.mean) * binary\n", + " diff **= 2\n", + " # regularize the estimate of std by adding 0.1\n", + " self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0)\n", + " return self\n", + "\n", + "\n", + " def transform(self, features):\n", + " if self.axis == 1:\n", + " features = features.T\n", + " binary = (features > 0)\n", + " features = features - self.mean\n", + " features /= self.std\n", + " features *= binary\n", + " if self.axis == 1:\n", + " features = features.T\n", + " return features\n", + "\n", + " def inverse_transform(self, features, copy=True):\n", + " if copy:\n", + " features = features.copy()\n", + " if self.axis == 1:\n", + " features = features.T\n", + " features *= self.std\n", + " features += self.mean\n", + " if self.axis == 1:\n", + " features = features.T\n", + " return features\n", + "\n", + " def fit_transform(self, features):\n", + " return self.fit(features).transform(features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can quickly visualize the matrix to see what our data looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1EYyVfo3xK+E/Zso49D+3nRatLFLpWo61VsqzdL4WrUxu275Hsk7IzMtVJCnT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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "binary = (train > 0)\n", + "fig,ax = plt.subplots()\n", + "# plot just 200x200 area for space reasons\n", + "ax.imshow(binary[:200, :200], interpolation='nearest')\n", + "ax.set_xlabel('User ID')\n", + "ax.set_ylabel('User ID')\n", + "fig.savefig('IMG_REC_01.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "def predict_positive_nn(otrain, necessary=None):\n", + " from scipy.spatial import distance\n", + " binary = (otrain > 0)\n", + " norm = NormalizePositive(axis=1)\n", + " train = norm.fit_transform(otrain)\n", + " \n", + " # compute all pair-wise distances:\n", + " dists = distance.pdist(binary, 'correlation')\n", + " # Convert to square form, so that `dists[i,j]`\n", + " # contains the distance between `binary[i]` and `binary[j]`:\n", + " dists = distance.squareform(dists)\n", + "\n", + " neighbors = dists.argsort(axis=1)\n", + " filled = train.copy()\n", + " for u in range(filled.shape[0]):\n", + " # n_u are the neighbors of user\n", + " n_u = neighbors[u, 1:]\n", + " for m in range(filled.shape[1]):\n", + " if necessary is not None and not necessary[u, m]:\n", + " continue\n", + " # This code could be faster using numpy indexing trickery as the\n", + " # cost of readibility (this is left as an exercise to the reader):\n", + " revs = [train[neigh, m]\n", + " for neigh in n_u\n", + " if binary[neigh, m]]\n", + " if len(revs):\n", + " n = len(revs)\n", + " n //= 2\n", + " n += 1\n", + " revs = revs[:n]\n", + " filled[u,m] = np.mean(revs)\n", + " # Finally, undo the normalization to get back the final reusl\n", + " return norm.inverse_transform(filled)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the `predict_positive_nn` function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "predicted = predict_positive_nn(train, test != 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the R² metric to evaluate how well we do" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 score (binary user neighbors): 30.4%\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "r2 = metrics.r2_score(test[test != 0], predicted[test != 0])\n", + "print('R2 score (binary user neighbors): {:.1%}'.format(r2))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 score (binary movie neighbors): 31.5%\n" + ] + } + ], + "source": [ + "predicted = predict_positive_nn(train.T, (test != 0).T).T\n", + "r2 = metrics.r2_score(test[test > 0], predicted[test > 0])\n", + "print('R2 score (binary movie neighbors): {:.1%}'.format(r2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression for recommendations\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use regression for recommendations as well" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def predict_regression(otrain):\n", + " from sklearn.linear_model import ElasticNetCV\n", + " binary = (otrain > 0)\n", + " norm = NormalizePositive(axis=1)\n", + " train = norm.fit_transform(otrain)\n", + "\n", + " reg = ElasticNetCV(alphas=[\n", + " 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])\n", + " filled = train.copy()\n", + " # iterate over all users:\n", + " for u in range(train.shape[0]):\n", + " curtrain = np.delete(train, u, axis=0)\n", + " bu = binary[u]\n", + " if np.sum(bu) > 10:\n", + " reg.fit(curtrain[:,bu].T, train[u, bu])\n", + " # Fill the values that were not there already\n", + " filled[u, ~bu] = reg.predict(curtrain[:,~bu].T)\n", + " return norm.inverse_transform(filled)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 score (user regression): 28.6%\n" + ] + } + ], + "source": [ + "predicted = predict_regression(train)\n", + "r2 = metrics.r2_score(test[test > 0], predicted[test > 0])\n", + "print('R2 score (user regression): {:.1%}'.format(r2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 score (movie regression): 26.2%\n" + ] + } + ], + "source": [ + "predicted = predict_regression(train.T).T\n", + "r2 = metrics.r2_score(test[test > 0], predicted[test > 0])\n", + "print('R2 score (movie regression): {:.1%}'.format(r2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combining multiple methods" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def predict_stacked(train):\n", + " from sklearn import linear_model\n", + " tr_train,tr_test = get_train_test(train)\n", + " tr_predicted0 = predict_positive_nn(tr_train, tr_test != 0)\n", + " tr_predicted1 = predict_positive_nn(tr_train.T, (tr_test != 0).T).T\n", + " tr_predicted2 = predict_regression(tr_train)\n", + " tr_predicted3 = predict_regression(tr_train.T).T\n", + " stack_tr = np.array([\n", + " tr_predicted0[tr_test > 0],\n", + " tr_predicted1[tr_test > 0],\n", + " tr_predicted2[tr_test > 0],\n", + " tr_predicted3[tr_test > 0],\n", + " ]).T\n", + "\n", + " lr = linear_model.LinearRegression()\n", + " lr.fit(stack_tr, tr_test[tr_test > 0])\n", + "\n", + " stack_te = np.array([\n", + " tr_predicted0.ravel(),\n", + " tr_predicted1.ravel(),\n", + " tr_predicted2.ravel(),\n", + " tr_predicted3.ravel(),\n", + " ]).T\n", + "\n", + " return lr.predict(stack_te).reshape(train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the same evaluation as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 score (stacked prediction): 32.0%\n" + ] + } + ], + "source": [ + "predicted = predict_stacked(train)\n", + "r2 = metrics.r2_score(test[test > 0], predicted[test > 0])\n", + "print('R2 score (stacked prediction): {:.1%}'.format(r2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SHOPPING BASKET ANALYSIS\n", + "This is the slow version of the code, which will take a long time to complete." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "from itertools import chain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You **must** have downloaded the data before running this analysis. The data is downloaded as a compressed file" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import gzip\n", + "# file format is a line per transaction\n", + "# of the form '12 34 342 5...'\n", + "dataset = [[int(tok) for tok in line.strip().split()]\n", + " for line in gzip.open('data/retail.dat.gz')]\n", + "dataset = [set(d) for d in dataset]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count how often each product was purchased:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "counts = defaultdict(int)\n", + "for elem in chain(*dataset):\n", + " counts[elem] += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print a little histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nr of baskets | Nr of products\n", + "--------------------------------\n", + " 1 - 2 | 2224\n", + " 2 - 4 | 2438\n", + " 4 - 8 | 2508\n", + " 8 - 16 | 2251\n", + " 16 - 32 | 2182\n", + " 32 - 64 | 1940\n", + " 64 - 128 | 1523\n", + " 128 - 512 | 1225\n", + " 512 - | 179\n" + ] + } + ], + "source": [ + "countsv = np.array(list(counts.values()))\n", + "bins = [1, 2, 4, 8, 16, 32, 64, 128, 512]\n", + "print(' {0:11} | {1:12}'.format('Nr of baskets', 'Nr of products'))\n", + "print('--------------------------------')\n", + "for i in range(len(bins)):\n", + " bot = bins[i]\n", + " top = (bins[i + 1] if (i + 1) < len(bins) else 100000000000)\n", + " print(' {0:4} - {1:3} | {2:12}'.format(\n", + " bot, (top if top < 1000 else ''), np.sum((countsv >= bot) & (countsv < top))))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "minsupport = 100\n", + "valid = set(k for k,v in counts.items() if (v >= minsupport))\n", + "dataset = [(valid&d) for d in dataset if len(valid&d)]\n", + "\n", + "baskets = defaultdict(set)\n", + "\n", + "for i, ds in enumerate(dataset): \n", + " for ell in ds:\n", + " baskets[ell].add(i) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n" + ] + } + ], + "source": [ + "itemsets = [frozenset([v]) for v in valid]\n", + "freqsets = []\n", + "for i in range(16):\n", + " nextsets = []\n", + " tested = set()\n", + " for it in itemsets:\n", + " for v in valid:\n", + " if v not in it:\n", + " # Create a new candidate set by adding v to it\n", + " c = (it | frozenset([v]))\n", + " # check if we have tested it already\n", + " if c in tested:\n", + " continue\n", + " tested.add(c)\n", + "\n", + " candidates = set()\n", + " for elem in c:\n", + " candidates.update(baskets[elem])\n", + " support_c = sum(1 for d in candidates if dataset[d].issuperset(c))\n", + " if support_c > minsupport:\n", + " nextsets.append(c)\n", + " freqsets.extend(nextsets)\n", + " itemsets = nextsets\n", + " if not len(itemsets):\n", + " break\n", + "print(\"Finished!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can _mine the baskets for interesting association rules_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "minlift = 5.0\n", + "nr_transactions = float(len(dataset))\n", + "for itemset in freqsets:\n", + " for item in itemset:\n", + " consequent = frozenset([item])\n", + " antecedent = itemset-consequent\n", + " base = 0.0\n", + " # acount: antecedent count\n", + " acount = 0.0\n", + "\n", + " # ccount : consequent count\n", + " ccount = 0.0\n", + " for d in dataset:\n", + " if item in d: base += 1\n", + " if d.issuperset(itemset): ccount += 1\n", + " if d.issuperset(antecedent): acount += 1\n", + " base /= nr_transactions\n", + " p_y_given_x = ccount/acount\n", + " lift = p_y_given_x / base\n", + " if lift > minlift:\n", + " print('Rule {0} -> {1} has lift {2}'\n", + " .format(antecedent, consequent,lift))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch08/all_correlations.py b/ch08/all_correlations.py deleted file mode 100644 index d3817bf0..00000000 --- a/ch08/all_correlations.py +++ /dev/null @@ -1,48 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np - -def all_correlations(y, X): - from scipy import spatial - y = np.atleast_2d(y) - sp = spatial.distance.cdist(X, y, 'correlation') - # The "correlation distance" is 1 - corr(x,y); so we invert that to obtain the correlation - return 1 - sp.ravel() - -# This is the version in the book (1st Edition): -def all_correlations_book_version(bait, target): - ''' - corrs = all_correlations(bait, target) - - corrs[i] is the correlation between bait and target[i] - ''' - return np.array( - [np.corrcoef(bait, c)[0, 1] - for c in target]) - -# This is a faster, but harder to read, implementation: -def all_correlations_fast_no_scipy(y, X): - ''' - Cs = all_correlations(y, X) - - Cs[i] = np.corrcoef(y, X[i])[0,1] - ''' - X = np.asanyarray(X, float) - y = np.asanyarray(y, float) - xy = np.dot(X, y) - y_ = y.mean() - ys_ = y.std() - x_ = X.mean(1) - xs_ = X.std(1) - n = float(len(y)) - ys_ += 1e-5 # Handle zeros in ys - xs_ += 1e-5 # Handle zeros in x - - return (xy - x_ * y_ * n) / n / xs_ / ys_ - - diff --git a/ch08/apriori/histogram.py b/ch08/apriori/histogram.py deleted file mode 100644 index 9344efbd..00000000 --- a/ch08/apriori/histogram.py +++ /dev/null @@ -1,25 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -from collections import defaultdict -from itertools import chain -from gzip import GzipFile -dataset = [[int(tok) for tok in line.strip().split()] - for line in GzipFile('retail.dat.gz')] -counts = defaultdict(int) -for elem in chain(*dataset): - counts[elem] += 1 -counts = np.array(list(counts.values())) -bins = [1, 2, 4, 8, 16, 32, 64, 128, 512] -print(' {0:11} | {1:12}'.format('Nr of baskets', 'Nr of products')) -print('--------------------------------') -for i in range(len(bins)): - bot = bins[i] - top = (bins[i + 1] if (i + 1) < len(bins) else 100000000000) - print(' {0:4} - {1:3} | {2:12}'.format( - bot, (top if top < 1000 else ''), np.sum((counts >= bot) & (counts < top)))) diff --git a/ch08/averaged.py b/ch08/averaged.py deleted file mode 100644 index 5b19bba7..00000000 --- a/ch08/averaged.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np -import load_ml100k -import regression -import corrneighbours -from sklearn import metrics -import norm - -def predict(train): - predicted0 = regression.predict(train) - predicted1 = regression.predict(train.T).T - predicted2 = corrneighbours.predict(train) - predicted3 = corrneighbours.predict(train.T).T - predicted4 = norm.predict(train) - predicted5 = norm.predict(train.T).T - stack = np.array([ - predicted0, - predicted1, - predicted2, - predicted3, - predicted4, - predicted5, - ]) - return stack.mean(0) - - -def main(): - train,test = load_ml100k.get_train_test(random_state=12) - predicted = predict(train) - r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) - print('R2 averaged: {:.2%}'.format(r2)) - -if __name__ == '__main__': - main() diff --git a/ch08/chapter.py b/ch08/chapter.py deleted file mode 100644 index d039d93f..00000000 --- a/ch08/chapter.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np # NOT IN BOOK -from matplotlib import pyplot as plt # NOT IN BOOK - -def load(): - import numpy as np - from scipy import sparse - - data = np.loadtxt('data/ml-100k/u.data') - ij = data[:, :2] - ij -= 1 # original data is in 1-based system - values = data[:, 2] - reviews = sparse.csc_matrix((values, ij.T)).astype(float) - return reviews.toarray() -reviews = load() -U,M = np.where(reviews) -import random -test_idxs = np.array(random.sample(range(len(U)), len(U)//10)) - -train = reviews.copy() -train[U[test_idxs], M[test_idxs]] = 0 - -test = np.zeros_like(reviews) -test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]] - -class NormalizePositive(object): - def __init__(self, axis=0): - self.axis = axis - - def fit(self, features, y=None): - if self.axis == 1: - features = features.T - # count features that are greater than zero in axis 0: - binary = (features > 0) - - count0 = binary.sum(axis=0) - - # to avoid division by zero, set zero counts to one: - count0[count0 == 0] = 1. - - # computing the mean is easy: - self.mean = features.sum(axis=0)/count0 - - # only consider differences where binary is True: - diff = (features - self.mean) * binary - diff **= 2 - # regularize the estimate of std by adding 0.1 - self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0) - return self - - - def transform(self, features): - if self.axis == 1: - features = features.T - binary = (features > 0) - features = features - self.mean - features /= self.std - features *= binary - if self.axis == 1: - features = features.T - return features - - def inverse_transform(self, features, copy=True): - if copy: - features = features.copy() - if self.axis == 1: - features = features.T - features *= self.std - features += self.mean - if self.axis == 1: - features = features.T - return features - - def fit_transform(self, features): - return self.fit(features).transform(features) - - -norm = NormalizePositive(axis=1) -binary = (train > 0) -train = norm.fit_transform(train) -# plot just 200x200 area for space reasons -plt.imshow(binary[:200, :200], interpolation='nearest') - -from scipy.spatial import distance -# compute all pair-wise distances: -dists = distance.pdist(binary, 'correlation') -# Convert to square form, so that dists[i,j] -# is distance between binary[i] and binary[j]: -dists = distance.squareform(dists) -neighbors = dists.argsort(axis=1) - -# We are going to fill this matrix with results -filled = train.copy() -for u in range(filled.shape[0]): - # n_u is neighbors of user - n_u = neighbors[u, 1:] - for m in range(filled.shape[1]): - # get relevant reviews in order! - revs = [train[neigh, m] - for neigh in n_u - if binary [neigh, m]] - if len(revs): - # n is the number of reviews for this movie - n = len(revs) - # take half of the reviews plus one into consideration: - n //= 2 - n += 1 - revs = revs[:n] - filled[u,m] = np.mean(revs) - -predicted = norm.inverse_transform(filled) -from sklearn import metrics -r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) -print('R2 score (binary neighbors): {:.1%}'.format(r2)) - -reviews = reviews.T -# use same code as before -r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) -print('R2 score (binary movie neighbors): {:.1%}'.format(r2)) - - -from sklearn.linear_model import ElasticNetCV # NOT IN BOOK - -reg = ElasticNetCV(alphas=[ - 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) -filled = train.copy() -# iterate over all users: -for u in range(train.shape[0]): - curtrain = np.delete(train, u, axis=0) - bu = binary[u] - reg.fit(curtrain[:,bu].T, train[u, bu]) - filled[u, ~bu] = reg.predict(curtrain[:,~bu].T) -predicted = norm.inverse_transform(filled) -r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) -print('R2 score (user regression): {:.1%}'.format(r2)) - - -# SHOPPING BASKET ANALYSIS -# This is the slow version of the code, which will take a long time to -# complete. - - -from collections import defaultdict -from itertools import chain - -# File is downloaded as a compressed file -import gzip -# file format is a line per transaction -# of the form '12 34 342 5...' -dataset = [[int(tok) for tok in line.strip().split()] - for line in gzip.open('data/retail.dat.gz')] -dataset = [set(d) for d in dataset] -# count how often each product was purchased: -counts = defaultdict(int) -for elem in chain(*dataset): - counts[elem] += 1 - -minsupport = 80 -valid = set(k for k,v in counts.items() if (v >= minsupport)) -itemsets = [frozenset([v]) for v in valid] -freqsets = [] -for i in range(16): - nextsets = [] - tested = set() - for it in itemsets: - for v in valid: - if v not in it: - # Create a new candidate set by adding v to it - c = (it | frozenset([v])) - # check If we have tested it already - if c in tested: - continue - tested.add(c) - - # Count support by looping over dataset - # This step is slow. - # Check `apriori.py` for a better implementation. - support_c = sum(1 for d in dataset if d.issuperset(c)) - if support_c > minsupport: - nextsets.append(c) - freqsets.extend(nextsets) - itemsets = nextsets - if not len(itemsets): - break -print("Finished!") - - -minlift = 5.0 -nr_transactions = float(len(dataset)) -for itemset in freqsets: - for item in itemset: - consequent = frozenset([item]) - antecedent = itemset-consequent - base = 0.0 - # acount: antecedent count - acount = 0.0 - - # ccount : consequent count - ccount = 0.0 - for d in dataset: - if item in d: base += 1 - if d.issuperset(itemset): ccount += 1 - if d.issuperset(antecedent): acount += 1 - base /= nr_transactions - p_y_given_x = ccount/acount - lift = p_y_given_x / base - if lift > minlift: - print('Rule {0} -> {1} has lift {2}' - .format(antecedent, consequent,lift)) diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py deleted file mode 100644 index eb30e685..00000000 --- a/ch08/corrneighbours.py +++ /dev/null @@ -1,58 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from __future__ import print_function -import numpy as np -from load_ml100k import get_train_test -from scipy.spatial import distance -from sklearn import metrics - -from norm import NormalizePositive - -def predict(otrain): - binary = (otrain > 0) - norm = NormalizePositive(axis=1) - train = norm.fit_transform(otrain) - - dists = distance.pdist(binary, 'correlation') - dists = distance.squareform(dists) - - neighbors = dists.argsort(axis=1) - filled = train.copy() - for u in range(filled.shape[0]): - # n_u are the neighbors of user - n_u = neighbors[u, 1:] - for m in range(filled.shape[1]): - # This code could be faster using numpy indexing trickery as the - # cost of readibility (this is left as an exercise to the reader): - revs = [train[neigh, m] - for neigh in n_u - if binary[neigh, m]] - if len(revs): - n = len(revs) - n //= 2 - n += 1 - revs = revs[:n] - filled[u,m] = np.mean(revs) - - return norm.inverse_transform(filled) - -def main(transpose_inputs=False): - train, test = get_train_test(random_state=12) - if transpose_inputs: - train = train.T - test = test.T - - predicted = predict(train) - r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) - print('R2 score (binary {} neighbours): {:.1%}'.format( - ('movie' if transpose_inputs else 'user'), - r2)) - -if __name__ == '__main__': - main() - main(transpose_inputs=True) diff --git a/ch08/figure3.py b/ch08/figure3.py deleted file mode 100644 index daafc300..00000000 --- a/ch08/figure3.py +++ /dev/null @@ -1,15 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from load_ml100k import load -from matplotlib import pyplot as plt -data = load() -plt.gray() -plt.imshow(data[:200, :200], interpolation='nearest') -plt.xlabel('User ID') -plt.ylabel('Film ID') -plt.savefig('Figure_08_03_DataMatrix.png') diff --git a/ch08/norm.py b/ch08/norm.py deleted file mode 100644 index 2925bbca..00000000 --- a/ch08/norm.py +++ /dev/null @@ -1,75 +0,0 @@ -import numpy as np - -class NormalizePositive(object): - - def __init__(self, axis=0): - self.axis = axis - - def fit(self, features, y=None): - # count features that are greater than zero in axis `self.axis`: - if self.axis == 1: - features = features.T - binary = (features > 0) - count = binary.sum(axis=0) - - # to avoid division by zero, set zero counts to one: - count[count == 0] = 1. - - self.mean = features.sum(axis=0)/count - - # Compute variance by average squared difference to the mean, but only - # consider differences where binary is True (i.e., where there was a - # true rating): - diff = (features - self.mean) * binary - diff **= 2 - # regularize the estimate of std by adding 0.1 - self.std = np.sqrt(0.1 + diff.sum(axis=0)/count) - return self - - def transform(self, features): - if self.axis == 1: - features = features.T - binary = (features > 0) - features = features - self.mean - features /= self.std - features *= binary - if self.axis == 1: - features = features.T - return features - - def inverse_transform(self, features, copy=True): - if copy: - features = features.copy() - if self.axis == 1: - features = features.T - features *= self.std - features += self.mean - if self.axis == 1: - features = features.T - return features - - def fit_transform(self, features): - return self.fit(features).transform(features) - - -def predict(train): - norm = NormalizePositive() - train = norm.fit_transform(train) - return norm.inverse_transform(train * 0.) - - -def main(transpose_inputs=False): - from load_ml100k import get_train_test - from sklearn import metrics - train,test = get_train_test(random_state=12) - if transpose_inputs: - train = train.T - test = test.T - predicted = predict(train) - r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) - print('R2 score ({} normalization): {:.1%}'.format( - ('movie' if transpose_inputs else 'user'), - r2)) -if __name__ == '__main__': - main() - main(transpose_inputs=True) diff --git a/ch08/regression.py b/ch08/regression.py deleted file mode 100644 index 693e99a4..00000000 --- a/ch08/regression.py +++ /dev/null @@ -1,50 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -from sklearn.linear_model import ElasticNetCV -from norm import NormalizePositive -from sklearn import metrics - - -def predict(train): - binary = (train > 0) - reg = ElasticNetCV(fit_intercept=True, alphas=[ - 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) - norm = NormalizePositive() - train = norm.fit_transform(train) - - filled = train.copy() - # iterate over all users - for u in range(train.shape[0]): - # remove the current user for training - curtrain = np.delete(train, u, axis=0) - bu = binary[u] - if np.sum(bu) > 5: - reg.fit(curtrain[:,bu].T, train[u, bu]) - - # Fill the values that were not there already - filled[u, ~bu] = reg.predict(curtrain[:,~bu].T) - return norm.inverse_transform(filled) - - -def main(transpose_inputs=False): - from load_ml100k import get_train_test - train,test = get_train_test(random_state=12) - if transpose_inputs: - train = train.T - test = test.T - filled = predict(train) - r2 = metrics.r2_score(test[test > 0], filled[test > 0]) - - print('R2 score ({} regression): {:.1%}'.format( - ('movie' if transpose_inputs else 'user'), - r2)) - -if __name__ == '__main__': - main() - main(transpose_inputs=True) diff --git a/ch08/similar_movie.py b/ch08/similar_movie.py deleted file mode 100644 index cd49a162..00000000 --- a/ch08/similar_movie.py +++ /dev/null @@ -1,74 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from __future__ import print_function -import numpy as np - - -def nn_movie(ureviews, reviews, uid, mid, k=1): - '''Movie neighbor based classifier - - Parameters - ---------- - ureviews : ndarray - reviews : ndarray - uid : int - index of user - mid : int - index of movie - k : int - index of neighbor to return - - Returns - ------- - pred : float - ''' - X = ureviews - y = ureviews[mid].copy() - y -= y.mean() - y /= (y.std() + 1e-5) - corrs = np.dot(X, y) - likes = corrs.argsort() - likes = likes[::-1] - c = 0 - pred = 3. - for ell in likes: - if ell == mid: - continue - if reviews[uid, ell] > 0: - pred = reviews[uid, ell] - if c == k: - return pred - c += 1 - return pred - - -def all_estimates(reviews, k=1): - '''Estimate all review ratings - ''' - reviews = reviews.astype(float) - k -= 1 - nusers, nmovies = reviews.shape - estimates = np.zeros_like(reviews) - for u in range(nusers): - ureviews = np.delete(reviews, u, axis=0) - ureviews -= ureviews.mean(0) - ureviews /= (ureviews.std(0) + 1e-5) - ureviews = ureviews.T.copy() - for m in np.where(reviews[u] > 0)[0]: - estimates[u, m] = nn_movie(ureviews, reviews, u, m, k) - return estimates - -if __name__ == '__main__': - from load_ml100k import load - reviews = load() - estimates = all_estimates(reviews) - error = (estimates - reviews) - error **= 2 - error = error[reviews > 0] - rmse = np.sqrt(error.mean()) - print("RMSE is {0}.".format(rmse)) diff --git a/ch09_3rd/chapter_09.ipynb b/ch09_3rd/chapter_09.ipynb new file mode 100644 index 00000000..8813dbcd --- /dev/null +++ b/ch09_3rd/chapter_09.ipynb @@ -0,0 +1,57544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing.\n", + "\n", + "It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter we are discussing two methods to reduce the feature space: filters and wrappers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities we will need" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "CHART_DIR = \"charts\"\n", + "if not os.path.exists(CHART_DIR):\n", + " os.mkdir(CHART_DIR)\n", + "\n", + "DATA_DIR = \"data\"\n", + "if not os.path.exists(DATA_DIR):\n", + " raise Exception(\"Data directory %s not found\" % os.path.abspath(DATA_DIR))\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "\n", + "import numpy as np\n", + "import scipy\n", + "\n", + "DPI = 300\n", + "\n", + "import collections\n", + "import csv\n", + "import json\n", + "\n", + "\n", + "def save_png(name):\n", + " fn = 'B09124_09_%s.png'%name # please ignore, it just helps our publisher :-)\n", + " plt.savefig(os.path.join(CHART_DIR, fn), bbox_inches=\"tight\")\n", + " \n", + " \n", + "def plot_pr(auc_score, name, precision, recall, label=None, plot_nr=None):\n", + " plt.clf()\n", + " plt.figure(num=None, figsize=(5, 4), dpi=DPI)\n", + " plt.grid(True)\n", + " plt.fill_between(recall, precision, alpha=0.5)\n", + " plt.plot(recall, precision, lw=1)\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('P/R curve (AUC=%0.2f) / %s' % (auc_score, label))\n", + " filename = name.replace(\" \", \"_\")\n", + " save_png(\"%s_pr_%s\" % (plot_nr, filename))\n", + "\n", + "\n", + "def tweak_labels(Y, pos_sent_list):\n", + " pos = Y == pos_sent_list[0]\n", + " for sent_label in pos_sent_list[1:]:\n", + " pos |= Y == sent_label\n", + "\n", + " Y = np.zeros(Y.shape[0])\n", + " Y[pos] = 1\n", + " Y = Y.astype(int)\n", + "\n", + " return Y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading data from Twitter\n", + "\n", + "This section is a slight adaptation of the Niek Sanders' [Twitter sentiment corpus install script](http://sananalytics.com/lab/twitter-sentiment/), which pulls tweet data from Twitter.\n", + "\n", + "In Sanders' original form, the code was using Twitter API 1.0.\n", + "Now that Twitter moved to 1.1, we had to make a few changes.\n", + "Cf. twitterauth.py for the details.\n", + "\n", + "Regarding rate limiting, please check\n", + "https://dev.twitter.com/rest/public/rate-limiting/n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "{'inList': 'data\\\\corpus.csv', 'outList': 'data\\\\full-corpus.csv', 'rawDir': 'data\\\\rawdata/'}\n", + "We will skip 2126 tweets that are not available or visible any more on twitter\n", + "We have already downloaded 0 tweets.\n", + "Fetching 4259 tweets...\n", + "['apple', 'positive', '126415614616154112']\n", + "--> downloading tweet #126415614616154112 (1 of 4259)\n", + "['apple', 'positive', '126402758403305474']\n", + "--> downloading tweet #126402758403305474 (2 of 4259)\n", + "['apple', 'positive', '126397179614068736']\n", + "--> downloading tweet #126397179614068736 (3 of 4259)\n", + "['apple', 'positive', '126379685453119488']\n", + "--> downloading tweet #126379685453119488 (4 of 4259)\n", + "['apple', 'positive', '126377656416612353']\n", + "--> downloading tweet #126377656416612353 (5 of 4259)\n", + "['apple', 'positive', '126373779483004928']\n", + "--> downloading tweet #126373779483004928 (6 of 4259)\n", + "['apple', 'positive', '126366353757179904']\n", + "--> downloading tweet #126366353757179904 (7 of 4259)\n", + "['apple', 'positive', '126365858481188864']\n", + "--> downloading tweet #126365858481188864 (8 of 4259)\n", + "['apple', 'positive', '126360935509135362']\n", + "--> downloading tweet #126360935509135362 (9 of 4259)\n", + "['apple', 'positive', '126360398885687296']\n", + "--> downloading tweet #126360398885687296 (10 of 4259)\n", + "['apple', 'positive', '126358340220616704']\n", + "--> downloading tweet #126358340220616704 (11 of 4259)\n", + "['apple', 'positive', '126357982685569024']\n", + "--> downloading tweet #126357982685569024 (12 of 4259)\n", + "['apple', 'positive', '126352268705538048']\n", + "--> downloading tweet #126352268705538048 (13 of 4259)\n", + "['apple', 'positive', '126342268603998208']\n", + "--> downloading tweet #126342268603998208 (14 of 4259)\n", + "['apple', 'positive', '126325800080392193']\n", + "--> downloading tweet #126325800080392193 (15 of 4259)\n", + "['apple', 'positive', '126324177501302784']\n", + "--> downloading tweet #126324177501302784 (16 of 4259)\n", + "['apple', 'positive', '126322063332999169']\n", + "--> downloading tweet #126322063332999169 (17 of 4259)\n", + "['apple', 'positive', '126318009647235072']\n", + "--> downloading tweet #126318009647235072 (18 of 4259)\n", + "['apple', 'positive', '126315223060709376']\n", + "--> downloading tweet #126315223060709376 (19 of 4259)\n", + "['apple', 'positive', '126315011600678913']\n", + "--> downloading tweet #126315011600678913 (20 of 4259)\n", + "['apple', 'positive', '126314687116750849']\n", + "--> downloading tweet #126314687116750849 (21 of 4259)\n", + "['apple', 'positive', '126311981564178432']\n", + "--> downloading tweet #126311981564178432 (22 of 4259)\n", + "['apple', 'positive', '126307801046847488']\n", + "--> downloading tweet #126307801046847488 (23 of 4259)\n", + "['apple', 'positive', '126302673820594176']\n", + "--> downloading tweet #126302673820594176 (24 of 4259)\n", + "['apple', 'positive', '126301956951117826']\n", + "--> downloading tweet #126301956951117826 (25 of 4259)\n", + "['apple', 'positive', '126287654093471745']\n", + "--> 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#125947912306954240 (61 of 4259)\n", + "['apple', 'positive', '125940394566483968']\n", + "--> downloading tweet #125940394566483968 (62 of 4259)\n", + "['apple', 'positive', '125925618486489088']\n", + "--> downloading tweet #125925618486489088 (63 of 4259)\n", + "['apple', 'positive', '125924446430183425']\n", + "--> downloading tweet #125924446430183425 (64 of 4259)\n", + "['apple', 'positive', '125922999651139584']\n", + "--> downloading tweet #125922999651139584 (65 of 4259)\n", + "['apple', 'positive', '125902301931126785']\n", + "--> downloading tweet #125902301931126785 (66 of 4259)\n", + "['apple', 'positive', '125901202591461376']\n", + "--> downloading tweet #125901202591461376 (67 of 4259)\n", + "['apple', 'positive', '125900497327636480']\n", + "--> downloading tweet #125900497327636480 (68 of 4259)\n", + "['apple', 'positive', '125850288488841217']\n", + "--> downloading tweet #125850288488841217 (69 of 4259)\n", + "['apple', 'positive', '125840039031738368']\n", + "--> 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4259)\n", + "['apple', 'irrelevant', '126394266145665025']\n", + "--> downloading tweet #126394266145665025 (836 of 4259)\n", + "['apple', 'irrelevant', '126391727408947200']\n", + "--> downloading tweet #126391727408947200 (837 of 4259)\n", + "['apple', 'irrelevant', '126387209824776192']\n", + "--> downloading tweet #126387209824776192 (838 of 4259)\n", + "['apple', 'irrelevant', '126381904621600768']\n", + "--> downloading tweet #126381904621600768 (839 of 4259)\n", + "['apple', 'irrelevant', '126379095004160001']\n", + "--> downloading tweet #126379095004160001 (840 of 4259)\n", + "['apple', 'irrelevant', '126373281099026432']\n", + "--> downloading tweet #126373281099026432 (841 of 4259)\n", + "['apple', 'irrelevant', '126367728754884609']\n", + "--> downloading tweet #126367728754884609 (842 of 4259)\n", + "['apple', 'irrelevant', '126362562865528832']\n", + "--> downloading tweet #126362562865528832 (843 of 4259)\n", + "['apple', 'irrelevant', '126355573586399232']\n", + "--> 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#126511426926944256 (989 of 4259)\n", + "['google', 'positive', '126510977335300096']\n", + "--> downloading tweet #126510977335300096 (990 of 4259)\n", + "['google', 'positive', '126510551789604864']\n", + "--> downloading tweet #126510551789604864 (991 of 4259)\n", + "['google', 'positive', '126509929619132417']\n", + "--> downloading tweet #126509929619132417 (992 of 4259)\n", + "['google', 'positive', '126509528287166464']\n", + "--> downloading tweet #126509528287166464 (993 of 4259)\n", + "['google', 'positive', '126508433582211072']\n", + "--> downloading tweet #126508433582211072 (994 of 4259)\n", + "['google', 'positive', '126507456019968000']\n", + "--> downloading tweet #126507456019968000 (995 of 4259)\n", + "['google', 'positive', '126507105023819776']\n", + "--> downloading tweet #126507105023819776 (996 of 4259)\n", + "['google', 'positive', '126506850781888512']\n", + "--> downloading tweet #126506850781888512 (997 of 4259)\n", + "['google', 'positive', 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4259)\n", + "['google', 'neutral', '126508842367463424']\n", + "--> downloading tweet #126508842367463424 (1393 of 4259)\n", + "['google', 'neutral', '126508789254979584']\n", + "--> downloading tweet #126508789254979584 (1394 of 4259)\n", + "['google', 'neutral', '126508753997668352']\n", + "--> downloading tweet #126508753997668352 (1395 of 4259)\n", + "['google', 'neutral', '126508642060083200']\n", + "--> downloading tweet #126508642060083200 (1396 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['google', 'neutral', '126508398522019840']\n", + "--> downloading tweet #126508398522019840 (1397 of 4259)\n", + "['google', 'neutral', '126508037400825857']\n", + "--> downloading tweet #126508037400825857 (1398 of 4259)\n", + "['google', 'neutral', '126508035416928256']\n", + "--> downloading tweet #126508035416928256 (1399 of 4259)\n", + "['google', 'neutral', '126507982543532034']\n", + "--> downloading tweet #126507982543532034 (1400 of 4259)\n", 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4259)\n", + "['google', 'neutral', '126503088461848576']\n", + "--> downloading tweet #126503088461848576 (1453 of 4259)\n", + "['google', 'neutral', '126502770130960384']\n", + "--> downloading tweet #126502770130960384 (1454 of 4259)\n", + "['google', 'neutral', '126502761608122368']\n", + "--> downloading tweet #126502761608122368 (1455 of 4259)\n", + "['google', 'neutral', '126502730264088576']\n", + "--> downloading tweet #126502730264088576 (1456 of 4259)\n", + "['google', 'neutral', '126502630578069504']\n", + "--> downloading tweet #126502630578069504 (1457 of 4259)\n", + "['google', 'neutral', '126502626085969920']\n", + "--> downloading tweet #126502626085969920 (1458 of 4259)\n", + "['google', 'neutral', '126502616128684032']\n", + "--> downloading tweet #126502616128684032 (1459 of 4259)\n", + "['google', 'neutral', '126502614086070273']\n", + "--> downloading tweet #126502614086070273 (1460 of 4259)\n", + "['google', 'neutral', '126502326356815872']\n", + "--> downloading 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4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['google', 'neutral', '126501463626223616']\n", + "--> downloading tweet #126501463626223616 (1479 of 4259)\n", + "['google', 'neutral', '126501463554924544']\n", + "--> downloading tweet #126501463554924544 (1480 of 4259)\n", + "['google', 'neutral', '126501463529754624']\n", + "--> downloading tweet #126501463529754624 (1481 of 4259)\n", + "['google', 'neutral', '126501428897382400']\n", + "--> downloading tweet #126501428897382400 (1482 of 4259)\n", + "['google', 'neutral', '126501392163684353']\n", + "--> downloading tweet #126501392163684353 (1483 of 4259)\n", + "['google', 'neutral', '126501360089825280']\n", + "--> downloading tweet #126501360089825280 (1484 of 4259)\n", + "['google', 'neutral', '126501176559677441']\n", + "--> downloading tweet #126501176559677441 (1485 of 4259)\n", + "['google', 'neutral', '126501155999203328']\n", + "--> downloading tweet #126501155999203328 (1486 of 4259)\n", 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4259)\n", + "['google', 'neutral', '126490034865045504']\n", + "--> downloading tweet #126490034865045504 (1625 of 4259)\n", + "['google', 'neutral', '126490011901231104']\n", + "--> downloading tweet #126490011901231104 (1626 of 4259)\n", + "['google', 'neutral', '126489950614073345']\n", + "--> downloading tweet #126489950614073345 (1627 of 4259)\n", + "['google', 'neutral', '126489908998176769']\n", + "--> downloading tweet #126489908998176769 (1628 of 4259)\n", + "['google', 'neutral', '126489751325908992']\n", + "--> downloading tweet #126489751325908992 (1629 of 4259)\n", + "['google', 'neutral', '126489719012990976']\n", + "--> downloading tweet #126489719012990976 (1630 of 4259)\n", + "['google', 'neutral', '126489665116192768']\n", + "--> downloading tweet #126489665116192768 (1631 of 4259)\n", + "['google', 'neutral', '126489609889783808']\n", + "--> downloading tweet #126489609889783808 (1632 of 4259)\n", + "['google', 'neutral', '126489523776536576']\n", + "--> downloading tweet #126489523776536576 (1633 of 4259)\n", + "['google', 'neutral', '126489506672160768']\n", + "--> downloading tweet #126489506672160768 (1634 of 4259)\n", + "['google', 'neutral', '126489489328705536']\n", + "--> downloading tweet #126489489328705536 (1635 of 4259)\n", + "['google', 'neutral', '126489300828307456']\n", + "--> downloading tweet #126489300828307456 (1636 of 4259)\n", + "['google', 'neutral', '126489263490596864']\n", + "--> downloading tweet #126489263490596864 (1637 of 4259)\n", + "['google', 'neutral', '126489146029129729']\n", + "--> downloading tweet #126489146029129729 (1638 of 4259)\n", + "['google', 'neutral', '126489064319881216']\n", + "--> downloading tweet #126489064319881216 (1639 of 4259)\n", + "['google', 'neutral', '126489048717074432']\n", + "--> downloading tweet #126489048717074432 (1640 of 4259)\n", + "['google', 'neutral', '126488912037289984']\n", + "--> downloading tweet #126488912037289984 (1641 of 4259)\n", + "['google', 'neutral', '126488727315943425']\n", + "--> downloading tweet #126488727315943425 (1642 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['google', 'neutral', '126488649905864705']\n", + "--> downloading tweet #126488649905864705 (1643 of 4259)\n", + "['google', 'neutral', '126488582218203136']\n", + "--> downloading tweet #126488582218203136 (1644 of 4259)\n", + "['google', 'neutral', '126488561888399360']\n", + "--> downloading tweet #126488561888399360 (1645 of 4259)\n", + "['google', 'neutral', '126487924043821057']\n", + "--> downloading tweet #126487924043821057 (1646 of 4259)\n", + "['google', 'neutral', '126487912433975297']\n", + "--> downloading tweet #126487912433975297 (1647 of 4259)\n", + "['google', 'neutral', '126487846038147073']\n", + "--> downloading tweet #126487846038147073 (1648 of 4259)\n", + "['google', 'neutral', '126487807156944899']\n", + "--> downloading tweet #126487807156944899 (1649 of 4259)\n", + "['google', 'neutral', '126487744787660800']\n", + "--> downloading tweet #126487744787660800 (1650 of 4259)\n", + "['google', 'neutral', '126487541569433600']\n", + "--> downloading tweet #126487541569433600 (1651 of 4259)\n", + "['google', 'neutral', '126487465203736576']\n", + "--> downloading tweet #126487465203736576 (1652 of 4259)\n", + "['google', 'neutral', '126487372039847937']\n", + "--> downloading tweet #126487372039847937 (1653 of 4259)\n", + "['google', 'neutral', '126487167823388673']\n", + "--> downloading tweet #126487167823388673 (1654 of 4259)\n", + "['google', 'neutral', '126487043462266880']\n", + "--> downloading tweet #126487043462266880 (1655 of 4259)\n", + "['google', 'neutral', '126486616343724032']\n", + "--> downloading tweet #126486616343724032 (1656 of 4259)\n", + "['google', 'neutral', '126486384902017024']\n", + "--> downloading tweet #126486384902017024 (1657 of 4259)\n", + "['google', 'neutral', '126486348713570304']\n", + "--> downloading tweet #126486348713570304 (1658 of 4259)\n", + "['google', 'neutral', '126486111689256960']\n", + "--> downloading tweet #126486111689256960 (1659 of 4259)\n", + "['google', 'neutral', '126485712836112384']\n", + "--> downloading tweet #126485712836112384 (1660 of 4259)\n", + "['google', 'neutral', '126485684113522689']\n", + "--> downloading tweet #126485684113522689 (1661 of 4259)\n", + "['google', 'neutral', '126484162302586880']\n", + "--> downloading tweet #126484162302586880 (1662 of 4259)\n", + "['google', 'neutral', '126484021369778177']\n", + "--> downloading tweet #126484021369778177 (1663 of 4259)\n", + "['google', 'neutral', '126484018211454976']\n", + "--> downloading tweet #126484018211454976 (1664 of 4259)\n", + "['google', 'irrelevant', '126535062148759552']\n", + "--> downloading tweet #126535062148759552 (1665 of 4259)\n", + "['google', 'irrelevant', '126534927637417984']\n", + "--> downloading tweet #126534927637417984 (1666 of 4259)\n", + "['google', 'irrelevant', '126534908670783489']\n", + "--> downloading tweet #126534908670783489 (1667 of 4259)\n", + "['google', 'irrelevant', '126534871299538944']\n", + "--> downloading tweet #126534871299538944 (1668 of 4259)\n", + "['google', 'irrelevant', '126534769105305600']\n", + "--> downloading tweet #126534769105305600 (1669 of 4259)\n", + "['google', 'irrelevant', '126534678156029953']\n", + "--> downloading tweet #126534678156029953 (1670 of 4259)\n", + "['google', 'irrelevant', '126534649995464704']\n", + "--> downloading tweet #126534649995464704 (1671 of 4259)\n", + "['google', 'irrelevant', '126534648800096256']\n", + "--> downloading tweet #126534648800096256 (1672 of 4259)\n", + "['google', 'irrelevant', '126534648611340288']\n", + "--> downloading tweet #126534648611340288 (1673 of 4259)\n", + "['google', 'irrelevant', '126534647264972800']\n", + "--> downloading tweet #126534647264972800 (1674 of 4259)\n", + "['google', 'irrelevant', '126534525089091584']\n", + "--> downloading tweet #126534525089091584 (1675 of 4259)\n", + "['google', 'irrelevant', '126534223950651392']\n", + "--> downloading tweet #126534223950651392 (1676 of 4259)\n", + "['google', 'irrelevant', '126534054739836929']\n", + "--> downloading tweet #126534054739836929 (1677 of 4259)\n", + "['google', 'irrelevant', '126534037929074688']\n", + "--> downloading tweet #126534037929074688 (1678 of 4259)\n", + "['google', 'irrelevant', '126533966156148736']\n", + "--> downloading tweet #126533966156148736 (1679 of 4259)\n", + "['google', 'irrelevant', '126533775411781632']\n", + "--> downloading tweet #126533775411781632 (1680 of 4259)\n", + "['google', 'irrelevant', '126533688321253376']\n", + "--> downloading tweet #126533688321253376 (1681 of 4259)\n", + "['google', 'irrelevant', '126533686282825728']\n", + "--> downloading tweet #126533686282825728 (1682 of 4259)\n", + "['google', 'irrelevant', '126533684252774401']\n", + "--> downloading tweet #126533684252774401 (1683 of 4259)\n", + "['google', 'irrelevant', '126533682273071105']\n", + 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4259)\n", + "['google', 'irrelevant', '126532704287199232']\n", + "--> downloading tweet #126532704287199232 (1693 of 4259)\n", + "['google', 'irrelevant', '126532561315958784']\n", + "--> downloading tweet #126532561315958784 (1694 of 4259)\n", + "['google', 'irrelevant', '126532543158820864']\n", + "--> downloading tweet #126532543158820864 (1695 of 4259)\n", + "['google', 'irrelevant', '126532472258301952']\n", + "--> downloading tweet #126532472258301952 (1696 of 4259)\n", + "['google', 'irrelevant', '126532467954950144']\n", + "--> downloading tweet #126532467954950144 (1697 of 4259)\n", + "['google', 'irrelevant', '126532295665532928']\n", + "--> downloading tweet #126532295665532928 (1698 of 4259)\n", + "['google', 'irrelevant', '126532294122024960']\n", + "--> downloading tweet #126532294122024960 (1699 of 4259)\n", + "['google', 'irrelevant', '126532119278264320']\n", + "--> downloading tweet #126532119278264320 (1700 of 4259)\n", + "['google', 'irrelevant', 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'irrelevant', '126531278685216768']\n", + "--> downloading tweet #126531278685216768 (1709 of 4259)\n", + "['google', 'irrelevant', '126531098279804928']\n", + "--> downloading tweet #126531098279804928 (1710 of 4259)\n", + "['google', 'irrelevant', '126530329820397568']\n", + "--> downloading tweet #126530329820397568 (1711 of 4259)\n", + "['google', 'irrelevant', '126530251684720641']\n", + "--> downloading tweet #126530251684720641 (1712 of 4259)\n", + "['google', 'irrelevant', '126530242612432898']\n", + "--> downloading tweet #126530242612432898 (1713 of 4259)\n", + "['google', 'irrelevant', '126530163029704705']\n", + "--> downloading tweet #126530163029704705 (1714 of 4259)\n", + "['google', 'irrelevant', '126530054023946240']\n", + "--> downloading tweet #126530054023946240 (1715 of 4259)\n", + "['google', 'irrelevant', '126530000303292416']\n", + "--> downloading tweet #126530000303292416 (1716 of 4259)\n", + "['google', 'irrelevant', '126529908850700289']\n", + "--> 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'126529003879272448']\n", + "--> downloading tweet #126529003879272448 (1725 of 4259)\n", + "['google', 'irrelevant', '126529001866002432']\n", + "--> downloading tweet #126529001866002432 (1726 of 4259)\n", + "['google', 'irrelevant', '126528999554949120']\n", + "--> downloading tweet #126528999554949120 (1727 of 4259)\n", + "['google', 'irrelevant', '126528997763989504']\n", + "--> downloading tweet #126528997763989504 (1728 of 4259)\n", + "['google', 'irrelevant', '126528993187999744']\n", + "--> downloading tweet #126528993187999744 (1729 of 4259)\n", + "['google', 'irrelevant', '126528658834853888']\n", + "--> downloading tweet #126528658834853888 (1730 of 4259)\n", + "['google', 'irrelevant', '126528444476555264']\n", + "--> downloading tweet #126528444476555264 (1731 of 4259)\n", + "['google', 'irrelevant', '126528018469494784']\n", + "--> downloading tweet #126528018469494784 (1732 of 4259)\n", + "['google', 'irrelevant', '126527955475251202']\n", + "--> downloading tweet #126527955475251202 (1733 of 4259)\n", + "['google', 'irrelevant', '126527760746295296']\n", + "--> downloading tweet #126527760746295296 (1734 of 4259)\n", + "['google', 'irrelevant', '126527053133656066']\n", + "--> downloading tweet #126527053133656066 (1735 of 4259)\n", + "['google', 'irrelevant', '126527051292356609']\n", + "--> downloading tweet #126527051292356609 (1736 of 4259)\n", + "['google', 'irrelevant', '126526946946457601']\n", + "--> downloading tweet #126526946946457601 (1737 of 4259)\n", + "['google', 'irrelevant', '126526850477469696']\n", + "--> downloading tweet #126526850477469696 (1738 of 4259)\n", + "['google', 'irrelevant', '126526815010426880']\n", + "--> downloading tweet #126526815010426880 (1739 of 4259)\n", + "['google', 'irrelevant', '126526781019787264']\n", + "--> downloading tweet #126526781019787264 (1740 of 4259)\n", + "['google', 'irrelevant', '126526765995802624']\n", + "--> downloading tweet #126526765995802624 (1741 of 4259)\n", + "['google', 'irrelevant', '126526082953379840']\n", + "--> downloading tweet #126526082953379840 (1742 of 4259)\n", + "['google', 'irrelevant', '126526068835368961']\n", + "--> downloading tweet #126526068835368961 (1743 of 4259)\n", + "['google', 'irrelevant', '126526020923834368']\n", + "--> downloading tweet #126526020923834368 (1744 of 4259)\n", + "['google', 'irrelevant', '126526019602628608']\n", + "--> downloading tweet #126526019602628608 (1745 of 4259)\n", + "['google', 'irrelevant', '126526019208351744']\n", + "--> downloading tweet #126526019208351744 (1746 of 4259)\n", + "['google', 'irrelevant', '126526017660653568']\n", + "--> downloading tweet #126526017660653568 (1747 of 4259)\n", + "['google', 'irrelevant', '126525994348711937']\n", + "--> downloading tweet #126525994348711937 (1748 of 4259)\n", + "['google', 'irrelevant', '126525817713991680']\n", + "--> downloading tweet #126525817713991680 (1749 of 4259)\n", + "['google', 'irrelevant', '126525815084158976']\n", + "--> 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+ "['google', 'irrelevant', '126523941035577344']\n", + "--> downloading tweet #126523941035577344 (1759 of 4259)\n", + "['google', 'irrelevant', '126523916817674240']\n", + "--> downloading tweet #126523916817674240 (1760 of 4259)\n", + "['google', 'irrelevant', '126523574470189057']\n", + "--> downloading tweet #126523574470189057 (1761 of 4259)\n", + "['google', 'irrelevant', '126523560096313344']\n", + "--> downloading tweet #126523560096313344 (1762 of 4259)\n", + "['google', 'irrelevant', '126523556958961664']\n", + "--> downloading tweet #126523556958961664 (1763 of 4259)\n", + "['google', 'irrelevant', '126523548914290688']\n", + "--> downloading tweet #126523548914290688 (1764 of 4259)\n", + "['google', 'irrelevant', '126523356773232641']\n", + "--> downloading tweet #126523356773232641 (1765 of 4259)\n", + "['google', 'irrelevant', '126523229400600576']\n", + "--> downloading tweet #126523229400600576 (1766 of 4259)\n", + "['google', 'irrelevant', '126523147091578880']\n", + 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4259)\n", + "['google', 'irrelevant', '126522646149087233']\n", + "--> downloading tweet #126522646149087233 (1776 of 4259)\n", + "['google', 'irrelevant', '126522540834304000']\n", + "--> downloading tweet #126522540834304000 (1777 of 4259)\n", + "['google', 'irrelevant', '126522492700471297']\n", + "--> downloading tweet #126522492700471297 (1778 of 4259)\n", + "['google', 'irrelevant', '126522273963319298']\n", + "--> downloading tweet #126522273963319298 (1779 of 4259)\n", + "['google', 'irrelevant', '126522127775047680']\n", + "--> downloading tweet #126522127775047680 (1780 of 4259)\n", + "['google', 'irrelevant', '126521734076698625']\n", + "--> downloading tweet #126521734076698625 (1781 of 4259)\n", + "['google', 'irrelevant', '126521694583132160']\n", + "--> downloading tweet #126521694583132160 (1782 of 4259)\n", + "['google', 'irrelevant', '126521635464425472']\n", + "--> downloading tweet #126521635464425472 (1783 of 4259)\n", + "['google', 'irrelevant', '126521523220652032']\n", + "--> downloading tweet #126521523220652032 (1784 of 4259)\n", + "['google', 'irrelevant', '126521505097068544']\n", + "--> downloading tweet #126521505097068544 (1785 of 4259)\n", + "['google', 'irrelevant', '126521233603960832']\n", + "--> downloading tweet #126521233603960832 (1786 of 4259)\n", + "['google', 'irrelevant', '126520774025678848']\n", + "--> downloading tweet #126520774025678848 (1787 of 4259)\n", + "['google', 'irrelevant', '126520080543649792']\n", + "--> downloading tweet #126520080543649792 (1788 of 4259)\n", + "['google', 'irrelevant', '126519943234732032']\n", + "--> downloading tweet #126519943234732032 (1789 of 4259)\n", + "['google', 'irrelevant', '126519837173358592']\n", + "--> downloading tweet #126519837173358592 (1790 of 4259)\n", + "['google', 'irrelevant', '126519472172445696']\n", + "--> downloading tweet #126519472172445696 (1791 of 4259)\n", + "['google', 'irrelevant', '126519359714766848']\n", + "--> downloading tweet #126519359714766848 (1792 of 4259)\n", + "['google', 'irrelevant', '126518917635125248']\n", + "--> downloading tweet #126518917635125248 (1793 of 4259)\n", + "['google', 'irrelevant', '126518845983830016']\n", + "--> downloading tweet #126518845983830016 (1794 of 4259)\n", + "['google', 'irrelevant', '126517747894075392']\n", + "--> downloading tweet #126517747894075392 (1795 of 4259)\n", + "['google', 'irrelevant', '126517575336214529']\n", + "--> downloading tweet #126517575336214529 (1796 of 4259)\n", + "['google', 'irrelevant', '126517570806358018']\n", + "--> downloading tweet #126517570806358018 (1797 of 4259)\n", + "['google', 'irrelevant', '126517567694180352']\n", + "--> downloading tweet #126517567694180352 (1798 of 4259)\n", + "['google', 'irrelevant', '126517492049915904']\n", + "--> downloading tweet #126517492049915904 (1799 of 4259)\n", + "['google', 'irrelevant', '126517413788401664']\n", + "--> downloading tweet #126517413788401664 (1800 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['google', 'irrelevant', '126517183139430400']\n", + "--> downloading tweet #126517183139430400 (1801 of 4259)\n", + "['google', 'irrelevant', '126516892029558784']\n", + "--> downloading tweet #126516892029558784 (1802 of 4259)\n", + "['google', 'irrelevant', '126516806360899584']\n", + "--> downloading tweet #126516806360899584 (1803 of 4259)\n", + "['google', 'irrelevant', '126516804108562433']\n", + "--> downloading tweet #126516804108562433 (1804 of 4259)\n", + "['google', 'irrelevant', '126516802011402240']\n", + "--> downloading tweet #126516802011402240 (1805 of 4259)\n", + "['google', 'irrelevant', '126516602316406784']\n", + "--> downloading tweet #126516602316406784 (1806 of 4259)\n", + "['google', 'irrelevant', '126516523408949248']\n", + "--> downloading tweet #126516523408949248 (1807 of 4259)\n", + "['google', 'irrelevant', '126516376566366208']\n", + "--> downloading tweet #126516376566366208 (1808 of 4259)\n", 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4259)\n", + "['google', 'irrelevant', '126514555743518720']\n", + "--> downloading tweet #126514555743518720 (1826 of 4259)\n", + "['google', 'irrelevant', '126514131544178688']\n", + "--> downloading tweet #126514131544178688 (1827 of 4259)\n", + "['google', 'irrelevant', '126513961553244160']\n", + "--> downloading tweet #126513961553244160 (1828 of 4259)\n", + "['google', 'irrelevant', '126513706912841729']\n", + "--> downloading tweet #126513706912841729 (1829 of 4259)\n", + "['google', 'irrelevant', '126513429409312768']\n", + "--> downloading tweet #126513429409312768 (1830 of 4259)\n", + "['google', 'irrelevant', '126513410128089088']\n", + "--> downloading tweet #126513410128089088 (1831 of 4259)\n", + "['google', 'irrelevant', '126513333191979008']\n", + "--> downloading tweet #126513333191979008 (1832 of 4259)\n", + "['google', 'irrelevant', '126512924385738752']\n", + "--> downloading tweet #126512924385738752 (1833 of 4259)\n", + "['google', 'irrelevant', 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'126494286316445696']\n", + "--> downloading tweet #126494286316445696 (1943 of 4259)\n", + "['google', 'irrelevant', '126494260269821952']\n", + "--> downloading tweet #126494260269821952 (1944 of 4259)\n", + "['google', 'irrelevant', '126494247082934272']\n", + "--> downloading tweet #126494247082934272 (1945 of 4259)\n", + "['google', 'irrelevant', '126494176551514112']\n", + "--> downloading tweet #126494176551514112 (1946 of 4259)\n", + "['google', 'irrelevant', '126494156368523267']\n", + "--> downloading tweet #126494156368523267 (1947 of 4259)\n", + "['google', 'irrelevant', '126494152375533568']\n", + "--> downloading tweet #126494152375533568 (1948 of 4259)\n", + "['google', 'irrelevant', '126494104187183104']\n", + "--> downloading tweet #126494104187183104 (1949 of 4259)\n", + "['google', 'irrelevant', '126494033882259458']\n", + "--> downloading tweet #126494033882259458 (1950 of 4259)\n", + "['google', 'irrelevant', '126493930794659840']\n", + "--> downloading tweet 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downloading tweet #126493525578747905 (1959 of 4259)\n", + "['google', 'irrelevant', '126493517622149121']\n", + "--> downloading tweet #126493517622149121 (1960 of 4259)\n", + "['google', 'irrelevant', '126493154219266048']\n", + "--> downloading tweet #126493154219266048 (1961 of 4259)\n", + "['google', 'irrelevant', '126493144824020993']\n", + "--> downloading tweet #126493144824020993 (1962 of 4259)\n", + "['google', 'irrelevant', '126493116554424320']\n", + "--> downloading tweet #126493116554424320 (1963 of 4259)\n", + "['google', 'irrelevant', '126493078717603840']\n", + "--> downloading tweet #126493078717603840 (1964 of 4259)\n", + "['google', 'irrelevant', '126493008244912128']\n", + "--> downloading tweet #126493008244912128 (1965 of 4259)\n", + "['google', 'irrelevant', '126492972735922177']\n", + "--> downloading tweet #126492972735922177 (1966 of 4259)\n", + "['google', 'irrelevant', '126492948740313088']\n", + "--> downloading tweet #126492948740313088 (1967 of 4259)\n", + "['google', 'irrelevant', '126492905476067328']\n", + "--> downloading tweet #126492905476067328 (1968 of 4259)\n", + "['google', 'irrelevant', '126492820130373632']\n", + "--> downloading tweet #126492820130373632 (1969 of 4259)\n", + "['google', 'irrelevant', '126492723673960448']\n", + "--> downloading tweet #126492723673960448 (1970 of 4259)\n", + "['google', 'irrelevant', '126492704707321856']\n", + "--> downloading tweet #126492704707321856 (1971 of 4259)\n", + "['google', 'irrelevant', '126492543146926080']\n", + "--> downloading tweet #126492543146926080 (1972 of 4259)\n", + "['google', 'irrelevant', '126492533860728832']\n", + "--> downloading tweet #126492533860728832 (1973 of 4259)\n", + "['google', 'irrelevant', '126492487111028736']\n", + "--> downloading tweet #126492487111028736 (1974 of 4259)\n", + "['google', 'irrelevant', '126492457276940288']\n", + "--> downloading tweet #126492457276940288 (1975 of 4259)\n", + "['google', 'irrelevant', '126492452990369792']\n", + 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4259)\n", + "['google', 'irrelevant', '126492053248020481']\n", + "--> downloading tweet #126492053248020481 (1985 of 4259)\n", + "['google', 'irrelevant', '126492052371406848']\n", + "--> downloading tweet #126492052371406848 (1986 of 4259)\n", + "['google', 'irrelevant', '126492019009916928']\n", + "--> downloading tweet #126492019009916928 (1987 of 4259)\n", + "['google', 'irrelevant', '126492010864574464']\n", + "--> downloading tweet #126492010864574464 (1988 of 4259)\n", + "['google', 'irrelevant', '126491986927685632']\n", + "--> downloading tweet #126491986927685632 (1989 of 4259)\n", + "['google', 'irrelevant', '126491961271136256']\n", + "--> downloading tweet #126491961271136256 (1990 of 4259)\n", + "['google', 'irrelevant', '126491942077992961']\n", + "--> downloading tweet #126491942077992961 (1991 of 4259)\n", + "['google', 'irrelevant', '126491928320688128']\n", + "--> downloading tweet #126491928320688128 (1992 of 4259)\n", + "['google', 'irrelevant', 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#126491616641953792 (2001 of 4259)\n", + "['google', 'irrelevant', '126491544558632960']\n", + "--> downloading tweet #126491544558632960 (2002 of 4259)\n", + "['google', 'irrelevant', '126491450035814400']\n", + "--> downloading tweet #126491450035814400 (2003 of 4259)\n", + "['google', 'irrelevant', '126491409871155200']\n", + "--> downloading tweet #126491409871155200 (2004 of 4259)\n", + "['google', 'irrelevant', '126491323774672896']\n", + "--> downloading tweet #126491323774672896 (2005 of 4259)\n", + "['google', 'irrelevant', '126491290627080194']\n", + "--> downloading tweet #126491290627080194 (2006 of 4259)\n", + "['google', 'irrelevant', '126491237720141825']\n", + "--> downloading tweet #126491237720141825 (2007 of 4259)\n", + "['google', 'irrelevant', '126491078797950976']\n", + "--> downloading tweet #126491078797950976 (2008 of 4259)\n", + "['google', 'irrelevant', '126491075870343168']\n", + "--> downloading tweet #126491075870343168 (2009 of 4259)\n", + "['google', 'irrelevant', '126490998816768001']\n", + "--> downloading tweet #126490998816768001 (2010 of 4259)\n", + "['google', 'irrelevant', '126490976565985281']\n", + "--> downloading tweet #126490976565985281 (2011 of 4259)\n", + "['google', 'irrelevant', '126490918885920768']\n", + "--> downloading tweet #126490918885920768 (2012 of 4259)\n", + "['google', 'irrelevant', '126490858735407104']\n", + "--> downloading tweet #126490858735407104 (2013 of 4259)\n", + "['google', 'irrelevant', '126490790150144000']\n", + "--> downloading tweet #126490790150144000 (2014 of 4259)\n", + "['google', 'irrelevant', '126490759766618112']\n", + "--> downloading tweet #126490759766618112 (2015 of 4259)\n", + "['google', 'irrelevant', '126490644616200192']\n", + "--> downloading tweet #126490644616200192 (2016 of 4259)\n", + "['google', 'irrelevant', '126490589125550080']\n", + "--> downloading tweet #126490589125550080 (2017 of 4259)\n", + "['google', 'irrelevant', '126490558230302721']\n", + "--> 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+ "['google', 'irrelevant', '126489957538873344']\n", + "--> downloading tweet #126489957538873344 (2027 of 4259)\n", + "['google', 'irrelevant', '126489934088511490']\n", + "--> downloading tweet #126489934088511490 (2028 of 4259)\n", + "['google', 'irrelevant', '126489915042168833']\n", + "--> downloading tweet #126489915042168833 (2029 of 4259)\n", + "['google', 'irrelevant', '126489907349831680']\n", + "--> downloading tweet #126489907349831680 (2030 of 4259)\n", + "['google', 'irrelevant', '126489892678144000']\n", + "--> downloading tweet #126489892678144000 (2031 of 4259)\n", + "['google', 'irrelevant', '126489830677942272']\n", + "--> downloading tweet #126489830677942272 (2032 of 4259)\n", + "['google', 'irrelevant', '126489823103041537']\n", + "--> downloading tweet #126489823103041537 (2033 of 4259)\n", + "['google', 'irrelevant', '126489703418568705']\n", + "--> downloading tweet #126489703418568705 (2034 of 4259)\n", + "['google', 'irrelevant', '126489580835848193']\n", + 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'irrelevant', '126488315041030144']\n", + "--> downloading tweet #126488315041030144 (2060 of 4259)\n", + "['google', 'irrelevant', '126488313187143681']\n", + "--> downloading tweet #126488313187143681 (2061 of 4259)\n", + "['google', 'irrelevant', '126488305859698688']\n", + "--> downloading tweet #126488305859698688 (2062 of 4259)\n", + "['google', 'irrelevant', '126488293717192704']\n", + "--> downloading tweet #126488293717192704 (2063 of 4259)\n", + "['google', 'irrelevant', '126488289988452352']\n", + "--> downloading tweet #126488289988452352 (2064 of 4259)\n", + "['google', 'irrelevant', '126488289157976064']\n", + "--> downloading tweet #126488289157976064 (2065 of 4259)\n", + "['google', 'irrelevant', '126488066977308673']\n", + "--> downloading tweet #126488066977308673 (2066 of 4259)\n", + "['google', 'irrelevant', '126488061369532417']\n", + "--> downloading tweet #126488061369532417 (2067 of 4259)\n", + "['google', 'irrelevant', '126488048962772992']\n", + "--> 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4259)\n", + "['google', 'irrelevant', '126486526631743488']\n", + "--> downloading tweet #126486526631743488 (2094 of 4259)\n", + "['google', 'irrelevant', '126486211824058368']\n", + "--> downloading tweet #126486211824058368 (2095 of 4259)\n", + "['google', 'irrelevant', '126486125874384896']\n", + "--> downloading tweet #126486125874384896 (2096 of 4259)\n", + "['google', 'irrelevant', '126485882265018368']\n", + "--> downloading tweet #126485882265018368 (2097 of 4259)\n", + "['google', 'irrelevant', '126485702056751105']\n", + "--> downloading tweet #126485702056751105 (2098 of 4259)\n", + "['google', 'irrelevant', '126485474016628736']\n", + "--> downloading tweet #126485474016628736 (2099 of 4259)\n", + "['google', 'irrelevant', '126484568239906817']\n", + "--> downloading tweet #126484568239906817 (2100 of 4259)\n", + "['google', 'irrelevant', '126484000075292672']\n", + "--> downloading tweet #126484000075292672 (2101 of 4259)\n", + "['microsoft', 'positive', 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4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['microsoft', 'neutral', '126519858035822594']\n", + "--> downloading tweet #126519858035822594 (2828 of 4259)\n", + "['microsoft', 'neutral', '126514963924787201']\n", + "--> downloading tweet #126514963924787201 (2829 of 4259)\n", + "['microsoft', 'neutral', '126514187647201280']\n", + "--> downloading tweet #126514187647201280 (2830 of 4259)\n", + "['microsoft', 'neutral', '126508567053340672']\n", + "--> downloading tweet #126508567053340672 (2831 of 4259)\n", + "['microsoft', 'neutral', '126507878382174208']\n", + "--> downloading tweet #126507878382174208 (2832 of 4259)\n", + "['microsoft', 'neutral', '126507753484193792']\n", + "--> downloading tweet #126507753484193792 (2833 of 4259)\n", + "['microsoft', 'neutral', '126507677919617024']\n", + "--> downloading tweet #126507677919617024 (2834 of 4259)\n", + "['microsoft', 'neutral', '126507292777652224']\n", + "--> downloading tweet 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"text": [ + "['microsoft', 'irrelevant', '126722505128935424']\n", + "--> downloading tweet #126722505128935424 (2983 of 4259)\n", + "['microsoft', 'irrelevant', '126721828432519170']\n", + "--> downloading tweet #126721828432519170 (2984 of 4259)\n", + "['microsoft', 'irrelevant', '126719767590604801']\n", + "--> downloading tweet #126719767590604801 (2985 of 4259)\n", + "['microsoft', 'irrelevant', '126719569179054080']\n", + "--> downloading tweet #126719569179054080 (2986 of 4259)\n", + "['microsoft', 'irrelevant', '126719029921579008']\n", + "--> downloading tweet #126719029921579008 (2987 of 4259)\n", + "['microsoft', 'irrelevant', '126717715657396224']\n", + "--> downloading tweet #126717715657396224 (2988 of 4259)\n", + "['microsoft', 'irrelevant', '126717214211575808']\n", + "--> downloading tweet #126717214211575808 (2989 of 4259)\n", + "['microsoft', 'irrelevant', '126715806565412864']\n", + "--> downloading tweet #126715806565412864 (2990 of 4259)\n", + "['microsoft', 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#126881835463614464 (3276 of 4259)\n", + "['twitter', 'positive', '126881080178507776']\n", + "--> downloading tweet #126881080178507776 (3277 of 4259)\n", + "['twitter', 'positive', '126880559162077184']\n", + "--> downloading tweet #126880559162077184 (3278 of 4259)\n", + "['twitter', 'positive', '126880385605976064']\n", + "--> downloading tweet #126880385605976064 (3279 of 4259)\n", + "['twitter', 'positive', '126879785908580352']\n", + "--> downloading tweet #126879785908580352 (3280 of 4259)\n", + "['twitter', 'positive', '126877750131818497']\n", + "--> downloading tweet #126877750131818497 (3281 of 4259)\n", + "['twitter', 'positive', '126877263311536128']\n", + "--> downloading tweet #126877263311536128 (3282 of 4259)\n", + "Twitter sent status 403 for URL: 1.1/statuses/show.json using parameters: (id=126877263311536128&oauth_consumer_key=zu7CKytyl7G8V4KQMXNOw&oauth_nonce=6730449425355855739&oauth_signature_method=HMAC-SHA1&oauth_timestamp=1520181543&oauth_token=12866362-GfEuXN31tyioMyPyTKT3YmOd0DnGhzAQXZ7Wjhw9s&oauth_version=1.0&oauth_signature=7BXZC79Aun1k7cnSbB3cfuoWjnE%3D)\n", + "details: {'errors': [{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]}\n", + "Not authorized to view this tweet.\n", + "['twitter', 'positive', '126877209813188608']\n", + "--> downloading tweet #126877209813188608 (3283 of 4259)\n", + "['twitter', 'positive', '126877056578486272']\n", + "--> downloading tweet #126877056578486272 (3284 of 4259)\n", + "['twitter', 'positive', '126876125107462144']\n", + "--> downloading tweet #126876125107462144 (3285 of 4259)\n", + "['twitter', 'positive', '126874748469788672']\n", + "--> downloading tweet #126874748469788672 (3286 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['twitter', 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(3363 of 4259)\n", + "['twitter', 'neutral', '126883224587739136']\n", + "--> downloading tweet #126883224587739136 (3364 of 4259)\n", + "['twitter', 'neutral', '126883185396170752']\n", + "--> downloading tweet #126883185396170752 (3365 of 4259)\n", + "['twitter', 'neutral', '126883158942695425']\n", + "--> downloading tweet #126883158942695425 (3366 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['twitter', 'neutral', '126883124595527681']\n", + "--> downloading tweet #126883124595527681 (3367 of 4259)\n", + "['twitter', 'neutral', '126882885553758208']\n", + "--> downloading tweet #126882885553758208 (3368 of 4259)\n", + "['twitter', 'neutral', '126882800585539585']\n", + "--> downloading tweet #126882800585539585 (3369 of 4259)\n", + "['twitter', 'neutral', '126882730154803200']\n", + "--> downloading tweet #126882730154803200 (3370 of 4259)\n", + "['twitter', 'neutral', '126882726061146112']\n", + "--> downloading tweet #126882726061146112 (3371 of 4259)\n", + "['twitter', 'neutral', '126882617843924992']\n", + "--> downloading tweet #126882617843924992 (3372 of 4259)\n", + "['twitter', 'neutral', '126882542610690049']\n", + "--> downloading tweet #126882542610690049 (3373 of 4259)\n", + "['twitter', 'neutral', '126882493059170304']\n", + "--> downloading tweet #126882493059170304 (3374 of 4259)\n", + "['twitter', 'neutral', '126882427661582336']\n", + "--> downloading tweet #126882427661582336 (3375 of 4259)\n", + "['twitter', 'neutral', '126882349588815873']\n", + "--> downloading tweet #126882349588815873 (3376 of 4259)\n", + "['twitter', 'neutral', '126882248644493312']\n", + "--> downloading tweet #126882248644493312 (3377 of 4259)\n", + "['twitter', 'neutral', '126882244982878208']\n", + "--> downloading tweet #126882244982878208 (3378 of 4259)\n", + "['twitter', 'neutral', '126882122077184000']\n", + "--> downloading tweet #126882122077184000 (3379 of 4259)\n", + "['twitter', 'neutral', '126882090343079937']\n", + 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"['twitter', 'neutral', '126881580521234432']\n", + "--> downloading tweet #126881580521234432 (3389 of 4259)\n", + "['twitter', 'neutral', '126881523755528192']\n", + "--> downloading tweet #126881523755528192 (3390 of 4259)\n", + "['twitter', 'neutral', '126881317894893568']\n", + "--> downloading tweet #126881317894893568 (3391 of 4259)\n", + "['twitter', 'neutral', '126881309015539712']\n", + "--> downloading tweet #126881309015539712 (3392 of 4259)\n", + "['twitter', 'neutral', '126881203642040320']\n", + "--> downloading tweet #126881203642040320 (3393 of 4259)\n", + "['twitter', 'neutral', '126881136398962688']\n", + "--> downloading tweet #126881136398962688 (3394 of 4259)\n", + "['twitter', 'neutral', '126881090446163968']\n", + "--> downloading tweet #126881090446163968 (3395 of 4259)\n", + "['twitter', 'neutral', '126881073366958080']\n", + "--> downloading tweet #126881073366958080 (3396 of 4259)\n", + "['twitter', 'neutral', '126880978273697792']\n", + "--> downloading 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(3516 of 4259)\n", + "['twitter', 'neutral', '126874038164393984']\n", + "--> downloading tweet #126874038164393984 (3517 of 4259)\n", + "['twitter', 'neutral', '126873977284079616']\n", + "--> downloading tweet #126873977284079616 (3518 of 4259)\n", + "['twitter', 'neutral', '126873903552405504']\n", + "--> downloading tweet #126873903552405504 (3519 of 4259)\n", + "['twitter', 'neutral', '126873883172274176']\n", + "--> downloading tweet #126873883172274176 (3520 of 4259)\n", + "['twitter', 'neutral', '126873866575425536']\n", + "--> downloading tweet #126873866575425536 (3521 of 4259)\n", + "['twitter', 'neutral', '126873786715873280']\n", + "--> downloading tweet #126873786715873280 (3522 of 4259)\n", + "['twitter', 'neutral', '126873686987902976']\n", + "--> downloading tweet #126873686987902976 (3523 of 4259)\n", + "['twitter', 'neutral', '126873680654516224']\n", + "--> downloading tweet #126873680654516224 (3524 of 4259)\n", + "['twitter', 'neutral', '126873665601146882']\n", + 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"['twitter', 'irrelevant', '126859860640940032']\n", + "--> downloading tweet #126859860640940032 (4216 of 4259)\n", + "['twitter', 'irrelevant', '126859846506127360']\n", + "--> downloading tweet #126859846506127360 (4217 of 4259)\n", + "['twitter', 'irrelevant', '126859794383515649']\n", + "--> downloading tweet #126859794383515649 (4218 of 4259)\n", + "['twitter', 'irrelevant', '126859627051757568']\n", + "--> downloading tweet #126859627051757568 (4219 of 4259)\n", + "['twitter', 'irrelevant', '126859610006110208']\n", + "--> downloading tweet #126859610006110208 (4220 of 4259)\n", + "['twitter', 'irrelevant', '126859509414100992']\n", + "--> downloading tweet #126859509414100992 (4221 of 4259)\n", + "['twitter', 'irrelevant', '126859503495938048']\n", + "--> downloading tweet #126859503495938048 (4222 of 4259)\n", + "['twitter', 'irrelevant', '126859490061598720']\n", + "--> downloading tweet #126859490061598720 (4223 of 4259)\n", + "['twitter', 'irrelevant', '126859443127332864']\n", + "--> downloading tweet #126859443127332864 (4224 of 4259)\n", + "['twitter', 'irrelevant', '126859428455657472']\n", + "--> downloading tweet #126859428455657472 (4225 of 4259)\n", + "['twitter', 'irrelevant', '126859363079041024']\n", + "--> downloading tweet #126859363079041024 (4226 of 4259)\n", + "['twitter', 'irrelevant', '126859354614939648']\n", + "--> downloading tweet #126859354614939648 (4227 of 4259)\n", + "['twitter', 'irrelevant', '126859286558158849']\n", + "--> downloading tweet #126859286558158849 (4228 of 4259)\n", + "['twitter', 'irrelevant', '126859211593351168']\n", + "--> downloading tweet #126859211593351168 (4229 of 4259)\n", + "['twitter', 'irrelevant', '126859155175772161']\n", + "--> downloading tweet #126859155175772161 (4230 of 4259)\n", + "['twitter', 'irrelevant', '126858835058098176']\n", + "--> downloading tweet #126858835058098176 (4231 of 4259)\n", + "['twitter', 'irrelevant', '126858789868670976']\n", + "--> downloading tweet #126858789868670976 (4232 of 4259)\n", + "['twitter', 'irrelevant', '126858639855206400']\n", + "--> downloading tweet #126858639855206400 (4233 of 4259)\n", + "['twitter', 'irrelevant', '126858516655898625']\n", + "--> downloading tweet #126858516655898625 (4234 of 4259)\n", + "['twitter', 'irrelevant', '126858260216160256']\n", + "--> downloading tweet #126858260216160256 (4235 of 4259)\n", + "['twitter', 'irrelevant', '126858248325308416']\n", + "--> downloading tweet #126858248325308416 (4236 of 4259)\n", + "['twitter', 'irrelevant', '126858186962644992']\n", + "--> downloading tweet #126858186962644992 (4237 of 4259)\n", + "['twitter', 'irrelevant', '126858032951996416']\n", + "--> downloading tweet #126858032951996416 (4238 of 4259)\n", + "['twitter', 'irrelevant', '126858004690767872']\n", + "--> downloading tweet #126858004690767872 (4239 of 4259)\n", + "['twitter', 'irrelevant', '126857921068941314']\n", + "--> downloading tweet #126857921068941314 (4240 of 4259)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['twitter', 'irrelevant', '126857918929838080']\n", + "--> downloading tweet #126857918929838080 (4241 of 4259)\n", + "['twitter', 'irrelevant', '126857746200014849']\n", + "--> downloading tweet #126857746200014849 (4242 of 4259)\n", + "['twitter', 'irrelevant', '126857736238530560']\n", + "--> downloading tweet #126857736238530560 (4243 of 4259)\n", + "['twitter', 'irrelevant', '126857518591901698']\n", + "--> downloading tweet #126857518591901698 (4244 of 4259)\n", + "['twitter', 'irrelevant', '126857511230902272']\n", + "--> downloading tweet #126857511230902272 (4245 of 4259)\n", + "['twitter', 'irrelevant', '126857421321797634']\n", + "--> downloading tweet #126857421321797634 (4246 of 4259)\n", + "['twitter', 'irrelevant', '126857383715676160']\n", + "--> downloading tweet #126857383715676160 (4247 of 4259)\n", + "['twitter', 'irrelevant', '126857361515216897']\n", + "--> downloading tweet #126857361515216897 (4248 of 4259)\n", + "['twitter', 'irrelevant', '126857211921174528']\n", + "--> downloading tweet #126857211921174528 (4249 of 4259)\n", + "['twitter', 'irrelevant', '126857071667847168']\n", + "--> downloading tweet #126857071667847168 (4250 of 4259)\n", + "['twitter', 'irrelevant', '126856764242137088']\n", + "--> downloading tweet #126856764242137088 (4251 of 4259)\n", + "['twitter', 'irrelevant', '126856732331884545']\n", + "--> downloading tweet #126856732331884545 (4252 of 4259)\n", + "['twitter', 'irrelevant', '126856425371746304']\n", + "--> downloading tweet #126856425371746304 (4253 of 4259)\n", + "['twitter', 'irrelevant', '126856097863708672']\n", + "--> downloading tweet #126856097863708672 (4254 of 4259)\n", + "['twitter', 'irrelevant', '126856097431699456']\n", + "--> downloading tweet #126856097431699456 (4255 of 4259)\n", + "['twitter', 'irrelevant', '126855687060987904']\n", + "--> downloading tweet #126855687060987904 (4256 of 4259)\n", + "['twitter', 'irrelevant', '126854999442587648']\n", + "--> downloading tweet #126854999442587648 (4257 of 4259)\n", + "['twitter', 'irrelevant', '126854818101858304']\n", + "--> downloading tweet #126854818101858304 (4258 of 4259)\n", + "['twitter', 'irrelevant', '126854423317188608']\n", + "--> downloading tweet #126854423317188608 (4259 of 4259)\n", + "We have already downloaded 4257 tweets.\n", + "\n", + "Starting second pass to retry 2 failed downloads...\n", + "['twitter', 'positive', '126877263311536128']\n", + "--> downloading tweet #126877263311536128 (1 of 2)\n", + "Twitter sent status 403 for URL: 1.1/statuses/show.json using parameters: (id=126877263311536128&oauth_consumer_key=zu7CKytyl7G8V4KQMXNOw&oauth_nonce=10011599810929708224&oauth_signature_method=HMAC-SHA1&oauth_timestamp=1520182306&oauth_token=12866362-GfEuXN31tyioMyPyTKT3YmOd0DnGhzAQXZ7Wjhw9s&oauth_version=1.0&oauth_signature=LbOhpXamdY6MnyhMw44pBRpBJAE%3D)\n", + "details: {'errors': [{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]}\n", + "Not authorized to view this tweet.\n", + "['twitter', 'neutral', '126870792960086018']\n", + "--> downloading tweet #126870792960086018 (2 of 2)\n", + "Twitter sent status 403 for URL: 1.1/statuses/show.json using parameters: (id=126870792960086018&oauth_consumer_key=zu7CKytyl7G8V4KQMXNOw&oauth_nonce=7035570845226684577&oauth_signature_method=HMAC-SHA1&oauth_timestamp=1520182306&oauth_token=12866362-GfEuXN31tyioMyPyTKT3YmOd0DnGhzAQXZ7Wjhw9s&oauth_version=1.0&oauth_signature=KFb%2FSl2bWBBgNezrfcgU5RqiUyg%3D)\n", + "details: {'errors': [{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]}\n", + "Not authorized to view this tweet.\n", + "--> missing tweet #126877263311536128\n", + "--> missing tweet #126870792960086018\n", + "\n", + "Missing 2 of 4259 tweets!\n", + "Partial output in: data\\full-corpus.csv\n", + "\n", + "4015.0309450626373\n" + ] + } + ], + "source": [ + "import csv\n", + "import json\n", + "import time\n", + "\n", + "try:\n", + " import twitter\n", + "except ImportError:\n", + " print(\"\"\"\\\n", + "You need to ...\n", + " pip install twitter\n", + "If pip is not found you might have to install it using easy_install.\n", + "If it does not work on your system, you might want to follow instructions\n", + "at https://github.com/sixohsix/twitter, most likely:\n", + " $ git clone https://github.com/sixohsix/twitter\n", + " $ cd twitter\n", + " $ sudo python setup.py install\n", + "\"\"\")\n", + "\n", + " sys.exit(1)\n", + " \n", + "print()\n", + "\n", + "from twitterauth import CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN_KEY, ACCESS_TOKEN_SECRET\n", + "api = twitter.Twitter(auth=twitter.OAuth(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET,\n", + " token=ACCESS_TOKEN_KEY, token_secret=ACCESS_TOKEN_SECRET))\n", + "\n", + "# for some reasons TWeets disappear. In this file we collect those\n", + "MISSING_ID_FILE = os.path.join(DATA_DIR, \"missing.tsv\")\n", + "NOT_AUTHORIZED_ID_FILE = os.path.join(DATA_DIR, \"not_authorized.tsv\")\n", + "\n", + "def get_user_params(DATA_DIR):\n", + " user_params = {}\n", + "\n", + " # get user input params\n", + " user_params['inList'] = os.path.join(DATA_DIR, 'corpus.csv')\n", + " user_params['outList'] = os.path.join(DATA_DIR, 'full-corpus.csv')\n", + " user_params['rawDir'] = os.path.join(DATA_DIR, 'rawdata/')\n", + "\n", + " return user_params\n", + "\n", + "\n", + "def read_total_list(in_filename):\n", + "\n", + " # read total fetch list csv\n", + " with open(in_filename, 'rt') as f:\n", + " reader = csv.reader(f, delimiter=',', quotechar='\"')\n", + "\n", + " if os.path.exists(MISSING_ID_FILE):\n", + " missing_ids = [line.strip()\n", + " for line in open(MISSING_ID_FILE, \"r\").readlines()]\n", + " else:\n", + " missing_ids = []\n", + "\n", + " if os.path.exists(NOT_AUTHORIZED_ID_FILE):\n", + " not_authed_ids = [line.strip()\n", + " for line in open(NOT_AUTHORIZED_ID_FILE, \"r\").readlines()]\n", + " else:\n", + " not_authed_ids = []\n", + "\n", + " print(\"We will skip %i tweets that are not available or visible any more on twitter\" % (\n", + " len(missing_ids) + len(not_authed_ids)))\n", + "\n", + " ignore_ids = set(missing_ids + not_authed_ids)\n", + " total_list = []\n", + "\n", + " for row in reader:\n", + " if row[2] not in ignore_ids:\n", + " total_list.append(row)\n", + "\n", + " return total_list\n", + "\n", + "\n", + "def purge_already_fetched(fetch_list, raw_dir):\n", + "\n", + " # list of tweet ids that still need downloading\n", + " rem_list = []\n", + " count_done = 0\n", + "\n", + " # check each tweet to see if we have it\n", + " for item in fetch_list:\n", + "\n", + " # check if json file exists\n", + " tweet_file = os.path.join(raw_dir, item[2] + '.json')\n", + " if os.path.exists(tweet_file):\n", + "\n", + " # attempt to parse json file\n", + " try:\n", + " parse_tweet_json(tweet_file)\n", + " count_done += 1\n", + " except RuntimeError:\n", + " print(\"Error parsing\", item)\n", + " rem_list.append(item)\n", + " else:\n", + " rem_list.append(item)\n", + "\n", + " print(\"We have already downloaded %i tweets.\" % count_done)\n", + "\n", + " return rem_list\n", + "\n", + "\n", + "def download_tweets(fetch_list, raw_dir):\n", + "\n", + " # ensure raw data directory exists\n", + " if not os.path.exists(raw_dir):\n", + " os.mkdir(raw_dir)\n", + "\n", + " # download tweets\n", + " for idx in range(0, len(fetch_list)):\n", + " # current item\n", + " item = fetch_list[idx]\n", + " print(item)\n", + "\n", + " print('--> downloading tweet #%s (%d of %d)' %\n", + " (item[2], idx + 1, len(fetch_list)))\n", + "\n", + " try:\n", + " response = api.statuses.show(_id=item[2])\n", + "\n", + " if response.rate_limit_remaining <= 0:\n", + " if response.rate_limit_reset == 0:\n", + " wait_seconds = 60\n", + " \n", + " print(\"Rate limiting exceeded - deliberately waiting %is seconds\" %\n", + " wait_seconds)\n", + " \n", + " else:\n", + " wait_seconds = response.rate_limit_reset - time.time()\n", + " print(\"Rate limiting requests us to wait %f seconds\" %\n", + " wait_seconds)\n", + " \n", + " time.sleep(wait_seconds+5)\n", + "\n", + " except twitter.TwitterError as e:\n", + " fatal = True\n", + " print(e)\n", + " for m in e.response_data['errors']:\n", + " if m['code'] in [34, 144]:\n", + " print(\"Tweet missing: \", item)\n", + " with open(MISSING_ID_FILE, \"at\") as f:\n", + " f.write(item[2] + \"\\n\")\n", + "\n", + " fatal = False\n", + " break\n", + " elif m['code'] == 63:\n", + " print(\"User of tweet '%s' has been suspended.\" % item)\n", + " with open(MISSING_ID_FILE, \"at\") as f:\n", + " f.write(item[2] + \"\\n\")\n", + "\n", + " fatal = False\n", + " break\n", + " elif m['code'] == 88:\n", + " print(\"Rate limit exceeded.\")\n", + " fatal = False\n", + " break\n", + " elif m['code'] == 179:\n", + " print(\"Not authorized to view this tweet.\")\n", + " with open(NOT_AUTHORIZED_ID_FILE, \"at\") as f:\n", + " f.write(item[2] + \"\\n\")\n", + " fatal = False\n", + " break\n", + " else:\n", + " fatal = True\n", + "\n", + " if fatal:\n", + " raise\n", + " else:\n", + " continue\n", + "\n", + " with open(raw_dir + item[2] + '.json', \"wt\") as f:\n", + " f.write(json.dumps(dict(response)) + \"\\n\")\n", + "\n", + " return\n", + "\n", + "\n", + "def parse_tweet_json(filename):\n", + "\n", + " # read tweet\n", + " fp = open(filename, 'r')\n", + "\n", + " # parse json\n", + " try:\n", + " tweet_json = json.load(fp)\n", + " except ValueError as e:\n", + " print(e)\n", + " raise RuntimeError('error parsing json')\n", + "\n", + " # look for twitter api error msgs\n", + " if 'error' in tweet_json or 'errors' in tweet_json:\n", + " raise RuntimeError('error in downloaded tweet')\n", + "\n", + " # extract creation date and tweet text\n", + " return [tweet_json['created_at'], tweet_json['text']]\n", + "\n", + "\n", + "def build_output_corpus(out_filename, raw_dir, total_list):\n", + " # open csv output file\n", + " with open(out_filename, 'w') as f:\n", + " writer = csv.writer(f, delimiter=',', quotechar='\"', escapechar='\\\\',\n", + " quoting=csv.QUOTE_ALL)\n", + "\n", + " # write header row\n", + " writer.writerow(['Topic', 'Sentiment', 'TweetId', 'TweetDate', 'TweetText'])\n", + "\n", + " # parse all downloaded tweets\n", + " missing_count = 0\n", + " for item in total_list:\n", + "\n", + " # ensure tweet exists\n", + " if os.path.exists(raw_dir + item[2] + '.json'):\n", + "\n", + " try:\n", + " # parse tweet\n", + " parsed_tweet = parse_tweet_json(raw_dir + item[2] + '.json')\n", + " full_row = item + parsed_tweet\n", + "\n", + " # character encoding for output\n", + " for i in range(0, len(full_row)):\n", + " full_row[i] = full_row[i].encode(\"utf-8\")\n", + "\n", + " # write csv row\n", + " writer.writerow(full_row)\n", + "\n", + " except RuntimeError:\n", + " print('--> bad data in tweet #' + item[2])\n", + " missing_count += 1\n", + "\n", + " else:\n", + " print('--> missing tweet #' + item[2])\n", + " missing_count += 1\n", + "\n", + " # indicate success\n", + " if missing_count == 0:\n", + " print('\\nSuccessfully downloaded corpus!')\n", + " print('Output in: ' + out_filename + '\\n')\n", + " else:\n", + " print('\\nMissing %d of %d tweets!' % (missing_count, len(total_list)))\n", + " print('Partial output in: ' + out_filename + '\\n')\n", + "\n", + "\n", + "def fetch_data():\n", + " # get user parameters\n", + " user_params = get_user_params(DATA_DIR)\n", + " print(user_params)\n", + "\n", + " # get fetch list\n", + " total_list = read_total_list(user_params['inList'])\n", + "\n", + " # remove already fetched or missing tweets\n", + " fetch_list = purge_already_fetched(total_list, user_params['rawDir'])\n", + " print(\"Fetching %i tweets...\" % len(fetch_list))\n", + "\n", + " if fetch_list:\n", + " # start fetching data from twitter\n", + " download_tweets(fetch_list, user_params['rawDir'])\n", + "\n", + " # second pass for any failed downloads\n", + " fetch_list = purge_already_fetched(total_list, user_params['rawDir'])\n", + " if fetch_list:\n", + " print('\\nStarting second pass to retry %i failed downloads...' %\n", + " len(fetch_list))\n", + " download_tweets(fetch_list, user_params['rawDir'])\n", + " else:\n", + " print(\"Nothing to fetch any more.\")\n", + "\n", + " # build output corpus\n", + " build_output_corpus(user_params['outList'], user_params['rawDir'],\n", + " total_list)\n", + "\n", + "\n", + "import time\n", + "start_time = time.time()\n", + "fetch_data()\n", + "print(time.time()-start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#irrelevant: 437\n", + "#negative: 448\n", + "#neutral: 1800\n", + "#positive: 390\n" + ] + } + ], + "source": [ + "def load_sanders_data(dirname=\".\", line_count=-1):\n", + " count = 0\n", + "\n", + " topics = []\n", + " labels = []\n", + " tweets = []\n", + "\n", + " with open(os.path.join(DATA_DIR, dirname, \"corpus.csv\"), \"r\") as csvfile:\n", + " metareader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n", + " for line in metareader:\n", + " count += 1\n", + " if line_count > 0 and count > line_count:\n", + " break\n", + "\n", + " topic, label, tweet_id = line\n", + "\n", + " tweet_fn = os.path.join(DATA_DIR, dirname, 'rawdata', '%s.json' % tweet_id)\n", + " try:\n", + " tweet = json.load(open(tweet_fn, \"r\"))\n", + " except IOError:\n", + " #print((\"Tweet '%s' not found. Skip.\" % tweet_fn))\n", + " continue\n", + "\n", + " if 'text' in tweet and tweet['user']['lang'] == \"en\":\n", + " topics.append(topic)\n", + " labels.append(label)\n", + " tweets.append(tweet['text'])\n", + "\n", + " tweets = np.asarray(tweets)\n", + " labels = np.asarray(labels)\n", + "\n", + " return tweets, labels\n", + "\n", + "\n", + "X_orig, Y_orig = load_sanders_data()\n", + "classes = np.unique(Y_orig)\n", + "for c in classes:\n", + " print(\"#%s: %i\" % (c, sum(Y_orig == c)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(Y_orig);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Some accuracy tests" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(precision=20) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.94065645841246544177e-324])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([2.48E-324])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([2.47E-324]) # ouch!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x = 0.00001" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1e-320" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x**64 # still fine" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x**65 # ouch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.float_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Relationship between probabilities and their logarithm" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_log():\n", + " plt.clf()\n", + " plt.figure(num=None, figsize=(6, 5), dpi=DPI)\n", + "\n", + " x = np.arange(0.001, 1, 0.001)\n", + " y = np.log(x)\n", + "\n", + " plt.title('Relationship between probabilities and their logarithm')\n", + " plt.plot(x, y)\n", + " plt.grid(True)\n", + " plt.xlabel('P')\n", + " plt.ylabel('log(P)')\n", + " filename = 'log_probs.png'\n", + " save_png(\"0_log\")\n", + "plot_log()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solving an easy problem first" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# first create a Boolean list having true for tweets\n", + "# that are either positive or negative\n", + "pos_neg_idx = np.logical_or(Y_orig==\"positive\", Y_orig ==\"negative\")\n", + "plt.hist(pos_neg_idx);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# now use that index to filter the data and the labels\n", + "X = X_orig [pos_neg_idx]\n", + "Y = Y_orig [pos_neg_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# finally convert the labels themselves into Boolean\n", + "Y = Y==\"positive\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "def create_ngram_model(params=None):\n", + " tfidf_ngrams = TfidfVectorizer(ngram_range=(1, 3), \n", + " analyzer=\"word\", binary=False)\n", + " clf = MultinomialNB()\n", + " pipeline = Pipeline([('tfidf', tfidf_ngrams), ('clf', clf)])\n", + " if params:\n", + " pipeline.set_params(**params)\n", + " return pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import precision_recall_curve, auc\n", + "from sklearn.model_selection import ShuffleSplit\n", + "\n", + "def train_model(clf_factory, X, Y, name=\"NB ngram\", plot=None):\n", + " cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)\n", + " \n", + " train_errors = []\n", + " test_errors = []\n", + "\n", + " scores = []\n", + " pr_scores = []\n", + " precisions, recalls, thresholds = [], [], []\n", + "\n", + " for train, test in cv.split(X, Y):\n", + " X_train, y_train = X[train], Y[train]\n", + " X_test, y_test = X[test], Y[test]\n", + "\n", + " clf = clf_factory()\n", + " clf.fit(X_train, y_train)\n", + "\n", + " train_score = clf.score(X_train, y_train)\n", + " test_score = clf.score(X_test, y_test)\n", + "\n", + " train_errors.append(1 - train_score)\n", + " test_errors.append(1 - test_score)\n", + "\n", + " scores.append(test_score)\n", + " proba = clf.predict_proba(X_test)\n", + "\n", + " precision, recall, pr_thresholds = precision_recall_curve(\n", + " y_test, proba[:, 1])\n", + "\n", + " pr_scores.append(auc(recall, precision))\n", + " precisions.append(precision)\n", + " recalls.append(recall)\n", + " thresholds.append(pr_thresholds)\n", + "\n", + " scores_to_sort = pr_scores\n", + " median = np.argsort(scores_to_sort)[len(scores_to_sort) // 2]\n", + "\n", + " if plot:\n", + " plot_pr(pr_scores[median], name, precisions[median],\n", + " recalls[median], label=name, plot_nr=plot)\n", + "\n", + " summary = (np.mean(scores), np.mean(pr_scores))\n", + " print(\"Mean acc=%.3f\\tMean P/R AUC=%.3f\" % summary)\n", + "\n", + " return np.mean(train_errors), np.mean(test_errors)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. neg ==\n", + "3075 838 0.27252032520325203\n" + ] + } + ], + "source": [ + "print(\"== Pos vs. neg ==\")\n", + "pos_neg = np.logical_or(Y_orig == \"positive\", Y_orig == \"negative\")\n", + "X = X_orig[pos_neg]\n", + "Y = Y_orig[pos_neg]\n", + "Y = tweak_labels(Y, [\"positive\"])\n", + "print(len(Y_orig), len(Y), len(Y)/len(Y_orig))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean acc=0.777\tMean P/R AUC=0.887\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0020477815699659007, 0.22261904761904763)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_model(create_ngram_model, X, Y, name=\"pos vs neg\", plot=\"1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using all classes" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos/neg vs. irrelevant/neutral ==\n", + "Mean acc=0.734\tMean P/R AUC=0.661\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.11226765799256506, 0.26608884073672806)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos/neg vs. irrelevant/neutral ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\", \"negative\"])\n", + "train_model(create_ngram_model, X, Y, name=\"sent vs rest\", plot=\"2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. rest ==\n", + "Mean acc=0.870\tMean P/R AUC=0.305\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.12513940520446099, 0.13011917659804983)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\"])\n", + "train_model(create_ngram_model, X, Y, name=\"pos vs rest\", plot=\"3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Neg vs. rest ==\n", + "Mean acc=0.852\tMean P/R AUC=0.490\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.13856877323420075, 0.14756229685807148)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Neg vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"negative\"])\n", + "train_model(create_ngram_model, X, Y, name=\"neg vs rest\", plot=\"4\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grid searching the best classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import make_scorer, f1_score\n", + "\n", + "def grid_search_model(clf_factory, X, Y):\n", + " cv = ShuffleSplit(n_splits=10, test_size=0.3,random_state=0)\n", + "\n", + " param_grid = dict(tfidf__ngram_range=[(1, 1), (1, 2), (1, 3)],\n", + " tfidf__min_df=[1, 2],\n", + " tfidf__stop_words=[None, \"english\"],\n", + " tfidf__smooth_idf=[False, True],\n", + " tfidf__use_idf=[False, True],\n", + " tfidf__sublinear_tf=[False, True],\n", + " tfidf__binary=[False, True],\n", + " clf__alpha=[0, 0.01, 0.05, 0.1, 0.5, 1],\n", + " )\n", + "\n", + " grid_search = GridSearchCV(clf_factory(),\n", + " param_grid=param_grid,\n", + " cv=cv,\n", + " scoring=make_scorer(f1_score),\n", + " verbose=10)\n", + " grid_search.fit(X, Y) \n", + "\n", + " return grid_search.best_estimator_" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 10 folds for each of 1152 candidates, totalling 11520 fits\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5010799136069115, total= 0.1s\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5201793721973095, total= 0.0s\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.3s remaining: 0.0s\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n", + "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s remaining: 0.0s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5677966101694916, total= 0.0s\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5622317596566524, total= 0.0s\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n", + "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.6s remaining: 0.0s\n", + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5384615384615385, total= 0.0s\n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5242290748898678, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6085192697768763, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5419354838709677, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5472837022132796, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5194805194805194, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5772357723577236, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5665961945031712, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5933609958506223, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5667351129363449, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5805084745762711, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5702479338842975, total= 0.0s\n", + "[CV] 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5450643776824035, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5494949494949494, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5269978401727862, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5772357723577236, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5788423153692615, total= 0.0s\n", + "[CV] 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5796460176991151, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6096033402922756, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.562015503875969, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6026871401151632, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.572, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5330578512396694, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5739514348785871, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5336322869955158, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5635593220338984, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5938144329896907, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.570841889117043, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5817409766454352, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5702479338842975, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5491803278688525, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6085192697768763, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5419354838709677, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5824847250509165, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5828343313373254, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5758754863813229, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5204918032786885, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5191489361702128, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5357142857142858, 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score=0.6144578313253012, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5692007797270954, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.569672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.664092664092664, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6215139442231076, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5747572815533981, total= 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.592885375494071, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6130841121495327, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6059479553903346, total= 0.5s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6247619047619049, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.603921568627451, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5708502024291497, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.567741935483871, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5766871165644171, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.602020202020202, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.584, total= 0.1s\n", + "[CV] 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.604206500956023, total= 0.6s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6219512195121951, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5875251509054326, total= 0.8s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5725338491295939, total= 0.8s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5625, total= 0.7s\n", + "[CV] 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5719844357976653, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5725971370143149, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5972495088408645, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6145251396648045, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6082089552238806, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6295585412667946, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6023622047244094, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5708502024291497, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.567741935483871, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5766871165644171, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.602020202020202, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.584, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5954825462012321, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5875251509054326, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.576402321083172, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6003976143141153, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5802707930367506, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5851703406813626, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5925925925925927, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.600375234521576, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6178217821782177, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5891783567134268, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5708502024291497, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5683760683760685, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5778688524590164, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6036217303822938, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5805168986083499, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5942622950819672, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5968379446640316, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6068702290076335, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6194331983805669, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5903614457831325, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5791505791505791, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5660377358490566, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.584, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5992063492063493, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5791505791505792, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5800000000000001, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.59375, 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6004228329809725, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6141414141414142, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6085192697768762, total= 0.2s\n", + "[CV] 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6203007518796992, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6086956521739131, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6183953033268101, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5900990099009901, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.581532416502947, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6004228329809725, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6141414141414142, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6085192697768762, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6204238921001927, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6056910569105691, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6003824091778203, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6189555125725338, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6575342465753424, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.616600790513834, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5808966861598441, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5848670756646217, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6097087378640775, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5961538461538463, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6261859582542694, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6126482213438735, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6034155597722961, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.620817843866171, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6287878787878788, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6061776061776061, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5810276679841897, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6046511627906976, total= 0.2s\n", 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6206896551724138, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6085192697768762, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6003824091778203, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6165703275529865, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.66015625, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6153846153846153, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5797665369649805, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, 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score=0.5950095969289827, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6238185255198488, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6126482213438735, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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score=0.5758754863813229, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5868263473053893, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6011787819253438, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5912698412698413, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5708502024291497, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.49889624724061815, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5727699530516432, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5054466230936819, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.595289079229122, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5382932166301969, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4881209503239741, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.55125284738041, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5690021231422505, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5664488017429194, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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tfidf__use_idf=True, score=0.5732217573221758, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5322245322245323, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5596707818930041, total= 0.0s\n", + "[CV] clf__alpha=0.01, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5708154506437768, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5162689804772235, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6012793176972281, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5426695842450766, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4860215053763441, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5479452054794521, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5750528541226216, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5557986870897156, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5580448065173116, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5626283367556468, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5294117647058822, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.48512585812356984, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5158150851581509, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5357967667436491, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5415778251599147, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5707964601769911, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5570175438596492, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5098039215686275, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5764192139737991, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5165562913907283, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4944812362030905, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5129411764705882, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5391304347826087, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5521739130434783, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5557809330628803, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5665961945031712, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, 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tfidf__use_idf=True, score=0.5560165975103734, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5083333333333334, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.48054919908466814, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5575757575757576, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5684210526315788, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5708502024291497, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5928853754940712, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5731958762886598, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5696969696969696, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5936254980079682, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6135458167330677, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.565130260521042, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5503080082135523, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5909090909090908, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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tfidf__use_idf=False, score=0.616600790513834, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5852631578947368, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5691056910569107, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5788423153692616, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6385542168674698, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.55, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5546218487394958, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5338809034907598, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5634920634920635, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5925925925925927, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.54989816700611, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5742574257425742, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5896414342629482, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.56, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5503080082135523, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5818181818181818, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5991735537190083, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5831622176591376, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6175298804780877, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5792811839323467, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.573170731707317, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5469061876247505, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5783664459161146, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", 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tfidf__use_idf=True, score=0.5912698412698413, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285715, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5696969696969696, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5936254980079682, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6147704590818363, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.565130260521042, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5503080082135523, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5909090909090908, total= 0.1s\n", 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.616600790513834, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5852631578947368, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6147704590818363, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5668662674650697, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5194805194805194, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5336426914153132, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5515789473684212, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5657370517928287, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5967078189300411, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5458248472505091, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5938144329896907, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5480572597137015, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5742574257425742, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5891783567134269, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.561122244488978, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6227544910179641, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5849802371541502, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6186770428015564, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5987780040733197, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5833333333333334, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5843137254901962, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6352941176470588, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5889328063241106, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5680933852140078, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, 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score=0.608187134502924, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5647969052224372, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5544554455445544, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5580448065173115, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5450819672131146, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5708502024291499, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6119096509240246, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5754527162977868, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5226337448559671, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5886939571150097, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5937500000000001, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.591111111111111, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6227544910179641, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5849802371541502, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5959183673469387, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.58051689860835, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5859375, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6379647749510764, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5873015873015872, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, 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tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6061776061776062, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.592, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5759368836291913, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, 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clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5964912280701754, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.56640625, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5294117647058824, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5321100917431193, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5685483870967741, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6109979633401222, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.579476861167002, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5226337448559671, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5190156599552572, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5652173913043479, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5389221556886227, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5692007797270955, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.592741935483871, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5748502994011976, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5886939571150097, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5949119373776909, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.55859375, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5197505197505198, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5814977973568282, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5899581589958158, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6055776892430278, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5786163522012578, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5882352941176471, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.539553752535497, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] 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tfidf__use_idf=True, score=0.5588822355289421, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5743801652892562, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.604, total= 0.0s\n", + "[CV] clf__alpha=0.01, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5899581589958158, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6055776892430278, total= 0.0s\n", + "[CV] 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"[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5899581589958158, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5736434108527133, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5577689243027889, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5841392649903289, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5725338491295938, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6091954022988505, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5708582834331338, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5148936170212767, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5389755011135858, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.574468085106383, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5436105476673427, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5364806866952789, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5743380855397149, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, 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tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5148936170212767, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5389755011135858, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6602316602316602, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.62, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5741811175337186, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5685071574642127, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6256983240223465, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.592885375494071, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5671641791044776, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5795918367346937, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6024096385542169, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5737051792828686, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5807692307692307, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5625, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.588, total= 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5795918367346937, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6024096385542169, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, 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score=0.5905511811023623, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6072106261859582, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6141414141414141, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5930232558139534, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6037037037037037, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6193293885601578, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5776892430278884, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5826771653543306, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6120689655172413, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6141414141414142, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6141414141414142, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6265060240963854, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5905511811023623, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6072106261859582, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5795918367346937, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6024096385542169, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5737051792828686, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5877551020408164, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5905511811023623, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6072106261859582, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6141414141414141, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.592, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.576402321083172, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.579654510556622, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5771543086172344, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5766871165644172, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6046511627906977, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6003824091778203, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6153846153846154, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5972495088408645, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5958254269449715, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6176470588235293, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6301886792452829, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6088631984585742, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5668016194331984, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5708154506437768, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5737373737373738, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6015936254980079, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.57429718875502, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5872689938398357, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5968379446640316, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6110056925996205, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6092184368737474, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6003976143141153, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5802707930367506, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5611814345991561, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5877712031558185, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5909980430528375, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5780346820809248, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5788423153692616, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.59765625, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6062846580406654, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.614785992217899, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5893909626719057, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5668016194331984, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5708154506437768, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5737373737373738, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6015936254980079, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.57429718875502, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5872689938398357, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5968379446640316, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6110056925996205, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6092184368737474, total= 0.1s\n", + "[CV] clf__alpha=0.01, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.606060606060606, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6003824091778203, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6138996138996139, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6498054474708171, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6138613861386139, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5786407766990291, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5836734693877551, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5872689938398357, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5968379446640316, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6110056925996205, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6092184368737474, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6003976143141153, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5968379446640316, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6110056925996205, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.55, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5507900677200902, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5690021231422505, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5617977528089887, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5622317596566524, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5221052631578947, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.48444444444444446, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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score=0.5507900677200902, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5690021231422505, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5617977528089887, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.540045766590389, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.576271186440678, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5121951219512195, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5080091533180779, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5665961945031712, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5458422174840085, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5450643776824033, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5322245322245323, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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score=0.47926267281105994, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5121951219512195, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5080091533180779, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5638766519823788, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.541019955654102, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5043478260869566, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5813449023861171, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5066079295154186, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5322245322245323, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5567010309278351, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, 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tfidf__use_idf=True, score=0.5557809330628803, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.586138613861386, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.575569358178054, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6147704590818363, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5566600397614314, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5882352941176471, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5691056910569107, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5816733067729083, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.64, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5720250521920669, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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"[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5988023952095808, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6147704590818363, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5566600397614314, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5215517241379309, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5219399538106235, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5617529880478088, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5503080082135523, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.64, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5720250521920669, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.54, total= 0.1s\n", + "[CV] clf__alpha=0.01, 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"[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5779625779625781, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6095617529880478, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5816733067729083, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.64, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5720250521920669, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.54, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5720620842572063, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5848670756646217, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5609756097560975, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.586138613861386, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5726141078838175, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5651302605210421, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5971943887775552, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6187624750499002, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.558882235528942, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5215517241379309, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5219399538106235, total= 0.0s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5511482254697286, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5473684210526317, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5790554414784395, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5787234042553191, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.58980044345898, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6222222222222222, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5737051792828686, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6174757281553398, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5987780040733197, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5765407554671967, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.58203125, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6381322957198444, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5844930417495029, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5607843137254903, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5627705627705628, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.588, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5631067961165048, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5941747572815533, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5856573705179283, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5691699604743083, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5953307392996109, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6108949416342413, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5576923076923078, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5551181102362206, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.58980044345898, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6222222222222222, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5737051792828686, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6174757281553398, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5987780040733197, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5765407554671967, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.58203125, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6381322957198444, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5844930417495029, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5607843137254903, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5627705627705628, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.588, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5631067961165048, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5941747572815533, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5856573705179283, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5691699604743083, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5953307392996109, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6108949416342413, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5576923076923078, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5283018867924528, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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"[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5870445344129555, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5892116182572614, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, 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score=0.5870445344129555, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5892116182572614, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.564516129032258, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6381322957198444, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5844930417495029, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5980582524271845, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6124031007751938, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.58203125, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6381322957198444, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5731225296442687, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5980582524271845, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6124031007751938, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5587668593448941, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5283018867924528, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5251141552511415, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5645161290322581, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5491803278688525, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5870445344129555, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5892116182572614, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.564516129032258, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5725806451612903, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.607645875251509, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5771543086172345, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5113402061855671, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.515695067264574, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5577689243027889, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5447316103379721, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5637065637065637, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5903614457831325, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5634920634920635, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5859375, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6000000000000001, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5653021442495128, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5283018867924528, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5251141552511415, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5870445344129555, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5892116182572614, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.564516129032258, 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5771543086172345, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5113402061855671, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.515695067264574, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5577689243027889, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5447316103379721, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5637065637065637, total= 0.1s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5903614457831325, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5634920634920635, total= 0.2s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5859375, total= 0.4s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6000000000000001, total= 0.3s\n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.01, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5653021442495128, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5458422174840085, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5863636363636363, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5887445887445888, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5831533477321815, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6016260162601627, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5826086956521739, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6000000000000001, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5341614906832297, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6317991631799164, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5577342047930283, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5368852459016393, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5829787234042552, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6085192697768763, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.597979797979798, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6007751937984496, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285713, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6114398422090729, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5626283367556468, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5508474576271186, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5863636363636363, total= 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5739130434782609, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5840336134453781, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6263048016701462, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5565217391304348, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5685884691848907, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5891783567134269, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, 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clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6128364389233955, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5819672131147541, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5682819383259912, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5970873786407768, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5842696629213484, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5882352941176471, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5758928571428572, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5909090909090909, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5827814569536424, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5840336134453781, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6070686070686071, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.604255319148936, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5971943887775552, total= 0.3s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6111111111111112, total= 0.3s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6448979591836734, total= 0.3s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5975103734439834, total= 0.3s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5726872246696035, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5906040268456375, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6025104602510462, total= 0.3s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6103092783505155, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 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clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6419753086419753, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5970772442588727, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.565121412803532, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.608695652173913, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5817409766454352, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6069868995633187, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5805084745762712, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6045548654244307, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6081370449678801, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5955734406438631, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6052104208416833, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6380368098159509, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4922394678492239, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5305164319248826, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5406593406593406, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5526315789473684, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5517241379310345, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5787234042553191, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5555555555555556, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5210084033613446, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5648535564853556, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5136842105263159, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5077951002227171, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5727923627684964, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5565610859728507, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5429864253393665, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5852631578947368, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.572072072072072, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, 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tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5529157667386609, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5064377682403434, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5727272727272728, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5658747300215984, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5708061002178649, total= 0.0s\n", 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285714, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5291666666666667, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5708333333333333, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5642105263157895, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5111111111111111, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5396825396825397, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5805084745762712, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5099778270509978, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5066079295154186, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.576036866359447, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5949367088607596, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.578512396694215, total= 0.1s\n", + "[CV] clf__alpha=0.05, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5855670103092783, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.58, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, 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clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6351084812623274, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5903614457831325, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.569672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5831435079726651, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5978947368421053, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5814432989690721, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6178217821782178, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5953878406708596, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5860655737704918, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.58, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6424242424242425, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5938144329896906, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.56, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.59375, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, 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tfidf__use_idf=True, score=0.5783132530120482, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.591715976331361, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.641732283464567, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5846774193548386, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5304347826086956, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5407925407925407, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.554371002132196, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5446808510638298, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5732217573221757, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5910064239828694, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.56, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5919282511210762, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.592, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.569672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5831435079726651, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6178217821782178, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5953878406708596, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6404715127701375, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5875251509054326, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5304347826086956, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5407925407925407, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.554371002132196, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, 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"[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6234309623430963, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.575, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5239085239085239, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5567928730512249, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5668016194331984, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5491803278688525, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5783132530120482, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5909090909090909, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5532786885245902, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5546558704453441, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6044624746450303, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5553319919517102, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5356371490280778, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5407925407925407, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5531914893617021, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.547008547008547, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5569620253164557, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5601659751037344, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6205450733752621, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.577962577962578, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5249999999999999, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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score=0.5991902834008097, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5703275529865125, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5956521739130435, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.578125, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5991902834008097, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5215605749486654, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5478841870824054, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6008064516129031, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.568, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5877712031558187, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6047430830039525, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.562992125984252, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5737051792828686, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5641025641025641, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5446808510638298, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5990990990990991, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5846153846153845, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.563265306122449, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5356371490280778, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5744234800838575, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5435684647302904, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5697445972495088, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5936254980079682, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5402061855670104, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5356371490280778, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5517241379310345, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5936254980079682, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5402061855670104, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5805084745762712, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5967365967365967, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6004319654427646, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5889570552147239, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6169354838709677, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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score=0.5807770961145194, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5986984815618221, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5864978902953587, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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"[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6286836935166994, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5995893223819303, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6038461538461538, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6086956521739131, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.65234375, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5987780040733197, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5805084745762712, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5967365967365967, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6004319654427646, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5884861407249466, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6204081632653061, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6035242290748899, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.594704684317719, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.588477366255144, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6459627329192547, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6186440677966102, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5731462925851704, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6017316017316018, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5860655737704917, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6166328600405678, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6286836935166994, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5995893223819303, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6038461538461538, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6086956521739131, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.65234375, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5987780040733197, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5628997867803839, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5668934240362812, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5714285714285714, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5683760683760682, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5732758620689654, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5900216919739696, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5826446280991735, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, 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total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5673469387755102, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5653104925053533, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, 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"[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6354378818737271, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6016597510373444, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.554585152838428, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5694444444444444, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5486725663716814, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5701754385964912, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5964912280701754, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5899581589958159, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5738045738045737, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6380368098159509, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5987525987525988, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5701754385964913, total= 0.2s\n", + 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score=0.5576519916142556, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5684647302904564, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5726495726495726, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5805168986083499, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.605427974947808, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.575, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5077951002227171, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5662100456621004, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5756929637526653, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5696202531645569, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5308641975308642, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5308641975308642, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5773195876288658, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5207373271889401, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5321100917431193, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5570175438596492, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.509719222462203, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5077951002227171, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5829383886255924, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5603271983640081, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5775862068965517, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5690021231422504, total= 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5077951002227171, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4853932584269663, total= 0.0s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5542168674698796, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5960264900662251, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5875251509054326, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5685071574642128, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5753968253968254, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5919282511210762, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=False, score=0.5939393939393939, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5609284332688588, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.591304347826087, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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"text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5980582524271845, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5955734406438632, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, 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score=0.5891783567134269, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6183953033268101, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6004140786749481, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.591304347826087, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6138613861386139, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5983935742971889, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5697211155378487, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5831702544031311, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.602020202020202, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5725646123260437, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5921568627450979, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6074950690335306, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5636007827788649, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5404255319148936, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5458715596330275, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5708418891170431, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5596707818930041, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5819672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5738396624472574, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5690721649484536, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5737704918032787, total= 0.2s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6298568507157465, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5824847250509164, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5197505197505198, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5458612975391499, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5788423153692615, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5537848605577689, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5831702544031311, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.602020202020202, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5725646123260437, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5921568627450979, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6074950690335306, total= 0.1s\n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.05, tfidf__binary=True, tfidf__min_df=2, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.591792656587473, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5707762557077626, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5871964679911699, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5987261146496814, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5979381443298969, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.55, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6128364389233955, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5497835497835497, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5649202733485194, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5827814569536424, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.54989816700611, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5892116182572614, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5836909871244637, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5896907216494846, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5701754385964912, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.498876404494382, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5463917525773196, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5533980582524272, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5726315789473684, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.556701030927835, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, 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clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5275229357798165, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5466970387243736, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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score=0.5434298440979956, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.531017369727047, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5081967213114755, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5642317380352645, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5424528301886793, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5778781038374717, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, 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tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5619469026548672, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5478841870824053, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5654205607476636, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5547785547785548, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5382716049382716, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5068119891008175, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5244215938303342, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5357142857142857, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5468354430379747, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5353535353535354, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5419664268585133, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5024154589371981, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5303030303030303, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5077720207253885, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5688073394495413, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5606060606060606, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5411764705882354, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5778781038374717, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.589041095890411, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5619047619047619, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5619469026548672, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5446428571428571, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5787037037037037, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5514018691588785, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5118483412322274, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5104166666666666, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5213032581453634, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.49753694581280794, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5169082125603864, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5260545905707196, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5505882352941176, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5185185185185186, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5427872860635696, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.504950495049505, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5442176870748299, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5339805825242717, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5629290617848971, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5243619489559165, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5504587155963303, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5475638051044083, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5357142857142857, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5244444444444445, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5553145336225597, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5182012847965739, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5646551724137931, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5085470085470085, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5864332603938731, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5756929637526653, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5571725571725572, total= 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5945945945945946, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5818181818181819, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6132264529058116, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5821205821205822, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5819672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True 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score=0.5829959514170041, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6144578313253013, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.587991718426501, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5819672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5771543086172344, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.632, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5843621399176955, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5339168490153173, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5526932084309133, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5418502202643172, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5434782608695652, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5708245243128964, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, 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tfidf__use_idf=False, score=0.5412262156448203, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6047516198704103, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5584415584415584, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.55, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.553014553014553, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285714, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.575, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5590062111801242, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.546972860125261, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5564853556485355, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5503080082135524, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5295404814004377, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5780590717299577, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5887445887445887, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5548387096774192, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5431578947368421, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6064516129032258, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5634408602150538, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5588235294117647, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5661157024793388, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5774058577405857, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5578512396694215, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5598377281947261, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6083333333333334, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5532786885245902, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5894736842105264, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5906976744186047, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, 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"[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5882352941176471, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5732484076433122, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, 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clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5982905982905983, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5875, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5919661733615222, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5742971887550201, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5865580448065173, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5992063492063493, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6324110671936759, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5891783567134268, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5870020964360587, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5925925925925926, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6033755274261604, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6322314049586776, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5919661733615222, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5828092243186583, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5887445887445887, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5555555555555556, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5369978858350951, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6114649681528662, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5544147843942505, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5836734693877552, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5867768595041322, total= 0.1s\n", + "[CV] clf__alpha=0.1, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.610655737704918, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5575757575757576, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5879828326180258, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5524625267665952, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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"text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5872340425531913, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5950413223140496, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6029723991507431, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5872340425531913, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5950413223140496, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5690021231422505, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5850622406639004, total= 0.0s\n", + "[CV] clf__alpha=0.1, 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tfidf__use_idf=False, score=0.5764966740576497, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5127020785219399, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5341880341880342, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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score=0.5569620253164557, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5755693581780538, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5517241379310344, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5656108597285069, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5752212389380531, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5978494623655914, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5811965811965812, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5975103734439835, total= 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5358649789029536, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6198347107438017, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5548387096774193, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5272727272727272, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.56, total= 0.0s\n", + "[CV] 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tfidf__use_idf=True, score=0.5887265135699373, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5488565488565489, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5599999999999998, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5678496868475992, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5521472392638037, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.592901878914405, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5129310344827586, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5272727272727272, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.56, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.52803738317757, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, 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tfidf__use_idf=False, score=0.5, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5764966740576497, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5127020785219399, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5591397849462364, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5887265135699373, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5488565488565489, total= 0.0s\n", + "[CV] 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5536992840095465, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5848214285714286, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5339366515837104, total= 0.1s\n", + 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"[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5515587529976019, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5673758865248226, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5852534562211982, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5536992840095465, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5939675174013921, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5803571428571428, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6079664570230608, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5780590717299577, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6163793103448276, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5903083700440529, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5223529411764706, total= 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clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5533769063180829, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5754716981132075, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5421412300683373, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5555555555555556, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5594713656387665, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5442477876106194, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, 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"[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5578231292517007, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5635910224438903, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5454545454545454, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5842696629213483, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5810810810810811, total= 0.2s\n", + "[CV] clf__alpha=0.1, 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tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5297029702970296, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5170731707317072, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5156626506024097, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.513715710723192, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5547785547785549, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5215419501133787, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5603864734299517, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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score=0.5318181818181819, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5475113122171946, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5529953917050692, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5629290617848971, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5318181818181819, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5555555555555555, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5501165501165501, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5363408521303258, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5362318840579711, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5035629453681709, total= 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tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5501165501165501, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5855855855855856, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5818181818181819, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5156626506024097, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.513715710723192, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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score=0.5215419501133787, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5603864734299517, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5134474327628362, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5377777777777778, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5133928571428571, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5851318944844125, total= 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5530973451327433, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5054466230936818, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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tfidf__use_idf=True, score=0.5631929046563193, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5690376569037656, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285714, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.569593147751606, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5128205128205129, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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score=0.509009009009009, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4966139954853273, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5308056872037915, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5312500000000001, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5528089887640449, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5504201680672269, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.570194384449244, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5533769063180827, total= 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5128205128205129, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5133928571428571, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5851318944844125, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5475638051044083, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.547945205479452, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.569593147751606, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5675675675675675, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5838779956427015, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5021645021645021, total= 0.0s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6102449888641425, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5530973451327433, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5032537960954446, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5635103926096998, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5557986870897155, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5631929046563193, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5726315789473684, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5758241758241759, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5745140388768899, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5265392781316348, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5426695842450766, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5650224215246638, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5543237250554324, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4966740576496675, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5842696629213483, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.509009009009009, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.49433106575963726, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5540540540540541, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5508474576271186, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5714285714285715, total= 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5650224215246638, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5543237250554324, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.49433106575963726, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5260663507109005, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5312500000000001, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5540540540540541, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5720338983050848, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.569672131147541, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6350515463917525, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.579957356076759, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5627530364372471, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6041666666666666, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5748987854251012, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6044624746450304, total= 0.0s\n", + "[CV] 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tfidf__use_idf=True, score=0.592901878914405, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5761316872427983, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5828343313373252, total= 0.0s\n", + "[CV] clf__alpha=0.1, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.569672131147541, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6350515463917525, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.579957356076759, total= 0.1s\n", + "[CV] 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tfidf__use_idf=True, score=0.5959183673469387, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.592901878914405, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5761316872427983, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5843621399176955, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5283842794759824, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5446009389671361, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5526315789473685, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5374449339207048, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.569620253164557, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5720524017467249, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5501066098081023, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5462184873949579, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.6043478260869565, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5634408602150538, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5247311827956989, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5429864253393665, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.549266247379455, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5576519916142556, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5649484536082473, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.570230607966457, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5555555555555556, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5609756097560975, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.6092436974789917, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5521472392638037, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5283842794759824, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5446009389671361, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5526315789473685, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5374449339207048, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.569620253164557, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5720524017467249, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5501066098081023, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5462184873949579, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.6043478260869565, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5634408602150538, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5247311827956989, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5429864253393665, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.549266247379455, total= 0.4s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5576519916142556, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5609756097560975, total= 0.0s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.6092436974789917, total= 0.1s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" 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tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5912240184757506, total= 1.4s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5966386554621849, total= 1.5s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5838509316770185, total= 1.7s\n", + "[CV] clf__alpha=0.1, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5797101449275363, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5912240184757506, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.5966386554621849, total= 0.3s\n", + 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5936254980079682, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.631163708086785, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5483146067415731, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5573770491803278, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5657370517928286, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5797101449275363, total= 0.4s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__use_idf=True, score=0.5921325051759835, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5841784989858012, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5964214711729623, total= 0.3s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5579399141630901, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.5562632696390658, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5483146067415731, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5562372188139059, total= 0.2s\n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.35625, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4117647058823529, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.33432835820895523, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.29411764705882354, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__use_idf=True, score=0.35692307692307695, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3890577507598784, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.39999999999999997, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22377622377622378, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2702702702702703, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2706270627062706, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34615384615384615, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3386581469648562, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3161290322580645, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3529411764705882, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.391566265060241, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.40853658536585363, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3890577507598784, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3892215568862275, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3468208092485549, total= 0.1s\n", + "[CV] 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.17647058823529413, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16788321167883213, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.27999999999999997, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24918032786885247, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.23052959501557632, total= 0.2s\n", 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.26315789473684215, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, 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clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32601880877742945, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.29253731343283584, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3192182410423453, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3279742765273312, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.22368421052631582, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.15658362989323843, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.24342105263157893, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.225705329153605, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + 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tfidf__use_idf=False, score=0.15658362989323843, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.14074074074074075, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1897810218978102, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.16, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.17454545454545453, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32587859424920135, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.23026315789473684, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2976190476190476, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32587859424920135, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.22950819672131148, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4173441734417344, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.40816326530612246, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.40697674418604657, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5075376884422111, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5294117647058824, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4578313253012048, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4987405541561713, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4463768115942029, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4305949008498583, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4699453551912569, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45938375350140054, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.459016393442623, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3957219251336898, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4444444444444445, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4067796610169492, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4831168831168831, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5027322404371586, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5274151436031331, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5363408521303258, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4619289340101522, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5116279069767442, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4831168831168831, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3954802259887006, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.42729970326409494, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.46285714285714286, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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score=0.4011142061281337, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.44385026737967914, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.46195652173913043, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.38309859154929576, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.465, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4475138121546961, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.43684210526315786, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4987277353689567, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4547803617571059, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.44385026737967914, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4636118598382749, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4475138121546961, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.44327176781002636, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3902439024390244, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32000000000000006, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3219814241486068, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4058823529411764, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2686567164179105, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3890577507598784, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.268370607028754, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.40571428571428575, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3738872403560831, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.44568245125348194, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.46961325966850836, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4462809917355372, total= 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clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3630573248407643, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3365079365079365, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.30225080385852093, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2298136645962733, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.40718562874251496, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.39169139465875363, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3505747126436782, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.38181818181818183, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2784810126582279, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16666666666666666, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12014134275618374, total= 0.3s\n", + "[CV] 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tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.12686567164179105, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1897810218978102, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19285714285714284, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32026143790849676, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.28093645484949836, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3081967213114754, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.24422442244224424, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), 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clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3217665615141956, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.28378378378378377, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33974358974358976, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3470031545741325, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2183098591549296, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.27609427609427606, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32026143790849676, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.28093645484949836, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.28013029315960913, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2183098591549296, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3270440251572327, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.27796610169491526, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.24422442244224424, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22499999999999995, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2585034013605442, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.18855218855218858, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3270440251572327, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.27796610169491526, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32026143790849676, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3428571428571428, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.33333333333333337, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3470031545741325, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32075471698113206, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.29673590504451036, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3205128205128205, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.22950819672131148, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4502617801047121, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4105571847507331, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4756756756756757, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4693333333333333, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3955431754874652, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.48081841432225064, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4666666666666667, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4731182795698924, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4883116883116883, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5276381909547739, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4948453608247422, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4987277353689567, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4292929292929292, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5416666666666666, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.443864229765013, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4502617801047121, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4105571847507331, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4330484330484331, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.48618784530386744, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3955431754874652, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.48081841432225064, total= 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.44041450777202074, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.42577030812324923, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.430939226519337, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.40223463687150834, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4256559766763849, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__use_idf=True, score=0.543640897755611, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.46268656716417905, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5282051282051283, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4256559766763849, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.46197183098591554, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4301675977653631, total= 0.3s\n", + 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tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.45945945945945943, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.38933333333333325, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4756756756756757, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.46831955922865015, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45945945945945943, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, 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tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4301675977653631, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.4756756756756757, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.46831955922865015, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.45945945945945943, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.38933333333333325, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5091863517060368, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5319693094629155, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5167958656330749, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5422885572139304, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.46, total= 0.1s\n", + "[CV] 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tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.44327176781002636, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4514285714285715, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4364640883977901, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4207650273224044, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.47395833333333326, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4829396325459318, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.48311688311688306, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4010282776349615, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4619565217391304, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3967391304347826, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4676616915422886, total= 0.0s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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"[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5144356955380577, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.43367346938775514, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4772117962466489, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4366576819407008, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.43548387096774194, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.42477876106194695, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.43059490084985835, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.41225626740947074, total= 0.5s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45576407506702415, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.47466666666666674, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45454545454545453, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3802083333333333, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.45251396648044695, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.38095238095238093, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.44783715012722647, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4577656675749318, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.44799999999999995, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.44215938303341895, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4681933842239186, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.48969072164948446, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.5050505050505051, total= 0.7s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.44717444717444715, total= 0.7s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4910485933503837, total= 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.42477876106194695, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.43059490084985835, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.41225626740947074, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.45576407506702415, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.47466666666666674, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.45454545454545453, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3802083333333333, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.45251396648044695, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.38095238095238093, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.44783715012722647, total= 0.1s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4577656675749318, total= 0.2s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.44799999999999995, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.44215938303341895, total= 0.5s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4681933842239186, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.48969072164948446, total= 0.4s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.5050505050505051, total= 0.3s\n", + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=0.5, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.07434944237918216, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03137254901960784, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.024, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.08270676691729323, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.047244094488188976, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03875968992248061, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030303030303030307, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0149812734082397, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2054794520547945, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1259259259259259, total= 0.1s\n", + 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.03773584905660378, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.021052631578947368, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.061538461538461535, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0149812734082397, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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}, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.22297297297297297, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.09774436090225565, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.10738255033557045, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.17142857142857143, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3076923076923077, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.26, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.10486891385767791, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.13432835820895522, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.20069204152249134, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22456140350877193, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.23208191126279865, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.14893617021276595, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.10702341137123746, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.17142857142857143, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.10638297872340426, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2622950819672131, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.20422535211267603, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.20640569395017794, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24242424242424246, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3178807947019867, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2533333333333333, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.19863013698630136, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.18589743589743588, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.23972602739726026, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15807560137457044, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.07434944237918216, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03137254901960784, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.024, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.08270676691729323, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.047244094488188976, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03875968992248061, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030303030303030307, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.021052631578947368, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.05405405405405406, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0149812734082397, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1993127147766323, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.12639405204460966, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.12781954887218044, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15714285714285714, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True 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tfidf__use_idf=True, score=0.1423487544483986, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08783783783783784, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1386861313868613, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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score=0.17857142857142855, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.13571428571428573, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10067114093959731, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.13186813186813187, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.058394160583941604, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.22297297297297297, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.09774436090225565, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.13432835820895522, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.18815331010452963, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.20567375886524822, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1978798586572438, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.20640569395017794, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2364864864864865, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3178807947019867, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0311284046692607, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.023904382470119518, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.18505338078291814, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1619718309859155, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08813559322033898, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.17204301075268816, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11347517730496455, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09230769230769231, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.13919413919413917, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.12075471698113208, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1391941391941392, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.07407407407407407, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.015267175572519085, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.007067137809187279, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.02352941176470588, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0, total= 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.09056603773584905, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.12075471698113208, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.08333333333333333, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.07407407407407407, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.021052631578947368, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.09811320754716982, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.05147058823529412, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12903225806451615, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.05426356589147287, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.09230769230769231, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12546125461254612, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12075471698113208, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1391941391941392, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.059701492537313446, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.021052631578947368, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0687022900763359, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.058394160583941604, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.19377162629757788, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11278195488721805, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.16911764705882354, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15884476534296027, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2064056939501779, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.18505338078291814, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.14893617021276595, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10101010101010102, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.17204301075268816, total= 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05426356589147287, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09230769230769231, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.13919413919413917, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.12075471698113208, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1391941391941392, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.07407407407407407, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.027972027972027972, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15217391304347827, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.19999999999999998, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", 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tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0075187969924812035, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10256410256410257, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08396946564885498, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11450381679389313, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10486891385767791, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11363636363636363, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.0981132075471698, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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score=0.04633204633204632, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.031746031746031744, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0234375, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.10218978102189782, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.05426356589147287, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1044776119402985, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.08494208494208494, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.014084507042253521, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.06896551724137932, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05426356589147287, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1044776119402985, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09961685823754789, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08333333333333333, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.04511278195488722, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.021052631578947368, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06896551724137932, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05860805860805861, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16254416961130744, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.08396946564885498, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1897810218978102, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16666666666666669, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16058394160583941, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15328467153284672, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.13571428571428573, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10101010101010102, total= 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030418250950570346, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.007936507936507936, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03968253968253968, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03875968992248062, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.031746031746031744, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0234375, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.007662835249042146, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.007067137809187279, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03137254901960784, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0075187969924812035, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.09558823529411765, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.06177606177606178, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__use_idf=False, score=0.03137254901960784, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0075187969924812035, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.09558823529411765, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.06923076923076923, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10727969348659003, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10486891385767791, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1064638783269962, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.08365019011406843, total= 0.4s\n", + "[CV] 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15602836879432622, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08396946564885498, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True 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tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.04511278195488722, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.021052631578947368, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.13970588235294118, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15999999999999998, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.14652014652014653, total= 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35435435435435436, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2763157894736842, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32380952380952377, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3486238532110092, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4476744186046512, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.36697247706422015, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3669724770642202, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3023255813953488, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.37267080745341613, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.29559748427672955, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2857142857142857, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.27540983606557373, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19314641744548286, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.29605263157894735, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22295081967213115, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.35435435435435436, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.38271604938271603, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.29999999999999993, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.34808259587020646, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.29508196721311475, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3248407643312102, total= 0.0s\n", + "[CV] clf__alpha=1, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.38805970149253727, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30952380952380953, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34808259587020646, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.411764705882353, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3965014577259475, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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}, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.25418060200668896, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2697368421052631, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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score=0.44117647058823534, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3619631901840491, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3619631901840491, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2591362126245847, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32587859424920124, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3435582822085889, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4516129032258065, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.37082066869300917, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.35582822085889576, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3023255813953488, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.29508196721311475, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3248407643312102, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3261538461538462, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.4176470588235294, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3834808259587021, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.33434650455927056, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2772861356932153, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.36307692307692313, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2670807453416149, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.38418079096045193, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3302180685358255, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.417391304347826, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.38323353293413176, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35097493036211697, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3928571428571428, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30538922155688625, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34808259587020646, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.29508196721311475, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.32380952380952377, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3312883435582822, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 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clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.28654970760233917, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3692307692307693, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2625, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3830985915492957, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 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"[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3893805309734513, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3417366946778712, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2380952380952381, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2953020134228188, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.20279720279720276, total= 0.2s\n", 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1978798586572438, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16494845360824742, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, 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clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3116883116883117, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.38271604938271603, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.29508196721311475, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34161490683229817, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22142857142857142, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.24324324324324326, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3122923588039867, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.20905923344947733, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2222222222222222, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1386138613861386, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19649122807017544, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1643835616438356, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3072100313479624, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24657534246575347, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.33333333333333337, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.30618892508143325, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.38271604938271603, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.29605263157894735, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.24573378839590448, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.29605263157894735, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2974683544303797, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.38532110091743116, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3217665615141955, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2903225806451613, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2611464968152866, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.37426900584795325, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3365079365079365, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3425076452599389, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34146341463414637, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.42651296829971186, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4023668639053254, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3663663663663663, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.29651162790697677, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35474006116207946, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32024169184290036, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.29605263157894735, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.30283911671924285, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3806646525679758, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.33228840125391845, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.32075471698113206, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.24316109422492405, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2903225806451613, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2611464968152866, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3790087463556851, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3417721518987342, total= 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tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4198250728862974, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4035608308605342, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.36904761904761907, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.29651162790697677, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.36969696969696975, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32024169184290036, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.22742474916387959, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.19795221843003413, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1386138613861386, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + 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clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1386138613861386, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19649122807017544, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1643835616438356, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3072100313479624, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24054982817869416, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.38271604938271603, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.30163934426229505, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34674922600619196, total= 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.30153846153846153, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2389078498293515, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.29605263157894735, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.30283911671924285, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3806646525679758, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.20143884892086333, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1527272727272727, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, 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score=0.1465201465201465, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.11347517730496455, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.27831715210355984, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1726618705035971, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30201342281879195, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2893890675241157, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.22291021671826622, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1773049645390071, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.21428571428571425, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2905405405405405, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.26755852842809363, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34504792332268375, total= 0.3s\n", + "[CV] 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tfidf__use_idf=True, score=0.22442244224422442, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.28750000000000003, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2105263157894737, total= 0.1s\n", + "[CV] 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.34810126582278483, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3086816720257235, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2532467532467532, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3483483483483484, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30718954248366015, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3301587301587301, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33125, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.393939393939394, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34161490683229817, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3197492163009404, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.29824561403508776, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3518518518518518, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32098765432098764, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.28660436137071654, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2105263157894737, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.28476821192052987, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2582781456953642, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34591194968553457, total= 1.0s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3184713375796178, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2718446601941748, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__use_idf=True, score=0.3890577507598784, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34161490683229817, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.325, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2932551319648094, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34674922600619196, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 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score=0.26174496644295303, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.335483870967742, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.25503355704697983, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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{ + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1773049645390071, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.21428571428571425, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.27831715210355984, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16606498194945848, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2905405405405405, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.26174496644295303, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.335483870967742, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24915824915824916, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2727272727272727, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.28750000000000003, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2105263157894737, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.27999999999999997, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2582781456953642, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.34810126582278483, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3086816720257235, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2549019607843137, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.20625, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2679738562091503, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2532467532467532, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3483483483483484, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3016393442622951, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33121019108280253, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33125, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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"[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2718446601941748, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19375, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2679738562091503, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.25242718446601936, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, 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score=0.2932551319648094, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3416149068322981, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=False, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32098765432098764, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.08148148148148149, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.023622047244094488, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.024, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0898876404494382, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0622568093385214, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.06130268199233716, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.04511278195488722, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.014925373134328358, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.18055555555555552, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.13284132841328414, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.19999999999999998, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.19148936170212766, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0622568093385214, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06130268199233716, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.04511278195488722, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.021052631578947368, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1702127659574468, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.19999999999999998, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.19148936170212766, total= 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.23129251700680273, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.15547703180212016, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.11333333333333333, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16487455197132617, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.11971830985915494, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2622950819672131, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1978798586572438, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2508474576271187, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15172413793103448, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.17421602787456447, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1259259259259259, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.12781954887218044, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15714285714285714, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1935483870967742, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.024, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0898876404494382, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, 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tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.12781954887218044, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15714285714285714, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + 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tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.23129251700680273, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.15547703180212016, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11333333333333333, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.16487455197132617, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11971830985915494, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2679738562091503, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1978798586572438, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24832214765100666, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.31125827814569534, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.25913621262458475, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.21088435374149664, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.18589743589743588, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.23972602739726026, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1453287197231834, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030418250950570346, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, 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tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.19377162629757788, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11278195488721805, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.12014134275618374, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.14184397163120568, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06923076923076923, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09923664122137404, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1386861313868613, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08118081180811808, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0348432055749129, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08333333333333334, total= 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tfidf__use_idf=False, score=0.1323529411764706, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08118081180811808, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0348432055749129, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08333333333333334, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.07246376811594202, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.18750000000000003, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.11278195488721805, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.19217081850533807, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15547703180212016, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10738255033557045, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16546762589928057, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.12014134275618374, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030418250950570346, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.015810276679841896, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.04743083003952569, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.05384615384615384, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03952569169960474, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0234375, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.015267175572519085, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.007067137809187279, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03137254901960784, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0075187969924812035, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10256410256410257, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.07662835249042146, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11406844106463877, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10486891385767791, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11594202898550725, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.05517241379310345, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11940298507462688, total= 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.015810276679841896, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.04743083003952569, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05384615384615384, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.03952569169960474, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0234375, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.015267175572519085, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.007067137809187279, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.03137254901960784, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0075187969924812035, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10256410256410257, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.07662835249042146, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.11406844106463877, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10486891385767791, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.12781954887218044, total= 0.8s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.08365019011406843, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.11594202898550725, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.05517241379310345, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.11940298507462688, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1003584229390681, total= 0.7s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] 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score=0.1423487544483986, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.10101010101010102, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15328467153284672, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05426356589147287, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09737827715355805, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.09923664122137406, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08301886792452831, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False 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tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.07633587786259542, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06569343065693431, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1494661921708185, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.09125475285171103, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.18909090909090906, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.17328519855595667, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16666666666666666, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16, total= 0.8s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1423487544483986, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10101010101010102, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.030418250950570346, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.015810276679841896, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.04743083003952569, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.05384615384615384, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.03952569169960474, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.09558823529411765, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.06923076923076923, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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score=0.12781954887218044, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08365019011406843, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.1090909090909091, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, 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tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.08664259927797834, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.030418250950570346, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.015810276679841896, total= 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.03952569169960474, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0234375, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, 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tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.06923076923076923, total= 0.9s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10727969348659003, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.10486891385767791, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.12781954887218044, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.08365019011406843, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1090909090909091, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.04166666666666667, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.09737827715355805, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.09923664122137406, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.08301886792452831, total= 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.07633587786259542, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.06569343065693431, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15602836879432622, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.09125475285171103, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.18909090909090906, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.13970588235294118, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15999999999999998, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.15328467153284672, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.14285714285714288, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.0945945945945946, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.16, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.11387900355871887, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11594202898550723, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.05426356589147287, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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score=0.09923664122137406, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.08301886792452831, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06691449814126393, total= 0.7s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.06569343065693431, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.15602836879432622, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.09125475285171103, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.18909090909090906, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.13970588235294118, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + 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tfidf__use_idf=True, score=0.14285714285714288, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.0945945945945946, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=1, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.26973684210526316, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3215434083601286, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] 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score=0.3746223564954682, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35365853658536583, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.31304347826086953, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2902208201892745, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.21602787456445993, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, 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tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.41520467836257313, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.39181286549707606, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), 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tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3384615384615384, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33230769230769225, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.40117994100294985, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3254437869822485, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34808259587020646, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3161290322580645, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3301587301587301, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3393939393939394, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.41520467836257313, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.33230769230769225, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.37209302325581395, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4481792717086835, total= 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tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.26402640264026406, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3225806451612903, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3486238532110092, total= 0.0s\n", + "[CV] 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tfidf__use_idf=True, score=0.38390092879256965, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3052959501557632, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2902208201892745, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, 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score=0.2987012987012987, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.26710097719869713, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.20923076923076925, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.30718954248366015, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.24104234527687296, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.35329341317365265, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.26402640264026406, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3225806451612903, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3486238532110092, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.44510385756676557, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.36474164133738596, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.35365853658536583, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.30813953488372087, total= 0.0s\n", 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.34808259587020646, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3161290322580645, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3301587301587301, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3393939393939394, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.41520467836257313, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.39181286549707606, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.33434650455927056, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.27988338192419826, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3692307692307693, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2670807453416149, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3898305084745763, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3384615384615384, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3261538461538461, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3673469387755102, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4481792717086835, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.4252873563218391, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.40462427745664736, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33239436619718304, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.40828402366863903, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.31547619047619047, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.34808259587020646, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3161290322580645, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3301587301587301, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3393939393939394, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.41520467836257313, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.39181286549707606, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", 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"[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3673469387755102, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.4481792717086835, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 1), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, 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tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1381578947368421, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.19649122807017544, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.1830508474576271, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.23859649122807014, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.25418060200668896, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3234323432343234, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2206896551724138, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2458471760797342, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1381578947368421, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19649122807017544, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1830508474576271, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3028391167192429, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3248407643312102, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.26282051282051283, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3058103975535168, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3115264797507788, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.37462235649546827, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.33228840125391845, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.33950617283950624, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.25825825825825827, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3826086956521739, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.33757961783439494, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, 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tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.33757961783439494, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3558282208588957, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34441087613293053, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.41279069767441867, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3869047619047619, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=False, tfidf__stop_words=english, 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tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.12639405204460966, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.23859649122807014, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.25418060200668896, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3234323432343234, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3028391167192429, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.24054982817869416, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3112582781456954, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30618892508143325, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3777089783281734, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.30163934426229505, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34355828220858897, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.25, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3141025641025641, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.25242718446601936, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.23920265780730895, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.12639405204460966, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.23859649122807014, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.25418060200668896, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3234323432343234, total= 0.0s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2206896551724138, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2458471760797342, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1381578947368421, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19649122807017544, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1830508474576271, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3028391167192429, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24054982817869416, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3777089783281734, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.30163934426229505, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34355828220858897, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.25, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3141025641025641, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3558282208588957, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34441087613293053, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.41520467836257313, total= 0.2s\n", + 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tfidf__ngram_range=(1, 2), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32432432432432434, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.19178082191780824, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0984848484848485, total= 0.5s\n", + 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tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.21908127208480566, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16487455197132614, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.15972222222222224, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.11371237458193979, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.15942028985507245, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.11971830985915494, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.27184466019417475, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.16606498194945848, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2733333333333334, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32797427652733124, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.24915824915824916, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2718446601941748, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2276923076923077, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.25333333333333335, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.23606557377049178, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.19178082191780824, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.0984848484848485, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.20289855072463767, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.1958041958041958, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.21908127208480566, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.16487455197132614, total= 0.6s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.15972222222222224, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11371237458193979, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.15942028985507245, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11971830985915494, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.27184466019417475, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.16606498194945848, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2733333333333334, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32797427652733124, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24915824915824916, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2718446601941748, total= 0.5s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2276923076923077, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.25333333333333335, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.23606557377049178, total= 0.4s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2962962962962963, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2105263157894737, total= 0.3s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2572347266881029, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.35119047619047616, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3171521035598705, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34700315457413244, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3507692307692308, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3180428134556575, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2962962962962963, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.345679012345679, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32298136645962733, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=False, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, 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tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.19178082191780824, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.0984848484848485, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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score=0.21908127208480566, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.16487455197132614, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.15972222222222224, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2733333333333334, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32903225806451614, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.24915824915824916, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2662337662337662, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2222222222222222, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.24749163879598665, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, 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tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.15942028985507245, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.11971830985915494, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2662337662337662, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.1527272727272727, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), 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tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2662337662337662, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2222222222222222, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.24749163879598665, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=None, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2185430463576159, total= 0.2s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2962962962962963, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2105263157894737, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3016393442622951, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.26229508196721313, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3448275862068966, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.3238095238095238, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2829581993569132, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.22085889570552147, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2671009771986971, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=False, score=0.2572347266881029, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, 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tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.34700315457413244, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3507692307692308, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3878787878787879, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3395061728395062, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.32398753894080995, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.2882352941176471, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.345679012345679, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=False, tfidf__use_idf=True, score=0.3128834355828221, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2962962962962963, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2105263157894737, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3016393442622951, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.26229508196721313, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3448275862068966, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.3238095238095238, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2829581993569132, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.22085889570552147, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2671009771986971, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=False, score=0.2572347266881029, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3522388059701492, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.30065359477124187, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.34700315457413244, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3507692307692308, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3878787878787879, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3395061728395062, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.32398753894080995, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.2882352941176471, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.345679012345679, total= 0.1s\n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True \n", + "[CV] clf__alpha=1, tfidf__binary=True, tfidf__min_df=2, tfidf__ngram_range=(1, 3), tfidf__smooth_idf=True, tfidf__stop_words=english, tfidf__sublinear_tf=True, tfidf__use_idf=True, score=0.3128834355828221, total= 0.1s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Done 11520 out of 11520 | elapsed: 50.8min finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pipeline(memory=None,\n", + " steps=[('tfidf', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n", + " dtype=, encoding='utf-8', input='content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=False,\n", + "...se,\n", + " vocabulary=None)), ('clf', MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True))])\n" + ] + } + ], + "source": [ + "Y = tweak_labels(Y_orig, [\"positive\", \"negative\"])\n", + "best = grid_search_model(create_ngram_model, X, Y)\n", + "print(best)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# we write out the parameters explicitly since we will be adding more later\n", + "best_params = dict(tfidf__ngram_range=(1, 2),\n", + " tfidf__min_df=1,\n", + " tfidf__stop_words=None,\n", + " tfidf__smooth_idf=False,\n", + " tfidf__use_idf=False,\n", + " tfidf__sublinear_tf=True,\n", + " tfidf__binary=False,\n", + " clf__alpha=0.01,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. neg ==\n", + "Mean acc=0.798\tMean P/R AUC=0.885\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0010238907849829503, 0.20158730158730162)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. neg ==\")\n", + "pos_neg = np.logical_or(Y_orig == \"positive\", Y_orig == \"negative\")\n", + "X = X_orig[pos_neg]\n", + "Y = Y_orig[pos_neg]\n", + "Y = tweak_labels(Y, [\"positive\"])\n", + "train_model(lambda: create_ngram_model(best_params), X, Y, name=\"pos vs neg\", plot=\"5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos/neg vs. irrelevant/neutral (BEST) ==\n", + "Mean acc=0.790\tMean P/R AUC=0.683\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0028345724907063441, 0.21007583965330445)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos/neg vs. irrelevant/neutral ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\", \"negative\"])\n", + "train_model(lambda: create_ngram_model(best_params), X, Y, name=\"sent vs rest\", plot=\"6\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. rest (BEST) ==\n", + "Mean acc=0.885\tMean P/R AUC=0.491\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0040427509293680438, 0.11495124593716141)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\"])\n", + "\n", + "train_model(lambda: create_ngram_model(best_params), X, Y, name=\"pos vs rest\", plot=\"7\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Neg vs. rest (BEST) ==\n", + "Mean acc=0.882\tMean P/R AUC=0.621\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0013011152416356642, 0.11830985915492957)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Neg vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"negative\"])\n", + "\n", + "train_model(lambda: create_ngram_model(best_params), X, Y, name=\"neg vs rest\", plot=\"8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cleaning tweets" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "emo_repl = {\n", + " # positive emoticons\n", + " \"<3\": \" good \",\n", + " \":d\": \" good \", # :D in lower case\n", + " \":dd\": \" good \", # :DD in lower case\n", + " \"8)\": \" good \",\n", + " \":-)\": \" good \",\n", + " \":)\": \" good \",\n", + " \";)\": \" good \",\n", + " \"(-:\": \" good \",\n", + " \"(:\": \" good \",\n", + "\n", + " # negative emoticons:\n", + " \":/\": \" bad \",\n", + " \":>\": \" sad \",\n", + " \":')\": \" sad \",\n", + " \":-(\": \" bad \",\n", + " \":(\": \" bad \",\n", + " \":S\": \" bad \",\n", + " \":-S\": \" bad \",\n", + " }\n", + "\n", + "# make sure that e.g. :dd is replaced before :d\n", + "emo_repl_order = [k for (k_len,k) in reversed(sorted([(len(k),k) for k in emo_repl.keys()]))]\n", + "\n", + "re_repl = {\n", + "r\"\\br\\b\": \"are\",\n", + "r\"\\bu\\b\": \"you\",\n", + "r\"\\bhaha\\b\": \"ha\",\n", + "r\"\\bhahaha\\b\": \"ha\",\n", + "r\"\\bdon't\\b\": \"do not\",\n", + "r\"\\bdoesn't\\b\": \"does not\",\n", + "r\"\\bdidn't\\b\": \"did not\",\n", + "r\"\\bhasn't\\b\": \"has not\",\n", + "r\"\\bhaven't\\b\": \"have not\",\n", + "r\"\\bhadn't\\b\": \"had not\",\n", + "r\"\\bwon't\\b\": \"will not\",\n", + "r\"\\bwouldn't\\b\": \"would not\",\n", + "r\"\\bcan't\\b\": \"can not\",\n", + "r\"\\bcannot\\b\": \"can not\",\n", + " }\n", + "\n", + "def create_ngram_model_emoji(params=None):\n", + " \n", + " def preprocessor(tweet):\n", + " tweet = tweet.lower()\n", + " for k in emo_repl_order:\n", + " tweet = tweet.replace(k, emo_repl[k])\n", + " for r, repl in re_repl.items():\n", + " tweet = re.sub(r, repl, tweet)\n", + "\n", + " return tweet\n", + "\n", + " tfidf_ngrams = TfidfVectorizer(preprocessor=preprocessor, analyzer=\"word\")\n", + " \n", + " clf = MultinomialNB()\n", + " pipeline = Pipeline([('tfidf', tfidf_ngrams), ('clf', clf)])\n", + "\n", + " if params:\n", + " pipeline.set_params(**params)\n", + " \n", + " return pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. neg ==\n", + "Mean acc=0.808\tMean P/R AUC=0.889\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0010238907849829503, 0.1924603174603175)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. neg ==\")\n", + "pos_neg = np.logical_or(Y_orig == \"positive\", Y_orig == \"negative\")\n", + "X = X_orig[pos_neg]\n", + "Y = Y_orig[pos_neg]\n", + "Y = tweak_labels(Y, [\"positive\"])\n", + "\n", + "train_model(lambda: create_ngram_model_emoji(best_params), X, Y, name=\"pos vs neg\", plot=\"9\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos/neg vs. irrelevant/neutral ==\n", + "Mean acc=0.791\tMean P/R AUC=0.689\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0026951672862453812, 0.20888407367280606)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos/neg vs. irrelevant/neutral ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\", \"negative\"])\n", + "train_model(lambda: create_ngram_model_emoji(best_params), X, Y, name=\"sent vs rest\", plot=\"10\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. rest ==\n", + "Mean acc=0.886\tMean P/R AUC=0.498\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.00390334572490707, 0.11365113759479957)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\"])\n", + "\n", + "train_model(lambda: create_ngram_model_emoji(best_params), X, Y, name=\"pos vs rest\", plot=\"11\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Neg vs. rest ==\n", + "Mean acc=0.883\tMean P/R AUC=0.632\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0013475836431226518, 0.11744312026002168)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Neg vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"negative\"])\n", + "\n", + "train_model(lambda: create_ngram_model_emoji(best_params), X, Y, name=\"neg vs rest\", plot=\"12\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using SentiWordNet\n", + "For the following, we need to download the SentiWordNet file from http://sentiwordnet.isti.cnr.it, unzip it, and store it as \"SentiWordNet_3.0.0_20130122.txt\" in the data directory." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] C:\\Users\\wilrich\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n" + ] + }, + { + "data": { + "text/plain": [ + "[('This', 'DT'),\n", + " ('is', 'VBZ'),\n", + " ('a', 'DT'),\n", + " ('good', 'JJ'),\n", + " ('book', 'NN'),\n", + " ('.', '.')]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "nltk.download('averaged_perceptron_tagger')\n", + "nltk.pos_tag(nltk.word_tokenize(\"This is a good book.\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Could', 'NNP'),\n", + " ('you', 'PRP'),\n", + " ('please', 'VBP'),\n", + " ('book', 'NN'),\n", + " ('the', 'DT'),\n", + " ('flight', 'NN'),\n", + " ('?', '.')]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nltk.pos_tag(nltk.word_tokenize(\"Could you please book the flight?\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import csv, collections\n", + "\n", + "def load_sent_word_net():\n", + " # making our life easier by using a dictionary that\n", + " # automatically creates an empty list whenever we access\n", + " # a not yet existing key\n", + " sent_scores = collections.defaultdict(list)\n", + " \n", + " with open(os.path.join(DATA_DIR, \"SentiWordNet_3.0.0_20130122.txt\"), \"r\") as csvfile:\n", + " reader = csv.reader(csvfile, delimiter='\\t', quotechar='\"')\n", + " for line in reader:\n", + " if line[0].startswith(\"#\"):\n", + " continue\n", + " if len(line)==1:\n", + " continue\n", + "\n", + " POS, ID, PosScore, NegScore, SynsetTerms, Gloss = line\n", + " if len(POS)==0 or len(ID)==0:\n", + " continue\n", + " for term in SynsetTerms.split(\" \"):\n", + " # drop number at the end of every term\n", + " term = term.split(\"#\")[0] \n", + " term = term.replace(\"-\", \" \").replace(\"_\", \" \")\n", + " key = \"%s/%s\"%(POS, term.split(\"#\")[0])\n", + " sent_scores[key].append((float(PosScore), float(NegScore)))\n", + "\n", + " for key, value in sent_scores.items():\n", + " sent_scores[key] = np.mean(value, axis=0)\n", + "\n", + " return sent_scores\n", + "\n", + "sent_word_net = load_sent_word_net()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator\n", + "\n", + "class LinguisticVectorizer(BaseEstimator):\n", + " def get_feature_names(self):\n", + " return np.array(['sent_neut', 'sent_pos', 'sent_neg',\n", + " 'nouns', 'adjectives', 'verbs', 'adverbs',\n", + " 'allcaps', 'exclamation', 'question', 'hashtag', \n", + " 'mentioning'])\n", + "\n", + " # we don't fit here but need to return the reference\n", + " # so that it can be used like fit(d).transform(d)\n", + " def fit(self, documents, y=None):\n", + " return self\n", + "\n", + " def _get_sentiments(self, d):\n", + " sent = tuple(d.split())\n", + " tagged = nltk.pos_tag(sent)\n", + "\n", + " pos_vals = []\n", + " neg_vals = []\n", + "\n", + " nouns = 0.\n", + " adjectives = 0.\n", + " verbs = 0.\n", + " adverbs = 0.\n", + "\n", + " for w,t in tagged:\n", + " p, n = 0,0\n", + " sent_pos_type = None\n", + " if t.startswith(\"NN\"):\n", + " sent_pos_type = \"n\"\n", + " nouns += 1\n", + " elif t.startswith(\"JJ\"):\n", + " sent_pos_type = \"a\"\n", + " adjectives += 1\n", + " elif t.startswith(\"VB\"):\n", + " sent_pos_type = \"v\"\n", + " verbs += 1\n", + " elif t.startswith(\"RB\"):\n", + " sent_pos_type = \"r\"\n", + " adverbs += 1\n", + "\n", + " if sent_pos_type is not None:\n", + " sent_word = \"%s/%s\" % (sent_pos_type, w)\n", + "\n", + " if sent_word in sent_word_net:\n", + " p,n = sent_word_net[sent_word]\n", + "\n", + " pos_vals.append(p)\n", + " neg_vals.append(n)\n", + "\n", + " l = len(sent)\n", + " avg_pos_val = np.mean(pos_vals)\n", + " avg_neg_val = np.mean(neg_vals)\n", + " \n", + " result = [1-avg_pos_val-avg_neg_val, avg_pos_val, avg_neg_val, \n", + " nouns/l, adjectives/l, verbs/l, adverbs/l]\n", + " \n", + " return result\n", + "\n", + "\n", + " def transform(self, documents):\n", + " obj_val, pos_val, neg_val, nouns, adjectives, \\\n", + "verbs, adverbs = np.array([self._get_sentiments(d) \\\n", + "for d in documents]).T\n", + "\n", + " allcaps = []\n", + " exclamation = []\n", + " question = []\n", + " hashtag = []\n", + " mentioning = []\n", + "\n", + " for d in documents:\n", + " allcaps.append(np.sum([t.isupper() \\\n", + " for t in d.split() if len(t)>2]))\n", + "\n", + " exclamation.append(d.count(\"!\"))\n", + " question.append(d.count(\"?\"))\n", + " hashtag.append(d.count(\"#\"))\n", + " mentioning.append(d.count(\"@\"))\n", + "\n", + " result = np.array([obj_val, pos_val, neg_val, nouns, adjectives, verbs, adverbs, allcaps, exclamation, question, \n", + "hashtag, mentioning]).T\n", + "\n", + " return result\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import FeatureUnion\n", + "\n", + "def create_union_model(params=None):\n", + " def preprocessor(tweet):\n", + " tweet = tweet.lower()\n", + "\n", + " for k in emo_repl_order:\n", + " tweet = tweet.replace(k, emo_repl[k])\n", + " for r, repl in re_repl.items():\n", + " tweet = re.sub(r, repl, tweet)\n", + "\n", + " return tweet.replace(\"-\", \" \").replace(\"_\", \" \")\n", + "\n", + " tfidf_ngrams = TfidfVectorizer(preprocessor=preprocessor, analyzer=\"word\")\n", + " ling_stats = LinguisticVectorizer()\n", + " all_features = FeatureUnion([('ling', ling_stats), \n", + " ('tfidf', tfidf_ngrams)])\n", + " clf = MultinomialNB()\n", + " pipeline = Pipeline([('all', all_features), ('clf', clf)])\n", + "\n", + " if params:\n", + " pipeline.set_params(**params)\n", + "\n", + " return pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "best_params = dict(all__tfidf__ngram_range=(1, 2),\n", + " all__tfidf__min_df=1,\n", + " all__tfidf__stop_words=None,\n", + " all__tfidf__smooth_idf=False,\n", + " all__tfidf__use_idf=False,\n", + " all__tfidf__sublinear_tf=True,\n", + " all__tfidf__binary=False,\n", + " clf__alpha=0.01,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. neg ==\n", + "Mean acc=0.793\tMean P/R AUC=0.882\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0010238907849829503, 0.20714285714285716)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. neg ==\")\n", + "pos_neg = np.logical_or(Y_orig == \"positive\", Y_orig == \"negative\")\n", + "X = X_orig[pos_neg]\n", + "Y = Y_orig[pos_neg]\n", + "Y = tweak_labels(Y, [\"positive\"])\n", + "\n", + "train_model(lambda: create_union_model(best_params), X, Y, name=\"pos vs neg\", plot=\"13\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos/neg vs. irrelevant/neutral ==\n", + "Mean acc=0.787\tMean P/R AUC=0.688\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0030669144981412709, 0.2134344528710726)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos/neg vs. irrelevant/neutral ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\", \"negative\"])\n", + "train_model(lambda: create_union_model(best_params), X, Y, name=\"sent vs rest\", plot=\"14\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Pos vs. rest ==\n", + "Mean acc=0.886\tMean P/R AUC=0.508\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0032527881040892324, 0.11365113759479957)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Pos vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"positive\"])\n", + "\n", + "train_model(lambda: create_union_model(best_params), X, Y, name=\"pos vs rest\", plot=\"15\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Neg vs. rest ==\n", + "Mean acc=0.876\tMean P/R AUC=0.617\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0032992565055762202, 0.12394366197183097)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"== Neg vs. rest ==\")\n", + "X = X_orig\n", + "Y = tweak_labels(Y_orig, [\"negative\"])\n", + "\n", + "train_model(lambda: create_union_model(best_params), X, Y, name=\"neg vs rest\", plot=\"16\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch09_3rd/data/corpus.csv b/ch09_3rd/data/corpus.csv new file mode 100644 index 00000000..3c2b6926 --- /dev/null +++ b/ch09_3rd/data/corpus.csv @@ -0,0 +1,5513 @@ +"apple","positive","126415614616154112" 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MIT License + +import sys + +CONSUMER_KEY = None +CONSUMER_SECRET = None + +ACCESS_TOKEN_KEY = None +ACCESS_TOKEN_SECRET = None + +if CONSUMER_KEY is None or CONSUMER_SECRET is None or ACCESS_TOKEN_KEY is None or ACCESS_TOKEN_SECRET is None: + print("""\ +When doing last code sanity checks for the book, Twitter +was using the API 1.0, which did not require authentication. +With its switch to version 1.1, this has now changed. + +It seems that you don't have already created your personal Twitter +access keys and tokens. Please do so at https://dev.twitter.com +and paste the keys/secrets into twitterauth.py. + +Sorry for the inconvenience, +The authors.""") + + sys.exit(1) diff --git a/ch10/Computer Vision.ipynb b/ch10/Computer Vision.ipynb new file mode 100644 index 00000000..4d494668 --- /dev/null +++ b/ch10/Computer Vision.ipynb @@ -0,0 +1,822 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computer Vision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We import `numpy` (as before) and `mahotas` (for image processsing/computer vision):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mahotas as mh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make plots inline:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic image processing\n", + "First example:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AwsKk1E9rnqTrdnHdkHYZRIjpRctCspPZsguUuE0R8pMju1jjSMMNUT5tN7oY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D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\npLaQLMHdLcAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image = mh.imread('scene00.jpg')\n", + "from matplotlib import pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bswWDbiiVCKNkq0KMJTk4TnZAhdGQviZVDAAyOVMUo4gAFMuk6QE4UgQM5Hin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\nVwJ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T+salVn1xyQicQ51xx+aJtY3KNuFpSb7Jy/tFD6hIOvlPS6A7lQCZWtEMrCeFXXcc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\n11tSQiuNbQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "image = mh.colors.rgb2grey(image, dtype=np.uint8)\n", + "ax.imshow(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Otsu threshold is 138.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MVElqxECVpEYMVElqxECVpEYMVElqxECVpEYMVElqxECVpEYMVElqxECVpEYMVElqZNFA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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "thresh = mh.thresholding.otsu(image)\n", + "print('Otsu threshold is {}.'.format(thresh))\n", + "# Otsu threshold is 138.\n", + "plt.imshow(image > thresh)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Otsu threshold is 137.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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qF/pqu7rT/f+Z5Be72nbgZF+bk11tUUn2JzmU5NAbnB2yi5I0Gis55V/Kx/n/j05PAe+t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\nVZIaMVAlqREDVZIaMVAlqZH/Bz5AC0R6FIPdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im16 = mh.gaussian_filter(image,16)\n", + "thresh = mh.thresholding.otsu(im16.astype(np.uint8))\n", + "print('Otsu threshold is {}.'.format(thresh))\n", + "# Otsu threshold is 138.\n", + "plt.imshow(im16 > thresh)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2494ZTb5QJRIOOPnuPKKsIK1aSA0beVTEybeJ5MCXZQwFRB2ioTiRUJwgEOk6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ra6yFGc3c0G01PjmEMbe+SSMpKUa3V2d9r4FhixBLKuIOviY/hXPo/ruQxHNoEAhp6l2xoiBiKW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8j9Bw955cXP850/+QGrWUVVSKoS2q02s9kKVYfT43OyrMa2bQzLRFd0ijIjLzJAwbQdojCC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\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zn/yMfMnCcSdcvnmDX/zibynlCnzy+AFCqcDUTFkjwW3vs/zmy9SW5tBFCbVY4pOnT8lY\nNnufPKZWKpPPWfRbHYYnQ2JNYpxEXHz1RaZSzDiXsv7CVdpCj/Zpn8J6ndxiBoUZre0dFpaXaXf6\nxIisb26Rz+dJwohHD0+5cOk2gRqzcWmNWm0ZM5PFjyLKuSonzUMWl+Y5Ojog9D0cJ+Hb3/4u737w\nAflihcbcAgtLdZzIIyFhuTDHw19+xqDdZzYNEEWV4XRGqVZDz2hYlQJWzkLK6gRRgIDIIOkxX67i\n+gkJIpadYXFphSdPH6NmZB598ozcaoHrr22RTgOCsYsgQLWcwfUj/CABzm1pSZhg5USEgkB1pcrB\nzjFn2z38aUyapL8e74QwCIl/fXUT/frPO0kTkiQljs9NiOe/KQIpsiThByG6plHM5RDSFMPUSZIU\nXTcJ4oSZ44EgnO8dCAIpECcxRl7FCcdImkoYRQw7Q+IoodzIo9cjyvM5rt+8widv735+guB/+l/+\nh7e+85++xO7BJxiGzKB/hCYLVEqLhHGXvFUgDBKSJCKbKxGnMfXGBarlLbzglHzeott5ztnOI073\nu+QqDWy7RKfvMQimkGR4/OwxjhtAJILrULYtcqZNwcpQKuT5zd/4XXb2O5RzDZLxBDWrkMoh+A5K\nGCAUTMbDMSurl/Bcl8nRLoYbc/nem5weDijnLay6wdG0S2qmKJ7EC+U1/J0n/OaXvk7vtMPTx+/j\n+BGSXSMUHRZW18lkZOaXljE0DVGMEGULlzLf/NptTEPByC5ydHyIrqQ4kwBBzSGZBbrDAFO1uHN5\nnvu/eg8cj1p1gaHnMnMjEtchSGZIskJG0ZhOHUzDYP/oBF1VGY9GRGlC2m/x+htf4dnTE06OnqIb\nJpXaOlLQYzB0ef7kmE8/fEDntI+uZcjnahipxg/+9mdcv/dFTo7bvP29/4ff/53fxJ1M0KwM5XKV\nvGXQPTuhYMVIokGhXGXqJ4SxgKDoaKqKICqkgKJbhGHKJEqQjRyWXWbzxjqaqrJwcQ0jaxDGNoph\nYVcbVBbW6HQdbi4oHO/tkEz6LNZK6EmT1VuL2JkTjve6zOctjh/dZ+PaJqeHh/y7v/gLaotrINZo\ndV1iQeXJ2Yz+cch7P33Et76yyfMHf0/O8rjwwiW6/QOkNMvDXz1ndXMBs2Tw0pde4WnrlLNgxmR/\nl0MvYnV9GV+VuHlhmUKhgB/LZCoKt774ZXrTDrde+SJWIULPFdHbXTLaCifDDmFrn0ngMnQ9fvb2\ne1x6+Q6xrVIQA/7gN36Llj/k//6r/xcXhXEGZorPceuM5w93UG2dvXhEezhhbWmD+59uc/LkPlfv\nXkc6a2G9sEZ9fZGjky5jP0RSRcqpgrhYRMiY3H/3V6xcreOMx4SdLr1Jl0pBJm62KBdqFAsZhtM2\n0XhKPlcgiUKePv+Mt3/8Q778tTcJmZAt1XC9ANPMUi6UsQ0dXVMoVfNMZiNu3b7GlWvX8MMEWZfQ\nTRPTyrBxYY2zsxZPHj0hZ/1/3L1nsCX5ed7363T65HzOPfecm/PMnTw7aWd3NmEXu1hgAQIEQYEk\nSJom7aJdpPVBtlW0ZLhYRUuySYkUpaIIUhQh0AAEIhAgiLB5d3ZmdvLcmZtzOPfknLpPJ38YlD/a\nrLJV5UJ/6u6qru4v/X/67ed93l+AjbubzJw6xU4pT9exeeqlczS6PaLJKKnRQQ7zOewu7K3vIUkK\nnXabUNJFfvuAZCpFsVLi1q0bLK4uc5AtMj0xgy43SSQSDAwNEfXFOVjZQ3HbhGdCSG6ZVuNx0lmW\nJVS3i2DKRWYww4MfrNHY1dC6FljW4zEyWEjyY7aaJAnYjoUsyYiiiOp2IwggiiApEg4/CYVJIMgS\ntvl49LWudVFlicNsFsOw0PuP24ERRHTTfMxIAHAcREkmOhCib3Tp9joEgyF0o8vIVJKxqQSSV2By\neopqscbardxPjxD8iz/4vS9+6heeY3LkSax+j+HBNILpRyt1aVTzbG5uEI9H6JsiWD2ifh+YUG2W\nCAXGiUWPMzl1CdHjxhVS6XSaj0lEspdoeIS56UuMjY1RrZUpFytICBw9coR6o0Y4FmV/mpB82AAA\nIABJREFUa4O1lVu0Ky0UdxDCAap6n93NfYxAAF1wEVaHKGxUGB+fwFJ9CJpFXAhRWsmSCkQYTU1w\n69pdXrh8hd2bjzjjDxBPJTn2xJO0jC5L65t0HQVDCdORU2iSB08gSPFgiQG/TS2/gSqBLqToaV32\n82U+WNhmeXWPdj3PQMRPX0+ynz+k0c6juFrMz41iaCZnz58nPThET+tQrlcYjMcp1U083iAu2SHq\nC3BifIRircrszBQuoU1E8ZAIham2GqzubOH2WQQch4IVwvb52cl16SAyOjRGLnuIJYj0dAMDF5Vm\nF8lsoxWqaHqPZ195hU6nQzKZotGp4fMHKRRzNNsdKn0vjstDyKciuQL0LQEHCds08cgutF6XMyeO\ncefWXRSPB0dyIUsylcMmQyODlMtZtL5AwOv9SQDHoFYssPjOX7F4+ybxmMVgJoIVGGUi48E1MURt\ntUNlL4cogpoep1XX8QQSnDx7FsHSaDbKTA8Ps7e9QdADkSGddttC7LdIRBKkU4N88L3XCQTGuJ3d\nI5hO8P2//AbzU2lqlQa1bocb3/wx1fVDnnvtFd5663USfi8u/wDnzx7n23/1TQajfmaOz1GoNVnb\nWGN7exXN0DlcztEJVBhNuHn66FN8+zs/4PwrL7K6vUmr2+Dc6YtIcpOqoXOQLXP52WdpbR8i9EwU\n2eGZS0+SGRlnf7fAy2fOkhyM89Vv/Q2hRJLp9Aj1chM7HqafreI+XMedGWdq+iixeIwdrU6/ZVFt\n1mn2Dbodh+PpKS7NXyQ84Ea0RHLrNQ5XchgNjX6vR3gixcryfSIBmf3CDhfOPsnh4QEzs5Po/d7j\n7rRuFU2vY1s6iixRruSo16sIokg4HCAQCBONhYjH4iguhV6vx0Qmg1av0ypV2SsWGZsdZ+RIGlu1\n8Yf9+HweFheXMQyN8akRgoMukpNxxucHyWeznDh+jt2NddJjg2j9LuFIkA/evEVQDXP3zSW8Lp1o\nNMr+3gEPf3QDX1wi+dQYsaE4sVQArW3RavTAAU3rgyOQ36lgaTa2JaCKIopHwROC2EAY0zLRuxaO\n5RAMhEB4XFHgOBj2Y146ODiig9er4vIoDI0labfaKLILVbAZHEgQjoRot7sgCOi6Sd+0MG37cbKY\nx3wC07ZA1AnHAo9ZIgq4vW4QLGRVoq11qdar1BpNCiudv7cQCI7j/Odcx/9fb0NTEed3v/QLGH2L\nzOAM9eoBcRFEs48nluDuxl3cviCNeoVIbIJUdJR6o4DZ7xCJJEglT4OgcfPWjxB8DqpjojgOfRPU\ncALHdtNqHiC4XJTKZdyWxMTQGKFQiGq7zrV3fkg6lSQazuDyJSlVc2g9g/FInHqpQsPUOD9/mVgk\nwfVr76ILNhkhyNmZY2itBlatzbGzp9na2mJpeQ2/0iUTFwnH0gQiQ1xdWGRqbJhoJI5h5alqEu26\nB9Hnprp9QG3zAdMXn8aQvXgCfrBtHNwUSwfYHY2nr8ho+h6vf7uAZ+AknXaVaMiPT45R0doYhoEj\ndNFbHRIDI/S6LTKjI2zsH2CZfRRZ4KPPnWXp0RKqy0OnUafb7KMLMqKnDxH4xOcyvPsfNumaMRLh\nEONjU+S2txB8XlLTx1BEL61Oh9XlFVy2QcPU8Lh9BAM++vU60VQS0RIZn5mg2e1x48NbeD0hzl16\nCsOw2D0oYNkSkupGcGz6poGh91ElGb9HRhBlrJ+Qo+xWg7GQRMM2UYM+NlY3GE4OovrDlJodDNtB\nqKzR2Fqm7/Ix/8THyZd2UAJheu0eRq+MInqYHE9y7+4CHsVg6dbf8Ytf/Fes336IjItuo0484ubw\ncI/EwDCOske74uLkhYuU1zbAMqlrLXJhlXv3rjMyc5qQy0fI7lNvtlg4OGDvcI9au8Kf/u9/zv5B\nltd/8G3sZpszTz+NeyjO3uYO/XqHNCofLt/GiQLVLl3DQ2Oniiq6qXerfP6//w0KzSpej8DegztI\nCS8+f5CDG1v0exLRgTCzZ2e5t3cP3YJMPI0iwP1bSzTaLUSfwGBikG69xu7DPX7m5VeJxGIUD1aI\n+E1qYobyYZGBwUFa+Ral3D5zL15iMBChvrPP3bUljLCPeuUQo61z/PIVlhfuIHgdLNXG1vq0ay0U\n2YtPitJ2dPRuh8xQkhde/TRvvvs3LC6vgiNz+cnn+e53v8NzLzz/GFUajdLt6KhelXKlwUGuzOWL\nlylvbNLMF9laWyc2NUF5v4rU6DI2M8X91WUQPRzul0jPD+Ly2syfmCcQjWHrPa6cfxbLhP18lq98\n7c/x+INkUmOokkg5V+b66ytINtiWjturEvR6aXVbeCcet3BW9mvUS02crortSDgW2Dg4joUiuVDd\nCo5pED4ZprhQIJ6OYqJR2OoiixKyqOBgYTsOMgIGJoIjIkg2iqqg9S0cwWZ4Oo0o2PT6NYaTQ+j7\nDdLDQ9iWgN7Vuf9wBU0QcRAfewqm+VgQRBiZiYGkg2jTNXTCsTB9W0eWLM6cOcXhQY6lO2vsPzDu\nOI7zxN9nnf3/fUXw+3/we188csFFp5ej32mi9KuMOF6OjB6nVD4kPnwUVZnA4/PglxOsry5hOhU6\nnTLf/ZvvosrQ7uzR1gxSiSG8Yp+A24ssuFAtm1p1n069RDx4nEwojl+U2PxwidnYINXGLn1LRxYE\nVNWHrAvkb+9Qe1TiYCeHXWgyP3uO//T1r3DmzDlGRkf48Te/h2kalFpV6obG4vV7JKMhHLNPpVzj\n6StPsPfgJqJHZm9ph9ziDg1XmHypyXhyBJ/jMJVJs7+1TzhQRg4Mslrv0eu0abS73F9cpK87vPa5\n4+hdjXsLh2xuCrSlAUqlEopgE4t2ufTcSXqVCqLixrFNhqIuzpyepFZcJzE4ztT0DE5fxo3Fzv3r\nyPUS/lCcM889i2mLzM0eQ2hvUa8+pN3dZCCVZGLoRbq2Q7/VZSKTZuXBAma/i8uucPP9t7l45jSO\npuH1+5ibnUbq28xeOcOdN99laGSMlfV1rr75Nk+cPkuuWKBvOnQ6TSYnhxg7MgFGnw9vXCWZTKMq\nEoKs0Olq1Kslshvr7C2v0eq0sFwyqaEklXqHoZEZavUukijT6mogq+iyH2/mBHJ0EFu0kFQFWzex\n7Q6iy03Ep/Ho0Sojk0cpFhs8/8LPkFtZIpVMcfedb9Eur5EemySVjjCYStE3C0w9MYdRzbG/cZ9Q\neBhTMrj2w79BsV0k4j7qvQL+gEwHlYG5oxyZP0IiKrJza5FyKc/8+YsUmjmyeoP3P/yQuWOn2F/b\nwhvwkK/UmJoYI7uZJ5UeIJEJUMu2GRjworkNxsePs7m8iCqbiG6H2eQM8YCfuZMz4Actt01OAN1Q\n0AyIRkcoF4u89OIrhGJRBlsNgm4/g9PTqB6VO9euozsym+t7RGNxNMNCsRQefPiAdqmB3MrSamwQ\njwTJ9qo8vPuI3/zkr7Jw+yG93UMc0SDqDxJIhNnb3KHV7uFWvYzOTXL61ElSw0nCHg9/8sd/zJVn\nn8Lt9zI0OolumhyZn2N0JM3hQY5kIkWxkKVaKJGKx7h19Sqp4UFcXpWttSW8bi+f+czPMhyLQaPG\nuecvkhoeIddrM3ZyHK/i4It4iETjlJsF8oUah/ksfUvCERwMp0tfM9l5uIvYgLvvLSJZKpLoYPZB\n69s0m23ig17iIwPU21VkTSSWTCEpCnq7h2GayKKMz+fFsk1Ms48hGBhti37LotPSwbQRBRc44Dg2\nguAgiBaiVyEzlUD1WYwOhynkqyiWTN+yqDWr+CbdBGN+Tlw6QWm9im31QYDx0TFEWaba7GD9xGNA\neOxZIAoYtobL83hUuIPxfx3bos3Jk/Osb6yTGI6wc7/+01MRjIwFnX/4e8eot9wEg0NMzxyjcLiJ\nrbcIZtLUyyWGBp/AsS0ikQi7O0vYrRr7+9vY7hhej4rhVNEMjUDITzoYxe1xoes6ji3g9XvYyudo\n1zQ2bz/iyjPnCPojWEIAs7BHV+4g+xUK+z3OT52mt3vI2MgRTE+X8s4WA14HU47xYGHjceLP8BF2\nLD7x889zd3mDC1NDyOIAh4d1hifGWL73fzA5PURVq5BK/Sy1/VUcW6TaqVBt95k7fpbs6iKmVaHc\n87G0vsD0yaep15qEw2EqlSrtlobX68XlMhGEAJbdw2w3OJqJMz09y8rOHoLbQ7ZaI5YKExdFJE+c\nRnaXwViAUrlC31FY3V4kHkkjix0unX4KVBEEnUg4xeLVD1ne3OLCK6cp1zqMDQ/iVU20XorVvQIB\nV5tAKMjb1/6Wo+cuIesJHFHAxEfXtJBti8Gwn16rQUez6FtdLn/kGW5cfYBP9eBSAUel3anhDwSw\n+xaFjkM8NUyt2cHj8yMIDpL0mEEhWhbDAwMcHGZpNpuoPjcyj1vrHNtEEQTGEwqbhyUkNYplmhha\nD9HQkRyLVimHaRlsZ0tMX7qChI7PaNGu5hkfGeOvvvwlXvrYZ4kFHHYrNfCkaLTr6Ht3ULxxDMFD\nrZblxGQEwVZ5+9a7hKIRLH+Cvlui2e8QU/x4E0FW8wfoeoOkKfHe9xc49+yTpI+N0TXrZPM53KEo\nF05fYvHmTTKhEDXDYNATZXpwiL996zu8/MlP8PA7b2AIGrsWyJkMXrdDN5ujoWl89ImLWFEf7rDC\n9/7jfyKSSOHJBCjUcgT9CSTT5JkLT/H9N17HVhxkR6LSrGL1JC4MHUHxqmTGx7jx1htUtvYJh8fo\nVVtUS01e+Nhx2k6XaDiG7uoxPP9Zvvb7/zP0RaptnaGpAbqtHp54gLbRY2IuwsNHWe4srvEP//Fv\nsnlzianTp7nz4Qe4JAdRgdDgEJYoMzNxlGs3rxKLJwkEQhQKBVyywsHuOmeOX2FtawNf0EdPa5KJ\nBGn3mrgdN/sfPkIVPTQVAc9olKmjR7h+9QNo9rDjIY4fO8+bb76JZWoMpIbY3jvk1Y+9wPL6AlpH\no1qsEWg7OBZsbTYI+WPk8kVMHr//gWEfCBZdsc3E6BjFSpNYLMGgJ8rK0iobSwfEBvwokkQ93+Hs\n5RPsrx+QO6iBKCDLMpZtY7t7DI6EEbHoljQi4ylMt4aEgSwJRBJhKo0WoWiIZqHFzp0sT5wb58kL\nl/nuV6+TjIQQTBvV46deb3Lv0Qqm4AIETNPAQUCQRUS3jeqHeCZIYshNNJZibHKUntZBUWXKpTqK\nIvHlf/rWT1FF8Ef/7IvPf/IyPl8Uv0dFsBR8Pp2oEqFjGkiSjV58RL90m1z2LvnDIs1GhcU7e5iq\nQK6cJRhwo7q9jI2foVXaQe80KeQPwONj53CPcm6PATXA3MQogbCHI5MvogkiHa1BVTcISiHiPgXT\ntIjgAlthIB5kYCDOVs9kp6nx1MhRguEYUsxHSqygd3SOjo4gyUGylSqhSAq/4sIwO1xfekhbHuMg\nL9Hri2h6no4CfbFCPOnF6FTR9TzhhIjLoxCRgyT8AZ771M+gihKFYhG328NLH/kYZreG1O9wcm6O\nzPQJSppOpdvCkTVOHD/O/BMzJE4+QyJosr9XpSN5MRAYiYdo4kNweenZMscunqXV6FA7uEs2+z6S\nz0MrOEZmYIhkLMjmapmdjTWWH72PS1Co9vw4UpDLZ55kc79OodmjbQrs7h/g9ro5ffEcjipgti3q\nPQPZG6HTrGEZFo7bS9dUmJqbppA/xBfLoAkxXL4QmtnC5/PS1zV03cCjSLjr+0jIVIuH+FQRweqx\nvrqOLIm0Wh0C0SS6BU1dxOWVqe0+wB+KoxkmUbNFCx3DlyI6OIxmi1w8OUd36wH7Cz8mM5wgPR4k\n7A5h2xLf/fbfUs3u8/xzz9B3e/AQRNDKxDN+DKHCiZlpYvEhunqXk3MnmBoKsXj9Bp+4cpmxsWEC\n8QS1WwsEWwaW43D0uXlEWaXS1jGqJWKxDH0sfG4HzWhAwk3XaCPFfTTdNl27z0H1ELwyB0KT05fP\nUm628bhUAq4AQ5EgSrjHyvINGocFTEtkd63MfqnGpacvUcrvUs3uUMpWmZk9wtOXn+P6O3c4OjNL\np9PlcLvEo6Uttg52aEomZkDAE7AZHgpxLB0mmhqkJvSZm59kcXOFRqOLJoPtFTlz5SJ3V5bxjyUp\ndlqYls3mao5mvs6/+ce/Q+XWQ7Y2tqhXawyPZSgeFGnXDDYfPGLAF2Vzc42g18+da3e4c/02w5kU\noXiE43NP8c1vfJtkOkIyFUPE5ltf/x5nxmaI+f1ImRBjF06RGR1CdywW7z7g6JFjrBxs0z7sU2jt\nkoyGmJs6x9TMScZnjrC+e59er4OmdRE8KtHxCI7PYWh6gmq2gUf1ozWbCH2HTqlLJVvD7VcxdI1O\nrYbW6zOaHqXZaCP6ewxNJCl1KmhNDbdXpHLYoNvtY5gGiqKghkTcMQ+WbLO9WsWxbOy+Tav/eO6Z\n7JYRXS5MLGrVDuFgiPJOmfPHp1hvlNEqNl6Xi0q1Sr3efOx31btYzuNfopbt4Ig/2ccgNRrAkfsk\nJlKMjI2hyCqyIvPw0T1GR0fYyR6w9WHxp8cs/lf/+ve++NzH0yRTAm4pguj02NnLondrmKZKo1Wg\nYfQR1UlmTr1G22ihyxXkQBBJNYhGFfY3tun2vCQG4xhlk+TUKJ6wFxSJwWicoFchlohTqlYQMdhv\nVqgW2mDVceo12jv79DsmJ+ZOkQn0+dJffYNr9xeR4gmu373Hkcwk7WYOJexl7+4dPv35XyU1dQrb\nqFAplRgdn6HZavJg7ZCV+++TGX6Gag8qjSKC4qduBOlqHrzucUTHx0DAIHnMTd88YCA8zNzp06iy\nyNW3P2Ag5CPkdnBaHUrZPJMDUO1a7BbqHBQL1OotvIrJWDpJrdkifeIKzdU1br13i8HMNAGfimPB\n9JSOVm9TajYZGR0md1ChUeuTGE6hhiYxdJnjR88SDbjxyCrJ0Ri9hoPqSiO4JPooeLwRDncXSXtt\nXL0qodAg4UAYt+SiVqpQ3jug18gzmInjC0bJlxv0TQPFF8KUHPSexu7GGomQn5beR5FlToymKR0e\nMBhUeP9vv8pHzp2irYPpCZEYTZKeHaaHgNYTUFwqkzMThAIehjIJysUcXkkkHnRjuEBstenV7iN6\nE0QjCWrNHptbu3TaDdquOOHJ48jeJNWKQ2ZinPRQkujQDLLfonG4Rq7RJaublDsWUnmLejhGtpxl\nv25z6ckrNAplKmWNzOQoX/k3/55P//qv48TcdHWNSq1I31K4c/sG82cGmJk/jWPm2Hu0jicaRcak\nkqty9uyz7OxuEQ6odAoVwqEg1Y0SeraC1baplnvUNmtMRIMUex3SR2fJHfaIR4do15pYisQLzz2D\nGpDQDOjUwOo6nD53gaOzM0wHgoyMuvEpAW5dvYPWMTl1aZz9fAG9ZyBLEh7ZQ002CUwlubmzQj+X\n4+rdOzQaEnWjzvZOkW5Hp200CceD6N0eZ2bP8cG1DxiYSOAbDrNR3ObRygKBWIRTk1OUd6tcfWOZ\n3G6RgZEMYk/iwfs3kXsyYZfIhal5qHXYvb+GaNUIp6K0ex2q1RzJRJRiJweChAzsZnMMxFM4gojT\n7uE0BB68dRMhECA1kiC3kyM5OcTWyipHj52irVXYXluk09Eo5KtE/CGmJy6yc7BPH53p6XFWb28h\nOgIe1YUsSCSDPiS9j1a26FZ7SI6EbnXY38/hUiWOHz+GoNj0nR6yW6KUb2FoDpIkYtk2jmTiHVAJ\nJhXSw1EUwaGj6QQSLprNJvVGC0SZw3wJvz+E26/SFU0ik2MguDELDRrVBl6Pl7GJCUKhCF5fgFyx\niI0DkgSAKIpIkoDoBlwOlqgh2g4ba+tsb22jqm7C8RA4sPje/k+PEPzLP/znX/zsL36EdsukXKqD\nGmJm7jm88QzN3AOMrgWiQk8roeu7DA/Mg1NnYvo4zXKeoBJiNnmMYHqAntHEI7cJJVM06lXEpkWj\nUMSDjFf1gSOxu7NHIDaEN9DGtDRUf5Ji0eD+1QdEM8Os7LT52Ec/R7PfpHBY4XC3g/dwjcTQIIdr\n6/zip7/Awq17/Pivv0xg+CIqbh4t3SbXctMVZMTUBE7PQuuYxGPDKLbImXPnmJmZwDA6LL3zY7az\nawiuOTqVFHpPJRbIsHjnLkuLt2k0Snh8ArOTCaIBHy0hym6+Tc/SkZG5cu4yUb+fcqHMqWc/Ru3h\nIpbWJOBvUT64h9406Gsmd+/vU9YEQiE/ilWl32/hOCIBfxyPN4AaUIjEPOyubbK3s0soFKdRLKIm\nBjj12mtMzicJyh7e+uqfUysUCQ+dRg0laPY0ZMdi8+EtvJKFpZgoks14XGZzZYVIepJytYboclOt\ntxmZmUPHjyi7EBDYP9hmNJOi1uqSyIxT68nYagtZ6VM9vMXG8g+RewlMQFFFTNOm3e5SqLUQbQet\nb5GvaugdnX63gNedxlbCtBwFW3ETiQcJRwIgqojeGm1TpK672K812a5ZtDUNbzzCyXMniA8luPjc\nJU4dP8LVO8v4ewamW2VjYYnDnSKj80c4/ulXyL3/PkOxJLmIyv0H17jxwX0uvXIZQ2zz0uUXeefW\ndRQlyMdOfZJKrcJmsc7So3UGhwcxrS6hqJdus8dEcpQ728tYkkFoRGZwOEmvW2I0CjPjFxiLBLn+\n3lV2drcoH2h0Wyat9T3OPPskik/lhz98kzsLi+zU9xgdSmO6QhzoXRbWsxTLh3zk2Y+weW+HslQn\nW+wSiofRzT7hVJxC9hC/J85v/fY/xdit0JMEBNUkv1/E7sF+oUs87WFjuYBji2zu7rKzUKZTt/C0\nFVrtBqfkEFmpz267hBiWOHZhmn/wmVcIC15uvbPAr/3Gr/H0hYsUC0UWtzaQoiE8w0kCMQ9NvU2j\nXMSfTDI4MMnND+7z6qd/gVazR2+7yOb1+6y+u0D5oMXmozWsgI9+S2Pu1BSvffZXKOUrTM3NUGuU\n2Nx4SMCwqCw26ZY0XvuZn+fdG28gNmzOnH6S+EiK1TtLaB2dyfFJWrUyht1HUWSCYREp4MYdhnqn\ng0sVcRyHZr+D2TeQXAIun0RyzEswphAZi+AaEYgFQrTNCicuzBNLDDB5JI1mtrEth+GRQVLjaRzZ\nJBIJgmQhSxKmZSLSp12r4dQh4osRjcRoNVsYBiytrGFYNo7tYNo2sux6HFx2bFx+hUDCjVdV2dw7\nAFuk0+kwf3yK0sEhu7e3ye38FAXK/uRLf/zFyy8NkwgFabfrTIaHSXoibO5cZ+XqDULpDNVyDb3Z\nR8WPS3QYTR9n73CbRGqQd95/m8hAkGgswUgqg673WbrxFkPRUVyqwemJ0/S7XUyXG1vTKDd03KKC\naHRx7e7zkeEpnkiP0B50UdjZZdQl4/WovPzSq5xJuvn4Z/4LtMP7zIxNEfZH6JpNOqaNKzLJ6n4B\nSzukVGsTDkdoNVpUc7tk4k3k2irW1gJaq8DinWusre1g9WwaggtdiT/2G/ou9HaV7c0iLl+UYEjl\noy+9yMT4PILjZ6PcITU1zZkLZ8hlD3EZDfy9NpWVFaZPnkSURa59+1/jEw6BHqZ1n/n5j2JJXi4+\n/wKyJVLr9NFNF16vj/FkitWtbUTFQynfptnsEQ+5GX/pBVypBOtbFapNnVaxyv7SLuNBidmTZ5FF\nhXRUxet20HSLeHoI2ePDUEIMZo4ynhpHEl00hQBoGpFkGlvwEAhEsS2FvmGgKC4QJVSPh1y+jCzK\nVOslOnqfQCxNU3ewhGFEZYK2IiBJLlTJgy3YOMjIdh9N04gGglhIxGMJ9F4TXyiExxvA7nc4fX4K\no1dm6e4jfvSXf8bRY3NcfH6W0WkRoV+l26pimzJ9Q2Brv869e4/Y+PA+73/16wwlfFw8d5bJgSHK\nuRLDoSjGvkXv4ICNtYcoQpWHNx8yNjNKsenw6N596Dq88fYiojvEidlR0uEoLVNjcCRFMh5jdu4I\n1WYDtybgdJrkumWefPLj7D1YoF9qUGp2mMyMsbK+x7PPHMcVSXN96RHxZIbVjV1+6bXPMZIZptrc\nYzw4RUc3qBQL/NrnfpbB4RH2dhdoVgs0c1XKhTLNmyt4/SGaOZ2AW+XS5TNMjI5Tqub4hU98gVMn\n5/mLr/8hS0YOzWPQKjnYMS8DmQQzJwbp5OssbxQIJ/yEgm6mpgaQW3BpZohmpUF3v0tiIk2jYWIJ\nAtVqie3yLi2tRWQ4iGXo3L59k1gsQkcWOPHCU+zub1DcK5BIDeHzKGjNLuXDfUa9ItnVHXq6zfyV\nS+w3s1z45GU8IZ1e38VAagBb1GnRRbdamI5FOpOh2aoiywqt3RK0LMDL26+/D/k+gg2V8i75epm9\n1SqqLFGrFrEFk5NHprGcDuHBIMmBIKIvSLvTJhDyIqrgkixMu48g2AwkYgyNDvLmO6tMTMaJh1Tq\njUOGZ4dJDU4R8sco5EvEM2Fkt4QacNPtaQwlRmjulVjZ2GN8ahhFcCG5FLwuD9W9Jp1al0ajSTAY\nwusLUG900AwTy7JwBAFwHpvRtkM4EcCfCqCoLjTNAktEcKBWK1Jrl+n2oLLd/+kRgt///d/94qmz\nXsq5LonAOK3mIb1SHtWl4R0J4QnG6GodSo0smelhuk6PnYNlHEeloxW4cOUC6dRpEC2MbpWQEicW\nH6Zy7ya2qdPcz7KxsMrxJy/h1WrouTx7twqUNw5xtAZOs8uPry6w1myQiabZL9S5MJFB6ZQJheco\nr7zD7PHnqPUc1OAg2ZJDrm1SqrZQRDhz8TnO/9xn2Vm8h2QLBD0tBiI2ycRxjj/1BInhM2xVe/S8\ncYr1Hn6/QiIeIptvEPaFUVw90kNpLMFLuSVyWMizvLjFdqWA5PGxt71NeWeVF5/7KIrXS2pyjmy7\nD90iPq9MMjBA+tTHWHjjLVKZy9xdz5KtFllaXkHTuuitOoosMxyLkX7xZcxWh83tbbqtHqbRQiit\nU3rv36GtrVKWEwR8foK+MI1uh2KpS9/W2NhcRXUsUuNnseQAB8UiNiIWAt1uA59fYQB/AAAgAElE\nQVTSQzEtah2NcDhKv+8wldxl7HiKmOSl0dKwu108gkGjlMflcRAEA5d/ENXrp9VuoSgeXLKE6hLx\nS15EvUNr/fscLjzkyudeo7H7GNAeU21qukGj3UH1BOnZDvV6g77WpF9r4NgqajjFa5/4CFNnZh6b\ndgs1BoIZ6pU65VoFt0ukUe1y7sggPqPOYe4Bn/uVT1E6WCA1lkIUvRwWbI5dPMfO9gqrzXuYsxn0\nyQGqzR7dqkJmcJyvf+N1/LqLMx89R7a0z6xPZWl/jbYJtmWwtrREdTdLu63R0Hv0W20OqqucHBvH\n7VLxJxPMjk5TKJZAkXh7cQE17KOlNZgbGqKye5PzLzzBtcUN3nlwE0mAkUSGC5eeptcs8t6P3kcw\nZTBCdPGSrZTAUNCKVSam3JyYSzA0MM5EfAJ3T+P7175H17ZwOiaNA4d/8t/9T4QebnDpyUts7G0Q\n9ki4DZFnrxxjRB1jb2eHyZEgKU+UmbFpSqKb19+7j2FoBGJR9rcP8If8dMwOqt+FKyFBAKrdBoIo\nsra2RbfaYGhknAe3HqKtVth4VMTx2qQnUvRkUD1uilurnD0zRW1ng+FQEFN2s1bMkUgmmZ06Qsdu\n02iUUVWZWrWOT/DQKpTJ51s83Dhk/tw8hUKZvmYS8oZwkiqyKSF6HSQXPP3UeZq9Gk5QYiQeRS+V\n6AkqR0czfPq5F8m2qoiSRCVfIZdvEI56ufv2CucuH+PsuZN0Oi0+8sILTE3OksvnKVZKTE1Oks8X\n6Rt1NN1AEmRKpQrtgwaReAC3T6GWr6HrBnubWeyWgNbsAVArV1BUF9s7B/QtC0cA2wHRAUtwwLaR\nZBnZY9EXQMVFo9pCtkW8EYmRkQyXLl/i2veXfnqE4F/+0b/44id/6RUEj5+MN8jmwi2mJ0cplvPU\n212iMQ+hkJeA10etVOfsqcvIYhDb9KDKXtKRiyw8fEQwqNDv9PjR317nO1+7Sq6ss1YoceHJJ3En\nUxSKW/j6BkPBDIrPSyYWp722i9UPceFjr3JiKsPRUJTRgRFmTl5E9QTQyhsEM2m6LYPk7CnyB4u4\nFYN8+ZBwwEUqEeH+ww1y6ws0W3VMy4tJD79/n918l2r2kIW8Qd/jwep2+czzH2UkUKHZaDF1NIZj\nd7l84RmSiUE2t/eYO3EGv6hz9sRJHly9h2GK2Kb1+Mtm4wbN3QW6lodOdYeuZvDgzn3Ke1lcYoXD\ntkqhrWPYXgzbZG5whKhgYRWv8dSnf424x4/dbpKcnWE2Hcbu2kyOjaJGRtGlNENH5hkYmuLWnfvY\ntkCn38WijzsQ4+zRk+TL+/RkP41Wm/HBEHGPSa3ZxOeVEEQbORyh0+lz5NQZtMO7vPfeAj5itJt5\nKju7tLK7vPd3X2ViaIDZ8XEMwUW12UVWJEJuBVXo43PbzJ2bpL63h0d0iGWmOHF8huVbd5h/4hgh\nv0HxYB2vbdOu7pMJtJk8Nk7y6Az9chvdgKAokG1plNttlu+tUq1LqKrA+u4B1Z6EpHc4MhKmVc5T\nPHjE+LSXbq1HNauzubxJq6KweOMuQY+Xam6Vezd+yKULF3jlyuewNjfIb+6TmUqCo/Lxjz/N/PQA\n3UaH/Y0Nho+c58yxF3j4zvfxh6NsLGwydXKOqbPHCQwMI3tkxH6X8HCCRDjJ9MQc79y8xWatQF8V\nWVvawDB13KKParNKrm2j2mFuL3yIPxLhqWdfodTqonpErv/wHURRBpdJLJZG08qUK10+8eLLVMpV\nwn4fjVqeh+/f5d3vfZ9r92/jiYYIeKIEPDF6SztcnjlJs1Dh2q2H/MyTl3n1iZdYvvGI0mqJF548\nzwc/XMByZK48+TRIFb77ne/R7qnMHB3BZzskPAkO61n8YT89w0brw1s/vo2oqViazZmhUZKSn/TM\nCSy3TiwQ5Zlf+QJeN3QaCkdPPsPDt9+gcVigsFPgwd01EsdmuL97QDDp5vIzT9HplxEFm0q5gKw6\nREJhBKNLRaszcXIQwQ0jMwHOXpzl5z7zCX741o8YSg8zdeoI5y6fwhWSaVg1eh4TSxQ4Mz7K+dl5\n6odN8mKHnGCQPyjQbLXp9wzi8QitXotGUSOU8CFKsLy8zInZ47z97vs0G00EWcSxHNZWt2mXDHJL\neVq7bQIhN82DGr64B9sycXSb7O4hCh78khcF5fFvH9WFI1iYQEfrIggShmlhOY8B95IoEA57aXZr\nOH0No6Ujdi3cjoPu6hEaDODzhrj1g7X/79pHBUEYBr4MDAAO8KeO4/yhIAhR4OvAGLAD/JzjOLWf\nXPOPgV8DLOC3HMf50U/OnwX+A+AB/g74bef/4QFmZjLO7/7hF+iLDi5FIOw1sVs5qodV9psFksNH\n8Pu9yNj4XD7uLmxz5NQlTKmFJRyicoy9rW3Wlx/gd/l4dHuXZqVFOBbmC//lzxN0BTCNBrJnlWBW\nZzhzHr+goI5M8KX/9Td4+tzn2N7Nkgj4OPXUk/gmZ8hv52iXa0xdeg7MA5p7GyhxN/WdKnqvTVPT\nUHomsaiHu9keVr2CqFrMzZyk3K7jURXW1lbweYI4chTsHpHECN1KHcfoc+SUjOka48Htm4iIyLIf\nizAejxe90sDjNjAsgSefeorrt6+j6jUyPoN65wBHHGFzdY2Pv/oruEcyvPntL7GXr3LhzAk8Pi+l\njozjiZItZwkoBi9evgDeOE6zQGFrj+iJY/zoy3/Ksac+ycrBDq+8+glWP7hOt2/iCicwjHdJBM6z\nc+0dWrVdTl44we6jDeKnn0UUoziym5HxGR5e+xpTxy4RHBii3+/hGhojf+cGQqVIvtIhOniEtjdE\ntV6jWCgzdWQWwbSpbtxg7vhZXn/3A+bOPIHqDlPvNXALKo6tMeLrE3FLON4gB3tVmi2LnlbnQsIg\nv79FYDBBcOYs//53/znD6UHGT44yduYKcjAJLpHW8i6LtRqO1EduVsGVwLJ7XLh8jMruQ5olA1Hw\ncWf5EEsAQa1zZD5B9VEOoVXgiWc+hSV6+cEPvodxsEMkAMnMIIkTp0inJ/jK177FTqHE6Ogoy8t3\niAQtYhNpcrUGYU+Kpz/6KuWHt9HVKlvrdSbPnmSn3sDnDvBo5V1OPHGWRvaQybljmPU21+8s89nP\n/DLVepf8/i3KZp1AMM7C/Q2ef/oFfMI6MdXDZOA8y6sfUqp32TfcmFaH7VwBxzD5B5//WQSXyOt/\n/SP63V0uTc2xk6sSTMZpbewzkEkxPj9JZvwS3/rWt9BtiWauygvPXCY54uEf/aPf53/5zc9jGI9x\niwOpKH/9jTfxuEXmLrzMD77/d1QKRcYmg3zv/av88mc/xXvffZvJU0kGTqX45vfXcMcfI1q1HYNz\nJyZ5//aHKG4Pn396mg4h8rJFv9PDHQzSNFtU6k16e23aBw0UjxvB7vFPfu+/YXVvmaqzz0D4CpFg\ngHd/+H1MJKqSxvjYCJVGG8sxefmFX2V17SZb9++S38xy9KkJRDHMD1+/zXMvfoRG9ZCWZhAdiHH/\n6k1UCbpmH1ERCPlD2JE0YaFPeGiID6+9j9ATUQUolA/xBiI0Sj1m5jM0WnUUyYXf60ZUH/OIvREv\nkdgAHinE0sIDOvU2SttketCHjIPicaPH/Ny/s8bxpy6xcncdf89HI1/FLUmILoFAKIhuuVhaWUeQ\n3GiaCZgIsohLsVBVBU9aRgGa9TYu2WFkKo2cVrhw4gz331rk776++PduH/37CMEgMOg4zl1BEALA\nHeBTwK8AVcdx/pkgCP8jEHEc538QBOEo8FXgPJAG3gBmHMexBEG4CfwW8OFPhOCPHMf5wf/d/ZOD\nHud3/uAkIf8ElsuH1tKx6ROOjlLKbdHIZUkPjtKoV7FwGJ09Tq8HSE0cbRPBiuAO+Qm5JukbAunE\nCMs3NvnLv/gTjh8bIjWtcvH8q3hVGXu1RMQXwm00SQ6naGzd4f5amZc/88tIiSjNnSL+Y5dorXzI\n9s4KarvK+u4qbnePZtngiSsfo6iF6estLLuLXxEIqAIud5BGt0+pXqTR99Cz+6jeCJ1Wm1TAxZHh\nKIZlEg8P09E7dASVpdVN3IqbeDzAfn6XjuZBxCbg1Qn6fYi2D39A5djsBI+uvwWuJPnCLrOzCooe\nYPjsE9y6ugaYtDsGLlmjsLWBXTlg9umXiKdiDIyM0sgeIrtNAqMjtLdz+AfHwBNnY3MDx9DYWN8n\nkhkiKosEPNBuNhnKzCP0Nuj3+8iyC28yyK1375AZHKAhhGlqIvXiIbHEEAGvhFe1iSUzuFywtvB1\nSmWb2aFZYk98ku2tXXw+ma1sDr/fz3AkhE8SqTc7ZPf3efjeDaY//UlCsptG9h52fp8rn3qV73zz\n2yTTZ3EHoo8NPSmIJtjIssjps1PcunYXS2vikmVsUcRQ/Tgtg8bubfrFt0gf+Xmuv/s6n/nFZ7G0\nJG+//T6xaBJZVTAdh77gwcZh/d4Njo/6+NHfvMPv/M5vs7m2TFvrUO1kScge3IKMrop0rQCf+MKv\n8+d/8gfkagrvfXCbJy7N8+zTc1RzWeYunGG7VGPl7ha//PmneeONqwzEQ3zl628QOTZEKhNl/6CM\ny9MlNXCaza0F5sePUi5WODF1kq/9x+/xhd/6Obq9Bu+9fY1jQ2OkpofIttYorjWpLRzy/GsvsX+4\nyuDIBF/569f5+H/1FAORYZbvvcegd5y5sbN8+d/+KZ//3DGufORlSIyz+e59rr13i8xEhOd+4b9F\nsCV+6dnXGEgNsbu9R3o4gRTwc3Y8gd8vsre7w8bqOr/0hf8a2yWx0+rwxrc/QHQpHKzv4UtKeDoO\n8ZSAZDlsCw47XQWn00K2oVXp8/GXzvOjN2/QNgwuH01y7Gyc6LlLfO+rX8MxZJo+mU6hQ6ug46gm\nP/9LL7G2ukwi4KPd7jI4M0Zpfx9B9hNPpDB6CqmMmw/u32Z0ahqvO4jc95HbW2J6epKe2UM2Zfb3\niliKi/BQCtEW+cs/+2tcjooiWrhlCd+AijcaIBhNM5BIsHznLvVeh77Z46WXP8pf/Nk3ePHlCxTz\nFbSOgS1pOLaEYVh4RTeSItDWW0QGfMwfPcPe5j73bi8Si4UJyBbRjJv5o2epdPuUy2U020QWvGhV\ng8KjXYSug0uWEQWLeDyObunoSo+OblM87GAYOh5J5n/7o8/S7bT4t//uGsVKg/+Tu7eOtW29rjx/\nazMzn70PM59z+Z6Lj9mPzBDb5TiJklTiKGAlXZG7uzqVistJp+yOHSjHbD/beX5Ml5kP3MOMm5l5\nr/7jvVZFrep0SkqXWlnSkpbGtz7N/76x5pxjzaFo1Diyz0cgkaZuUjB88gCykoSv/vrL/3JE8N8g\nhleBb3xwnxJFMfgBWVwURbHng2wAURT/wwfvvwt8hfezhguiKPZ+gH/8g/2/8k/F6+xxiH/y54dI\nboZxN3mQtzzB+vYk4egOPb5mRpqH0Wg8nL/5XSpIKBaktFg9JOMheh0KztyaRGf1oUFBNbOHquHA\n1zvM9KXrSMQSiUiCX/vNXyO8ukFJmSO2uUird5xujxeNwYNCq2D64ksoNC3ovV00P/ZFaBRIzp8j\nMDnDztokjzz7GLmkH6N5gEQ8y0ZwjWqjitlhoFIo4XINsb61ycGJE9y+P4/eYCKWSJNKxBHKZbp9\nVlRihWwBUhIdu9H4+9NU5TLshjJ1iUA+b6JSLKEQanS0OjFYzPg3t9Er1dgNdXTyOkaHhUKxQU3Z\nwdbKMmZFHoVRg9Pj4/7UEg6LEbvHSmhllu2dWxw69iJrq0vYm/uQGQXEeoosBTxtBwkt56hUKuzs\nbiPR6NE2GvRYVVw98w76/qOYrC6U0iL3rl5gYP8EaoOJXDZLVYRKvUFHrwarwcPGch6DvIFaq0Il\nVLl3+z5KjZHtzVmsnk5c7S2IokBBMGKVlqhV4ih0HhQNCOQKFJGi1qqYvHIHm0mLohJgoKsDhcNJ\nLFkilc7R0+4hXWggrcKG34/J7KaQS3FktINUYI28qEREgafTQy6xzs7CEijXQDjO8L4+5m5dIRCK\ncvRxC6X6OpVKM9cvyRHLcYwGGfGtMMpqGntnF+1OB/n4BuG9LUKJBDoZOHoHkWn6aW9p5c7Zl1kO\nBUgWYjQdMGHRHsaoUqASCqTXlpHaRolmotxfW+XBBw5z+mOf5fOffpjTJ5/hys27NHeZ0ekcKORy\nXFYnr597jeGRcVBvENj043U5sEgN7POdYLFR4uzLL3Oqa5i+jg7efO8i9/ZiOKwCR/a1oWnSUmjU\nKfqTvPajJfYdOoi9yU0wGKSazEOtzDOPP0xJ7WE5tEfdv4fdrOLGrTnSqRI2h4XWXh8j/SMYTBb8\n0Wus7oWxKcwktvyU8kWklRzewUGUmha+8/0f0NtlweLowtmsp6TJoArb+M//5ceojVa0KoFQJI3D\nqKAmAYWo5BPPP8q1e3cJE6fVaWHDv4VottLh6yJ9bRKlxUQynaP/YD9r5T0eOvUkc7P3kKnlKJV1\ninkRvV5PyB9Ao5aj0sqIrucpNKBWzdPZpiNfl2ATDazuZHC2e0l8YNST2t5B2eImGIrTZfNgsFjR\n6LRs7O5x69JN7IIWtVWFyaOjqbeTYDRGLpcjFYozPjpOJpchmoyi1ujIxlIkgwVURlBopHg8HsSa\nlI21bdKZAlqdkpYeI3ZbJ9VSnVQqRTYfQ6VwM315ltaOJtTxKlKFiNltpCRvMLx/P8HAFgcPdqJS\nmkFMUCwEeOvnk9y9VqRpQI3RYsLVZEaqFvHPbiAYlBx85DibGzu89JWr/2wikP13kkArMMb7X/RO\nURSDHyyFeL90BNAE3PxH2/Y+wKofPP/f8f9WnC8CXwRwuU2UwxBr6PDf26ZLtkpbUx9Lq0vMhWZ5\nft+zaBxOFjfH2AqtIJbqWGVtRBJb5BQmDgyexOR1US3k2FoTaXL3Yld4KLT5OPHYCb7/je9QSFSR\nSrXoiimkviYaCgNKi4Pw+m0UUg1jhx6kVFSRqaRIXvxrUokqbSeOYRyv4WiysrlbIp6UIG5cQ5Bo\nEZEjl5eQZXIk0lIcTw6xE4vy3qU7ZMslxL04Gr0Gnd6Ez2WgXEgSTSVRmH0E43lQyKiXa8jkCvpH\nxmjqqXLmR0sk4ilyqSim/g4WZpeZOHYQxCrXb9/DrtfSKa0yv75JqrhOb5eeTNFAYtPP+s4kGkkT\nlaqVO1enWfcvcPDAI6QqJmT6doKhPbzKbhaXAoTiYTp3Z+lrH2EhtIJBraXQqKLVKtD42jn04udQ\nqxTcuL+ETFrjxU9/CsHZzMLtefoGR2nkM2SjfvKbObYa6zQkOrYjcazuTuYun+OxZ54ikCtQc3cg\nSiGvlCCXSPGos7z71T8mFauBXEuz04On24d74jRSWYnjI03kGkocjSB6nUg4vU5id5K6apB6o4ZC\nMOI9cgTZsomm7m5mbkyylygTLOppiAIKoUFqMUytKnJ+KohLY2Ksp0YyUKWn5wBbgQvMzUjI5Cxk\nkmFkgo3RDgfmkWEyRZErVy6yurLBres3aO30oTW4sdR11PMRXHYdsUSIK6/doG+glwc+9ymuXr7B\n+uYSDaWC8d4xhIrI+aVr1NIzOJr1jLl7EGVq3v3ht+lCy+VXLzB4ZBS1XIlUSFEtyNDbXBzq6qR3\n6DEk8iCR4N9SEzSkc3nO3HgXMVZmSG9hZnadTHiH0fERcrktbB4b5YaExm6e8m4Ii0PFvkPDDE/0\nEZta5teefJo33z1LQyLB3uTm8tUbtFr1zMRXEeVG1rc3+M43v83b586hkKe5cP5VMimBWCKIPxpG\nqxV5ZuwAmUyRj//yF/n3f/anNGpyPv3px2k0Elg9KRSyIlubWl6/eJ7f/b1P0m9T8su/+3UOHh6i\nls2hKsP4kw/SfmI/WbeG8Mo8pVIKuUZHJd1gKx4EsxW1VYGnu5lgKEIsFmPWMcXMzXmsahWjQ17e\ne+MmZr0Bs8dNMlKkYanidGupFpS0OTUkliJsynWcn1nj6L4hrr1+BUerk0o6hkKlxOv1oNMZsKh1\nLM8tIZfK0JgMTDx6lHpNRCzlsLgcJMJxIjtRXD4HPYdbaTQaqDVK9FUT29vb6HVmjpwaJpOLotJI\nEWRKLFoT2ztbNLc5KIslBvpHmZ5cJpVKIdYa6Ix6jHYtB0/209ziZXt6A51ZTt+JQ9i1DsLRdYKx\nEMVKD4url2hxmYhE/VQbclqHVHhHjfg8fZTLdUxGK6lUEaVKyp2rd6nKyv89R/s/PyMQBEEHXAL+\nN1EUXxYEISWKoukfrSdFUTQLgvAN4KYoij/4AP8vwNu8nxH8qSiKD32AHwf+QBTFp/6puE0dBvGJ\njzs4cPhDFBNhPK0O8vU65y6+S0sNemR6hg58iLBFQzC+gCDRIQRyZHajuF3NxLJbOJwGSskCbUeP\nIY0lWb7yKnWlHXldg6ykoMNr4cbl80yc/jBjn3qMzfOvc/7VH3Ly1Am8A08gChJW9+LoDGrquRi1\nQgm3V0epvEixqqKYtuLx9ZJMRKimKyhlu/i6TnL/7mVk5hGSxQxZoYJEUKKTa7CYDEwtLSJKFNQL\nFXpam1HooNPnJZxcIx6rM7/mR6XWU6kUaHXUMEiaCQbTeH0OMqUAW7s7tDZ5UJutBMIpxJpIqdFA\nIpMjqUKnSUINkbpEw1vv/oSPf+zDBHeSnH7qMRBUlEs1lBI5e6tTvP3yN3nxw59lYTmE3dqgu2eA\nkH+ddMKEXw6iWkulUESvUCOX1NGmlyiQ5+CH/g1n376Nw+zAYNCi1xvY29slGw9i9znIFkT2P/kM\nFOI0EJCoVOQWlwlu7nLh9iQj42PIVWo69/dw58zrqOo6GkodwYAfl9GKp3c/iegeB0eMvPz9vycS\nUrM3fZt//5PvISrK1PJb1OplkokcibSXYCqPyeakUMih1asol+sEVpfRVivsOzROWa4kHI7i31rC\n6W6nu0XP/E4EhbhNx2AXGr2Fi+d3QRQplmpkV3/GjVsx/u2Xfw+vToteLUEUGwSzKX789W+wf/gg\nsVoaU4uHN/7hTV6cOAjSCs1Nen5xa4rRxz/L9v27rM5t8OhnPsPq+k0k/iCdJ55m9tJrVJ1u2n3d\nbKbukd2uYnFoqWkVJMN1rDoN8fgm5mYvOqUFhSbP7uYK5rycfRPDvDl9AW/Jy9r9HdRWOY888yFk\nRRXT96YplEsUUwV0+xTE7y8z3tfP+asb6A0aXHozx48d5Vtf/3vy5Sy//0d/wMXrd5mfXqK7T8TR\nPMz89BqH9x9gPrgG9RJvvreA12hHb1Bgttlp72nD3d7Dzt1bNI0dJJLdwNXczPbSMhe/9wpf/q3P\nML2yiHvMTV1i4Oa7IfSWPQqpMvqUlGsXN1BbtEQLKfRdWl74zU+QiRe5fOEsx3p7efPsbUKRKulc\nBUFWo73fhKelhWomz0YgwICg5KHRw3zr9iV6e10U0hIKigr97U1kUyGazRo8Xiez96dIxi1sNsq0\nd/VjrOd57ZVruI0GTEg58MAgUaucjeU4tdr7B/7lty7ywOMPks/mkMt0rG2sk9gN0jY6RL1cIJaI\n0NXfzPLWLmqFBKEoIR3JsbuW5dmPPkGmvIvdZWVzew8DSsqilJ7BMS6cO8P2eoCWThu5QgWFUoLW\nIOPwoZNYjRoKRTk3L13nYx/+EGdeeQdJWsS4X6Cv+yQzszeIxWJozBKaLf1UyxK248vILWrKyToz\nU5ucOj1BqRGiUSwgkxnZ2dnm1g8i/7KlIUEQ5MAbwLuiKP75B9gy/wNKQ752q/jv/vwJdKo2ookk\nlXIWrRHEogJx4xryeB7B0UfcGqchqilkSnR7WvnEyS9STJUx6OVcvfgSQ0OPcvGNn9Da3MfC9pu8\n+DtPcf/MPToHfx1ji5fa+h1KSQgEAlQLGbKpKB19fdjcrUQ3FmgIVZKhWXqHT7Gx66eULSNRmLk9\nvcD4vv3U60Uq2RCdrXZqEhmmjpNU0jF29rYJ7U1x+uFnKealvP69b9E1eJiV+SW0dhsaUzNqQwVP\niweX1YlKa+C9t65g1JsJZyNUG+8baStUeoS6FLVMoL+jFYddSlWuQClTcP32CvOz9xga7icUS5DM\nlZHW68ikAgq5CotZi1CvoNdpiESj6PVmjj/7ApViEUWySk3Is7m6RCxbRllOkIyHaR+aYPnWJR77\nrf+JyM4Wv/8Hf8RHP/Y8LXYVyUKKbEaBUadAWq3jUMJeMIbK7cNg1HLvvR9z/PHPU0AgVgW7TEUt\n9/74ZKW5iWpdRKVQ0GjUkCBQFyQI9QaN7DYbyzNMHJ4gcPcu0vZBJHo7umqSvbUp9Nom8qU848Md\nBDNxhHIGm6OL7XiZek2KzW5CoVGwE4ujRk0hscvIYBf1SobdvQQWowmTyYQ/GsZm1PPuT75HXV6h\nZ9+jNFmMaEwGri2HyVVziJUakfgaGzem6OnsxeZxUm7UaFQzmD0u8psbTBweR2uxEQmF+ekv3mR3\ndYsvff5pljfX2ShmGBocY3p6mv1PfZLFGxfYunaZttZ++vftJ5bKspfcpmmwA1GmIpUKYNKr2Yz4\nMbs17GxEKNTztHa0sDgVwmLV09vVRnRlk8xuCPfJcVKVDO2ag+zt3qecL5GoRBhyDSBNxqBUZTIZ\n5uHHTrM0s8Dd6RWOHTnK8t1VFNT44mc/wb0752hrHuWNM69x+sQRWrvHuXLjLsurCwhCGcOgBpPg\n5cqFRTp9TkRRBGmGRGqXof5HUeusTC/MIRgqjHWPcO/WIondIO2tGtb9KeRWBbNrS3zpt3+Hipgk\ntJkkcnmT5bkt8qoKA0cGaNBAaq+zvRoisLyLSa1nLQRSm5xssUB/TzuLa5u09TTh7XSzsxZg1NtE\n6tYcm9kaRT0oTVr8kRwmgxKzycD69g5jrXZ8ciU6p4GZeJ3nP/wpHNltbvl3iWXLmApFAnUBlcHM\nhbPnefETz7KyMo1MIiBIZYQ2Y/isNnLbMRpyORa3m1u3bjH+wD46hzu4dth2pJcAACAASURBVHGO\nQi5JJZrC4/GQk8spF8qkCxlEoYpSJSEWzNHZ1cbQvjH2tncQGyWmp+9TiovIy3Iy6SIOjxT7sJGJ\n40+glMioNUKs3t0lsVPAsV+CNF6ixeyi7jKjt7fi39nGqm8jnUjy7itXKearKFQSWk4oaW53YdaY\nkUmcyKRS/vRz3/kXbRYLwHd5vzH82/8I/yoQ/0fNYosoir8vCMIA8CP+a7P4HND1/9As/rooim/9\nU/E7e23iV7/5eaRigYt3FvG429AqlWiEBs8dfowr771EMblFuqObq++doWuwH5lEzrDVi0OtR5Is\nUC74WV2rUKnUObj/JBZ5jo3Va7R2CjjsTQj0sLGyjsYzjlwvZXVhhc7ObvQuG8vXfoKzaxhv+yi5\nRI7LP/4GD3/0BTZXp1jaKNM/fAipTktbTyeUAa0JjCYygRjJyA67myHUQhyX08OVyTlsdh+2Jgcr\ni1toTHrI1zAbqkw89jDZcJx6qQCiFIVGy9TUDNlyHoMO+vr6uHhpkWK+hNOoQZZfJlhxkKvLqNeK\nPPXwSYzSPOlYiKurcTKZFA6zi4ZCRr5a51Nf+DyonBSX7yI0IiT8CYKRBIMjA8j7xpHIDVz4+Q+R\n1iocnjiKQqnDvzLP5HtvYO8bJ1Mp0Wup4+5q4p1buxRLEgyChEMDzUSSEQLxElqtloPPPgalKrV8\ng/WtELVUhnK5SL1eRe0wE0vWUWm1NKpV1Eo1KpWKailLZ5uJ1376El1NXopbd9GrnbQ8/xSx3Ry7\nC6u4Xe143ToyyS2kUjk7aRkOrYBJ1aBWLRLwZwhka7T39pIT6sjCFfZm3sPd4cNrMSCzWFGbTUjt\ndvyb21SCcdZ2V1GausCkp1xqgNigXMkjCiq8qixuh5vZjXlq1SjFYp7pa5c59eghDh1/mOTmFu72\nPu4tLtFnNGC297B+7xpz128gMSl56rf/kIbRjVTtYfrOe7Tp9Vx5/esM7DtFq62VizduEJifZ7uS\noffRU0xPnWHE0k1KLSEnbCMJWigiojbrSAslkpEkqlqcBw8fZ6T1JGevXyMoJJAq/BTSVWIhgXyq\nwMmuYcKbEVoOd79PxGE/Sp0UqdzC7twqkXCSExP7Wbx/l/6uIcI7KUbHJeTENIf3fZxXX/8JTz/x\nYc6du8Z0YIfuXhu3Ly7z3FOfYCN+HbFuZn1mDotRYGS8G6FhYSNZolIsEwsHye6m+MhnnsEf2GWs\nfxSV1cDX/vwvEBoFLDI14q6MG5EAgWKcP/jjLzE3cwOP18zNq/NYXW7MNgPVnJ6bCzMsbm7ywgvP\n8drL5zA6dQjkGT9+gL2zUyRNIqMuG48ff5wv/8X3KJTqyOQi+WIOpVJJs1XPx4+e5Ny1y0QdShxu\nG337ushEE0S24hSLcWSCEptSSziepNvnoNvh4fbmFkszy1jtNnoG+rG2eYhXKmg1CpZvTtJ19Dh7\nqyvIkdDqa+bN117HabURSiXYDsewmAxkMjkUWjmlchWdVoVco6BaKiMRa8hVWjRmM7HFBNntJBq3\njsMfauP4gaeIJgJkywFmLi6T2czRPWJlzG6krDUjbWklnCwxeecqR4+dQCKBybNzTN66j9qiY+hD\nVorpCiq5koGeUabvTfP2f/6XVQ0dA64As0DjA/gPPzjMfwo0A9u8Lx9NfLDnj4DPAzXgt/8vZZAg\nCPv5r/LRt4Hf/H+Tj3rdCvFP/t1RytUSmd0yfac+TUvnAKv3f0i9ZkGoK9idu8mHHn+cYDLG5OQk\nG9shOtscuKUFBrrHUcrU2K2DKPRGRKmcu+9+C6GcRqF2sbG2yGPPvUioakOiMRBNlpAIdcSaQDEf\no9ulIiVYiWfLVDNxWm0mLB4Xgd0AHe2D3Ju8SbZcpy59f0RsslhBqVJRr4uoJVCXSqnkcjSqOeSh\nabTqFBl1Ex/+3K8i1hq8+vPLHD4whEajYWZmGqVSTiyWwGT10Nnfj6O9Df/yCleu3adUytHb6UNS\nrhLPBqkoLeyGdmhIFYy1tdHm8bG5vMJeKEJbqw+jVkVTuw+lzsj62g61bAqLvZ1IyE+pGKZch329\nXYT9MwTmrzD+5B+it3kp5PNMrSywtb2KVm9EIZaRZlP0jQ6h09jYjWXYi0fwepsZbOvg5oWfMvGh\n5ygmk0hsJi69cRO9p5Pc3A1quTy5SgVzWwvtLjc/+8VZHvnUp6lW64iAVITQ6gUef+YJCnkZe7Eq\n+WyGGrX3R/sWksgKebQ2N3JJlduv/zX+aIbjTzxLVelk4ea7nDj8CO2tZqQyEcHq49LLf0/NPEpd\npaFeK/HEQweI3rlBqCYnVhaxOvREdvw8ePQQs0sr7IWDdGqqTE2+wwMvPMwbb89zfPgA2fAk8YhI\nrV6gZ183tu4+ipFdrvziHVJCEalWhs87ihgI09TXj97dRXI9zrmzr/KxX/1VdoOTdB99AaPHRnDy\nFnfeep2jEyexeHws70b56c/fodmpJFEPUdTnaLPpMLis+IMKKg01bR3DvP7aa/T2DNKQJpldvMtR\nZzM1m4JoSYHNkMLv99Pf8QA6vYuzP/8xLTYXfX37aPW5eOXsRXYifp54/BHCyQSB1W1aWzpZWpvj\n8NgozVo3r/z8VdQ2EwmxhlMqo8VnoFDcotCQYFKb8TV1g1zkxIMv8rOffJtQosTt2yugkvKFf/vr\nbM3dZGZhBf96hEI6j8WhYW95k9OnT+KPB9GbtRg9Ak6rFLX1Ea58+x84+OgInQMdvPSzd7CaFWiV\nJZy2Ht65vYJSbUIjhZGjnawGNggXCty6sEStUcHQriG5nmPf0YO0dzZx/fIF0qE4pfz740pUKhkO\nj4ETzx4mPrdI09gIQrbCwuIuWr0JvVtEq1ZRzFcJ+CM0CgVaPF5YC2Du6OHS25ex6w2kSxXSpRJq\nvRKlSoa8UUNtN1JWC/QPD5FMJyltxGjq6+PCe+cw1iUky0WUnSYefvgkk5NzLCxv09nTQV9PNxfP\nnWdkdIBmr4/XX3/7fRluQ0NqO8uTX5xAlNWpZJU0d3jY3Fjj7plJ2kwOBk+Nkc6lUGnkJGt5ZKjQ\nqfRUxQpvvXERU0VHPJhH4azTf9rKzlYEoSFhdOAAC3eXufdK4P871dD/6KvdpxG//NEWpNZO7izO\nMjQ2RmffMJuxC5RzdazVGtq6m6HuA+SlCqb35pm9usCjgwc4MNKOf2eJWDSMUS5Db2qjY/wAf/fN\nr7JvyIc/uIFO1oNEVkLpO4REa8FlM7O4cJUOZwdpUUBWy1OtFcnFcyjlWkYnxskk02QSOZBKKBQz\nKLQONgMhMqUytUoZldmCTWdkbKCDqck50pkU7UIFuXWWXGIZrf0ZbB05csljbGysUyvVKTTyxJIF\nGkio1t7/w1Cl1qNVqyhnUwz3u8gn42STCRzuVoyuFmpVCXZVivuLs6QLarQ6IzWxweMf+QiXz77F\nxMRpUoEwSysbxOt1spkCVpOIJL6NSWujJlXisJpoaXaycPcNRvY9RIV2wtkc8WySSCyM3mSmrb2T\nSCBIMp1DKxcxmYwgq9A53MP0tXWk5RSCzoBSpyG2MYPR3IbDpUfMp1nL6JAaLCjlCmq5NHW5Aqko\noVosoDWaSJcrmOUCbi2Ucyn8ZQnJTBK7xUoNKXNv/RSZYOPA44/TSPq5cv49JEKVz/z+lxEMJuqB\nBP7l15AqG+jUzaRLGTbyZgwaO4VaDRKbpOIxrG4PpXIVrdmMKNZZunCOwdNPkIoE8HotrF66jWpk\nkGg0jrWwRpfXCMUSSxsFtA4LlcwyR557GKlSzcVXbjM3v8yTD43S3HecWilGTW7gwntv8uTpF3nt\npb/k9PPP89Pv/pxUQ83AwAATpybYS/nZ29ujrWmIG++9RCVZQtfXiV1Rxb+T5egjhylpFEzNz7AZ\nn0Oj1GG3DmOy55GK7UzPvYmkLuBzOvG2PkIytEX7YB+RrV12FtfZd3Ccmkxg9cJl4rkCsqYmevt9\n/PAHP0OtkHOwa5iPPP0sP/nLryOxu7k5NcXw+H7kop47G0vYlUpCu2HafG4MagW+cTlm+ziPvfBL\nnP2b/53LV8/Q09HKj346TWt/M93jnTxy4Bjzt6dZXVolkEwhyGU8++AIgVSVrb0ANoeGAhmUYoi5\neQkjNjsZocpaNotFL+G5J8fY8me4fO0+U4spigYdw91DbN6aJSvLYGtux6xXMzzSzuXbZ1i+kkbr\nUPHCLz3L2ffOIghSQrtZUpEMcoWUerXBw8+MoafA0r11Doz2oxweYPbOEkqrknhok4mHJ6ijQFqv\nE/KHMEgU+Dw23vzemzQ1+ej0OtmppGh2NxPb3CFcTpNO5bAZnCRDIeQ2PUafh3q1Ql+zl7vLK4iB\nNAWPliefepGdvV3kCgnRaBipQko5V2B7bRWbU8/+oX383V+9TCKcR1VVo3cpwFqltcdJb88o6XSC\n4OweVQp0Hx6nnK8wvn+Cy1ffwb8Z5vDRQ2zurHP5zBSSJGiU0HnIjbVDjVowsbMbQiEto5RqeOOv\n1v71jKH+i2987SsPPjlGQ5XF5hnj8NCzBFYuMzbxHMXEDM2yPlQ5JfnMLkLFgE2i5JMf+RTtLh3b\nywu4fQ46+hIYHJ24ug/TQInN10I+VcLg6EVt7cTl7cJilJPJJ3HoCmgVaqbvvYJOJqO7y0dTUw/J\nVIWqrIbTrGfm5j0UOj0dxyYI7CQIRNN4vF4SuQylap1qKU/Qv4XdpCAUDFKtlzjywqM4zUModEEW\nF+aJxarvuxjJJCxthpDI5EglapqbPZQrCXpbHTQZZQy2qxHIU6qpkCidhFMVFm9eZ/7SeUpVGTvF\nIg8+/Dg+Xw/VsoivuZ35mTliwTSLc1OkkzG0CjkaWYMBY5zdjRyuoccZ7bFz784KErmeah0k9nYm\nt0KEC372YgFytTpyiZa6KNDk8nDn5k002XUEUUAhSVOWmtgJFUEipa2rl6pEgUSUEk+m0Gr1xBIV\nlufmmF9cwd7UjLRWJbi7hxALolOW0Zv1rMzdxm61UWhIKOUa5CJ+UrkymrqEcmINtUSGvnUUY18/\nJamcaF2KTCHh8KkThCMZhFiUKxde4/ADTxELl7D2DOLoHsYhqZENbaMQ5RhqKbpbDPT1DCElh7ma\nZX36KvY2LxMTh+nu9eHo9HL9vddxqm2oSusY3APMFSQolCpyG4vUyinkFhfVooRobAu9Uk17ayu5\nTIqZM7+g4I0yd+MWrYOdXJ56l0ee/hyzC1tkwtuc2D9KLh1FrgSZwUJDbuf27D2SQpjZUBCry8kr\nb29h6TyENV/m0tRVClKBshgnGa9RqlehWmbp9hyxWBqFRuBzz/wS5XCR+TPvUK5nyYV2OdY9xrm3\nzhBfXqKoLqNt96DQq0gHonjbfGQzRew2M0avlQuvnkErgsJj4gufeAG3u45CUsLVZKdWa/DZz3+a\nQNhPZDdFLrPH/YvXQSqnXqoR2omj1pox21SYqVMp1glsbjEzvUar3czC5h6NbJHMzhalTIaPfPiT\nGNUyNgJ1xKKaUrmKpp6lr7UbncXCI498hkhBZDMco27S0TPURsC/Sus+Hw8+8SgXz1/H7JNTlZYw\naOSMHOwgsJvi3t1JjGYF2UKKbLKKxqzghc8fYfSBNlIpP8YOK11jvex//CP83d98k4auSnuvF5vD\nht1pYWFqDq/HQXhymuXVbZI7BX7puSdIBGPEogEMTg87y5vUGlCv1TAUBTxOG019bTQfHiLs32Vr\n00+pFGff6cdoGh2go6+b3cAWmXwGf2ALlUaBtFzDZjTS09lPi0fPG69epIgclUSCIC9jtdqpZgpo\nLXJsDhv9PfuQ61UsrS6SKcbI5yMEw7vkoxlCiwmMzXJWZtZQKQXEUo3hE11IzRJsVheVUg2TwYxQ\nhWqpwtrkP9+Y5v/3RPBnX/0PX3ng1EHu3p6hx2NiIzzDejTCa6/8jInmYaIbftZW79Pi6aMuKVMN\nbyLktnnnH17GZWvC5WknHk0zf/MWJXGTzQAEIiVsVj1dI0epi7vcvPEjSlmBHl8z5VwAo66Z0fEH\ncfS2sXD3Dh53MwaLnLXp22TjSVytbSisXhavXCI+f4F8Zpq4P4pYk9Li9mHWSshEUhSrDepijfW9\nTexmLzevXEQQ2lAILhp1C6EQVKs5joyUUUvaqTXqiEKNzo5m1AYl4/u8GExldnYS2KyDyPUG1vd2\nkDqakLZ7KcoFtBoJM4u7rKwHUEtEtGo5I/v6aWtvom+wF4fNzt0718iVRZKBLKLFg6Re5NqVsyi9\nHYQrZSJVkVCyBCjwtKnQKCUIChPJaJZKA1bmV3j80ce5M32Dtv0HsTX3UMjl6XRaKWWi5NMJKuUS\nXo8Dp81BNJnB4nRidbWjdTejUBuoVWsEJt/l2GgbgiCiUZTYmb3H4aE22tp1TL37IzqGjlBFgVSn\nYai3E5taJBJJIshV0CijysepZFNcv73MViQMKhOrS3u49E6unPkR+3sGWZjfIBCMI9UayObjaNzN\n1CplFOU8MqUGVDrSUheirZvdUIbpQJqVlSAaayd2o5ZoIkRy6Qqf/J+/wtprr1Eu1IiWRYxVNQuT\n9zEbbITTGWbuzpPbS7OciVJWCBgMOh554pdxNYG7tZO1zSW0rV6WtvzkM2W2tveoFONkUjKkUhX3\n708x0NGCkGww9sAx3px6jcBygP2Dg6QrOaTaKjazBbEgw+ftQKsQqNbzeM02jAobL/30h7Qe9ZKT\npRA0ZTTlMO5uGe42I9auPjRmL3M373FgcB9vv/wuBqWeSiDB1KXLHHziYSReCSPHjrETCnHl8jWq\n6TzDg0cIRLYJRsK4dEpqKi2Li7ssTi/ja21mK5ViPZLhiYkDbMyt4HY4OX7yFCu5GDNrMdbjWSoy\nJSmZlru7cea2MizPLaIqpfDpzXR5LMxMLnLk8CB3bk1iamrmpb/9IRdeP8fo0AHWtrfxB1OcfuA0\nqUyRRCqI2+cmGcmRjCRpczsIFdd54NkRpGop3jYfziY7TpeCB04d5s0fXWNtKU7XkVHy2QIlRYNQ\ncJG6TGBi4iC/eP019u3fz87WKmINPIYmrDYnGbHI2vYqCztbPDB+gmQ8RHxrk3AqT00UqOWSeAwG\nhFwFqURCKzru3plEppKTSSepSsusL09hLJYpl1OsvXcHq9dEYG2NSqrA7bNXqcVSLJyZx6Y3EApF\nkZk1PPb842wuTqOzK9FqlaSSedpbe1lbXcJqN1JrlMlmKuRSRRauBpBr1JQlURRyLRIF9B5qxdfe\nilZtRCqoKRWqxGMxVGopWxthwqv/iqaP/vU3/+wrX3ymi36VkaGmLsL+IrP37qCTybl7bZkv/cHv\n4et2US9DPrhOox4mX1bz0Ed/A71CRr4mx9lzDKNFhfXUb1BMZ0EhUK9JqFQlmDVASYbF3s/M0jYj\nR54mHPSzsDCNt60HrdLGzZtn6O7uZ2ZqE6O7B7PXhZiN4vbayEg8KFVyGko740cP0eRxUCqWiaRy\nxPIlCiIotAa2ggnkUhlCWUJTUys9Pb1oy2vIUqvM3gkQT6t4/jd/lRaXDYdFj7xR5Wd//X1aWo5z\n9rUz1Moh/P49RK2ebL7Eo6ce5IlnnqZ/7AjB7W2SuSwPHn8AiVSFAhnhQJqlpS083V0MD4/SP7aP\nEhJUDQkyMc+R4w/hbW7D0+QgHIhiVysY6emm2dpNYHkdq15NT3sr4WAQh9tNtVLCYtETzgtE0nVS\n2QpymYpssUZDkCNVqkhnCuxF0ySiCfwbK1itRmhIkTYa9PqaMDe1MnnzMqUqVMsiQ6MDGFrbETRO\nHEYDyVgYQaZGUChJFCREKjIEjRmNTIpNKUUrgWL0Lt4uL46mLjLpNM19fQTC52kdeZb1SJWCTI2i\nlEInLeJ2mQn4d/DvBmjt6uTnP/kmJlmOA/tGkNIgvDiPRyxQSCwjq+yyc+ce7W47TSYFidk5+seP\n4O3vwWQ20zZowWhzYnF30Nw/TqGSobWtk5KiQKmqRqq0sbfjp9k7TkkwEt1KoWoowR9mcmEFl6cF\naVnBaI8TnTnF+MRp1pfWyVdh7tpthHyNUwcnsHgdLC8tk23o6O/bj9XmpFStojXquXN9loa6SEkn\nYjO4ySXKhPNxDhx4jstLG0SKUoqyJu5evUSpUKCai1BNBHnwMRP1hgSNYMbX2sLkzftYbG2sze8Q\n3Nqjy+Ok1e1BJpcRCiV4+53LDLc72VuPcOhkH76WdiZnFvH4vOgbNTxmEwcPH+OlN99E4TQys7bD\no08fZuzAIINdTh566kHqJYG97QB7iQxXF/zsre1QrpQoKSRM7+6wHcuTKKbQ+Jy4enz4t/bY9+Q4\n3uY6gVCZgxOnCIfC6HQSFqaXcbs6UBitmF29OKz9KJVy5GoV6VgMhBoqoUH/cBsuExTTSYLpAB57\nE5Vklo7ubtJLu/R1dLO9vkVyap5asoSuIXD36n00ehV5WQ231YrbpcflNCHXqSjmahRTDdy+ZvYd\nfYrlm1fIZPJMze/itLmw6mH41D6UDTDKJBQpUBBrWB06nCoj8XCEfUf3k8jmaO7rYjMYoiYVGD48\nysjIAAt3b9Hic5GpF5FIlbi9NubmZwmHowjyOvVGg2S8TCZWRswLiIoqZqeRXLJAOV+kUigjUwpE\nolFi8SCh4B6NahlHk41SFXamk/96rCo9Tq34v3x6mCNdHpA4aBo/wRvvfp8WRxPHDz/G8uwNGnUB\nvaUFi75AJDSDVKOjUnLRcfAES9euENrd5dSzH2bx0ju4D53G1NcF2TS3f/x11OltQiUTxv6PIqqk\nqIp+6mk/fQOt3Lt1nWCyysTJ08zOriPX2DE7nUjKFYrZAFK5lI1EDafZilSpYWlnG7EmUqiXqNfr\nSCSgUiip1yoYlTJa3VZaXM2Y9FIisSQaTYON4B7Fepl6DYaPPk8ysEpidRWrWkRmVKOUWnH4XLz5\n2i8oGjwk0nmkchmN6vv+qTqNCjkSKg2Rjz3/FPq2flJLc0zfuomntY9KJUksHAO5kVo1itMhoaOj\nlcn7cRwaLeGdVfoPnmRh+j5DoyOkg7vINCr88Rg1hZxEIoHb5cJhtCBozExt7KGUK8jnizSSOZRm\nE/lKAYPehEohQSsXuPzGL3j8qQkctha+/53vM/HgswhqA6VaFRl1dIKISSsS2pqjv3vwfaWHQsel\ni+d47rO/xd/9p/+VL/zuvyG2HeLln71GW98Bevt7uHr9DkePjGB2d7C5MENKbsAkpBjq8TG1WmVv\ndYHR/QNk8xVMegPVco5wNMFAlxupkOHW7XUK4RX2Hxzg1kKautlHuVpB0RBQSSukdu/z0U9/kvOv\n/C2VnV1Ofvy3iAV3aTabuXTpbeKpMiq1FL3VhrQuo6OrnwvTZ7k6dZYTpyeoZeBTv/Jl/v6rv4fd\n2IRMqaH1yElqiRSDA4f4xff/jqQ/gEobIYSFoe7DLNyZ4tCzT1Cu1NmavUeyXCIRS/OFL3yer33n\n/0BqUSKr1pA3EmjdRmSGLPvHHkanGeG7f/lVLK1qQtk49bqNgwMHUWq1JMPLlO+H2I2lOXlyH9PX\nZ8nUZazOb9PSZMHsbMHeWcW/lUSnkaKXGBjv6cK/GeXFT/0yi2s3uTA1g16dppquEU9IQK5mz58g\nF80Sy5fIZGIYRSWnjh9iN5uhUMswO7+BWaGjxWJnKVNkLxjCbDIwONRDdHmJ4+MHCDcCSD+wWZUY\nlbQcmKAhSvnBV79G/6EuHF2tRBIZxocPcvvKHVr7Orl59QoGmQ6LRsQ70EwpGUCGgN7RxfS9u9SU\nAoXVBGqJl1gySv94E7PJHTqdTkKVHMcOH2L53DymgkiTw4pEnqNkaSMQ2KMiCLSMdHP1yiW0Tjs6\nQY42HscqE5HV1AS3MigUKuqqBs0tbnLJPDK5hq1wCquhRqpQpqPZyex2jJaD7Sj0cvxrYSqlAjKN\nCqmyQTCc4/78Gt2jAySDcXwtbmpiDZnQYGBsgPX1TRwOG7t7IaKh913nwpEIBqOORlWgmhdIB0sY\nnRoalgT1mJpqTqBSziNTSGntdqDWgEKqpl4TaBtrRiJX8a0vvfGvp0fw7b/+q6986lQrbZ0FEnsS\nFhfvcWJ0jNDMPWYuvoHF1s/S7E1mp+6htA3Q/cQXSIZWCayUuHP5Grtr09hNMqa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kB42U\nlU9+7gtU0nl2Dw7IFTepFpLIeieHB+vcubvI1NwE0fEoVk+IV7/zHVaW1gnFhyhWe3i9DvZXNikW\ny1xbymA3Wjj3xDnUjkBZTVHuVfEqVrpihGgwztD4URKri5STJcLBIZbvPeZouI/k+gqzp6JktjaJ\nDbhYu7fI7MgwDocZWawTnQwzujCCrl7i1MIoVy/NcPe1B9jsYbw+Bw6vndR+ifyuyL3F+/z73/ss\nS5ubGFUjX/g3v4s3EGRvc5WZvgn2lh7x+Np9IoMDnH/qefYyO8SjA7g9MQoP11hc3iPf6RI/Ooei\nmHjq/BP4Ix7e2voZVq+bvXaVtewuoqnDQe0mxWKC/cwOHZ0OhToWt4XR4VPcu38bpS2R3D3A3+/H\n64pisehpSW1aPZUPv/xJsmu7OCWNpZv32Vkts58TcQfdvPv91/B3uig9A8lshsjIAPVyE6PJgcVi\non/Yz/5eGq/PgcnmplltcPzIBB63gxu33yWXKVA4TPB7X/hdXn3rBpeGo5S9CslSDY9zkqWlbfb3\nUwTHBvBEXCzuLvGhjzyDLOu4+2ANk86Ib3iAuqzg6CgELBLOoAfBZWXr/U2MRgO7GztMjQ5Qy5Up\nFVU8YR/pTBm704qYrlHYTKIoRlRBQVBVFFlGrPcQdEZ8ATejxyO4hoIELW6CgT4y2X2kXov1e4+h\npyAIKk6vg4A/xJ03Nn5xIPjv/+VPv/j5Z8ewSXU27r+G0shTPlxCFCucuvox6Oqw+PqoGgU0Uc9A\n3xhhbxwNBZtaxGi2sn2YpCd78URCBGNGvFYnQv4RhbqDXMdOrtlmajCAw2ohVcuja0nY1C4tuY7V\noGd/ex2PDHqlzUg8QiKRo93TcDl1pHfXKWwtsr+1iMmosHl4QLFhoFprU20L1DQDdq+fUqaATZCI\njLiwBSZZ2thFZzCh1dN4+2YYe+Yz0LMilwtEAkYGpk8iNQ5Y20iw9fA29Y5MJZdBNdi4fe1t+vsH\nMbis2N1hrt+9TaMt8e71RYIzx9HKJU7OTVBLrREwKVDfZ7emUJTMHJZryNYAo319FLIJ5J5G30CY\nWqdNpVwH1ULyYB9rt0tVrDEQ9CKnlnHbbdzdrcLy17h97xH6TpmnTo5zmMphsMfpdjrEJwbI1FrY\n7BZmji5AK4NOddGTNXodEZPLjC00jD/iwxUKopk87JY0dq+/yVDAiCrmsERniEzMIJhsuMNR5hbc\nJG59GSmzQXJ3hflnf4V33v0RjYMEamWXlinG2g/+Dr/DwdDUINl0jbHZcXL5IuVijZArwuTscWot\nHXK3SHLxFp52AWf2MXIjQ3/sCFZvlL2ESCK5yxf+6P/AFZggdZhk++FrRHxePH2zOF0eOqpCKBRC\n19Vx+9VHLN9KEp9c4NU3PmD+9HGk9CYYIO5zcG+pyPd+eAd9R0PrmZh/8WW+8+3voeuYuPjEOFNn\nFhC7KsevHuf1tx/y0nNXeefuq5icJp6cucDmcpPMbo6GKFHd3EUvNchna7z11vt4vG7S5Twtk54I\nUY7NTLB//REeXxit1CAQkxmY9HJQWaeeKmIy2UgoNRotDYffTdTpx2nUsf+oQNTiZnR6kMm+af7u\nR68Rm5qh3x/l5IlT3PzgZ/gcLpKJFGaPm/nzM2QbVcqdKoOBMKuPt6mWmhQOS9hMFsr7WXKHCWyj\nLg5ye/zwmz9k8GQ/zY6C1O7h9nsIRsc52O1x9/VDGnWJ0elxgoEQB8k2cqdHtVREFfSU8x3c1gB9\n/T6WF/cwaiZadYlivkCj2MTm8FJpSgwOhXD1aUiGJqrXiOxwEDoyhGisoDr0ePQOwgEfNreRrtZG\nb7CRXq0gShIeh5H4UJQBrx9Tr0sTlVqpxdrhKk9Oj2L1B6hXjRxW2sTnhsFYo+UQGT85xeLGGoHJ\nYUpija5eJmKz01jewWsz0SxlMfWFSe2IdDslLKoeodumUJSoNnsU8hKdfA+lreFz2pENbebPziPK\nXcROD51qQmpqVCs1xoeCHDvSj9QVeeb54+zv71HLFkkkslSrOR4tJghFnPh9cbweB94BH+2enu3d\nDZLL4i8OBF/9ype++IWPXaXdNGO0uhgYGUPSnNTELssPV5k5Noc5GKadShL0WLBYbOgMZiqtDrbg\nEGMvfISpMxcx2Cw47GZuvveIvoCPN17/Nntrj7jzwdtMH5vFb+3x8O4bBMwuvCYJn03GaPNQbbQJ\nN5MsLa5hd1g5f3mOkclRfLo81UyetupGM9vBGmI/XUPTbET8XnzhIB/6xIfJ724zFBvGqG/R7ipI\nFZHNg32cjiBKTyYSGaBjdrF45w7h/hiHhQJtzYieNtJhErtBQGunsHhG8PTP0D88yoc//ml0ej09\nGWJnzzA9PwdiDdkRpC1WcatdnKjcfvctTLoOdr2LgUiUdmqTktjA6nZwav4UXVFC6sjEgw4cFgOz\nx0/gdQnETh0hs7lGR9ZRyxZwWaCgmthfv48QXCDWF0Wqd6nXWihyD53Ni2J10esoiLk8jtmnKEoq\nQ0N++kJDOP0xsvkaiVKDQNBPviIhdUCUBRAMNA0mkgWR4Og0Op2Az2an126gtSts37rH8lqd7c0C\n85cucef+ClbXOIRiXL70FHKhytrWCo/3s3gGT+MMRtg82GV6wEuymKOiGTEYDKTX7jI4NUC7VqKo\nNlGHXyRjjlC2hMk1VWRNxSFAzNojs7vMxGA/4cgIO4ebXPnQr3Lrzge8+MIvYTM7uPD0C3xw6w5f\n+OM/YPL4AgaTwpn5F7j1zlus33lILCAzfGQKSYFri+sYQwHWV9b50LMf4fjJMaLRAd58/bvovFb8\nXoVwdIrdxD7NdJpjAyFWthKMnDrLRmaZmWNx0uUMn/v0b3Ht2pv8xqc+wyc/8Qk8DjedepfllR0G\nPR62RQnv5CSXfuVFnj6/gMU6SKlUJBgf41Fxl2KvQbKeRrAZGXFGEVNlZi6eBU3E4/Hy+CDJyHw/\nA7Y+jh27wI0PfkwxnWLh9Cn2i4fcWF1ibWOP8mGOitahVBWJBOPYjVbsVjsb95foKWa6XRm/w4zB\nZSZbLhNwR7lz7RGnzs9RSCa5MHWGuiyit3cYGBqk2uzhC4yh5Dt0uh2qlRaXLj2H2WgkNtrPa99+\nm2qhjoaOoNdH16xjfHaG9dUNkvsF4kfHyReqNHsNAmY3DreNxOM9Qu5BBJ8HLDpsLiulcolqsUG1\nJGNTLTSqFYb6ozQrdQSDiWIuj2wzoun0pHbLVII6UmqBnr6FWfVw9okFGp0EfZEQzUoZi12H0DWw\nvnnI6Xgf7eIBlW4XzawihDwIej3nTkzTFx2gkczzzMXTbDzewuq1YbSa8dgdyJKGyaAy43Vglao0\n0zWkkkJqL4ddb8Jud1FOSOzvJQkMeqh3ewg2Fza3l14XcgcNonEbZpOVilgjHAvh9LlJZg6QpDbZ\nFekXB4K//su/+OKFhXPocLF2WKdtjIAjgtnmwRUdp90z8fDet1Ad8/TaTWjtkdl7F4vBRsjvRC3m\n6JbLCJqO9f06QwPH8EX6iA6fxB7rIzB+mlSxjKI6UNEjqTn209s09FFQBAS5jcXlITp3gp5tgEQh\nTbXSpqPZGJw+zeZWhnpbIpnN02y1aXeK9PR2WppCLpOlmNjBYW1hMwUpNJuU8pCutOkoPcxGHX2h\nKAfJPaKhCJ6Am263wcmTZ7D7Q4xevErrYI92q8hI3zgHjRarqxuYzT0iFgsry3eoHWZ47ZW3UGWZ\nofgAz166wvSZGcJjY+g6XU5eeZq20YzWKqP2Ktj1Roy1Boerj7j48Y8Tn5jhRz99ha99+fvMzU7R\nlTQe37hNf2SUds9CT9VIb91FrMtcvjCBwR4iq/Oh2kP03BF6rj50Rjuy0UpHMSC7+jCZrWgGI6Ji\no6aZKdSbREJuBoemkHUG6rUGgl6PYDCh1xSMRjOb9+4QdbqwaD0clg7pnSzZwz0mji/g7R9j5Pg5\nHDYDQWeLTmkXXb1Javseq3fvUSh2OP7x38RiMjI+FCVXb1JI57H0UsxMHSEYCzNy+iS5rWUSxR7O\ngRAGUw+TzYpOE1B6zZ9nKVN7zB4d4p3XXkFsSYyNT9OSzCRSu1x98iKi3GL38RJmux2rXcDhtnLv\nvfd5/tO/zX/78//Cxz71r7BKhzRlF/3HjxGfdmEXND7x0eeYHOpn5cF1SqU6C09eZW1jG13PSqYk\nsruxz3BsCKfTxdLjHapZiebOAb/56c/xxrffJn3YINd6zKlLcZYPlyhJO2wuZzFrHYYXJlgu5fG6\n7LQOtlndesBq5iEnpxdYebTO7dv3OHv+Ai6vQsgeo8/Qz7tLdwiNjKHvqGwubmMYHcLgstJpVDFK\nCmPBAFI+jcPjYmVjBVEqk8xXmbM6iAkKHqeD0SMzhEIxCpkkcq3E8alhTLQJ+8OsJzMkUgfMHJ1j\nb2Ofj3/sKnv5LRwWH/m6SK2Qx+UO4/H6aes0Gu8u4hr0srm/hs/ro1IpsPJwEb1Yw9HT8cufeI5a\nq8bw2AC1XBaH1Yhs1nBEXaB2aWsSHp8bk92EWTAR6ziYHRkmmUxRaVTpaD06ioagdQiPRQjHY1w4\nc4JypYbD68LmtxCan6eQSKLrwIDNxc5KmvmBIfYfPMZogscPlxEsepC61CtFtu4n2FhKE4v2k6tu\n0D8zhm0mghYUMBoDWFxODtoNIuOT+OIz/NV//joOQ4tLR+eolqo4LWZsgpknLx7Dotexup4iU20j\ntTVcFgs+jw2DwcTs0SEcYSMT88dYXd8inUyhx4hsMDI0d4KSVKGtiOhsRor5Ml21g8ls4sjsUe6/\ntvWLA8FX/8fffPHf//GHUaQkPmcLTedHbzdiRMXv8yE2K5hcR9BMZnpGI6NHTmMfWqBncrFXaP98\nli9YURUNsdIiVSyzkTjkMJmiIsk0FYUz50/RP+jBoFho1HaxCP0cO3oWpVfDbLSitwXp6fQ0W230\nlgC5chqDwUqhmOL80WMcP3KSgNv18+SjM8z0+BgOUw+xeoDd7KTSNNMzmxATh0SdDtL5AjidNLpt\nbBYHRlSC0X5+9uY1DvMtEskMYrnC1qOffxfcyKmcuHAFp0klXyrRrhTJHiwRCEbZy2cYHO3DbYfi\n9j0e3rvJ4kaKdKaGrgu72Rz1ahtVKTE1u4CssxKdGaUp2Hh8/SZdg55Qn4vZ+aOYdDoyhQbV7WXc\n3iDL66tkN1/H7YmSyIsMT8zQRkdTkgk4bOhEkczeHkZPAKNBx5GYgahTI+gUcFtM1OsNdGoXi65G\nU7RSL2fIZ1OMjY5Qyq3SVaz09EaSmTRjs8cQMaGzudlNpPilT4yycCJCu3iA9Pgu03NT6Hs1/N44\nMyev8qU//ytu3DsgISmcuXCUX3rpOTa31/D57Xzwytc4PhEhEpjgtR9+m4WrT2D02Ni6/R4T4/20\n1t8hMjRFaX+PF8+e4Jt/9iecODlNutLAbPdz9MhRvveNv2f+6Dz1Zp2p8VG+/uW/wYHMjRuLyFID\nSSyS3HxEyB9g/+FdaOQ53N+h1VJoNOs8un6frdUDfv33r2C07HDv9k1CkSA2r4lCsoBTp0dXPyRV\nyjPeP8mDu/e4cOU5XvrYr2HQ9AxPjPM3f/v35PNV0tUKuKxk93pIHQcZVcbqiqPzetHrDXziiSfR\ndXqYfDqMepkzUyd569rbrD3exO8M0e8JY6nB9nYSg0nD5fXhwMTBXoLf+l/+HelSAYvDxtqt+9h7\nAvvZJBWxhFHRmJ2Z5fylS+i6dfxxG6pZ45c/+XlWH1wn0xHJik3OT8wRHh3lzr27XH3yCtcX1/jc\nb32Sva1tZkYnGIkNUUsmaFd7WPQWNhaTaKoZVelSr1bxj8fp6SoEggP0Dwyy9PAxPpOd8Eic6MQE\n+1tbTA8PExqOIygSzp6KUVMQy2XsEQdH5qao1aq0my0sLSMHpTIZrYmiU9DpdUiyjNFoIhiNs7O2\nTqfeYPVRCq/fTb0rYvd7UAQBg1nA5jAjRFycu3CGd965TyjkRO+04AjbGHI5ESx2LAY9PpcTi8GN\n1hHY3M2TSh/gDXo4TJSQJBGrzYLDaGXjwSobd28xcHyCobCPoMOOVOkgWHWUsm1u31unWmtjNhkx\nGCX6on687iCqTkC1tLAOuTEH/WR3DhgZH2P24hPUimUEgwy6Luamh8JOieNHxygVCmgCgEylUmHv\nbvkXB4L/56//8ou/+fwVapUSYzMfJ2LXc/e9r9CrrjM+fopQMIxRr+Ky27AJAq1qhdD4FEKng9ni\nwuyOspnNcZAuUZAVKtUi1WqDtqpjfmGK8eFRcrkUO+s7SF0rM2PnCfgD6A1Ger0uzr5rGN1vEwkL\nnD16mZa4jEGtYDB2cBtcBIJ2ssUWwVCIpUerZKsNXBYTyWSSg3SBTKlBs1HHKJjwWMzQLdB/ZJ5G\nrUqnI7NbSLOfT6EcPkY82Ob5j73IwmSMcnaPkM3GmXPneerqVZJ7y6RTGUodDUk10jW5KaYOeO7S\nRbqKxivffpvZhVNcuHiFw2wZpacnFu1jOBzAYTOwMH2a+xurdCwCtaZIutqm28zTaBaxagIT46dY\nWV9l7WALa8BHARNmt4/Y8BTB/iinzz1BYmcLo9PDxPAIrVqdZlfGPjCBpumwmiG58ohSKUmm50HH\nIR/87JuMmMwkb7+FpCiogoFA3xipUpm+0BDltgyqzJljwxjpIPd6KJLEjENj9fYN8nmBxMptZk48\nz43332BsfJ6dSgKzo4UhMsnC1Wd46SPPMz0+SjKxjaaZKNUyaFWRh7fv4/JbUR1GvveNf8Eotlhd\n3eLWK7eo1LsEI4Psl0U2Nh6QK2fYXVlhJGBGRSE8OsjZkydIpHZI7T3i1R99B7/TxFZij9/4rT9g\ne/MxraYRqV3gzPMv8ej+IiOT45RS6zRKFao1lZpiYfJUHzOrw+/IAAAfKElEQVTTRmyWLgY5zJG5\nl0nvFmkWd7HZNMRqgYX5J7E4ND7yqS/g89j5j//bH/HmrQc8fPyI0xMhdIKR//Rnv8Or37rB1VPn\naVdrTFh9zIZCaJ0mT515mpDbxebGPUb6J9l5vIXROcDdzU38Q8PMzy8wOjbBvcX7lHaz2BWJTCXH\n6sYeMZ+HcjKJ3+lBy5RZPzxkZqgft8WEyW6nqSqk9g/JlqvoYhGKO3d5+sUFHJYOgtagWOpgMjlZ\n2VhjZXeD08fnefDuHdSgg3NjU9x/7zrWpow/GObxzhp9fhfmoo3tgyypvV2sDhNXnnwaSadiswaw\nKjq2tjaxGe0cmT9O5fEh45Eouysr+Dw+vv/Wa9gjURZ3d6lXunijcSZmj5E9TFPKVVldTNHtGZg8\nNoOsyVTrdXxBH16/D6vZSiaTxeVz4/L5sYdUQrEgXo+DdDVHvVBBKYlIjTatdotOp4tmg/p+h+BI\nGKfXw16lQKlYwIDAwXaSgMvBwU6CsMOI3etkcOg42XSOjtLD5QlTahwwEnGwtrhNwOvFFY7z3Z9e\n59QLs0xNjLCSreL0mqiJNY4fmUbVqsxOTmGM6rD2G2lpCsXlAj6HQNekI7+4gyi3Wb75GLffSX67\nSnW3QK/VY/n2NnStSE0Nk8GCgo7k0v93CP5/vyw+OTep/cUfPkfUu4DVYOUH3/4nBo+ewhkbQWrX\nmBiM0RXr5LJFhibHiY73o+8q3Lm/S1UUsZgNWA0daKzjsrlRdGY0fOxXazSaTbROC5+hx9jUAqVC\nk0ajSCGfoqcHb6hM0GXkyNQ5lG4/FbFLcn+bqYkZup0kgmqmXpNwuRwUSkkMBoGNzVXcvjAdRaUL\nXLp6Bb9Nx8oPvwLdIod7XSqWSUoeBxWpicVgRNHrcBvArMDC1ATdVh6trXDu0y9z85XX6Ha7lIoV\ndEY/lZbMqZPzSGKbtlhHJ0v0el2agp3RcBizpclI3zg/fvVH9CwRDFoPq1VHJBpgeHgSm9dJulwh\n5oly50d/jzsURaqIHOyuMTL/Eu/+7X/gX//Jn7P04BuMzP46769vU89nqC1+gF5nYHzhCAf5Ci5v\nlI2dApLZhmASOHf2BIIsY3YEUAQ9R52HmCMjrKyVOXHyND954wNsRpg5cgyvy8z7b3yLeknE4I8Q\n93qpJTZ4/qMf5e7d+5g1ha986esIwTinL19i7b0P+Le//2v84GtfIdbfx9ErT3H97Rucv3wahz1I\nLpPDosDQuI90ap+NR4+oNmQyhzma1QwDoT7CR6Y5celpFMFCev+QWn6XTr3C/vpDfG4vk0dP849f\n/p+E+/0IQptGR4ff58LQKDE7E6PaKnP7xhr/7v/8z1idLt5581V61RR76TR2swkUI4VSkbnpIXYz\nZbYPt/nT//uv0GkCX//qX2A1hVHVNs8+9zG++8/fxOYwcvnJ57j+2usE3FbyySSq18bFq1d461++\nS8PjRt/tceT4Saq1Ao9W9rAZjLgsFvqiAdLpQ6ROnbHpIZqaE0+8w3f+6SYvf/QjfH/9BgOBYa5c\nusyDrVt8ZOY5Pvjpm9y9dRfCXU48N0N6s8WL88+zWy6QyZWRJInVvVVevHSJO6++zcjEOMVCgfGx\nSeaeuMTbH7yNTqfSNvRwu6OkH24gOdw0DU2uXnmaV/7pfxIzWLA5rKwl99E7TAzb/bTbddp6Gdnu\npt/spJIXyWs1DpIil565wOULx9h6/IhEvoipJZHVKaTLEjuJQyacAWbiYaxmEz29gBzzsJnPEozH\n8dtddFS4/dYd7C4TcrtDMVmjU+2xcHWGmlRCbEtY7CaMditBTxCp06ElioTdHm6sLRJzB7AoRgqF\nBlbVhNxpYzAZiUX9WCNO8uUKHblH4aCOzWPCHzTT6TYJ+Pw4HC5KmQIGrweDw4O53aPR+nlmNhQK\n0JJ66C0yfpsOrarxeDHLzkYRk0WHwWRDJ/TwBPVISg8hJzMaC3L+4gyrG4cclvPsS3Usghs5JzFy\nIsL+dg5FNWOyu8guJ+nodLRbbTAIGC0mDCY9otjCYjHRNmnEJg0sf7/4i7Ms/tJ//69f/NgLT9Lo\ndiiLEsZAjPWlJQyCjkA0BnIdQ7tAemuFM1cucPMn38cqCBSTW/gdBjw2HfV6h51sg4woUJJUqs0a\nJh10pRpio4HDZqdeaZLeu0V2bwvZ6kJUjOgMHrpVI7VChekj4xj14A/GCEVdtKolmo0cbo+Pbq+B\n2WAn6A8jKSaanSalTI4Bv4fS3h7Lr34NGxqSWKXrHWL8+Y+zvbfHqAMuzI9isphRdEY0c5DNdJlU\nDRr1JvPnT9M3Mkp/JIZYk2g0FEaPjHH0+Cz9M5MMjk2gyQbE3Ab2oI1KTUe5K7C0fUBX76anqAwN\nxZkaCVGpFwn1D1EqSxh0ZtKZDKmdTdYe3yDTbFOupRCzW/zaH/7v7O1ss3RriUePFlGbPQaOX2D6\nzEWeevnXiE7OYTerxGNxvAN9jA2PMRjo8d5rrzM7cxSpvIEg2Hhv8TFd2YVVLbKzvMTTp+Y52HkP\nnydOs7JKcj/DxvV3mR71Ee3z43e0uXH9A37yvVfIlkTsUR8fffElsNs5c/oY6YYMsQk07xQWqYHD\noDE3f5VM4pC438PSzS+T2NngK199B8/x5zCERnEPjGAbnqMomch1egwMjNOqiOh6bexeP+Nzp5k6\ne4nba2XKCmQzGbp6M0PDIzzzoZcoJvYIREa58OwLfPc7P2Xh3GWqYp1aqUJ6axVVF8FpNRKKOIn3\n9dOsloj29bO9m6TeMfPP3/oZA2NHGDl2FsVSQdGMvPfjn1BvaegFNzure+SzOXRWFy/98ueZPH2V\nNmYep3KcP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ZZJMz/zM+q7jpC3aVy6PMpi3k10cYFsJkV7i4cdO9sIzU3gtGsExqZo9njQ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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im = mh.imread('forest.jpeg')\n", + "fig,ax = plt.subplots()\n", + "ax.imshow(im)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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F4p7zekWHCGVmPAUG2+EX4brOrO7UktklQXeBd3f3xEEpVpBL5rqe2Y/KtVSGEYYYudRG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RjphWO2nNHXHbevLnn29QVf/Oweo3UEq7i7u6PvBxSe9x4+5M2LZ8yqGVddB6PkwJTM5jlS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r,g,b = im.transpose(2,0,1) \n", + "r12 = mh.gaussian_filter(r, 24.)\n", + "g12 = mh.gaussian_filter(g, 24.)\n", + "b12 = mh.gaussian_filter(b, 24.)\n", + "im12 = mh.as_rgb(r12,g12,b12)\n", + "h, w = r.shape # height and width\n", + "Y, X = np.mgrid[:h,:w]\n", + "Y = Y-h/2. # center at h/2\n", + "Y = Y / Y.max() # normalize to -1 .. +1\n", + "\n", + "X = X-w/2.\n", + "X = X / X.max()\n", + "\n", + "C = np.exp(-2.*(X**2+ Y**2))\n", + "\n", + "# Normalize again to 0..1\n", + "C = C - C.min()\n", + "C = C / C.ptp()\n", + "C = C[:,:,None] # This adds a dummy third dimension to C\n", + "\n", + "ringed = mh.stretch(im*C + (1-C)*im12)\n", + "fig,ax = plt.subplots()\n", + "ax.imshow(ringed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification\n", + "\n", + "A first peek at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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TIUUiSlUl7DJ/tetg1YD05jJWLtdKjPY87xxF1SqM7gC8CLk1hhhAHa02YrDsfjWMzK0S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\nrXjCGeZKpRLGpGD1RKPe93JOfusLujonYdpdyHeaGq669FIaqQt2KE3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Ybe7iO01uWIgiGmT8xidZRjjRzgpb/8iSBUrFg0HRyLMJ9n5NB2bGBCSwOf/exnyRcK/Po3v+TA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "text = mh.imread(\"../SimpleImageDataset/text21.jpg\")\n", + "building = mh.imread(\"../SimpleImageDataset/building00.jpg\")\n", + "h, w, _ = text.shape\n", + "canvas = np.zeros((h, 2 * w + 128, 3), np.uint8)\n", + "canvas[:, -w:] = building\n", + "canvas[:, :w] = text\n", + "canvas = canvas[::4, ::4]\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(canvas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For classification, we compute _features_:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from glob import glob\n", + "\n", + "images = glob('../SimpleImageDataset/*.jpg')\n", + "features = []\n", + "labels = []\n", + "for im in images:\n", + " labels.append(im[:-len('00.jpg')])\n", + " im = mh.imread(im)\n", + " im = mh.colors.rgb2gray(im, dtype=np.uint8)\n", + " features.append(mh.features.haralick(im).ravel())\n", + "\n", + "features = np.array(features)\n", + "labels = np.array(labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can use a standard classifier from `scikit-learn`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 81.1%\n" + ] + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "clf = Pipeline([('preproc', StandardScaler()),\n", + " ('classifier', LogisticRegression())])\n", + "\n", + "\n", + "from sklearn import model_selection\n", + "cv = model_selection.LeaveOneOut()\n", + "scores = model_selection.cross_val_score(\n", + " clf, features, labels, cv=cv)\n", + "print('Accuracy: {:.1%}'.format(scores.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us define our own features:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def chist(im):\n", + " im = im // 64\n", + " r,g,b = im.transpose((2,0,1))\n", + " pixels = 1 * r + 4 * b + 16 * g\n", + " hist = np.bincount(pixels.ravel(), minlength=64)\n", + " hist = hist.astype(float)\n", + " hist = np.log1p(hist)\n", + " return hist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use these features for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 96.7%\n" + ] + } + ], + "source": [ + "features = []\n", + "for im in images:\n", + " imcolor = mh.imread(im)\n", + " im = mh.colors.rgb2gray(imcolor, dtype=np.uint8)\n", + " features.append(np.concatenate([\n", + " mh.features.haralick(im).ravel(),\n", + " chist(imcolor),\n", + " ]))\n", + "\n", + "\n", + "scores = model_selection.cross_val_score(\n", + " clf, features, labels, cv=cv)\n", + "print('Accuracy: {:.1%}'.format(scores.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ignore the areas close to the border:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nearest neighbor\n", + "\n", + "We re-compute the features, _while ignoring the border areas_:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = []\n", + "for im in images:\n", + " imcolor = mh.imread(im)\n", + " # Ignore everything in the 200 pixels close to the borders\n", + " imcolor = imcolor[200:-200, 200:-200]\n", + " im = mh.colors.rgb2gray(imcolor, dtype=np.uint8)\n", + " features.append(np.concatenate([\n", + " mh.features.haralick(im).ravel(),\n", + " chist(imcolor),\n", + " ]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we\n", + "\n", + "1. scale the features\n", + "2. find nearest neighbors\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MTNsi9Hw0UQ3DMCyEMCA0QJsEWpGIxbFNCykljzz2KEqF6FATj8V2KFEiUcDE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pz+gR7Ov/Bo0ukkdRvmcPppP0B4Ald8uC7jX5YLoMsEVXkBzjhwFu9uWUckt4BNW5uYP38BOeEA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\n7GgW9AjBYITWqdPYvmsnl557L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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc = StandardScaler()\n", + "features = sc.fit_transform(features)\n", + "from scipy.spatial import distance\n", + "dists = distance.squareform(distance.pdist(features))\n", + "\n", + "\n", + "fig, axes = plt.subplots(2, 9)\n", + "for ci,i in enumerate(range(0,90,10)):\n", + " left = images[i]\n", + " dists_left = dists[i]\n", + " right = dists_left.argsort()\n", + " # right[0] is the same as left[i], so pick the next closest element\n", + " right = right[1]\n", + " right = images[right]\n", + " left = mh.imread(left)\n", + " right = mh.imread(right)\n", + " axes[0, ci].imshow(left)\n", + " axes[1, ci].imshow(right)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Larger dataset\n", + "\n", + "First, download the big dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from os import path, system\n", + "basedir = 'AnimTransDistr'\n", + "if not path.exists(basedir):\n", + " !./download.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute same features as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing whole-image texture features [can take a while]...\n" + ] + } + ], + "source": [ + "classes = [\n", + " 'Anims',\n", + " 'Cars',\n", + " 'Distras',\n", + " 'Trans',\n", + "]\n", + "\n", + "images = []\n", + "labels = []\n", + "for ci, cl in enumerate(classes):\n", + " current = glob('{}/{}/*.jpg'.format(basedir, cl))\n", + " for im in sorted(current):\n", + " labels.append(cl)\n", + " images.append(im)\n", + "\n", + "\n", + "print('Computing whole-image texture features [can take a while]...')\n", + "ifeatures = []\n", + "for im in images:\n", + " im = mh.imread(im)\n", + " img = mh.colors.rgb2grey(im).astype(np.uint8)\n", + " fs = np.concatenate([mh.features.haralick(img).ravel(),\n", + " chist(im)])\n", + " ifeatures.append(fs)\n", + "\n", + "ifeatures = np.array(ifeatures)\n", + "labels = np.array(labels)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use these for classification:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy (5 fold x-val) with Logistic Regression [image features]: 73.4%\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "C_range = 10.0 ** np.arange(-4, 3)\n", + "grid = GridSearchCV(LogisticRegression(), param_grid={'C' : C_range})\n", + "clf = Pipeline([('preproc', StandardScaler()),\n", + " ('classifier', grid)])\n", + "\n", + "cv = model_selection.KFold(n_splits=5,\n", + " shuffle=True, random_state=123)\n", + "scores = model_selection.cross_val_score(\n", + " clf, ifeatures, labels, n_jobs=-1, cv=cv)\n", + "print('Accuracy (5 fold x-val) with Logistic Regression [image features]: {:.1%}'.format(scores.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SURF features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing SURF (this will take a while)\n" + ] + } + ], + "source": [ + "from mahotas.features import surf\n", + "\n", + "alldescriptors = []\n", + "print(\"Computing SURF (this will take a while)\")\n", + "for im in images:\n", + " im = mh.imread(im, as_grey=True)\n", + " im = im.astype(np.uint8)\n", + " desc = surf.surf(im, descriptor_only=True)\n", + " # To use dense sampling, you can try the following line:\n", + " # desc = surf.dense(im, spacing=16)\n", + " alldescriptors.append(desc)\n", + "# get all descriptors into a single array\n", + "concatenated = np.concatenate(alldescriptors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, compute the k-means structure, using 256 clusters:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total nr descriptors: 2489095\n" + ] + } + ], + "source": [ + "print(\"Total nr descriptors: {}\".format(len(concatenated)))\n", + "concatenated = concatenated[::64]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", + " n_clusters=256, n_init=10, n_jobs=1, precompute_distances='auto',\n", + " random_state=None, tol=0.0001, verbose=0)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "k = 256\n", + "km = KMeans(k)\n", + "km.fit(concatenated)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now use the kmean structure on **all the descriptors**:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy (5 fold x-val) with Logistic Regression [SURF]: 62.6%\n" + ] + } + ], + "source": [ + "sfeatures = []\n", + "\n", + "for d in alldescriptors:\n", + " c = km.predict(d)\n", + " sfeatures.append(np.bincount(c, minlength=256))\n", + " \n", + "# build single array and convert to float\n", + "sfeatures = np.array(sfeatures, dtype=float)\n", + "scores = model_selection.cross_val_score(\n", + " clf, sfeatures, labels, cv=cv)\n", + "print('Accuracy (5 fold x-val) with Logistic Regression [SURF]: {:.1%}'.format(scores.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine the features for the final result" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy (5 fold x-val) with Logistic Regression [All features]: 76.7%\n" + ] + } + ], + "source": [ + "allfeatures = np.hstack([sfeatures, ifeatures])\n", + "score_SURF_global = model_selection.cross_val_score(\n", + " clf, allfeatures, labels, cv=cv).mean()\n", + "print('Accuracy (5 fold x-val) with Logistic Regression [All features]: {:.1%}'.format(\n", + " score_SURF_global.mean()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch10/chapter.py b/ch10/chapter.py deleted file mode 100644 index 233720bb..00000000 --- a/ch10/chapter.py +++ /dev/null @@ -1,186 +0,0 @@ -import numpy as np -import mahotas as mh -image = mh.imread('scene00.jpg') -from matplotlib import pyplot as plt -plt.imshow(image) -plt.show() -image = mh.colors.rgb2grey(image, dtype=np.uint8) -plt.imshow(image) # Display the image -plt.gray() -thresh = mh.thresholding.otsu(image) -print('Otsu threshold is {}.'.format(thresh)) -# Otsu threshold is 138. -plt.imshow(image > thresh) - -im16 = mh.gaussian_filter(image,16) -im = mh.demos.load('lenna') - -r,g,b = im.transpose(2,0,1) -r12 = mh.gaussian_filter(r, 12.) -g12 = mh.gaussian_filter(g, 12.) -b12 = mh.gaussian_filter(b, 12.) -im12 = mh.as_rgb(r12,g12,b12) -h, w = r.shape # height and width -Y, X = np.mgrid[:h,:w] -Y = Y-h/2. # center at h/2 -Y = Y / Y.max() # normalize to -1 .. +1 - -X = X-w/2. -X = X / X.max() - -C = np.exp(-2.*(X**2+ Y**2)) - -# Normalize again to 0..1 -C = C - C.min() -C = C / C.ptp() -C = C[:,:,None] # This adds a dummy third dimension to C - -ringed = mh.stretch(im*C + (1-C)*im12) - -haralick_features = mh.features.haralick(image) -haralick_features_mean = np.mean(haralick_features, axis=0) -haralick_features_all = np.ravel(haralick_features) - -from glob import glob -images = glob('../SimpleImageDataset/*.jpg') -features = [] -labels = [] -for im in images: - labels.append(im[:-len('00.jpg')]) - im = mh.imread(im) - im = mh.colors.rgb2gray(im, dtype=np.uint8) - features.append(mh.features.haralick(im).ravel()) - -features = np.array(features) -labels = np.array(labels) - -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.linear_model import LogisticRegression -clf = Pipeline([('preproc', StandardScaler()), - ('classifier', LogisticRegression())]) - - -from sklearn import cross_validation -cv = cross_validation.LeaveOneOut(len(images)) -scores = cross_validation.cross_val_score( - clf, features, labels, cv=cv) -print('Accuracy: {:.1%}'.format(scores.mean())) -# Accuracy: 81.1% - -def chist(im): - im = im // 64 - r,g,b = im.transpose((2,0,1)) - pixels = 1 * r + 4 * b + 16 * g - hist = np.bincount(pixels.ravel(), minlength=64) - hist = hist.astype(float) - hist = np.log1p(hist) - return hist - -features = [] -for im in images: - im = mh.imread(im) - features.append(chist(im)) - - - -features = [] -for im in images: - imcolor = mh.imread(im) - im = mh.colors.rgb2gray(imcolor, dtype=np.uint8) - features.append(np.concatenate([ - mh.features.haralick(im).ravel(), - chist(imcolor), - ])) - - -scores = cross_validation.cross_val_score( - clf, features, labels, cv=cv) -print('Accuracy: {:.1%}'.format(scores.mean())) -# Accuracy: 95.6% - - -features = [] -for im in images: - imcolor = mh.imread(im) - # Ignore everything in the 200 pixels close to the borders - imcolor = imcolor[200:-200, 200:-200] - im = mh.colors.rgb2gray(imcolor, dtype=np.uint8) - features.append(np.concatenate([ - mh.features.haralick(im).ravel(), - chist(imcolor), - ])) - -sc = StandardScaler() -features = sc.fit_transform(features) -from scipy.spatial import distance -dists = distance.squareform(distance.pdist(features)) - - -fig, axes = plt.subplots(2, 9) -for ci,i in enumerate(range(0,90,10)): - left = images[i] - dists_left = dists[i] - right = dists_left.argsort() - # right[0] is the same as left[i], so pick the next closest element - right = right[1] - right = images[right] - left = mh.imread(left) - right = mh.imread(right) - axes[0, ci].imshow(left) - axes[1, ci].imshow(right) - - - -from sklearn.grid_search import GridSearchCV -C_range = 10.0 ** np.arange(-4, 3) -grid = GridSearchCV(LogisticRegression(), param_grid={'C' : C_range}) -clf = Pipeline([('preproc', StandardScaler()), - ('classifier', grid)]) - -cv = cross_validation.KFold(len(features), 5, - shuffle=True, random_state=123) -scores = cross_validation.cross_val_score( - clf, features, labels, cv=cv) -print('Accuracy: {:.1%}'.format(scores.mean())) - - - - -from mahotas.features import surf -image = mh.demos.load('lena') -image = mh.colors.rgb2gray(image, dtype=np.uint8) -descriptors = surf.surf(image, descriptor_only=True) - -from mahotas.features import surf -descriptors = surf.dense(image, spacing=16) -alldescriptors = [] -for im in images: - im = mh.imread(im, as_grey=True) - im = im.astype(np.uint8) - alldescriptors.append(surf.dense(image, spacing=16)) -# get all descriptors into a single array -concatenated = np.concatenate(alldescriptors) -print('Number of descriptors: {}'.format( - len(concatenated))) -# use only every 64th vector -concatenated = concatenated[::64] -from sklearn.cluster import KMeans # FIXME CAPITALIZATION -k = 256 -km = KMeans(k) -km.fit(concatenated) - -features = [] -for d in alldescriptors: - c = km.predict(d) - features.append( - np.array([np.sum(c == ci) for ci in range(k)]) - ) -# build single array and convert to float -features = np.array(features, dtype=float) -scores = cross_validation.cross_val_score( - clf, features, labels, cv=cv) -print('Accuracy: {:.1%}'.format(scores.mean())) -# Accuracy: 62.6% - - diff --git a/ch10/features.py b/ch10/features.py deleted file mode 100644 index 42847b30..00000000 --- a/ch10/features.py +++ /dev/null @@ -1,70 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -import mahotas as mh - - -def edginess_sobel(image): - '''Measure the "edginess" of an image - - image should be a 2d numpy array (an image) - - Returns a floating point value which is higher the "edgier" the image is. - - ''' - edges = mh.sobel(image, just_filter=True) - edges = edges.ravel() - return np.sqrt(np.dot(edges, edges)) - -def texture(im): - '''Compute features for an image - - Parameters - ---------- - im : ndarray - - Returns - ------- - fs : ndarray - 1-D array of features - ''' - im = im.astype(np.uint8) - return mh.features.haralick(im).ravel() - - -def chist(im): - '''Compute color histogram of input image - - Parameters - ---------- - im : ndarray - should be an RGB image - - Returns - ------- - c : ndarray - 1-D array of histogram values - ''' - - # Downsample pixel values: - im = im // 64 - - # We can also implement the following by using np.histogramdd - # im = im.reshape((-1,3)) - # bins = [np.arange(5), np.arange(5), np.arange(5)] - # hist = np.histogramdd(im, bins=bins)[0] - # hist = hist.ravel() - - # Separate RGB channels: - r,g,b = im.transpose((2,0,1)) - - pixels = 1 * r + 4 * g + 16 * b - hist = np.bincount(pixels.ravel(), minlength=64) - hist = hist.astype(float) - return np.log1p(hist) - diff --git a/ch10/figure10.py b/ch10/figure10.py deleted file mode 100644 index 6cb45e7a..00000000 --- a/ch10/figure10.py +++ /dev/null @@ -1,20 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -import mahotas as mh - -# This little script just builds an image with two examples, side-by-side: - -text = mh.imread("../SimpleImageDataset/text21.jpg") -building = mh.imread("../SimpleImageDataset/building00.jpg") -h, w, _ = text.shape -canvas = np.zeros((h, 2 * w + 128, 3), np.uint8) -canvas[:, -w:] = building -canvas[:, :w] = text -canvas = canvas[::4, ::4] -mh.imsave('figure10.jpg', canvas) diff --git a/ch10/forest.jpeg b/ch10/forest.jpeg new file mode 100644 index 00000000..b8d977c2 Binary files /dev/null and b/ch10/forest.jpeg differ diff --git a/ch10/large_classification.py b/ch10/large_classification.py deleted file mode 100644 index 8db3571b..00000000 --- a/ch10/large_classification.py +++ /dev/null @@ -1,108 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -from __future__ import print_function -import mahotas as mh -from glob import glob -from sklearn import cross_validation -from sklearn.linear_model import LogisticRegression -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.grid_search import GridSearchCV -import numpy as np - -basedir = 'AnimTransDistr' -print('This script will test classification of the AnimTransDistr dataset') - -C_range = 10.0 ** np.arange(-4, 3) -grid = GridSearchCV(LogisticRegression(), param_grid={'C' : C_range}) -clf = Pipeline([('preproc', StandardScaler()), - ('classifier', grid)]) - -def features_for(im): - from features import chist - im = mh.imread(im) - img = mh.colors.rgb2grey(im).astype(np.uint8) - return np.concatenate([mh.features.haralick(img).ravel(), - chist(im)]) - -def images(): - '''Iterate over all (image,label) pairs - - This function will return - ''' - for ci, cl in enumerate(classes): - images = glob('{}/{}/*.jpg'.format(basedir, cl)) - for im in sorted(images): - yield im, ci - -classes = [ - 'Anims', - 'Cars', - 'Distras', - 'Trans', -] - -print('Computing whole-image texture features...') -ifeatures = [] -labels = [] -for im, ell in images(): - ifeatures.append(features_for(im)) - labels.append(ell) - -ifeatures = np.array(ifeatures) -labels = np.array(labels) - -cv = cross_validation.KFold(len(ifeatures), 5, shuffle=True, random_state=123) -scores0 = cross_validation.cross_val_score( - clf, ifeatures, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regression [image features]: {:.1%}'.format( - scores0.mean())) - - -from sklearn.cluster import KMeans -from mahotas.features import surf - - -print('Computing SURF descriptors...') -alldescriptors = [] -for im,_ in images(): - im = mh.imread(im, as_grey=True) - im = im.astype(np.uint8) - - # To use dense sampling, you can try the following line: - # alldescriptors.append(surf.dense(im, spacing=16)) - alldescriptors.append(surf.surf(im, descriptor_only=True)) - -print('Descriptor computation complete.') -k = 256 -km = KMeans(k) - -concatenated = np.concatenate(alldescriptors) -print('Number of descriptors: {}'.format( - len(concatenated))) -concatenated = concatenated[::64] -print('Clustering with K-means...') -km.fit(concatenated) -sfeatures = [] -for d in alldescriptors: - c = km.predict(d) - sfeatures.append(np.bincount(c, minlength=k)) -sfeatures = np.array(sfeatures, dtype=float) -print('predicting...') -score_SURF = cross_validation.cross_val_score( - clf, sfeatures, labels, cv=cv).mean() -print('Accuracy (5 fold x-val) with Logistic Regression [SURF features]: {:.1%}'.format( - score_SURF.mean())) - - -print('Performing classification with all features combined...') -allfeatures = np.hstack([sfeatures, ifeatures]) -score_SURF_global = cross_validation.cross_val_score( - clf, allfeatures, labels, cv=cv).mean() -print('Accuracy (5 fold x-val) with Logistic Regression [All features]: {:.1%}'.format( - score_SURF_global.mean())) diff --git a/ch10/lena-ring.py b/ch10/lena-ring.py deleted file mode 100644 index fb28b53d..00000000 --- a/ch10/lena-ring.py +++ /dev/null @@ -1,41 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import mahotas as mh -import numpy as np - -# Read in the image -im = mh.demos.load('lena') - -# This breaks up the image into RGB channels -r, g, b = im.transpose(2, 0, 1) -h, w = r.shape - -# smooth the image per channel: -r12 = mh.gaussian_filter(r, 12.) -g12 = mh.gaussian_filter(g, 12.) -b12 = mh.gaussian_filter(b, 12.) - -# build back the RGB image -im12 = mh.as_rgb(r12, g12, b12) - -X, Y = np.mgrid[:h, :w] -X = X - h / 2. -Y = Y - w / 2. -X /= X.max() -Y /= Y.max() - -# Array C will have the highest values in the center, fading out to the edges: - -C = np.exp(-2. * (X ** 2 + Y ** 2)) -C -= C.min() -C /= C.ptp() -C = C[:, :, None] - -# The final result is sharp in the centre and smooths out to the borders: -ring = mh.stretch(im * C + (1 - C) * im12) -mh.imsave('lena-ring.jpg', ring) diff --git a/ch10/neighbors.py b/ch10/neighbors.py deleted file mode 100644 index 1f71d0de..00000000 --- a/ch10/neighbors.py +++ /dev/null @@ -1,64 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing - -import numpy as np -import mahotas as mh -from glob import glob -from features import texture, chist -from matplotlib import pyplot as plt -from sklearn.preprocessing import StandardScaler -from scipy.spatial import distance - -basedir = '../SimpleImageDataset/' - - -haralicks = [] -chists = [] - -print('Computing features...') -# Use glob to get all the images -images = glob('{}/*.jpg'.format(basedir)) -# We sort the images to ensure that they are always processed in the same order -# Otherwise, this would introduce some variation just based on the random -# ordering that the filesystem uses -images.sort() - -for fname in images: - imc = mh.imread(fname) - imc = imc[200:-200,200:-200] - haralicks.append(texture(mh.colors.rgb2grey(imc))) - chists.append(chist(imc)) - -haralicks = np.array(haralicks) -chists = np.array(chists) -features = np.hstack([chists, haralicks]) - -print('Computing neighbors...') -sc = StandardScaler() -features = sc.fit_transform(features) -dists = distance.squareform(distance.pdist(features)) - -print('Plotting...') -fig, axes = plt.subplots(2, 9, figsize=(16,8)) - -# Remove ticks from all subplots -for ax in axes.flat: - ax.set_xticks([]) - ax.set_yticks([]) - -for ci,i in enumerate(range(0,90,10)): - left = images[i] - dists_left = dists[i] - right = dists_left.argsort() - # right[0] is the same as left[i], so pick the next closest element - right = right[1] - right = images[right] - left = mh.imread(left) - right = mh.imread(right) - axes[0, ci].imshow(left) - axes[1, ci].imshow(right) - -fig.tight_layout() -fig.savefig('figure_neighbors.png', dpi=300) diff --git a/ch10/simple_classification.py b/ch10/simple_classification.py deleted file mode 100644 index a5a448d2..00000000 --- a/ch10/simple_classification.py +++ /dev/null @@ -1,70 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import mahotas as mh -import numpy as np -from glob import glob - -from features import texture, chist -from sklearn.linear_model import LogisticRegression -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler - -basedir = '../SimpleImageDataset/' - - -haralicks = [] -labels = [] -chists = [] - -print('This script will test (with cross-validation) classification of the simple 3 class dataset') -print('Computing features...') -# Use glob to get all the images -images = glob('{}/*.jpg'.format(basedir)) - -# We sort the images to ensure that they are always processed in the same order -# Otherwise, this would introduce some variation just based on the random -# ordering that the filesystem uses -for fname in sorted(images): - imc = mh.imread(fname) - haralicks.append(texture(mh.colors.rgb2grey(imc))) - chists.append(chist(imc)) - - # Files are named like building00.jpg, scene23.jpg... - labels.append(fname[:-len('xx.jpg')]) - -print('Finished computing features.') - -haralicks = np.array(haralicks) -labels = np.array(labels) -chists = np.array(chists) - -haralick_plus_chists = np.hstack([chists, haralicks]) - - -# We use Logistic Regression because it achieves high accuracy on small(ish) datasets -# Feel free to experiment with other classifiers -clf = Pipeline([('preproc', StandardScaler()), - ('classifier', LogisticRegression())]) - -from sklearn import cross_validation -cv = cross_validation.LeaveOneOut(len(images)) -scores = cross_validation.cross_val_score( - clf, haralicks, labels, cv=cv) -print('Accuracy (Leave-one-out) with Logistic Regression [haralick features]: {:.1%}'.format( - scores.mean())) - -scores = cross_validation.cross_val_score( - clf, chists, labels, cv=cv) -print('Accuracy (Leave-one-out) with Logistic Regression [color histograms]: {:.1%}'.format( - scores.mean())) - -scores = cross_validation.cross_val_score( - clf, haralick_plus_chists, labels, cv=cv) -print('Accuracy (Leave-one-out) with Logistic Regression [texture features + color histograms]: {:.1%}'.format( - scores.mean())) - diff --git a/ch10/threshold.py b/ch10/threshold.py deleted file mode 100644 index d7361bfb..00000000 --- a/ch10/threshold.py +++ /dev/null @@ -1,34 +0,0 @@ -# This code is supporting material for the book -# Building Machine Learning Systems with Python -# by Willi Richert and Luis Pedro Coelho -# published by PACKT Publishing -# -# It is made available under the MIT License - -import numpy as np -import mahotas as mh - -# Load our example image: -image = mh.imread('../SimpleImageDataset/building05.jpg') - -# Convert to greyscale -image = mh.colors.rgb2gray(image, dtype=np.uint8) - -# Compute a threshold value: -thresh = mh.thresholding.otsu(image) -print('Otsu threshold is {0}'.format(thresh)) - -# Compute the thresholded image -otsubin = (image > thresh) -print('Saving thresholded image (with Otsu threshold) to otsu-threshold.jpeg') -mh.imsave('otsu-threshold.jpeg', otsubin.astype(np.uint8) * 255) - -# Execute morphological opening to smooth out the edges -otsubin = mh.open(otsubin, np.ones((15, 15))) -mh.imsave('otsu-closed.jpeg', otsubin.astype(np.uint8) * 255) - -# An alternative thresholding method: -thresh = mh.thresholding.rc(image) -print('Ridley-Calvard threshold is {0}'.format(thresh)) -print('Saving thresholded image (with Ridley-Calvard threshold) to rc-threshold.jpeg') -mh.imsave('rc-threshold.jpeg', (image > thresh).astype(np.uint8) * 255) diff --git a/ch10/thresholded_figure.py b/ch10/thresholded_figure.py deleted file mode 100644 index 947762e8..00000000 --- a/ch10/thresholded_figure.py +++ /dev/null @@ -1,31 +0,0 @@ -import mahotas as mh -import numpy as np -from matplotlib import pyplot as plt - -# Load image & convert to B&W -image = mh.imread('../SimpleImageDataset/scene00.jpg') -image = mh.colors.rgb2grey(image, dtype=np.uint8) -plt.imshow(image) -plt.gray() -plt.title('original image') - -thresh = mh.thresholding.otsu(image) -print('Otsu threshold is {}.'.format(thresh)) - -threshed = (image > thresh) -plt.figure() -plt.imshow(threshed) -plt.title('threholded image') -mh.imsave('thresholded.png', threshed.astype(np.uint8)*255) - -im16 = mh.gaussian_filter(image, 16) - -# Repeat the thresholding operations with the blurred image -thresh = mh.thresholding.otsu(im16.astype(np.uint8)) -threshed = (im16 > thresh) -plt.figure() -plt.imshow(threshed) -plt.title('threholded image (after blurring)') -print('Otsu threshold after blurring is {}.'.format(thresh)) -mh.imsave('thresholded16.png', threshed.astype(np.uint8)*255) -plt.show() diff --git a/ch11_3rd/chapter_11.ipynb b/ch11_3rd/chapter_11.ipynb new file mode 100644 index 00000000..6524fdff --- /dev/null +++ b/ch11_3rd/chapter_11.ipynb @@ -0,0 +1,4398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Machine Learning Systems with Python - Chapter 11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter we will create a music genre classifier. While the ML algorithm itself (logistic regression) is nothing fancy by now, we will look into fancy features like Fast Fourier Transforms and Mel Frequency Cepstral Coefficients, use P/R and ROC curves to analyze what works best and then figure out which version to use.\n", + "\n", + "This code is supporting material for the book `Building Machine Learning Systems with Python` by [Willi Richert](https://www.linkedin.com/in/willirichert/) and [Luis Pedro Coelho](https://www.linkedin.com/in/luispedrocoelho/) published by PACKT Publishing. It is made available under the MIT License.\n", + "\n", + "All code examples use Python in version..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.3 |Anaconda custom (64-bit)| (default, Nov 8 2017, 15:10:56) [MSC v.1900 64 bit (AMD64)]'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities we will need" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob \n", + "from pathlib import Path\n", + "\n", + "CHART_DIR = \"charts\"\n", + "if not Path(CHART_DIR).exists():\n", + " os.mkdir(CHART_DIR)\n", + "\n", + "DATA_DIR = \"data\"\n", + "if not Path(DATA_DIR).exists():\n", + " os.mkdir(DATA_DIR)\n", + "\n", + "GENRE_DIR = Path(DATA_DIR) / 'genres'\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn')\n", + "\n", + "import numpy as np\n", + "import scipy\n", + "\n", + "DPI = 100\n", + "\n", + "import collections\n", + "import csv\n", + "\n", + "def save_png(name):\n", + " fn = 'B09124_11_%s.png'%name # please ignore, it just helps our publisher :-)\n", + " plt.savefig(str(Path(CHART_DIR) / fn), bbox_inches=\"tight\")\n", + " \n", + " \n", + "def plot_pr(auc_score, name, precision, recall, label=None, plot_nr=None):\n", + " plt.figure(num=None, figsize=(5, 4), dpi=DPI)\n", + " plt.grid(True)\n", + " plt.fill_between(recall, precision, alpha=0.5)\n", + " plt.plot(recall, precision, lw=1)\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('P/R curve (AUC=%0.2f) / %s' % (auc_score, label))\n", + " filename = name.replace(\" \", \"_\")\n", + " save_png(\"%s_pr_%s\" % (plot_nr, filename))\n", + " \n", + "def plot_roc(auc_score, name, tpr, fpr, label=None, plot_nr=None):\n", + " plt.figure(num=None, figsize=(5, 4), dpi=DPI)\n", + " plt.grid(True)\n", + " plt.plot([0, 1], [0, 1], 'k--')\n", + " plt.plot(fpr, tpr)\n", + " plt.fill_between(fpr, tpr, alpha=0.5)\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.0])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('ROC curve (AUC = %0.2f) / %s' % (auc_score, label), verticalalignment=\"bottom\")\n", + " plt.legend(loc=\"lower right\")\n", + " save_png('%i_auc_%s' % (plot_nr, name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(WindowsPath('data/genres.tar.gz'), )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import urllib.request\n", + "\n", + "genre_fn = 'http://opihi.cs.uvic.ca/sound/genres.tar.gz'\n", + "urllib.request.urlretrieve(genre_fn, Path(DATA_DIR) / 'genres.tar.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import tarfile\n", + "\n", + "cwd = os.getcwd()\n", + "\n", + "os.chdir(DATA_DIR)\n", + "\n", + "try:\n", + " f = tarfile.open('genres.tar.gz', 'r:gz')\n", + " try: \n", + " f.extractall()\n", + " finally: \n", + " f.close()\n", + "finally:\n", + " os.chdir(cwd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download and install https://sourceforge.net/projects/sox/files/sox/. For this notebook, we are using SOX 14.4.2 to convert the downloaded genre files from `.au` into `.wav` format, which is easier to handle." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "SOX_PATH = r'C:\\Program Files (x86)\\sox-14-4-2'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data\\genres\\blues\\blues.00000.au\n", + "data\\genres\\blues\\blues.00001.au\n", + "data\\genres\\blues\\blues.00002.au\n", + "data\\genres\\blues\\blues.00003.au\n", + "data\\genres\\blues\\blues.00004.au\n", + "data\\genres\\blues\\blues.00005.au\n", + "data\\genres\\blues\\blues.00006.au\n", + "data\\genres\\blues\\blues.00007.au\n", + "data\\genres\\blues\\blues.00008.au\n", + "data\\genres\\blues\\blues.00009.au\n", + "data\\genres\\blues\\blues.00010.au\n", + "data\\genres\\blues\\blues.00011.au\n", + 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+ "text": [ + "data\\genres\\rock\\rock.00070.au\n", + "data\\genres\\rock\\rock.00071.au\n", + "data\\genres\\rock\\rock.00072.au\n", + "data\\genres\\rock\\rock.00073.au\n", + "data\\genres\\rock\\rock.00074.au\n", + "data\\genres\\rock\\rock.00075.au\n", + "data\\genres\\rock\\rock.00076.au\n", + "data\\genres\\rock\\rock.00077.au\n", + "data\\genres\\rock\\rock.00078.au\n", + "data\\genres\\rock\\rock.00079.au\n", + "data\\genres\\rock\\rock.00080.au\n", + "data\\genres\\rock\\rock.00081.au\n", + "data\\genres\\rock\\rock.00082.au\n", + "data\\genres\\rock\\rock.00083.au\n", + "data\\genres\\rock\\rock.00084.au\n", + "data\\genres\\rock\\rock.00085.au\n", + "data\\genres\\rock\\rock.00086.au\n", + "data\\genres\\rock\\rock.00087.au\n", + "data\\genres\\rock\\rock.00088.au\n", + "data\\genres\\rock\\rock.00089.au\n", + "data\\genres\\rock\\rock.00090.au\n", + "data\\genres\\rock\\rock.00091.au\n", + "data\\genres\\rock\\rock.00092.au\n", + "data\\genres\\rock\\rock.00093.au\n", + "data\\genres\\rock\\rock.00094.au\n", + "data\\genres\\rock\\rock.00095.au\n", + "data\\genres\\rock\\rock.00096.au\n", + "data\\genres\\rock\\rock.00097.au\n", + "data\\genres\\rock\\rock.00098.au\n", + "data\\genres\\rock\\rock.00099.au\n" + ] + } + ], + "source": [ + "for au_fn in Path(GENRE_DIR).glob('**/*.au'):\n", + " print(au_fn)\n", + " !\"{SOX_PATH}/sox.exe\" {au_fn} {au_fn.with_suffix('.wav')}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Looking at music" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.io.wavfile\n", + "\n", + "from matplotlib.ticker import EngFormatter\n", + "\n", + "def plot_specgram(ax, fn):\n", + " sample_rate, X = scipy.io.wavfile.read(fn)\n", + " ax.specgram(X, Fs=sample_rate, xextent=(0, 30), cmap='hot')\n", + "\n", + "GENRES = [\"classical\", \"jazz\", \"country\", \"pop\", \"rock\", \"metal\"]\n", + "\n", + "def plot_specgrams():\n", + " \"\"\"\n", + " Plot a bunch of spectrograms of wav files in different genres\n", + " \"\"\"\n", + " plt.clf()\n", + " \n", + " num_files = 3\n", + " f, axes = plt.subplots(len(GENRES), num_files, dpi=DPI, figsize=(6, 8))\n", + " \n", + " for genre_idx, genre in enumerate(GENRES):\n", + " for idx, fn in enumerate((Path(GENRE_DIR) / genre).glob('*.wav')):\n", + " if idx == num_files:\n", + " break\n", + " \n", + " axis = axes[genre_idx, idx]\n", + " axis.tick_params(direction='out', length=0, width=1, labelsize=5)\n", + " \n", + " axis.yaxis.set_major_formatter(EngFormatter())\n", + " axis.set_title(\"%s song %i\" % (genre, idx + 1), fontsize=7)\n", + " plot_specgram(axis, fn)\n", + " \n", + " plt.subplots_adjust(hspace=0.5)\n", + " save_png(\"5_Spectrogram_Genres\")\n", + " \n", + "plot_specgrams()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Playing with waves" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "!\"C:\\Program Files (x86)\\sox-14-4-2\\sox.exe\" --null -r 22050 sine_a.wav synth 0.2 sine 400" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "!\"C:\\Program Files (x86)\\sox-14-4-2\\sox.exe\" --null -r 22050 sine_b.wav synth 0.2 sine 3000" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Program Files (x86)\\sox-14-4-2\\sox.exe WARN sox: mix-combining clipped 140 samples; decrease volume?\n" + ] + } + ], + "source": [ + "!\"C:\\Program Files (x86)\\sox-14-4-2\\sox.exe\" --combine mix --volume 1 sine_b.wav --volume 0.5 sine_a.wav sine_mix.wav" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have three files `sine_a.wav`, `sine_b.wav`, `sine_mix.wav` in the current directory, which we can visualize." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy\n", + "\n", + "def plot_wav_fft(wav_filename, desc=None, plot=0):\n", + " plt.clf()\n", + " plt.figure(num=None, figsize=(6, 4), dpi=DPI)\n", + " sample_rate, X = scipy.io.wavfile.read(wav_filename)\n", + " spectrum = np.fft.fft(X)\n", + " freq = np.fft.fftfreq(len(X), 1.0 / sample_rate)\n", + "\n", + " plt.subplot(211)\n", + " num_samples = 200\n", + " plt.xlim(0, num_samples / sample_rate)\n", + " plt.xlabel(\"time [s]\")\n", + " plt.title(desc or wav_filename)\n", + " plt.plot(np.arange(num_samples) / sample_rate, X[:num_samples])\n", + " plt.grid(True)\n", + "\n", + " plt.subplot(212)\n", + " plt.xlim(0, 5000)\n", + " plt.xlabel(\"frequency [Hz]\")\n", + " plt.xticks(np.arange(5) * 1000)\n", + " if desc:\n", + " desc = desc.strip()\n", + " fft_desc = desc[0].lower() + desc[1:]\n", + " else:\n", + " fft_desc = wav_filename\n", + " plt.title(\"FFT of %s\" % fft_desc)\n", + " plt.plot(freq, abs(spectrum), linewidth=2)\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " rel_filename = os.path.split(wav_filename)[1]\n", + " save_png(\"%i_%s_wav_fft\" % (plot, os.path.splitext(rel_filename)[0]))\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "plot_wav_fft(\"sine_a.wav\", \"400Hz sine wave\", 1)\n", + "plot_wav_fft(\"sine_b.wav\", \"3,000Hz sine wave\", 2)\n", + "plot_wav_fft(\"sine_mix.wav\", \"Mixed sine wave\", 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A \"real\" music file looks a bit noisier:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_wav_fft(Path(GENRE_DIR) / 'disco' / 'disco.00000.wav', \"some sample song\", 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First classifier using FFT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating FFT features" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converting data\\genres\\blues\\blues.00000.wav ...\n", + "Converting data\\genres\\blues\\blues.00001.wav ...\n", + "Converting data\\genres\\blues\\blues.00002.wav ...\n", + "Converting data\\genres\\blues\\blues.00003.wav ...\n", + "Converting data\\genres\\blues\\blues.00004.wav ...\n", + "Converting data\\genres\\blues\\blues.00005.wav ...\n", + "Converting data\\genres\\blues\\blues.00006.wav ...\n", + "Converting data\\genres\\blues\\blues.00007.wav ...\n", + "Converting data\\genres\\blues\\blues.00008.wav ...\n", + "Converting data\\genres\\blues\\blues.00009.wav ...\n", + "Converting data\\genres\\blues\\blues.00010.wav ...\n", + "Converting data\\genres\\blues\\blues.00011.wav ...\n", + "Converting data\\genres\\blues\\blues.00012.wav ...\n", + "Converting data\\genres\\blues\\blues.00013.wav ...\n", + "Converting data\\genres\\blues\\blues.00014.wav ...\n", + "Converting data\\genres\\blues\\blues.00015.wav ...\n", + "Converting data\\genres\\blues\\blues.00016.wav ...\n", + "Converting data\\genres\\blues\\blues.00017.wav ...\n", + "Converting data\\genres\\blues\\blues.00018.wav ...\n", + "Converting data\\genres\\blues\\blues.00019.wav ...\n", + "Converting data\\genres\\blues\\blues.00020.wav ...\n", + "Converting data\\genres\\blues\\blues.00021.wav ...\n", + "Converting data\\genres\\blues\\blues.00022.wav ...\n", + "Converting data\\genres\\blues\\blues.00023.wav ...\n", 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fft_features)\n", + " \n", + "for wav_fn in Path(GENRE_DIR).glob('**/*.wav'):\n", + " print('Converting %s ...' % str(wav_fn))\n", + " create_fft(wav_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def read_fft(genre_list, base_dir=GENRE_DIR):\n", + " X = []\n", + " y = []\n", + " for label, genre in enumerate(genre_list):\n", + " genre_dir = Path(base_dir) / genre\n", + " for fn in genre_dir.glob(\"*.fft.npy\"):\n", + " fft_features = np.load(fn)\n", + "\n", + " X.append(fft_features[:1000])\n", + " y.append(label)\n", + "\n", + " return np.array(X), np.array(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the FFT-based classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the model..." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model.logistic import LogisticRegression\n", + "\n", + "def create_model():\n", + " return LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "from sklearn.metrics import precision_recall_curve, roc_curve, confusion_matrix\n", + "from sklearn.metrics import auc\n", + "from sklearn.model_selection import ShuffleSplit\n", + "\n", + "def train_model(clf_factory, X, Y, name, plot=False):\n", + " labels = np.unique(Y)\n", + "\n", + " cv = ShuffleSplit(n_splits=1, test_size=0.3, random_state=0)\n", + "\n", + " train_errors = []\n", + " test_errors = []\n", + "\n", + " scores = []\n", + " pr_scores = defaultdict(list)\n", + " precisions, recalls, thresholds = defaultdict(list), defaultdict(list), defaultdict(list)\n", + "\n", + " roc_scores = defaultdict(list)\n", + " tprs = defaultdict(list)\n", + " fprs = defaultdict(list)\n", + "\n", + " clfs = [] # just to later get the median\n", + "\n", + " cms = []\n", + "\n", + " for train, test in cv.split(X, Y):\n", + " X_train, y_train = X[train], Y[train]\n", + " X_test, y_test = X[test], Y[test]\n", + "\n", + " clf = clf_factory()\n", + " clf.fit(X_train, y_train)\n", + " clfs.append(clf)\n", + "\n", + " train_score = clf.score(X_train, y_train)\n", + " test_score = clf.score(X_test, y_test)\n", + " scores.append(test_score)\n", + "\n", + " train_errors.append(1 - train_score)\n", + " test_errors.append(1 - test_score)\n", + "\n", + " y_pred = clf.predict(X_test)\n", + " cm = confusion_matrix(y_test, y_pred)\n", + " cms.append(cm)\n", + "\n", + " for label in labels:\n", + " y_label_test = np.asarray(y_test == label, dtype=int)\n", + " proba = clf.predict_proba(X_test)\n", + " proba_label = proba[:, label]\n", + "\n", + " precision, recall, pr_thresholds = precision_recall_curve(\n", + " y_label_test, proba_label)\n", + " pr_scores[label].append(auc(recall, precision))\n", + " precisions[label].append(precision)\n", + " recalls[label].append(recall)\n", + " thresholds[label].append(pr_thresholds)\n", + "\n", + " fpr, tpr, roc_thresholds = roc_curve(y_label_test, proba_label)\n", + " roc_scores[label].append(auc(fpr, tpr))\n", + " tprs[label].append(tpr)\n", + " fprs[label].append(fpr)\n", + "\n", + " if plot:\n", + " for idx, label in enumerate(labels):\n", + " print(\"Plotting %s\" % GENRES[label])\n", + " scores_to_sort = roc_scores[label]\n", + " median = np.argsort(scores_to_sort)[len(scores_to_sort) // 2]\n", + "\n", + " desc = \"%s %s\" % (name, GENRES[label])\n", + " plot_pr(pr_scores[label][median], desc, precisions[label][median],\n", + " recalls[label][median], label='%s vs rest' % GENRES[label], plot_nr=plot+idx)\n", + " plot_roc(roc_scores[label][median], desc, tprs[label][median],\n", + " fprs[label][median], label='%s vs rest' % GENRES[label], plot_nr=plot+len(labels)+idx)\n", + "\n", + " all_pr_scores = np.asarray(list(pr_scores.values())).flatten()\n", + " #import pdb;pdb.set_trace()\n", + " summary = (np.mean(scores), np.std(scores),\n", + " np.mean(all_pr_scores), np.std(all_pr_scores))\n", + " print(\"%.3f\\t%.3f\\t%.3f\\t%.3f\\t\" % summary)\n", + "\n", + " return np.mean(train_errors), np.mean(test_errors), np.asarray(cms)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_confusion_matrix(cm, genre_list, name, title, plot_nr=None):\n", + " plt.figure(num=None, figsize=(5, 4), dpi=DPI)\n", + " plt.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)\n", + " ax = plt.axes()\n", + " ax.set_xticks(range(len(genre_list)))\n", + " ax.set_xticklabels(genre_list)\n", + " ax.xaxis.set_ticks_position(\"bottom\")\n", + " ax.set_yticks(range(len(genre_list)))\n", + " ax.set_yticklabels(genre_list)\n", + " ax.tick_params(axis='both', which='both', bottom='off', left='off')\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " plt.grid(False)\n", + " plt.xlabel('Predicted class')\n", + " plt.ylabel('True class')\n", + " if plot_nr is not None:\n", + " save_png('%i_confusion_%s' % (plot_nr, name))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\sklearn\\linear_model\\base.py:340: RuntimeWarning: overflow encountered in exp\n", + " np.exp(prob, prob)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting classical\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting jazz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting country\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting pop\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting rock\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n", + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting metal\n", + "0.406\t0.000\t0.322\t0.122\t\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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zJbCHc+6RCvZ1Nv5beHt8a3Um/sPyQeBfroKSd1HbrsYngXn4luyT+C6oAyOjScFP2H8BX/3mKXyr6KoYXutX+O7Q5fjkF/3ca/iJ/1tG9j0NP3jqEFdBkYooM/HdcYcBRL5A7MSGR1o+HPk5KHLsX/AfPJ/gpyW8gf+g7ggc5ZyLnk5A5EP6GPx8vt0j+3sK3zq6BegeOZ81YRi+sMIZ+PmiHYGjXWQub8RZ+BGu0dcGT8S/zwbjz9nO+GIRZVN1nC9zdy4+IT2Hn2J0unNuvRG2ZtYen4CiW6zRDqdqU1vi4pxbi3+vvoN/L76Mb41fhX99pUbju1NfxX/RGIwf6HMz/gvhWZHfHwD2ifTyVDWGYvzcz1T8tJdoQ/FfzEoLqNwRiaH8tJiqHGdoJO7j8T0KI/G9Rt2jvjQeF9n/cPzf1Xn498lNkee/jsR6IX+Phm/wQuFwhdMBRRocMxsH7OSc2+jADak+M7sBX+Rg90pGEYvUeWqJivxtBL7brlvQgdR3ZtYM39K5WglUkpmSqEiE81VizsMP3JCadQ1+UFhl10NFkoK6c0VEROKklqiIiEiclERFRETipCQqIiISpzpRbCFS8/U94CxX8f0PMbNe+DsrdMRPir7cOfdirQUpIiJSTuAtUTPbD59AK7uHIWa2Hb5s3FB8BZgbgCfMbPNaCVJERKQCgSZR83eKn87GbzJ7BjDXOfdcpJzdE/iSWOfUdIwiIiKVCbol+iqwrXNuxkbW25n1748HvpB4lxqJSkREpAoCvSYamdxeFc34570ec6n67bMACIfD4VAoltv9iYhIfXHlDaP5Zs12zBxzTMISQZ0YWFQFa/F3AYnWGH93kSoLhULk5ORRXFyy8ZWF1NQUsrOzdM5ipPMWO52z+Oi8xaZ375P45uHKbrMbn2RJol/h73IRbSci92aMRXFxCUVFerPFQucsPjpvsdM5i4/O2z+Fw2GmTJlEt257s8suuwJQUgMF+pIliU4DBpvZifjbNh2Pv73SxUEGJSIidc/atWu5/PJLeOqpGWy9dQdef30OLVq0rJFjBT2wqFJmtsbM+gFEbnh8LL5o9Qr8fRB7O+e+DzBEERGpY37++Ud69TqEp57y41UzMjJYsWJFjR2vzrREnXOhco+blnv8Kn40r4iIyD+8/PJLXHjhIFavzgHgqKOO5a677qVp02Y1dsw6k0RFRETiUVxczG233cxdd40BIDU1leuvH865515ATc/IUBIVEZGkVVBQQL9+J/Lf/74FQJs2bZk06WH22We/Wjl+nb0mKiIisjGZmZl07NgRgG7d9ubNN+fWWgIFtURFRCTJDR9+G9tssy1nnTWI9PT0Wj22WqIiIpI08vLyuPrqISxYML9sWWZmJuedd2GtJ1BQS1RERJLEr7/+woABp/HVV//j448/YubMV2nUqFGgMaklKiIidd5rr73MoYf24Kuv/gfA5ptvQVFRYcBRKYmKiEgdVjp95dRT+7Jq1UpSUlIYOvQmpkx5pEbnf1aVunNFRKROWr58GeeeO5C3354NQOvWrZk4cQoHHNAj4Mj+piQqIiJ1zuLFizjiiEP57bcFAOyxx55MnjyV9u03Dziy9ak7V0RE6pxNN23Hrrt2AWDAgLN5/vmX61wCBbVERUSkDgqFQowbN57evU/kqKOOCTqcSqklKiIigVuwYD5nnHEKy5cvK1vWrFl2whJobn4Rv/+5NiH7iqaWqIiIBGr27Nc577yzWLFiBfn5eUyf/hSpqakJ239ufhFXjJ9HbkFRwvZZSi1REREJRElJCaNH38bJJ/dhxYoVpKSksO+++yf8ziuLlq+tkQQKaomKiEgAVq5cwfnnn80bb7wGQKtWrZgw4UF69DioRo+723atE7o/JVEREalVX375Bf37n8aCBb8CsPvuezB58jQ233yLGj92duPE1tdVd66IiNSaTz75iCOOOLQsgZ5xxkCef/6VWkmgNUFJVEREak3nzrvRpUtXGjVqxN13j2fUqDvJzMwMOqy4qTtXRERqTXp6OpMmPczSpUvZddfOQYdTbWqJiohIjZkz5y169z6a3NzcsmW+GlHyJ1BQEhURkRpQUlLC2LGj6dv3OObOfZsrrxwcdEg1Qt25IiKSUKtWreTCCwfx6qsvA9CyZUuOO65PlbfPzS9i0fLEVReqiUpFpZRERUQkYb7++iv69+/Hr7/+AkCXLl2ZPHkqW221dZW2r8nqQjVB3bkiIpIQTz75OL16HVyWQE899Qxmzny1ygkUara6UHpqCs0aZyR0n2qJiohItT3++KNcdNF5AGRmZnLbbWPo1+/0au2zS6dWZCcw6TXNSic9LbFtRyVRERGptiOPPIZ7772L3NxcHnxwGl26dK32PrMbZ9CyWd2eQ6okKiIicSkpKSElxbfsmjZtyrRpM8jOzmaTTVoFHFnt0TVRERGJSTgcZty4sfTtexxFRX9fv+zQYZsGlUBBSVRERGKQk7OK/v1PZfjw65kz5y3GjLk96JACpe5cERGpkm+//Yb+/fvx888/AbDLLp3p2/eUgKMKllqiIiKyUc888ySHH96zLIH27XsKL730Oh06bBNwZMFSS1RERCq1bt06hg27jgcemABARkYGI0aM5PTT+xMKhZKqulBNUBIVEZFKjRgxrCyBbr75Fjz44DS6dt0DSL7qQjVB3bkiIlKpCy+8hM02a0+PHgfxxhtzyxIo1Hx1oaZZ6TWy70RSS1RERMqEw2HWrVtXdqPsNm3a8MILr7DFFluSmppa6XbJUF2oJiiJiogIAGvWrObiiy8gNTWFiROnEAqFANh66w4b3TYZqgvVBCVRERHh++8d/fv344cfvgegZ89DOemkfgFHVffV/bayiIjUqBdeeJbDDjuoLIH26dOXo48+LuCokoNaoiIiDVRhYSE33XQ9EyfeC0B6ejrDh99G//5nlXXlyoYpiYqINEBLlizh7LPP4P335wGw2WbtmTx5Kv/6V7eAI0su6s4VEWmALr74vLIEesABPXjjjblKoHFQS1REpAG69dbR/PvfB3LGGQO46qrrSEurOB1sqCJRslUXqglKoiIiDcCaNWvIzMwkPd0XMNhmm468996ntG7dutJtVJFo49SdKyJSz/3ww/ccfnhPbrpp6HrLN5RAoeoViZKlulBNUEtURKQemznzeS666DzWrl2Dc99x5JHHstdee8e8nw1VJEqW6kI1QUlURKQeKioqYsSIYdx7710ApKWlMWzYCLp12yuu/TXUikQboyQqIlLPLF26lEGD+vPuu3MB2HTTdkyaNDWuFqhsmJKoiEg98tFHHzBw4OksXrwIgH333Z+JE6ew6aabBhxZ/dQwO7FFROqhoqIi/vOfc8sS6PnnX8RTT72gBFqDAm2Jmllb4H7gQKAIeAQY4pz7x3AwM7sYuARoBfwKDHPOPV1rwYqI1HFpaWlMnPggJ510PCNH3slRRx0bdEj1XtAt0RnAGqA90A04BLi0/EpmdjhwDfB/zrlsYBjwhJl1qL1QRUTqnoULFxIOh8sed+nSlY8//koJtJYElkTNrBO+BXqFcy7XOfczMBy4sILVdwRCQIqZhYBiYB2+9Soi0iC99NJMdt55Z8aNG7ve8iZNmsS1v9z8In76Y1XZP1Uk2rggu3N3BpY75/6IWvYNsJWZtXDOrYxa/hjQP/J8MRAGTnXOLYz1oKmpQTe+k0fpudI5i43OW+x0zmJTVFTELbcMZ+zYMQDceuvNnHDCSWy22WZx7zM3v4grJswjN7/itkkoBCkpyX9nl0S/hiCTaDOg/Nec3MjPpkB0Es0APgcGAF8A/YDJZvaNc+7LWA6anZ0VX7QNmM5ZfHTeYqdztnF//vknJ598Mm+++SYA7dq1Y8aMGey0U6dq7Xfp/OWVJtD0tBTabNKEjPTUah2jPgoyia4FGpdbVvp4dbnl9wDvOuc+ijyeYmanAGcCl8Vy0JycPIqLS2IMtWFKTU0hOztL5yxGOm+x0zmrmk8++ZgzzujHH3/8DsA+++zL008/RZMmLVixonpdr6tX55f9vtt2rclu/HcZv2aNMyguKiavqLhax6gL6lNL9CuglZlt6pxbElm2E7DQObeq3LpbAR+XW1aIvy4ak+LiEoqK9EcaC52z+Oi8xU7nrGLhcJiHH36Qa6+9gsLCQgAGDTqfm24aQdu2PoFW97wVRX15aZaVToum61cnKikJl99ECDCJOud+MLN3gLFmdg7QGhgKTK5g9ReAC81sJr5b93jgIPyIXRGRem3ZsmXceutNFBYW0rhxE8aOvYdjj+1NWgOtV1uXBP0/0AefyH8BPgBewY/QxczWmFm/yHrDgHuBp4EVwFXAsc65z2s9YhGRWta6dWvGj5/M9tsbr7wym2OP7R10SBIRaLGFSDfuCZU81zTq9yLgxsg/EZF679tvv2HHHXcqe9yz5yF0735gpTfPlmAE3RIVEZEoxcXF3HbbcHr02JsZM6av95wSaN2jJCoiUkcsX76Mk0/uzR13jAJg5MhbKCgoCDgq2RB9rRERqQM+//xTBgw4jcVLl9Gi3XbsuOPOXHvtDSxclg/kV7hNWmoKzXIKWL06f73RtfFQdaL4KImKiATskUce5qqrLqOENHqedT8ZjfyQkAmz5gPzgw1ONkhJVEQkIHl5eVx99RCmT58GwKZbb1+WQIOSnppC06z0ja8ogJKoiEhg5s//lWeeeRKAbbbpyIgx9zL9nRUAdOnUiuzGGRvcPhSCzMx0CgoKCSeoFkLTrHTSNf+0ypRERUQCssMOOzJq1Fheemkm48aN56+1IXjnEwCyG2fQslnmBrdPSQmRlZVBXl6qKgoFRF83RERqSUlJCe+99+56y/r2PYWHH55O8+YtAopKqkNJVESkFqxYsZxTTz2RY4/txezZb6z3XCiU/LcYa6iUREVEatiXX37BoYf24I03XiMcDvPAA+ODDkkSRElURKQGPfbYI/TqdQgLFvipKmecMZCHHpq+ka0kWWhgkYhIDcjPz+faa69g2rSHAGjUqBEjR97JSSf12/CGklSUREVEEuy33xYwcOBpfP75ZwBsvXUHHnzwEXbdtXPAkUmiqTtXRCTB5s//lf/97wsADj30MF5/fY4SaD2llqiISILtv393hg69iYKCfC65ZAgpKWqv1FdKoiIi1bRq1Uo++eRjevY8pGzZBRdcFGBEUlv09UhEpBq++upLDj20B2eccTJffPFZ0OFILVMSFRGJ04wZ0zniiEP49ddfKCgo4OWXXwo6JKll6s4VEYlRQUEBQ4dexUMPTQYgMzOT22+/g1NOOS3gyKS2KYmKiMTg998XMnDgaXz6qS8Uv9VWW/Pgg9Po3Hm3gCOTIKg7V0SkiubOncMhhxxQlkB79jyE11+fowTagCmJiohU0bJlf7Fs2TJCoRBDhlzF9OlP0bLlJkGHJQFSd66IyAbk5hexaPlaAHbtdgjnDx7ODjvsyJ7d9uKXxasTeqzf/1yb0P1JzVMSFRGpxGdffMnE1/5kXXH00l1Z8B289t0nQYUldUhcSdTMDgeuAAzYBxgA/OScm5rA2EREAvP0008w7Pa7+Nfxw2v92OmpKTTNSq/140rsYk6iZnYo8CzwOLA3kBrZz4Nmluqcm5LYEEVEas+6deu48cZrmTRpIi3abVe2vEunVmQ3zqiVGJpmpZOepiErySCelugw4Crn3Fgz6w3gnLvWzFYAlwFKoiKSlBYt+oOBA0/n448/BKBt203LnstunEHLZplBhSZ1VDxfdXYFZlaw/GmgY/XCEREJxrvvzuXggw8oS6AHHtiTceMmBByV1HXxJNFVwOYVLN8FWF69cEREat+ECffQp8/R/PXXnwAMHnw5jz32NNnNmwccmdR18XTnPgrcZWYDgTDQNDLQ6B5gRiKDExGpDSkpKRQXF5Od3Zx7772fww47POiQJEnEk0SvA7YEPo48/gwIAS9GnhMRSSpnn30ey5cvo2/ffmyzja5KSdXF3J3rnCt0zp0CbA+cCJwM7OKcOxrIT3B8IiIJ9/zzz/DRRx+UPQ6FQlx11VAlUIlZPFNcfgb+5Zz7EfgxavnmwBdA68SFJyLJIrqyT6zSUlNollPA6tX5FBWXJDjJxZ+vAAAgAElEQVSyvxUXFTF58v08++xTtGrdhnvGTaRFyxaVrq8KQrIxVUqiZtYXOCzysANwr5nllVutA/4aqYg0MLn5RVwxfh65BUVBh7JxWXux/yl7AXDvzJ8CDkaSXVVbovOAQfhrnwBbAeuing8Da4AzEheaiCSLRcvXJkcCjZMqCEllqpREnXO/AT0BzOwt4Hjn3IqaDExEklM8lX1CIcjMTKegoJBwAvuzwuEwH737CrNnPU64xBfA3fvAo+h+aG9SU6t+NUsVhKQyMV8Tdc4dVNlzZraVc25B9UISkWQWT2WflJQQWVkZ5OWlUlKSmCxaUlLC5LHX8NG7rwDQqHFTBvxnOLt1q/QjTCRm8Qws2hq4A+iMr5sLvps3E2gbzz5FRBItJSWFVm02A6D9Vp047/IxbNp+64CjkvomnoR3D/7uLTPwd3IZHXl8HP66qYhInXDMKReQ1aQZPXudTGajrKDDkXoonk7+A4CznHPXAV8BzznnegO3AL0SGZyISFUVFxfx3PR7WLTwl7JlqalpHH78ACVQqTHxJNFGQOm79Ft8ty7AVPyt0UREalXOymXcOexcZj09iQmjLiM/LzfokKSBiKc792f8nVx+A34AdossTwWaJSguEZEq+em7z5k45nJWLvfF45s1b0lhYQGNshoHHJk0BPEk0SnANDM7A5gFvG1m84F/4ysWiUg9tKGKREFU9gmHw7z18gyeeGg0JcV+juq/jzmD4/r9J6bpKyLVEc8Ul1FmVgiEnXMfmtmN+MLzvwGnJTg+EakD6lpFooL8PKZNuIkP574MQGajxpx54U3ssc8hAUcmDU1cX9ecc2Ojfh8JjAQwsyYJiktE6pCqViSqjco+uWtzGHndAP5Y4Et3b7ZFR869fAybbbFNjR5XpCJVTqJmloWvWlQMzHHO5ZV7/gjgPkATsUTqsQ1VJKqNyj5ZjZux1TY78MeCH/nXvv/m9PNv1PVPCUxVC9B3Bl7FF1MIAb+a2YHOuQVm1hK4FzgJP1pXROqxeCoSJVIoFKLfoGvZYZc92eegowmFQhvfSKSGVPUr4+3AUuBA/DSW+cBoM9se+BzoDdwMdK2BGEWkActZtZwH7riKlSv+LFuWmZnFvj2PUQKVwFW1O7cb0Mc5NxfAzAbgR+LuCOQARznn/lczIYpIQ/Xz918ycfQQVixbworlSxh84/2kpeluKlJ3VLUl2hxwpQ+cc78AGcASoJsSqIgkUjgc5u1XnmDU0P6sWLYEgK233SngqET+qaot0RSg/NC8QuD68gOMRESqo6Agj+n3j+C9t18EILNRFqeffyN77ndYwJGJ/FN1ZyQvrs7GZtYWuB9/rbUIeAQY4pz7x1h6M+uBn0qzM7ACuM85d2t1ji8idcvSRb9x3+2DWTj/ewA2bd+B864YQ/sttw04MpGKVbU7Nxz5t7FlsZoBrAHa46+7HgJcWn4lM9sBXx3pPnxpwSOAy8ysTzWPLyJ1xB+//czwISeXJdCuex3MNbc/ogQqdVpVW6IhYLGZlV/2Y7llOOdSqQIz64RvgW7unMsFfjaz4fjW5qhyq1+Av1vMw5HH/zOzffGDmkTqlNz8IpbOX87q1fkUFZcEHU5C1EZZv3btt6aj7cq3X3zA8adexKFHn67Rt1LnVTWJ9q+BY+8MLHfO/RG17BtgKzNr4ZxbGbW8G/CGmT0GHAr8CdzpnLs/1oOmptbsRPD6pPRc6ZxVXW5+EYPveYfc/LpRHq8mhEKQkpLY5JaSEiIlNZVBg29j4fwf2X7nPRK6//qq9P8h0f8f9Vmiz1WVkmhUCzCRmgHlv96W3r+oKRCdRDcBLsIXdDgN2Bd40cyWO+eeiuWg2dm6r2CsdM6qbun85fU6gaanpdBmkyZkpFepw2mDfnJf8sRDd3LxdXeRmeVvANWqTRtatWlT7X03NJmZmvYTlCBvdbAWKF+rq/Tx6nLLC4DnnXMvRR7/18ymAScCMSXRnJw8iutJF1tNS01NITs7S+csBqtX55f9vvv2rWu8jmxta9Y4g+KiYvKKiuPeRzgc5r+vP81jD9xGUVEh9428iguvvpNGjTIoKCikpKS6Qy0ajpSUEJmZ6TpvMQikJVpDvgJamdmmzrklkWU7AQudc6vKrfsNUL7OWCr+umxMiotLKCpSQoiFzlnVRV8DbZqVToumwZXHqynV+bBeV5DP9Em3Mm/28wBkZDZi930OIRz+e99KBrHTeQtOYEnUOfeDmb0DjDWzc4DWwFBgcgWrTwBeNbNTgUeBA4B+kX8ikgT+XLyQCaOH8Nsv3wHQtt2WnHvFHWyx9XYBRyYSv6BHjPTBJ/JfgA+AV4DhAGa2xsz6ATjnZgNHAxcDq/A3Bh/inHshiKBFJDZffjKXEVecXJZAu+x5INeMnK4EKkkv7paomXXH186dDmwJ/OCcK4xlH5Fu3BMqea5puccvAy/HF62IBOWLj+Zw720XAxBKSeHYky/ksGPPJCUl6O/wItUXcxI1s2b426LtjS+28DpwG7C9mR3inFuY2BBFJJnt1GVvtt52J5b9uYizL72NHTvvFXRIIgkTT0u0tNTetkBp4fnL8dcqRwEnJyAuEUli4XC4rFBCekYm514+hlAoxCat2wUcmUhixZNEjwJOds79UlqtyDnnzOwCYGYigxMJQm5+EYuWx1ehpzYq+9R177z5HO/PmcnFQ8eTnp4BQKs2mwUclUjNiCeJtqHiwvM5QJPqhSMSrNz8Iq4YP4/cgvpbMKGmFK4r4LHJt/HOG88C8PTUOzlp4JUBRyVSs+K5sv8R0DfqcenkpIuAT6sdkUiAFi1fm5AEmp6WQrPGGQmIKDksW/oHI6/rX5ZAW2+6BfsdfGzAUYnUvHhaolfj69juA6QD15nZzkBXQDf8k3qjS6dWZMeRCEMhaLNJE4qLihvEBPivP5vHpLFXs3aNr5HSeY/u9L/oZpo0zQ44MpGaF3MSdc7NM7O9gSHAj8A++OpDFzvnPkhwfCKByW6cQctmsVccSkkJkZGeWq3SeMmgpKSEWU9PYuaM8WUDiY4+6XwOP36gpq9IgxHPFJeDnHNvAafXQDwikiRefW4KLzx+HwBNmrXgrEtuYefd9g04KpHaFU937utmthB4GJjqnPspwTGJSBLocdgJvPPmczRu0oxzh4ymVdv2QYckUuviSaJbA6fi69ZeZ2bz8Al1hnOu/N1XRKQeKSosJC3d35mmcZNsLr1+As1btiY9o/4V2hepipgvXDjnfnfO3e6c6wzsAbwPXA8sNrNHEh2giASvsHAdj04cwX0jL6Wk5O871bTedHMlUGnQqnX13zn3OfA48BhQBByZiKBEpO5Y/tdiRg0dwJzXnuSrT9/hzZceDTokkTojrgL0ZtaRv29F1gl4G7gAeDphkYlUQ7xVh1RxaH3ffPE+k8ZezZqcFQDs0nU/9j3w6ICjEqk74hmd+z6wJ/72ZVOBh5xzCxIdmEi8VHWo+kpKSnj1uSk899i9hEtKCIVCHHniII7oc46mr4hEiacl+i1wpXNuTqKDEUmERFQdSk9NoWlWeoIiSi65a1czZdxQvvjobQAaN81m4MW3sOvu+wcbmEgdFE+xhf41EYhITYi36lDTrHTS0xpmi2v6A7eUJdCtOu7IuUNG03rTzYMNSqSOqlISNbNiYDPn3FIzK+Hvern/4JxLTVRwItUVb9Whhqz3aZfw7f8+oPMe3Tnl7Ks1+lZkA6raEh0ArIr8rpaoSD1SVFhIOFxSlixbttqUoWNm0KJlm4AjE6n7qpREnXMPRz0M4wsrFESvY2ZNgHMSGJuI1LAVy5YwcczltN9iW04//4ay5UqgIlVT1e7c1kDjyMMpwFdm9le51XYDbgHuTFx4IlJT3Fcfcf8dV7J61XJ+dv9jt24H0flf3YMOSySpVLU7txfwEL4VGsLfU7S8EDArMWGJSE0Jh8O89vzDPPvoOEpK/J1mjuhzDrt03S/gyESST1W7c6ea2a/4CkezgT7A8qhVwsAa4MsExyciCZSXu4aH7rmBzz54E4DGTZox4KIRaoGKxKnKU1ycc/8Ffys04F3nnGayS62qahUiVR2q2B8LfmT8qMtY8sd8ALbosD3nXj6Gtu22DDgykeRV1Wui1wOjnXO5QA+gh5lVuK5z7qbEhSfiqQpR9YTDYabcc31ZAt3nwCM55ZxryczMCjgykeRW1ZZof+BeIJcNT3EJA0qiknDxVCFqyFWHyguFQvT/z3BGXTeAY0+5kO7/7kMoFAo6LJGkV9VrottU9LtIEKpahaghVx0CyFm1nKZNm5OS6uuftN9yW24Z/xJZjZsGHJlI/RHvXVyygGLn3Doz2xE4ApjnnJuX0OhEKqAqRBv3wzefMnHMFRxwyHEcc/IFZcuVQEUSK+av6WbWHfgd2N/M2gEfANcB/zWzExIcn4jEIBwO8/rMaYy54WxyVv7FrKcnsfj3X4MOS6TeiqclegvwHH6u6AAgB9gu8vs1wJMJi05Eqiw/by1T7xvGx/NeA6BR46YM+M9w2m3eIdjAROqxeC4Y7Q7c7JxbDRwGvOScywNeBHZIZHAiUjWLFv7MrVedVpZA22/ViWtvf5Tduh0UcGQi9Vs8LdG1QIaZZQLd+Xu0bjv+LlIvIrXkk/de56F7bqAgPxeAvbr34tRBQ8lspOkrIjUtniT6FjCKvysWvWJmuwF3R54TkVpSkJ/HjAdHUpCfS0pqGn37D+HA/+ur6SsitSSeJHoBMAHoDJzqnFttZqcBhcCliQxOatbGKgClpabQLKeA1avzKSouqcXI/klViCqW2SiLsweP5MG7r+WsS25lW+sSdEgiDUooHK70/tpVZmYZzrl1CYinpoVXrFhLUVGwCaEuSOYKQAd03qxOT3FJSQmRlZVBXt46Skqq//dV3uLff2XT9luv19osKiwkLT15C0vU9Dmrr3TeYpeSEmLgsZ0T1lUT7zzRLfAt0s74FujXZjbRObcgUYFJzYqnAlBd0JCrEIXDYWbPeownH76Dvv2HcNDhJ5U9l8wJVCSZxZxEzWwX4L/4EoAfAqnAmcAFZrafc+7rhEYoNa6yCkChEGRmplNQUEgCOiwSoqFWISrIz2Pa+GF8+M4rALwwYwJ79zhSxRNEAhZPS3QU/nZo/ZxzBQBm1gh4BLgdODJx4UltqKwC0N9dRanqKgrQkj/mM37kYP747ScANtuiI+ddcYcSqEgdEE8SPQDYuzSBAjjn8s3sJnwLVUQS5LMPZjNl3FDy8/zAqn/tdxinn3cDjbIaBxyZiEB8SXQ1UNGojro70kMkyRQXF/H89Ht55bkpAKSkpnHC6ZfS84hTNH1FpA6J5+LSm8AoM9ukdIGZtcZ35c5OVGAiDdmKZUt4+zVfQTO7RWsuG/YABx/ZTwlUpI6JpyV6FTAPWGBm3+PvIWrACvwNu0Wkmlq33Zz+F97EGy8+ytmDb6NFyzZBhyQiFYg5iTrnFprZTsBpwC5ACJgETHfOqeyfSBzC4TA/uy/YdofdypZ13asnXfY8kJSUhjcaWSRZxJREI9NbCpxzPwDjayYkqQnlqxOpAlDdUVCQxyMTbuaD/77EoCGj2WOfQ8qeUwIVqduqlEQjxRVm4osrYGafASc6536uwdgkQZK5OlF9t3TRAiaMGsLC+d8D8OpzU+i6V08lT5EkUdW/1FFAY+BU4GQgA5hYU0FJYm2oOlFDrgAUtM8/epsRV5xSlkB33+cQBt94vxKoSBKpanduD+AE59y7AJEBRR+aWWb0fFGp+8pXJ2qoFYCCVFJczAszxjPr6UkApKSk0vu0SzjkqFM1+lYkyVQ1ibYBortuv4j8bAv8ltCIpEZVVp1IasfqnBVMGns1337xPgDZLVpx9uDbsZ3/FXBkIhKPqjZBUoHi0gfOuTBQAKgfUCQGy5b+zvdffwzAttaF60Y9pgQqksTiuouLiMSnQ6ddOGnAlSxa+DN9Th+su6+IJLlYkug+ZrYi6nEK0C0ycreMc071c0Ui1hXk88M3n7Jz133LlvU47IQAIxKRRIoliT6DL6wQbXq5x2F812+VmFlb4H7gQKAIfyeYIc65SudiROaqfgj0cs69XdVjidS2Pxcv5L6Rg/l9/o8MvvF+tt95j6BDEpEEq2oS3aaGjj8D+B1oD7QDXgAuxU+p+Qczaww8BmTVUDwiCfH5h3O49/bLyV27GoAP5s5SEhWph6qURJ1z8xN9YDPrhG+Bbu6cywV+NrPhwEgqSaLAfcCz+HKDgSpfAaguU3Wi2lNSXMzMGfcz8wk/jTqUksJx/f7DYcecGWxgIlIjghxYtDOw3Dn3R9Syb4CtzKyFc25l9MpmdjrQCRgIDI33oKmp1Z8TmZtfxBUT5pGbn3wVgEIhf7Ptqihdr6rrN3RrVq9i0p1X89Vn7wLQrHlLzhl8Ozt23ivgyOo+vdfio/MWu0SfqyCTaDOgfBMpN/KzKVCWRM1sB2AEsJ9zrtjM4j5odnb1e4KXzl+elAk0PS2FNps0ISO9ypetAcjM1AjSjfnlh6+56+aL+XPJ7wB02qELF107llZt2gUcWXLRey0+Om/BCTKJrsWXEoxW+nh16QIza4S/dnqJc25BdQ+ak5NHcXFJtfaxenV+2e+7bdea7MbJ8QZu1jiD4qJi8oqKN74y/htbZmY6BQWFlJSEazi65LZ08aKyBNqz10mccd7VFJeEyMtbF3BkyUHvtfjovMWuPrVEvwJamdmmzrklkWU7AQvL3VJtT2B7YLKZTY5a/qKZTXXOnR/LQYuLSygqql4SLYpKws2y0mnRNHkqAMXzh1ZSEtYf6EbssvsBHHvKhWzSejP2PehI0tIzKMxbp/MWI73X4qPzFpy4kqiZdQEuBnYATgCOAb51zr1V1X04534ws3eAsWZ2DtAaf61zcrn15lJuNK6ZhYEjNcVFgvLX0t9ZtPAXdt19/7JlvXqfFWBEIhKEmEfZmNkewPtAR2APIBPoCrxmZkfGuLs++ET+C/AB8AowPHKcNWbWL9b4RGra15/NY8TlpzBx9BB+X/Bj0OGISIDiaYneDoxxzl1nZqsBnHNnm9lK4AbgxaruKNKNW2H5Fudc0w1sp6FoUutKSkqY9fQkZs4YTzgcJpSSwk/ffc7mW3UKOjQRCUg8SfRfQEXXIScA51UvHJG6ae2aHB68+1q+/GQuAE2ateDsS29lpy77BByZiAQpniS6DsiuYPlW/HPKikjS++2X7xg/agh/LVkIQIdOOzNoyGhatdks4MhEJGjxJNHngNvMrG/kcdjMdgTuJoauXJFk8P6cl5g24SYK1/l7z3f/dx/6DriC9PSMjWwpIg1BPEl0CPAysBRfkP5TfMv0C+DyxIUmEryiokIK1xWQnpFJv7OvYd+exwQdkojUITEnUedcDrCfmR2MH5Wbgp/z+YpzrnoTMEXqmP0PPpblf/7Bbt16slXHHYIOR0TqmLiLLTjn3gTeTGAsIoH75ov3SUlJZYdd9yxbdvRJMdXzEJEGJOYkama/4O8bWiHnXMdqRSQSgJKSEl559kGef/w+mjRtznWjHmOT1qp7KyIbFk9L9GHWT6LpwHbA4cB1iQhKpDblrs1hyrjr+eKjtwEoKSlm2dI/lERFZKPiuSZ6Y0XLzexCYH/8KF2RpLDw1+8ZP+oy/lz8GwBbddyRc4eMpvWmmwccmYgkg0QWoJ8J3JLA/YnUKD99ZTiF6/xdefY/+DhOPusq0jOS54YCIhKsRCbRA4H8ja0kErRwOMyMB0cye9ZjAKSlZ3DyWVdxwCHHBxyZiCSbeAYWvcX610RDQHOgM3BXguISqTGhUIhmzTcBoFWbzRg0ZDQdOu0ccFQikoziaYn+WsGydcBY4NFqRSNSSw4/fiAAPQ47gabNWgQcjYgkq3iS6BvAy8655YkORqQmhMNhXn9hKjt23ostt/EFE1JSUjiiz9kBRyYiyS6eJDoO2A9QEpU6Ly93DQ/dcz2ffTCb1m0359pRj9GkaUX3TxARiV08SfR7/PXPbxMci0hC/bHgR8aPuowlf8wHoFHjJuTnrVUSFZGEiSeJfgU8amaXAz8AedFPOucGJCIwker4cO7LTB0/jHUFfsD4PgcexSnnXENmZlbAkYlIfRJPEt0WmBv5XSVdpE4pKizkqal3/D19JS2dvgOvpPuhvQmFQgFHJyL1TTwViw6qiUBEqqtwXQF3DBvET999DkDL1u04d8gottlu14AjE5H6KqUqK5lZsZm1relgRKojPSOTLTsYADt23ovrRk5XAhWRGlXVlqj6wSQpnHjmENpv2ZHuh/YhJTU16HBEpJ6rUktUpC7Kz1vL1PuG8deS38uWpaWnc+D/9VUCFZFaEcs10RPNLGdjKznnplYjnjovN7+I3/9cG3QYDd6ihT8zfuRlLP79Fxb88h1X3DyFjMxGQYclIg1MLEm0Krc4CwP1Nonm5hdxxfh55BYUBR1Kg/bxvNd4+N4bKcjPBWCzLbYhXPl94kVEakwsSbSdc25pjUWSBBYtX7teAk1PTaFpVnqAETUsRUWFPPvI3bw+cxoAqWlpnHjmEA78v76aviIigahqEtXX/HK6dGpF+1ZNSE/TZeXasGrFX9w/5gp++PZTAFps0pZBQ0axrXUJODIRacg0OjdO2Y0zlEBryfK/FnPrVaexasWfANgue3L24NvJjtzOTEQkKFXNAg9TrryfSG1p2WrTshbnYceeySXXj1cCFZE6oUotUedc/5oORKQyoVCIMy8cxr49j6bzHt2DDkdEpIz6I6XOWfz7r9x18wWszllRtqxRVhMlUBGpc5REpU759P03ueXKfnz92btMGns1JcXFQYckIlKpeO7iIpJwxcVFPDf9Hl597iEAUlLT6LxHd0Ip+p4nInWXkqgELmfVch6440rcVx8B0LxlGwYNGUmnHboGHJmIyIYpiUqgfnJfMHH05axc7ut4bLfTHpwz+Haat2wdcGQiIhunJCqB+fG7zxhzw9kUF/kqUIcefTrH9fsPaWmqAiUiyUFJVALTodMudNy+Cwt+/pYzLxzGHvscGnRIIiIxURKVWhUOh8vq3KalpXPO4NvJXZvDZlt0DDgyEZHYaeij1JrPP3qb0UMHUpD/d/Gr5i1bK4GKSNJSEpUaV1JczLOPjuO+2y7hh28/ZfoDtwQdkohIQqg7V2rU6pwVTLrzKr793wcAZLdoxX49jw04KhGRxFASlRrzyw9fMmH05az4azEA2+6wG4MuG0mLTdoGHJmISGIoiUrChcNh/vvaU8x4cCRFRYUAHHxkP3qfdommr4hIvaIkKgn3zhvP8Oj9IwDIyGzE6effSLf9/y/gqEREEk9JVBKu2wG9ePOl6RQXF3He5WNov1WnoEMSEakRSqKSEMXFRaSm+rdTZqMsLrzmbpo0bU5W46YBRyYiUnM0xUWqpaS4mBcev4+xN51HcXFR2fLWbTdXAhWRek9JVOK2ZvVKxt3yH1588n7cVx/x0lMPBB2SiEitUneuxGX+T98wYdRlLPtzEQAdt+/MAYccH3BUIiK1S0lUYjb3jWd4bNJtFBWuA+DA/+vLiWcOIS1d01dEpGFREpUqK1xXwGOTbuOdN58FID2jEaedO5S9exwRcGQiIsFQEpUqe3ra2LIE2rbdlpx7+Ri26LB9wFGJiAQn0CRqZm2B+4EDgSLgEWCIc66ognXPBS4F2gOLgLHOuftqL1o5os/ZfPbBbLbquCP9/3MTjZtkBx2SiEiggm6JzgB+xyfGdsAL+EQ5KnolMzsWuBU4HPgA2BuYZWZLnHNP12rEDUhJSQnrCvIpHcTdrPkmXHXrVJq3bENKigZ2i4gElkTNrBO+Bbq5cy4X+NnMhgMjKZdE8Un2Nufc+5HH75nZW0B3IKYkmpoa/4d/WtS2oRCkpITi3lddt3ZNDpPHXkNW4yacc9ntZa+1VZt2AUdW95Weq/r8/kg0nbP46LzFLtHnKsiW6M7AcufcH1HLvgG2MrMWzrmVpQvLd9tGuoG7A4NjPWh2dlac4UKznIKy3zMz08nKyoh7X3XZrz9+w9ibL+bPxQsB6LpXDw44RLcvi1VmpkYrx0rnLD46b8EJMok2A9aWW5Yb+dkUWEkFzKwd8BLwCTA91oPm5ORRXFwS62YArF6dX/Z7QUEheXmpce2nLnt39vM8MnEEhev8F4aDj+hL170PJS9vXcCRJY+UlBCZmekUFBRSUhIOOpykoHMWH5232NWnluhaoHG5ZaWPV1e0gZntDTwJzAX6VzQAaWOKi0soKooviRZFJd9wmHr1pi0sXMfjk29n7uu+dzw9I5NTB13HIUf0IS9vXb16rbWlpCSs8xYjnbP46LwFJ8gk+hXQysw2dc4tiSzbCVjonFtVfmUzGwCMA653zo2pxTjrvWV/LmLi6CH8+uPXgK97e94VY9h62x0DjkxEpG4LLIk6534ws3eAsWZ2DtAaGApMLr+umfUGxgNHO+derd1I67+p44eVJdBd9ziAAReNoElTTV8REdmYoOcp9MEn8l/wU1deAYYDmNkaM+sXWe+GyHpPR5aX/psQRND1zannXEeTZi045uQLuOCqu5RARUSqKNB5opFu3BMqea5p1O+day2oBiB3bQ4ZGVlltW7btNuCm+95QclTRCRGQbdEpZb99qtjxBX9eGrqHestVwIVEYld0BWLApWbX8Si5eVn2VTu9z+rvm5d9N7bL/LIxJspXJfP7FmP0e2AXnTcftegwxIRSVoNNonm5hdxxfh55BbEPEsm6RQWruOJKaOY8+qTAKSlZ3DKWVcrgYqIVFODTaKLlq+NO4Gmp6bQNCs5KoSsWLaECaOG8MsPXwLQqm17zh0ymq233SngyEREkl+DTaLRunRqRXbjqpfwa5qVTnpa3b+c/N2XH/LAHVeyOmcFADt33Y+BF4+gabMWAUcmIlI/KIkC2Y0zaNksM+gwEos97JUAABHbSURBVKqoqJBHJgxndc4KQqEQR5xwDkf2OYeU1PpXqlBEJCh1vzklcUlLS+fsy0bSvGUbLrz6bo7ue54SqIhIgqklWo/8teR3NmmzWdm9PrfuuCO33Pci6Rn1q5UtIlJXqCVaT3wwdxY3XtqbV597aL3lSqAiIjVHSTTJFRUW8vjk25k89hrWFeTz4pP3s2rFX0GHJSLSIKg7N4mtXL6UiaMv5yf3BQAtW7fjvMtH07xl64AjExFpGJREk5T7+mMeuONKclYuA2DHLntz1iW30iy7ZcCRiYg0HEqiSSYcDvP6zGk8M+0uSkqKAejV+yyNvhURCYCSaJJZvWo5Lz/zICUlxWQ1bsqAi0bQZc8eQYclItIgaWBRkslu0YqzL72VLbfZgWtHTlcCFREJkFqiSWD+T9+sV+t2py77sMOue5XNBxURkWDoU7gOKyoq5IkpoxlxxSnMm/38es8pgYqIBE+fxHXUqhV/ceeNg3jjxUcAmPX0JIqKCgOOSkREoqk7tw764dvPmDj6cnJW+qIJO+zajbMuvY20tOS4/ZqISEOhJFqHhMPh/2/vzsOkqs48jn+7UVkEzagD2EHcYF6NQUVFRKPihgtGETURF0AnGoy4DWJGjAYCPqKAy4wiMfEBiaKgRuMaTUAF3JcRQeOLKCBLQGQREWikrfnj3GqKSnV3VXV33aL793kenq6+99a9bx+q+XHOXQ7TnpvEYxPv5PuKMNfpKWddwpl9fkWTJvqrEhEpNvqXuUhs3LCeifcN493XXgSgWYuWXDzwd3TuenzMlYmISFUUokVi1YqlzHrnVQDK2nfg8sFjaFO2Z8xViYhIdRSiRaKsfQf6Xn4Ts9+fyUUDbqZps+ZxlyQiIjVQiMakomIzPuddfnTQEZXLuh7Tk8OPPo2SkpIYKxMRkWzpFpcYrF2zkjuHDeDu4Zcz+/2ZW61TgIqIbDsUogX2mc9ixODzmPvRuyQSCWb87Ym4SxIRkTxpOLdAEokEL78wmcceHE3F5nD7So8z+nLWhVfFXJmIiORLIVoA5Rs38NDvh/PW9OcBaNqsBf0HDuPQbifFXJmIiNSGQrSeLV+6kPtGDWLpF/MA2L3dPgwYPIbd2+0dc2UiIlJbCtF6tmb1CpYtng/Aod1Oot8VQ2nWfMeYqxIRkbqgEK1ndsBhnN33GgBOPP1CXX0rItKAKETr2Ddfr2L+p3M48LBjKped9NOLYqxIRETqi25xqUOfz53NiMF9GDdqEAvmzYm7HBERqWcK0TqQSCR49cXHGHXTxaxeuZzNm7/j41lvxl2WiIjUMw3n1lJ5+QYm3X8Lb7zyLABNmzWn76+G0uWok2OuTERE6ptCtBa+XLaIcbcPYvHCuQC0KduLywePpqx9h5grExGRQlCI5unD96bzwF1D2LB+HQCdu55A/4HDaN6iZcyViYhIoShE87SpfCMb1q+jpLSU3hdcRY8z++n2FRGRRkYhmqfDjuzB8qUL2dcOZr9OXeIuR0REYqAQzdKCeR/xzdrVdDrkJ5XLep5zaYwViYhI3BSiNUgkEsz8+5955I8j2W6Hptx428O0Kdsz7rJERKQIKESrsal8I4/8cSSvTXsKgNImTVi+dKFCVEREAIVolb5avoT7Rg1i0fxPAGjddg8GXH8H7fbsGHNlIiJSLBSiGcx+fyYP3D2E9evWAnBQl+5cfOVwWuzYKubKRESkmChE0zz3+B94+tGxJBIJSkpL6dVnICf36k9pqZ6QKCIiW1OIpmneoiWJRIKWO/0bl147kv0P7Bp3SSIiUqQUommOO/U8NqxfR7fuP2WX3drGXY6IiBQxjVECixZ45euSkhJ6nnOpAlRERGrUaEN0U/mmytdPTbqHtWtWxliNiIhsixpliC5a9AXXXXd15ffbbbc93677OsaKRERkWxTrOVEzaw3cD3QHNgMPAde5++YM254G3AbsA3wBDHb3Z3M95rRpU7n00v4kmu5Gm+iRt/0HDmf3Nrvk+2OIiEgjFXdPdDKwDigDDgdOBK5N38jMOgJPADcBOwO/BaaY2Q9zOdiIESM499xerF69eqsZV5q32DHvH0BERBqv2ELUzDoQeqDXu/t6d/8cGA4MzLB5P2CGuz/l7pvdfQrwKnBZLsccc+9Edm7TgT06HsqgIbfV8icQEZHGLs7h3AOAVe6+NGXZx0B7M/uBu69J23Z22vs/Bg7K5YA/OX9U5es3529ZXlICpaWaCzRdsk3UNrlRu+VObZYftVvu6rqt4gzRVsC3acvWR19bAmuy2LZlLgd8ZsyZ+qSJiEidifOc6LdAi7Rlye+/yXLb9O1EREQKJs4QnQPsamZtUpb9CFjs7un3m8whDOmStu2ceqxPRESkWiWJRCK2g5vZDGAx4QKh3YBngMfdfWjadvsB/0e4wOjPQG/gQeAgd59byJpFRESS4r7F5RzCedn5wFvAXwlX6GJm68zsAgB3/wToBQwBVgM3A2crQEVEJE6x9kRFRES2ZXH3REVERLZZClEREZE8KURFRETypBAVERHJU6yzuNS1OGaF2dbl2GYDCBMElAH/BO5y97GFq7Z45NJuKe/5MfA2cJq7v1KAMotKjp+1Y4HbCfeHrwbGuvuthau2eOTYblcD1wC7AguAYe7+RMGKLTJm9u/AG8Avqvqdq20WNLSeaEFnhWkgsm2zXsCthHt1d4q+3mJmZxeu1KKSVbslmVkL4BGgeUGqK07Zftb2A54HxhIe+dkTGGRm5xSu1KKSbbudSrgN8BR33wkYRvh3ba/ClVo8zOwoQoDuW802tc6CBhOiccwKs63Lsc3KgJHu/qa7J9z9DeBl4JiCFVwkcmy3pLHAkwUoryjl2GZXAE+5+4PRZ+1D4EhgZsEKLhI5ttv+QAlQamYlQAWwidB7bVTMrB8wCbixhk1rnQUNJkSpYVaYDNvWelaYBiDrNnP3se5eOX9cNMR0DPBeQSotLrl81jCzvkAHQs+gscqlzQ4HFpjZI2b2lZn9A+ju7ssKVWwRyaXdHgGWR+u/Ax4D+rv74oJUWlxeBPZ198k1bFfrLGhIIVrTrDDZbJvTrDANQC5tVsnM2gIvEAJ0Uv2UVtSybrdoaPIW4Hx3ryhAbcUql8/aLsBVhHN/bYFfAqMb6XBuLu22A/AB4T8hLQi9qQfMrFO9VliE3H1ZddcnpKh1FjSkENWsMLnLpc0AMLMjgHcAB87I8oPa0GTVbmbWjHA+6xp3/6JAtRWrXD5r5cBf3P25aIhtOvAn4Gf1XGMxyqXd7gE+cvd33H2Tu48nnBPsX78lbtNqnQUNKUQ1K0zucmkzzOwSYCrhqtzz3b28QHUWm2zbrQvwH4TewBozS86R+6yZNbarmnP5rH0MNE1b1oRwvq+xyaXd2vOv7fYd4byoZFbrLGhQz87VrDC5y6HNziYM3Z7h7i8Wus5ik227ZXhfAjiukd7iku1n7XjCOa2LgYeBo4HngAvc/elC1lwMcmi34cAA4GTCsG5vQg++m7t/UMiai0l1v3N1kQUNqScKmhUmH1m1GeHS7+2AJ6LlyT/j4ii6CGTbbrJFtr+f04AzgKuBr4HxhPsiG12ARrL9rA0D7iXcsrEa+G+gV2MO0EzqOgsaVE9URESkkBpaT1RERKRgFKIiIiJ5UoiKiIjkSSEqIiKSJ4WoiIhInhSiIiIieVKIioiI5EkhKiIikqft4i5ApJDM7BXg2CpW3+3u12Sxj+6EuVT3dvcFdVbclv3vRXg6TaoKYAVhrsPB7r6ojo61AJjg7kOjOSj7Ai+4+5dm1h8Y7+718sza5P7TFn9PeErR28Cv3X1WDvtrDxzp7o/WWZEiNVBPVBqjKcDuGf7cFGdRGZzNltr2JMxicgjhAfZ1FWxdgNHR62OACWyZ1WJydOz6lvp3sCdwLmES+JfMbMcc9vMgcErdlydSNfVEpTHasI1M8Lwqrc4lZjaU8FD2TsCHtT2Au69I+bYkbd0GYENtj5FFDel/F4vNbCCh13084YHr2WiMs7xIzBSiImnM7AfArcDphEmhVwJPAv8VBUv69h2B/wW6EUZ3Xic8MH12tH5nYBRwFmHi5PeA69393TzKS87fWh7te4+o1hMJEwzPIAz3Jo/dmvBQ8uOAHYH3gSHu/mq0fgGh9/kKYYgaYL6ZXRy9Hu/uJWY2Adjf3bum/NztgIVAD3efamZHAiMJvdsVhPC7wd3X5vFzboy+VkTHKgGuAy4B9onWzwCudPf5KcP0x5pZd3ffy8x2IDyo/UJgZ8L0Vje7+0t51COSkYZzRf7Vg0BXwuwZHYFrCdNyXVbF9o8CS4HDovdVEEI3+Y//89F+To/Wvwm8Zmadsy3IzErN7GDCkPMHwKdm1gp4DWhHmPWkG2GS4enR+UGA+wjDs8cSeq9zgb9kGCZ9nTB8DHA4YSg31QTgcDPrkLLsAmAJ8LKZHUiYa/Yl4EDgfOBQwpBsTj1EM9sbuB34gtAbBbiGMNPG9YQ5Ws8EDLgjWt+bMAH1FEKIJ2s+hRCinaN1z5pZz1zqEamOeqLSGF1gZuekLXvd3XtEr/8GzEi5qGVBNLx4YBX725cQHvPdfXM0efl+ZlZK6AEeCbROGTodYmZHEab66l9NnS+YWUX0uilhuHI6cJm7f29mFxLmlzw0ue9oiqfPgCuAX0e1zQY+c/eNZnY1YTi4IvVA7r7JzFZF365w9w1mlrrJq8DnhOAcFi27EJgY1TIYmOruI6J1n5pZn+g9xxJ6uhmZ2bqUb7cnTCL9EtDf3b+Nls8D+rl7cmh3oZlNAc6L6l9lZpsIQ/UrorDvA3RJ6fHfYWYHAYMJ85OK1JpCVBqjpwkBkyp1mHYscEYUUh0IPbh9gE+r2N+NwF3A5WY2jTDf45QoXA6JtpmfFkpNgWY11PkLwvyRAN8BX6YNJ3cC5qae14yC8m22BP4w4CGgt5lNJ0x2PdndN5IDd0+Y2USiEI16xT9mS+/1EKBjWiAm7U81IQocHH1tC/wOaAPcmHrls7s/Y2ZdzWwYoVe/f3T8JVXsM9nLfyWt3bcH1lRTi0hOFKLSGH3j7vMyrYiGHp8hhNDDwOOEYcT7q9qZu99rZo8BpwEnEM5RJoOmFFhLGNpMV15DnUuqqjNSAmSaELgJIXRx9yfNrIwwrHkiYTh0uJkd4e4f1XD8dBOA35pZF+DnhN57cvLiUkJ73ZLhfSsyLKuU8jPOM7PTCbe3vGRmnd19JYCZXU/4D8F4QiD/D2FIt08Vu02eqjoa+CZtXQUidUQhKrK1zoQwPMLd3wIws+0JPdLP0zc2szaE85Qj3X0CMMHMfggsJgxjzgF2ApqmhpaZ/QGYBdxTi1pnA/3MrLW7fxnttxnh3OxEM2tKCPQ/uftkYLKZtQD+CfQE0kM0UyBXcveF0QU85xJCdHjK6jnAAamhb6ELOBq4gXDvZ43cfX00JP0O4YKo86JVvwGGuvttKfsfzNZX5KbWPyf6Wubuz6W85xbCvajFdjuTbKMUoiJbW0a4AvZnZvYlsCthuLYtYQg23UrCBUP7mtkNhF7nJYTzeu8BCwgXAk0xsysJF8v8MtqmR4b95WJSVNuUqKe2EbgZaAn83t3LzawrcHR07GWE8GxFuAgnXXIo9mAz+6qKY44nhNv2bH3x0RhghpmNI/QSWxGGxVtR9TB4Ru4+y8xuA35jZg9H50EXAT3M7BlCT/IiwsVEy9Pq38vM2rn7R2b2LDDOzK4ghGpvQqD/Zy71iFRHV+eKpHD3pUA/wtWu/yAM5y4B7gS6pF9p6u6bgVMJvZuphN7dCUBPd//M3SuAkwhDlJMJ93YeD/R296m1rHUNobe7Bvg7MJNwJe5R7p584tG5hB7004ATrjA+391nZNjlbMKVxJMJQZ/JE9HXp9y9snfp7m8CJxPO075HuHDnM+BEd69p2DqTEcAnwNjoKuSLop/tXcLFVZ2AAUDr6AlPAOMI50k/NLMmhN7y49Hyjwn/cbnM3dOfkiSSt5JEotoRHBEREamCeqIiIiJ5UoiKiIjkSSEqIiKSJ4WoiIhInhSiIiIieVKIioiI5EkhKiIikieFqIiISJ4UoiIiInlSiIqIiORJISoiIpKn/wdzhYOM7MQcggAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, Y = read_fft(GENRES)\n", + "\n", + "train_avg, test_avg, cms = train_model(create_model, X, Y, \"Log Reg FFT\", plot=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm_avg = np.mean(cms, axis=0)\n", + "cm_norm = cm_avg / np.sum(cm_avg, axis=0)\n", + "\n", + "plot_confusion_matrix(cm_norm, GENRES, \"fft\", \"Confusion matrix of an FFT based classifier\", 17)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Improving classification performance with Mel Frequency Cepstral Coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4135, 13)\n" + ] + } + ], + "source": [ + "from python_speech_features import mfcc\n", + "\n", + "fn = Path(GENRE_DIR) / 'jazz' / 'jazz.00000.wav'\n", + "sample_rate, X = scipy.io.wavfile.read(fn)\n", + "ceps = mfcc(X)\n", + "print(ceps.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 16.43787597, 7.44767565, -13.48062285, -7.49451887,\n", + " -8.14466849, -4.79407047, -5.53101133, -5.42776074,\n", + " -8.69278344, -6.41223865, -3.01527269, -2.75974429, -3.61836327])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_ceps = len(ceps)\n", + "np.mean(ceps[int(num_ceps*0.1):int(num_ceps*0.9)], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.35924851, 12.06182075, 9.8524351 , 6.17551375,\n", + " 6.88436778, 6.98513479, 8.53439104, 7.27414944,\n", + " 8.5409396 , 8.99238561, 9.18711851, 8.83647839, 8.49855748])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.std(ceps[int(num_ceps*0.1):int(num_ceps*0.9)], axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating MFCC features" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converting data\\genres\\blues\\blues.00000.wav ...\n", + "Converting data\\genres\\blues\\blues.00001.wav ...\n", + "Converting data\\genres\\blues\\blues.00002.wav ...\n", + "Converting data\\genres\\blues\\blues.00003.wav ...\n", + "Converting data\\genres\\blues\\blues.00004.wav ...\n", + "Converting data\\genres\\blues\\blues.00005.wav ...\n", + "Converting data\\genres\\blues\\blues.00006.wav ...\n", + "Converting data\\genres\\blues\\blues.00007.wav ...\n", + "Converting data\\genres\\blues\\blues.00008.wav ...\n", + "Converting data\\genres\\blues\\blues.00009.wav ...\n", + "Converting data\\genres\\blues\\blues.00010.wav ...\n", + "Converting data\\genres\\blues\\blues.00011.wav ...\n", + "Converting data\\genres\\blues\\blues.00012.wav ...\n", + "Converting data\\genres\\blues\\blues.00013.wav ...\n", + 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num_ceps = len(ceps)\n", + " X.append(np.mean(ceps[int(num_ceps / 10):int(num_ceps * 9 / 10)], axis=0))\n", + " y.append(label)\n", + "\n", + " return np.array(X), np.array(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the MFCC-based classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting classical\n", + "Plotting jazz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n", + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting country\n", + "Plotting pop\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n", + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting rock\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting metal\n", + "0.672\t0.000\t0.676\t0.226\t\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, Y = read_ceps(GENRES)\n", + "\n", + "train_avg, test_avg, cms = train_model(create_model, X, Y, \"Log Reg CEPS\", plot=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\local\\Anaconda3-4.1.1-Windows-x86_64\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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dQWbRW0TEeZ2UtwOjyR/5tuR+Hk72ht6dbDesVSOeQlaT3UjubzOOIDPRXSOidsA8lDyoj+6nOv8bZPD8Cvn5jyTfr81aPOHYh+xscyT5+exa/P1z8sSgz05GRcecNcmTtG+T36+TyYPs52N6T+N6B5EHy94eJ7RQ7vpyHEh+NnOQ78EtwGnkZ7FWRPyhidV8g6ya/DXZU/lzEVGrAZjh51909tmEbIM8h2xHXB3YKopLcyKvg9yEzNquIHteTyF7rt5VzPMCmTU+Tp7s/rTYl95OPOvfg6fI3+FTZNX2aDKQfqroa9Fohp97RNxE1n58BLiKvCRmAtnT/MVm9rlVEfF3sh12Gvn+XEE28XypqLa3Qs+0aX114jQzM7NmODM1MzPrkIOpmZlZhxxMzczMOuRgamZm1iEHUzMzsw45mJqZmXXIwdTMzKxDDqZmZmYdcjA1MzPrkIOpmZlZhxxMzczMOuRgamZm1iEHUzMzsw45mJqZmXXIwdTMzKxDDqZmZmYdcjA1MzPrkIOpmZlZh2Yf6AKYmZnNLJIWAe4Edo2IMX3MsxlwHPBB4Elgv4i4rpXtODM1M7NKkrQeGUiX72eeDwFXAgcDCwCHApdJWqqVbTmYmplZ5UjaEbgY+OEMZt0RuD0iro6IKRFxGfB7YLdWtudgamZmVXQzsHxEXDqD+VYB7m+Y9hCwWisbc5tpyT60343TBroM3fDnUZ8Z6CJ0zTxz+mcw2Lz+5tSBLkJX9Ax0Abpogbln68ruzP3xPdo+Bk6695SmyxAR/2ly1vmACQ3TJgLzNrstcGZqZmZD2wRgnoZp8wCvtbISn5KbmVl5egZdDvcAsHrDtJWBv7ayEgdTMzMrT8+gq/z+BbCPpG2Bq4AtgRHAnq2sZNCdIpiZWYX1DGv/0SWSxkv6GkBE/AP4EnAg8BJwCLBVRPyzlXU6MzUzs0qLiJ6G5/M2PL+Z7P3bNgdTMzMrz+Cr5u0KB1MzMyvP4OuA1BUOpmZmVh5npmZmZh1yZmpmZtYhZ6ZmZmYdqmhmWs29MjMzK5EzUzMzK4+rec3MzDpU0WpeB1MzMyuPM1MzM7MOOTM1MzPrUEWDaTX3yszMrETOTM3MrDzD3GZqZmbWGVfzlkPSCEnTZvI2zpB0RhfWM1bSTl0okpnZ0NDT0/5jEBuSmWlEfGugy2BmNiRVNDMd0GAqaXXgRGAN4DXgbOC2hnk2Bw4AVgDmBf4C7BoRj0iaD/g5sDEwBfg/YK+IeFjSksA5wNrAROBuYGREPCPpfICI2KnYxp7Ad4HFgH8C+0XErZLmB04ARgBLAi8Dp0bE0TPj/TAzq7xBnmG2a8BOESQtCPyWDJ4LAxsAOwMfrpvn/cDlwDERsSiwNNADHFLMsi8wfzH9A8AzwLHFa8cA48gAuRIZiH/QSzl2Kta3A7AAcDpwbVG+Y4HlgDWL5b8HHCVphS68BWZmQ0/PsPYfg9hAZqabA5OAwyNiGvCYpI3JwFXzLLBKRDxWZKFLA88DSxWvTwJWA/4X+A2wS0S8VffaJ4HtgVuATeteq7cjcGZE3Fk8P1vSQ8Xyo8iM91Xg/cDrxTxLAo92sO9mZlYhAxnqlwCeKgIpABERZDZZ8ybwFUnjgIeAo4FFmV7u44DjgW8AjwAPS9qyeO17wKXAfsU675G0QR/l+Ff9hIj4U0RMKrZ1OfAC8Gtgi2KWwX2KZGY2WFW0A9JABoWngKUlvf0OSdqCzD5rtiXbMkdExNIRsRlwb93rqwLXRsRawELA+cClkhYAViczzlXJqt47gKv6KMcy9RMkHSlpJTKQ/hVYJCJWJ9tuzcysXRWt5h3I0l0PzAEcKGm4pOWBk4C56+ZZAJgKTJLUI2lTskp3ePH6rsAFkhYlq2JfAcYDbwA/BE4pOhG9BEwgq4gbnQfsJmlNScMk7QzsUcy7AFndO1XSIsDJxTLDe1mPmZnNiDPT7oqIl4FNgE8D/wHGAGeSvWlrRgO/Ax4EngMOIgOuJA0nM8VHi9dfIzswbRERrwO7kfv3BBlM1wa26aUcF5NtoxeSvXV3Bz4XEc8V69uODNT3kNXF9wIf7cqbYGY21FQ0M+2ZNm2m3h/BGnxovxsr8Yb/edRnBroIXTPPnEPycutB7fU3pw50EbpicOdSrVlg7tm6sjtzf/5nbR8DJ13/vUH7lvooYmZm5RnkGWa7qrlXZmZmJXJmamZm5aloZupgamZm5RnkvXLb5WBqZmblcWZqZmbWIWemZmZmHapoZlrNvTIzMyuRM1MzMyuPq3nNzMw60+NgamZm1hkHUzMzs05VM5Y6mJqZWXmcmZqZmXWoqsHUl8aYmZl1yJmpmZmVpqqZqYOpmZmVxsHUzMysU9WMpQ6mZmZWHmem1hXX7rPhQBehK6564N8DXYSu2eqj7x/oInTFyxPfHOgidM2UqW8NdBG6YumF5hnoIgw6VQ2m7s1rZmbWIWemZmZWmqpmpg6mZmZWGgdTMzOzTlUzljqYmplZeZyZmpmZdaisYCppUeAsYAQwBbgQ2DcipvQy757AXsBCwFjgsIi4spXtuTevmZmVpqenp+1Hiy4FxgNLAmsBGwN7N84k6XPAgcCmETE/cBhwmaRlW9mYg6mZmVWKpBXIjHT/iJgYEY8DRwB79DL7SmRL7jBJPcBUYDKZzTbNwdTMzMrT08GjeasAL0bE03XTHgKWkfTehnkvAf5bvP4mcDmwU0SMa2WDDqZmZlaakqp55wMmNEybWPw/b8P04cB9ZFXwPMBuwDmSPtrKBt0ByczMSlNSB6QJZGCsV3v+WsP0U4A/RsRfiufnSfoqsBPw/WY36MzUzMxKU1Jm+gCwkKTF6qatDIyLiFca5l0GmLNh2ptku2nTnJmamVlpyshMI+IRSXcAJ0naDVgYOBg4p5fZrwH2kHQtWd27JbAR2cO3aQ6mZmZWRVuTVbhPAG8BF5A9epE0Htg9Ii4iL4WZClwJLAg8AnwpIu5rZWMOpmZmVp6SboAUEf8FtunjtXnr/p4CjCoebXMwNTOz0vh2gmZmZh1yMDUzM+uQg6mZmVmnqhlLHUzNzKw8zkxncZKWIe+9uHJEPDnQ5TEzs+oYMsG0CKCN92Q0M7MSOTOdxRVj0z0BLEeOb3cksCJ5ke4DwB4RcZekU8h7MtbMBsxFjoW3F3lnjJo5yJskrxARj83kXTAzm+U5mFbH3MC1wCHA6cXzc4HjgQ0iYg+KMe8kzQncTA7Pc2tE3FJbiaT3AX8ErncgNTNrjoNpdUwG1gEeJTPOZYEXgDXrZyoGib2AzD53jIhpda/NRd7P8UFg/1JKbWZWBdWMpUMymE4FPg3cSLahPkiOENA4gs6JwOrAJyLi9dpEScOAC8nq3x3qg6yZmfXPmWl1LAacDKwbEfcASPo+2X5K8XwfYAcykD7fsPxJwMdoCLJmZjZjDqbVsRo5gsAkAEnrAHtSvBeStiNHEdgkIh6pX1DS/sB2wPoR8VyZhTYzs8FrKAbT3wKnAX+QNBvZw/dnwLHFQLLHkO/LNUUHpNpp1NFkD+CJwJ8lzVH3Wm0oHzMz60dFE9MhFUxrbaJTIuL7wPcbXv9x8f8H+1nH0V0vlZnZEOJq3llY0fv2o2TnoxcGuDhmZkNWRWPp0AimwKHASOC4iJg40IUxMxuqnJnOwiLiAOCAgS6HmdlQV9FY+q5rK83MzKxFQyIzNTOzwWHYsGqmpg6mZmZWmqpW8zqYmplZadwByczMrEMVjaUOpmZmVh5npmZmZh2qajD1pTFmZmYdcmZqZmalqWhi6mBqZmblqWo1r4OpmZmVpqKx1MHUzMzK48zUzMysQxWNpe7Na2Zm1ilnpmZmVhpX85qZmXWoorHUwdTMzMrjzNS6YpH55xzoIliD7131wEAXoSsO31QDXYSumX/uOQa6CF3xxptTB7oIXTPX7LN1ZT0VjaUOpmZmVh5npmZmZh2qaCz1pTFmZmadcmZqZmalcTWvmZlZhyoaSx1MzcysPM5MzczMOuRgamZm1qGKxlL35jUzM+uUM1MzMyuNq3nNzMw6VNFY6mBqZmblcWZqZmbWoYrGUgdTMzMrz7CKRlMHUzMzK01FY2nnwVTSHMBqwD8iYnznRTIzM+uMpEWBs4ARwBTgQmDfiJjSy7yfBH4ErAK8BJwWEce0sr2WrzOVtLSk30haS9JcwL3A3cBYSR9rdX1mZjZ09PT0tP1o0aXAeGBJYC1gY2DvxpkkrQjcAJwGzAd8Hvi+pK1b2Vg7melPgAWAZ4GtgA8A6wPfICP7Z9tYp5mZDQHDSqjmlbQCmZEuFRETgcclHUHGqOMbZh8JXB0Ro4vnf5e0LvBqK9ts5w5InwJ2j4ixwGbAjRHxJ+A44BNtrM/MzIaIkjLTVYAXI+LpumkPActIem/DvGuRNauXSHpe0sPAiIj4TysbbCeYzgG8WPy9MfC7unW9qy56ViJpNknLDXQ5zMyqqqen/UcL5gMmNEybWPw/b8P0BYHvkW2qiwO7Az8uo5r3XmBXSU8DCwM3SBoO/AC4r431DSa/BB4ERg1wOczMKqmHUrrzTgDmaZhWe/5aw/Q3gF9HxPXF8z9I+gWwLXBFsxtsJzP9Phm5TwWOi4hxwEnAl8iAOitbZKALYGZmHXsAWEjSYnXTVgbGRcQrDfM+BMzZMG02aC3qt5yZRsRfJC0OLBARLxeTTwIOjogXWl1fXyStDpwIrEGeSZwNHEp2djoKWJXswnwhcGREvCFpFFnXPaJuPWOBURFxvqQxwJ3AesDqwFPAoRFxmaSzgQ2AdSWtAXwXeKIowy7AVcCWwMiIuLhY93DgGWCbiLi1W/tuZlZVZXRAiohHJN0BnCRpN7IW9WDgnF5mPwO4WdLXgYvIOPC14tG0dodgm4ui/lnSSsAXAbW5rneRtCDwW+A28k3YANgZ2K2YfiWwKPCZYts/amH1uwF7kvXkVwJnSZorInYFbgeOjojN6+afD1iMzMgvAXaoe21zssfXbS3uopnZkFTipTFbkwnjE8CfgZuAIwAkjZf0NYAiEfoiGRdeAc4jr0e9ppWNtZyZStoQuBrYWtJDRSHfAuaV9JWIuLzVdfZic2AScHhETAMek7Qx8P+Av0fET4v5HpV0AHCFpHddP9SHyyPi3mJfRgM/JAPzk33MPzoiJgOTJZ0L3CVp8aKn147A+UUZzcxsBsq6A1JE/BfYpo/X5m14fiNwYyfbayczPZoMpn8BtiMzsyXIqH5gJ4WpswTwVH2QiogA3gQeb5j3CWBuMiA2o76785vF//29D293rY6Iv5L1618p7q6xCTC6rwXNzOydhvX0tP0YzNoJpquTbZSvkcHk+oiYBFwHrNilcj0FLC3p7XdP0hbAOGD5hnmXJ3tjvQhMBYbXLTOMrM7tRGPWeR6wPVmFcHtxva2ZmTWhpEtjStdOMJ0ADJc0J7Ah068zXZysb+6G68nrWQ+UNFzS8mQnp/8CK0vas2760cBFRVXsw8CqklaRNDuwP+++pqg/r5N3d+rPheS9iL9JBlYzM2tSiW2mpWonmN5G3o7prOL5TcU9eX9GlzriFL2ENwE+TVbLjgHOjIiziulbk7czvIPskLRHsejVZLC7hayeXbiYp1kXALtIur2fsj1H3sdxObKHr5mZDXHt3LRhJNmVeFXg6xHxmqQdyPbHZjsBzVBE3EfeurBx+h1k797elpkCfKt49Pb6iIbnY6m7lqi45OXiuln6OhUaC1xSVG+bmVmTBnmC2bZ2rjN9jrzBfb0DimrWSpO0NPAhshfvpwe4OGZms5zB3pGoXW2NZyppEeDD5F0iAHqKNtS1I+KIbhVuEPomsA9556dZ/daJZmalq2Yobe860+2Bc8kbN0wj35taj9exFBfFVlFEHAIcMtDlMDObVQ32jkTtaqcD0g/JdkWRvXfXJO/L+zR5uz8zM7NeDetp/zGYtRNMPwQcHxGPkCPILBIR15I3bdirm4UzM7Nq8aUx070O1Dob/RP4SPH3X8lAa2ZmNqS0E0zvZvqlJw8Bny3+XpnpQdbMzOxdqnoHpHZ68x4G/EbSc+QdgEZJehBYGri0m4UzM7NqGezVte1qOTONiD+S1blXFuOXrkfeUvBw8oYOZmZmvarTPfKsAAAeiklEQVRqB6S2rjONiPqRVP5Bdj4yMzPrV1Uz06aCqaRbm11hRLzrFoBmZmbgmzb8a6aWwszMbBbWVDCNiJ0bp0kaXrsfr6SlI+KpbhfOzMyqxffmLUhaFLiCHNrswGLyvZLuA7aJiJe6WD4zM6uQisbStq4z/Sk5cPcv6qZ9BngPOc6pmZlZr3wHpOk+A+weEQ/XJkTEveQA3V/sVsHMzKx6fNOGdy4zWy/T3yRHkjEzM+tVVdtM28lMbwOOlbRAbYKk+cibNvyhWwUzM7PqcWY63T5k56Nxkv5JjmX6YeBFpt+n18zMbMjomTZt2oznaiBpfmB74KNk9e5DwEURMam7xaue16fQ+hs+CE18Y8pAF6FrBnvHhmZtfEJ1KoZu2XfDgS5CV8w1R28tYrOmuWbvzv0WRv7q4baPgad+eaVB+2Nt93aCrwJndbksZmZWce20Lc4K2gqmZmZm7ahKTVAjB1MzMyvNYB/9pV0OpmZmVpqqBtOqVl+bmZmVpq3MVNJq5BimKwLbAFsAD0fEbV0sm5mZVUxV20xbzkwlrQHcBXwQWAOYE/g48BtJX+hu8czMrEqG9bT/GMzaqeY9DjghIkYAkwEi4pvAScCh3SuamZlVTVXvgNROMP0f4IJepp8BrNRZcczMrMqG9fS0/RjM2mkznQzM38v0ZYAJnRXHzMyqrKq9XtvZr6vJG90vVDyfJmkl4GfAdV0rmZmZVY6reafbF5gbeJYcEPxvwAPkPXr3617RzMzMZg0tV/MW9+VdT9KnyV68w8hgelNEvNXl8pmZWYUM9rbPdrV9B6SIuAW4pYtlMTOziqtoLG09mEp6AvoeRiwiPthRiczMrLIG+/Wi7WonMx3NO4PpHMCHgM8BB3WjUGZmVk2u5i1ExKjepkvaA1if7NVrZmb2LhWNpV295OdaYLMurs/MzGyW0M0h2EYAr3dxfWZmVjFuMy1Iuo13tpn2AAsAqwI/7VK5zMysgnqoZjRtJzMd28u0yeSN7i/qqDRmZlZpzkyn+x1wY0S82O3CmJlZtTmYTncysB4wSwZTScsCTwDfA35I3hrxGmBkRLwqaVdgb2Bp4F/AsRFxUbHsGOAesn14ReAfwJ4RcUe5e2FmNmvy4ODT/ZNsH53VbQV8FBB5neypknYCTgC+C7wP2BM4TdKX65bbjbw/8fuAq4Br6276b2Zm/ajq4ODtZKYPABdJ2g94BJhU/2JE7NKNgpVg74h4DkDSIWR2ugJwZkTcWsxzq6Qzgd2BXxXTzo2I24rljga+DWwOnF9i2c3MbBBpJ5guD9xe/L14F8tStkfq/n4SmBNYEHi8Yb4ngC/2tlxETJM0DlhiZhXSzKxKyqrllbQocBbZLDcFuBDYNyKm9LPMR4C7gc0iYkwr22vnDkgbtbrMILUUEMXfywETgXHkyUK95YFnGpYDQNIwclD0J2deMc3MqqPE2wleCvwbWJJM/K4h+8Mc39vMkuYBLiH70bSsqWAqaSqwREQ8285GBqljJe0IzAscDlwA3AqcI+lG4PfAhmQb6ci65XaVdAVwP3Ag2e7sQdHNzJpQRtunpBXIjHSpiJgIPC7pCOBH9BFMgdPI5ryPtLPNZjsgDfKm37Y8Srb/3g/cSbahXk6euZwMvAKcDuwXEb+oW24McCrwPLAR8JmIeKXEcpuZzbJ6etp/tGAV4MWIeLpu2kPAMpLe2zizpP8l+8wc1u5+dfN2grOaUyNiv8aJEXEOcE4/yz0YEdvMvGKZmVXXsHJys/mACQ3TJhb/zwu8XJsoaUXgKGC9iJgqqa0NthJMt5X06oxmiogL2iqJmZlVXklNphOAeRqm1Z6/VpsgaS6ybXWviOio70srwbSZodWmkW2PZmZmA+UBYCFJi0XEf4tpKwPjGprl1gQ+TPaVqa+RvE7SBRHxnWY32EowXbwKHZAiYixttgFHxIiuFsbMbIgpowNSRDwi6Q7gJEm7AQsDB9PQhBcRt9PQe1fSNOALrV4a02wHpGkznsXMzKx/w3p62n60aGsyYXwC+DNwE3AEgKTxkr7Wzf1qNjOtYm9eMzMrWVmXmRbVu712Fo2IeftZrq0SNhtMR9Nw20AzM7NWlXjThlI1FUwjYueZXRAzM6u+isbStkaNMTMzszpD+aYNZmZWsqpmcA6mZmZWmqoODu5gamZmpalmKHUwNTOzEg3p3rxmZmbdUM1Q6mBqZmYlqmhiWtmOVWZmZqVxZmpmZqVxb14zM7MOVbU61MHUzMxK48zUzMysQ9UMpQ6mpZvwxpSBLkJXvDh+8kAXoWv+/XI1BkS6Yc/1B7oIXbPpT+8Y6CJ0xbUj1x3oInTNXPPN0ZX1VDUzrWr1tZmZWWmcmZqZWWmqmsE5mJqZWWmqWs3rYGpmZqWpZih1MDUzsxJVNDF1MDUzs/IMq2hu6mBqZmalqWpmWtWOVWZmZqVxZmpmZqXpcTWvmZlZZ6pazetgamZmpXEHJDMzsw45MzUzM+tQVYOpe/OamZl1yJmpmZmVxr15zczMOjSsmrHUwdTMzMrjzNTMzKxDVe2A5GBqZmalcWZqZmbWoaq2mfrSmBZJGitpp4Euh5mZDR7OTM3MrDSu5p2FSVoWeAI4EdgFuBi4F9gbWBr4F3BsRFxUzP8e4DhgW2A48Cfg2xHxr4b1bgJcBuwaEZeXsjNmZrOwqnZAGmrVvPMBiwGPACcA3wXeB+wJnCbpy8V8pwJrAmsU8/8H+GX9iiRtVkz7igOpmVlzejp4DGZDIjOtMzoiJkvaEjgzIm4tpt8q6Uxgd0nXA9sDX4yIpwAk7QOsULeezYAtgB0i4oYSy29mNksbVtHUdKhlpk8X/y8GPN7w2hPAssCCwJxk1S8AEfFyRPy1bt6Ngb8BO82sgpqZVVFVM9OhFkynFf+PBZZveG154BngWeANYJnaC5IWlXSCpLmLST8AtgbWlbT7TC2xmVmVVDSaDrVgWnM2WaX7KUmzSdoI2A04NyLeAi4ADpO0pKS5gCOBT0TEpGL5yRHxb7ID0wmSGgOzmZkNIUMymBYdhvYGTgZeAU4H9ouIXxSz7AP8BbibrBpemMxEG9dzHvB7YLSkIflempm1oqeDf4NZz7Rp02Y8l3XNCxOmVOINf3H85IEuQtf8++VJM55pFrDSEvMPdBG6ZovT/jTQReiKa0euO9BF6JpF5pujK9Hs7sdfafsYuNYHFxi0EXWo9eY1M7MBNGijYYccTM3MrDwVjaYOpmZmVprB3vbZLneaMTMz65AzUzMzK01Fb4DkYGpmZuWpaCx1MDUzsxKVFE0lLQqcBYwApgAXAvtGxJRe5v0Wee+BJck74Z0UEae1sj23mZqZWWlKvGnDpcB4MkCuRd5Tfe/GmSR9CTgG2BGYv/j/KElbtbIxZ6ZmZlaaMtpMJa1AZqRLRcRE4HFJRwA/Ao5vmH1Jcjzru4rnd0q6DdgQuLLZbTqYmplZaUqq5V0FeDEinq6b9hCwjKT3RsTLtYmN1blF9fCG5G1lm+ZqXjMzq5r5gAkN0yYW/8/b10KSFgduBO4BLm5lgw6mZmZWnnKGYJsAzNMwrfb8td4WkLQOOcBJAF/sraNSfxxMzcysNCV1QHoAWEjSYnXTVgbGRcQrjTNL2gW4hezF+9WIeKPV/XIwNTOz0vT0tP9oVkQ8AtwBnCRpPknLAQcD5zTOW/TaPR3YMiJOaHe/3AHJzMxKU+JNG7YGTgGeAN4CLgCOAJA0Htg9Ii4CDiVj4ZWS6pe/MCK+1ezGHEzNzKw8JUXTiPgvsE0fr81b9/eq3dieq3nNzMw65MzUzMxKU9Uh2BxMzcysNB41xszMrEMVjaX0TJs2baDLMKS8PgW/4YPMC+MnD3QRumKheYcPdBG65vU3pw50Ebpiia+fP9BF6JpJV36jK3Hw4WcmtH0MXGmJ9wzaWOzM1MzMSuM2UzMzsw5Vtc3Ul8aYmZl1yJmpmZmVpqKJqYOpmZmVqKLR1MHUzMxK4w5IZmZmHapqByQHUzMzK01FY6l785qZmXXKmamZmZWnoqmpg6mZmZXGHZDMzMw65A5IZmZmHapoLHUwNTOzElU0mjqYmplZaaraZupLY8zMzDrkzNTMzErjDkhmZmYdqmgsdTA1M7PyVDUzdZtpLyStMNBlMDOrpp4OHoOXg2kDSccDBzU57whJ02ZykczMKqOnp/3HYOZg+m6LDHQBzMxs1lKJNlNJywJPADsCRwALA5cB5wCnAssDfwa2A14AvguMBBYD7gf2ioh7JB0MfK1Y58cjYjVJ6wJHAisCCwIPAHtExF2l7aCZWUUM8gSzbVXLTDcDVgLWBnYATimmLQssA3yneHwf2IbMQs8DfidpsYg4ArgIuKgIpHMD1wJXAu8HFgIeA44vcZ/MzCqjqtW8lchM6/w4IiYCD0h6BhgdEf8GkHQnGVS3B46OiL8Xy5wraVfg68AJDeubDKwDPArMVSz/ArDmTN4PM7NKquodkKoWTF+o+3sq8FLd87fITHxZ4ARJx9W9Ngfw18aVRcRUSRsBNwLzAg8Cb1K9jN7MrBzVjKWVC6bN9KwdBxwSEb+sTZC0PO8MxLXpawMnA+tGxD3FtO+T7admZtaiisbSIZlhnQUcJGlFAEmbkBnnhsXrrwMLFH8vQGa0k4p51wH2BIaXWWAzs6pwm2l1/IQ8ObpG0hLAv8neudcUr18KXCrpSeADwGnAHyTNRvYY/hlwrKTFyi+6mZkNRj3TpvmeA2V6fUpTVdFWohfGTx7oInTFQvNWp8Lk9TenDnQRumKJr58/0EXomklXfqMrueFzr01p+xi4yHyzD9r8dChmpmZmNlAGbTjsjIOpmZmVpqKx1MHUzMzKM9g7ErXLwdTMzErjmzaYmZl1qKqZ6VC8ztTMzKyrHEzNzMw65GpeMzMrTVWreR1MzcysNO6AZGZm1iFnpmZmZh2qaCx1ByQzM7NOOTM1M7PyVDQ1dTA1M7PSuAOSmZlZh9wByczMrENlxVJJiwJnASOAKcCFwL4RMaWXeTcDjgM+CDwJ7BcR17WyPXdAMjOz8vR08GjNpcB4YElgLWBjYO/GmSR9CLgSOBhYADgUuEzSUq1szMHUzMxK09PBv2ZJWoHMSPePiIkR8ThwBLBHL7PvCNweEVdHxJSIuAz4PbBbK/vlYGpmZlWzCvBiRDxdN+0hYBlJ7+1l3vsbpj0ErNbKBt1mWrK5Zq9oV7ZZ2FLvHT7QRbAGc80+20AXoSsmXfmNgS7CoDP3HKUcA+cDJjRMm1j8Py/wchPzztvKBp2ZmplZ1UwA5mmYVnv+WpPzNs7XLwdTMzOrmgeAhSQtVjdtZWBcRLzSy7yrNExbuZjetJ5p06a1XEozM7PBTNLtwDiyI9HCwLXAFRExqmG+FYF7yY5IVwFbAqOB1SLin81uz5mpmZlV0dZkv6AngD8DN5E9epE0XtLXACLiH8CXgAOBl4BDgK1aCaTgzNTMzKxjzkzNzMw65GBqZmbWIQdTMzOzDjmYmplVRHEbPRsADqaDgKQRkmZqTzBJZ0g6owvrGStppzaXXaboRbdMp+WoEkmzSVpuoMthqZPv+ECSdDxwUJPzzvRjzlDj2wkOERHxrUFQhidp8RZdQ8QvgQeBUQNcDpu1LTLQBRjKHExLJml14ERgDfJ2VWcDtzXMszlwALACGXz+AuwaEY9Img/4OTmc0BTg/4C9IuJhSUsC5wBrk/eWvBsYGRHPSDofICJ2KraxJ/BdYDHgn+T4fbdKmh84gRxxYUnyHpanRsTRXdj3ZclrvpYr1n0ksCKwIHm3kT0i4i5JpwA71S06GzBXsc97ARvVvTYHMBxYISIe67SMA2RAD4J1n8v3gB8CcwPXkN+dVyXtSg5dtTTwL+DYiLioWHYMcA/5fVkR+AewZ0TcMUD7cCKwC3AxeSF+X+V+Dzl+5bbk9+dPwLcj4l8N690EuIz8/V0+E8u9I3kN5MLF9s4BTgWWJ6+R3A54gfzNjiR/t/eTv/17JB0MfK1Y58cjYjVJ69LHb6zb+2EOpqWStCDwW+BnwCbA+4ExwL/r5nk/cDmwTURcK2kh4FfkhcQ7APsC85MHiLeAM4FjgS2AY8g7fnyRDD5XAj8A9mwox07F+r5A/lB3Aa6VtDT541sOWBN4hbwbyBWSLouIR7v0VsxN3o3kEOD04vm5wPHABhGxB8VQSZLmBG4G/gvcGhG31O3H+4A/Atd3K5D2cbJzKLA+cBSwKnlh94XAkRHxhqRRwIiIGFG3nrHAqIg4vwg4dwLrAasDTwGHRsRlks4GNgDWlbQGebCsDwq1O7KMjIiLi3UPB54hvyO3dmO/C1sBHyVPXq4GTpV0C3ly9WVyWKpPAr+SNDEiflUstxv5nfsjsB/5XVohIl7oYtmaNR8ZaL5D/+U+FViJ/JyfJb+HvwQ+UVtRMWD0RcBXIuKGmVzuzYryfBC4D/h4Me0N8rvzHTKYfh/YHHiYPB78TtKKEXGEpOUhT5gl9fsbm8n7MiQ5mJZrc2AScHhETAMek7QxGbhqngVWiYjHiix0aeB5oDZQ7SRyaKD/BX4D7BIRb9W99klge+AWYNO61+rtCJwZEXcWz8+W9FCx/Cgy432VDPavF/MsCXQrmE4G1inWNxewLHmgqH8fkNQDXEBmnzsW71nttbnI7OlBYP9uFGoGJzs/Bf4fmR0vQ56ozE/DiUo/diuWfZA8wJ0l6ZqI2LXoNDImIkYVmQpMDwrzkAfUHchsC/J79CoNNRpdsHdEPAcg6RDy/V2B/K7Ugvatks4EdidP8gDOjYjbiuWOBr5dlPH8LpevGaMjYrKkLemj3JKuJ38jX4yIp4py70Pua81m5AnqDiUEUoAfR8RE4AFJzxT78e+ibHeSv5HtgaMj4u/FMucWtQZfJ08c6jX1G7PucQekci0BPFUfFCIiyGyy5k3gK5LGkWPqHQ0syvTP6jjy7PIbwCPAw8WBA7Ka7lIyOxgH3COpt7PQJchqr7dFxJ8iYlKxrcvJH96vyQMKdPe7MpWsqn2EzMJ+Rp6RN27jRDKT2yIiakEdScPIzHA28mDXrY4U9Sc7bxTZ7sZktfnfI+KnETG5yNAPAL5ZlKUZl0fEvRExmbzv5wLke92X0cW2XiYzis9IWrx4bUfg/C7ud80jdX8/CcxJVg8+3jDfE+TB+V3LFWUaR37HBkJt/MrF6LvcC5L79vZvICJejoi/1s27MfA33tncMDPVZ/FTydqPmrfI38aywAmSXq49yBPrDzSuLCKa/Y1Zl/iNLddTwNJFxgWApC3I7LNmW7Kqb0RELB0Rm5FtPzWrAtdGxFrAQuTZ/6WSFiADz5kRsSp5MLmDrCbsrRzv6FEr6UhJK5GB9K/AIhGxOhk0um0x4GRgu4hYPCI+TWaE9eXZh8zGNouI5xuWPwn4GA1Btgv6Otl5k94PzHPTf0Cs95+6v98s/u/v9/f2oMbFQf4h8iRrUTJrHt3kdluxVN3fy5Ht7uPIdrt6y5PVzO9arji5WIYMxgOh9tmNpe9yP0tm+2//BiQtKumEonoUsnlka7L6ffeZWuLUzInROLLt9r21B3k8OKRxRklrM4PfmHWXg2m5rierLA+UNLxo4ziJPCjXLECemU6S1CNpU7JKtzaC9a7ABcVB9VWyXXM8eXD4IXBK0YnoJXKcvsZABHAesJukNSUNk7Qz2Ub5fLH9ScBUSYuQP0jqtt8Nq5Fn25MAJK1DVpcOL55vBxxGVsPVZ0tI2p/sjPG5WpVkF/V1stNXQHkDeJH8vIbXLTOMzH460XhwPY+s5tsauD0ixna4/t4cK2n+oiPb4WQV+xlk1einikt4NiKrrM+tW25XSWsUbbmHkMeV62ZC+VpxNn2Uu2j6uAA4TNKSRZPBkcAnitoZgMlFNeveZDbY+PkPhLOAg5SjnNQ6Rz0IbFi8/jr5+6X4v8/fmHWfg2mJiiq7TYBPk5nKGLIDUf3oBKOB35E/kufI68ZOAlQcrA4g20EeJDvI7Mz0DG038jN9ggymawPb9FKOi8m20QvJ3rq7Mz047UwGq1fJXprjyMz4o115E9JvgdOAP0h6qfj7Z8CiyvEHjyHb86+R9Jry2tTxkg4kO1u9B/hzw2tf60K5+jrZ+S+wsqQ966YfDVxUVNs+DKwqaRVJs5NtuK1cAlR/EOzLheRJyDfJwDozPEr2+Lyf7PSyd9GDdW/ypOoVsjPLfhHxi7rlxpAdep4nqxY/08uYkaVqotz7kL3k7yZrARYmT1Qa13Me2YFpdAtV+jPLT8jjwzWSXiPb8feIiGuK1y8F1pP0JDP+jVmXedQYK42kDwKPAcvUOn4MNpI+RrbVfozM7E+NiGMlrU8G+Y+SZ/sXAwdFxKQigJ5CDuM0jMx61iKzoFpv3jG1cRRVd4lQRIyV9FXyYP93smr77dcaynYV8ClgiboMqhv7/I7ytLjsGOr2zWyocjC1UhRVaZtQ9IItei5aCySdCMwdEd/u8nqXxcHUrCO+NMbKcih5sflxDqStUV7/+yGyF++nB7g4ZtYLZ6Zmg5ykw8k2vuMi4oiBLo+ZvZuDqZmZWYcGuneamZnZLM/B1MzMrEMOpmZmZh1yMDUzM+uQL42xIU05VFr9jcLfIu8sdS9wSETc3uXtjSBHe6ndsGEMMDaKcWZnsOx7gJ0i4tQOtr8seU3pRhExpo3lx9Bkec2GEmemZjl81RLF4/3k2KWvATcpx5edmbak+WHc9iVHBDKzQcaZqRmMj4j6UV2ekfQtchzTLcl7ms4UEfFiC7P3zHgWMxsIDqZmvZtS/P8GvF0d/CvyloiLkTdFH0Nmit8CFicHLDg+Ii6qraQYT/bH5FBZ/6DhJvWN1aaS1iBv5v8J8t7AV5M3bNiPvIsUkqYxvZp4Z/LG+suSw46dAZxcGxRe0kfIk4G1yZODY2e0432VISIm9DLv5uRwZauRx5O/AwdGxO+K1z9E3mz+E2RN2J+AfSPi/uL1zwFHACuTox/dQN5g/yXMZiGu5jVrIGkp8sb144Eb6176NjkA+yZkUDiKvEXi98gb4P8UOF3Sd4r1LAf8hmx//Tg5zNeh/Wx3WXKEkmeBdYAvk6OwnEkG5BOYPvD2U5J2K6YdDqxCjjD0A4qAWYxxews5AtBaRVnfNfZlC2VonHcNMtBeVez/2sVyFxUjHAH8khyV5X+K16eSJyVIWrj4+1xgpWJbGwLH91dGs8HImalZDrm2b/H37MCc5LBq20ZE/SDXN9RlXO8hh/jaISJqY3c+VgSj/ckhr3Yjh9obGRFTgX8U99n9SR/l2I0cH3WniHiz2M6uwCcjYryk8cDUWpW0pIOBoyPikmL5x4uxbE+TdAg5/uk8wI7FkGgPStqLIpi1WoZe5p0KfK++Q5Skn5InEIuR48MuXzx/IiKmSNoFWLEYzuz95Hv9ZET8C/hXken6uGSzHH9pzbJqtNYuOhV4sY/xOOsHKl8ZmIscqP38uumzA3NKmpvM1u4tAmnNn/opx6rAPbUgBhARfwD+0DhjMXD7+4EjJI2qe2lYUa7liu0/0rAv/W2/pTJExH2SXiwGbBfwYTIDB5it+P+H5Jiw35Z0K3ATcFlRDX2fpEuAayU9RY7BeT1wDWazGAdTswyejzYxX/0YorUmkm3JttBGbxT/N3YaerNxxobXmr1Zdm37e5ODyTeqZdStbL+lMkjakMw6bwBuJ6t05yGrfgGIiFMlXQ5sRo54cwxwmKSPRcR/I+Krkg4DPgd8BrgE+CM5bqvZLMNtpmbt+QfZSekDEfFo7UEGjX2LzOteYM269kOANftZ50PA6pJqWR2SvixpnKR5eGeQe7Z4LN+w/TXIttmeYvsq2iab2X4zZai3L3BbRGwZET+JiN8CyxSv9UhaTNIpwPCIOD8idiAz38WBT0paR9JPIp0UEZ8HdgE2krToDMppNqg4MzVrQ0S8IukM4EhJr5LZ1AbAj4DjitlOB/YAzpV0FNl+2GcHJOBUsjPTGcVA4AsX6/pNREws2kzfJ+nD5I0XfgQcLelJMjv8CNlWe11EvCHpl2SnpEuKNuH3klWu/ZlRGernfQr4kqT1yY5RG5E9cyHbQp8CvgAsL+kAsiPULsBk4J5inpGSJgM/B+YGvkJWpz8/g3KaDSrOTM3atzdwItmb9mGyp+xhwCiAiHiarK5cGvgb2fP2yL5WVsz/WbL98W/AZWQb4shiliuBZ8jLT1aPiBOKMowstn8K2TN2t2J9E8gAN5kM9r9geqBvtwz1DgHuAq4D7gO+SQbLScDaETGFrL59i+xV/CBZ1fv5iHgsIh4ir+P9VLH8HWQ18+dql/aYzSo8nqmZmVmHnJmamZl1yMHUzMysQw6mZmZmHXIwNTMz65CDqZmZWYccTM3MzDrkYGr2/9urYwEAAACAQf7Wg9hbEgFMMgWASaYAMMkUACaZAsAkUwCYAuYUfs9yAkcIAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm_avg = np.mean(cms, axis=0)\n", + "cm_norm = cm_avg / np.sum(cm_avg, axis=0)\n", + "\n", + "plot_confusion_matrix(cm_norm, GENRES, \"ceps\",\"Confusion matrix of a CEPS based classifier\", 19)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch12/features.py b/ch12/features.py deleted file mode 120000 index 142a324d..00000000 --- a/ch12/features.py +++ /dev/null @@ -1 +0,0 @@ -../ch10/features.py \ No newline at end of file diff --git a/ch12/features.py b/ch12/features.py new file mode 100644 index 00000000..c183d909 --- /dev/null +++ b/ch12/features.py @@ -0,0 +1,70 @@ +# This code is supporting material for the book +# Building Machine Learning Systems with Python +# by Willi Richert and Luis Pedro Coelho +# published by PACKT Publishing +# +# It is made available under the MIT License + +import numpy as np +import mahotas as mh + + +def edginess_sobel(image): + '''Measure the "edginess" of an image + + image should be a 2d numpy array (an image) + + Returns a floating point value which is higher the "edgier" the image is. + + ''' + edges = mh.sobel(image, just_filter=True) + edges = edges.ravel() + return np.sqrt(np.dot(edges, edges)) + +def texture(im): + '''Compute features for an image + + Parameters + ---------- + im : ndarray + + Returns + ------- + fs : ndarray + 1-D array of features + ''' + im = im.astype(np.uint8) + return mh.features.haralick(im).ravel() + + +def color_histogram(im): + '''Compute color histogram of input image + + Parameters + ---------- + im : ndarray + should be an RGB image + + Returns + ------- + c : ndarray + 1-D array of histogram values + ''' + + # Downsample pixel values: + im = im // 64 + + # We can also implement the following by using np.histogramdd + # im = im.reshape((-1,3)) + # bins = [np.arange(5), np.arange(5), np.arange(5)] + # hist = np.histogramdd(im, bins=bins)[0] + # hist = hist.ravel() + + # Separate RGB channels: + r,g,b = im.transpose((2,0,1)) + + pixels = 1 * r + 4 * g + 16 * b + hist = np.bincount(pixels.ravel(), minlength=64) + hist = hist.astype(float) + return np.log1p(hist) + diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..64277953 --- /dev/null +++ b/environment.yml @@ -0,0 +1,105 @@ +name: BMLS3 +channels: +- conda-forge +- defaults +dependencies: +- bottle=0.12.9=py36_0 +- jug=1.6.4=py_0 +- pyyaml=3.12=py36_1 +- yaml=0.1.6=0 +- bleach=1.5.0=py36_0 +- cairo=1.14.8=0 +- certifi=2016.2.28=py36_0 +- cycler=0.10.0=py36_0 +- dbus=1.10.20=0 +- decorator=4.1.2=py36_0 +- entrypoints=0.2.3=py36_0 +- expat=2.1.0=0 +- fontconfig=2.12.1=3 +- freetype=2.5.5=2 +- glib=2.50.2=1 +- graphviz=2.38.0=5 +- gst-plugins-base=1.8.0=0 +- gstreamer=1.8.0=0 +- harfbuzz=0.9.39=2 +- html5lib=0.9999999=py36_0 +- icu=54.1=0 +- ipykernel=4.6.1=py36_0 +- ipython=6.1.0=py36_0 +- ipython_genutils=0.2.0=py36_0 +- ipywidgets=6.0.0=py36_0 +- jbig=2.1=0 +- jedi=0.10.2=py36_2 +- jinja2=2.9.6=py36_0 +- jpeg=9b=0 +- jsonschema=2.6.0=py36_0 +- jupyter=1.0.0=py36_3 +- jupyter_client=5.1.0=py36_0 +- jupyter_console=5.2.0=py36_0 +- jupyter_core=4.3.0=py36_0 +- libffi=3.2.1=1 +- libgcc=5.2.0=0 +- libgfortran=3.0.0=1 +- libiconv=1.14=0 +- libpng=1.6.30=1 +- libsodium=1.0.10=0 +- libtiff=4.0.6=3 +- libtool=2.4.2=0 +- libxcb=1.12=1 +- libxml2=2.9.4=0 +- markupsafe=1.0=py36_0 +- matplotlib=2.0.2=np113py36_0 +- mistune=0.7.4=py36_0 +- mkl=2017.0.3=0 +- nbconvert=5.2.1=py36_0 +- nbformat=4.4.0=py36_0 +- notebook=5.0.0=py36_0 +- numpy=1.13.1=py36_0 +- openssl=1.0.2l=0 +- pandocfilters=1.4.2=py36_0 +- pango=1.40.3=1 +- path.py=10.3.1=py36_0 +- pcre=8.39=1 +- pexpect=4.2.1=py36_0 +- pickleshare=0.7.4=py36_0 +- pip=9.0.1=py36_1 +- pixman=0.34.0=0 +- prompt_toolkit=1.0.15=py36_0 +- ptyprocess=0.5.2=py36_0 +- pygments=2.2.0=py36_0 +- pyparsing=2.2.0=py36_0 +- pyqt=5.6.0=py36_2 +- python=3.6.2=0 +- python-dateutil=2.6.1=py36_0 +- python-graphviz=0.5.2=py36_0 +- pytz=2017.2=py36_0 +- pyzmq=16.0.2=py36_0 +- qt=5.6.2=5 +- qtconsole=4.3.1=py36_0 +- readline=6.2=2 +- scikit-learn=0.19.0=np113py36_0 +- scipy=0.19.1=np113py36_0 +- setuptools=36.4.0=py36_1 +- simplegeneric=0.8.1=py36_1 +- sip=4.18=py36_0 +- six=1.10.0=py36_0 +- sqlite=3.13.0=0 +- terminado=0.6=py36_0 +- testpath=0.3.1=py36_0 +- tk=8.5.18=0 +- tornado=4.5.2=py36_0 +- traitlets=4.3.2=py36_0 +- wcwidth=0.1.7=py36_0 +- wheel=0.29.0=py36_0 +- widgetsnbextension=3.0.2=py36_0 +- xz=5.2.3=0 +- zeromq=4.1.5=0 +- zlib=1.2.11=0 +- pip: + - ipython-genutils==0.2.0 + - jupyter-client==5.1.0 + - jupyter-console==5.2.0 + - jupyter-core==4.3.0 + - prompt-toolkit==1.0.15 +prefix: /home/luispedro/.conda/envs/BMLS3 +