From a580caca7dc5f49401facf829a2679d2f60fdbd5 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 10 Oct 2014 08:20:27 +0200 Subject: [PATCH 01/87] ENH Download recommendation data in chapter 8 --- ch08/data | 1 - ch08/data/download.sh | 4 ++++ 2 files changed, 4 insertions(+), 1 deletion(-) delete mode 120000 ch08/data create mode 100755 ch08/data/download.sh diff --git a/ch08/data b/ch08/data deleted file mode 120000 index 32d37b53..00000000 --- a/ch08/data +++ /dev/null @@ -1 +0,0 @@ -../ch07/data \ No newline at end of file diff --git a/ch08/data/download.sh b/ch08/data/download.sh new file mode 100755 index 00000000..bc5091d0 --- /dev/null +++ b/ch08/data/download.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +curl -O http://www.grouplens.org/system/files/ml-100k.zip +unzip ml-100k.zip +curl -O http://fimi.ua.ac.be/data/retail.dat.gz From 437da2aff4e793ae649bb60774c78538eb902fa9 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 10 Oct 2014 08:20:48 +0200 Subject: [PATCH 02/87] ENH Use curl instead of wget This is more widely available --- ch07/data/download.sh | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/ch07/data/download.sh b/ch07/data/download.sh index 6f938e06..74753364 100755 --- a/ch07/data/download.sh +++ b/ch07/data/download.sh @@ -1,5 +1,3 @@ #!/usr/bin/env bash -wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.train.bz2 +curl -O http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.train.bz2 bunzip2 E2006.train.bz2 -wget http://www.grouplens.org/system/files/ml-100k.zip -unzip ml-100k.zip From efce70f71936bcacc21955d8c5c9cbc1ffc40a51 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 10 Oct 2014 18:33:51 +0200 Subject: [PATCH 03/87] ENH Add color histogram function --- ch10/features.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/ch10/features.py b/ch10/features.py index 27fd3e91..c651e7b9 100644 --- a/ch10/features.py +++ b/ch10/features.py @@ -37,3 +37,23 @@ def texture(im): return mh.features.haralick(im).mean(0) +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 + ''' + im = mh.stretch_rgb(im) + im = im // 64 + pixels = (im * (1, 4, 16)).sum(2).ravel() + hist = np.bincount(pixels, minlength=64) + hist = hist.astype(float) + return np.log1p(hist) + From 8209239b53ea2919075d8e4a75fe520e6d465828 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 12 Oct 2014 10:41:07 +0200 Subject: [PATCH 04/87] RFCT Use scipy instead of rolling our own This makes the code simpler --- ch08/all_correlations.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/ch08/all_correlations.py b/ch08/all_correlations.py index 509e5b8e..d3817bf0 100644 --- a/ch08/all_correlations.py +++ b/ch08/all_correlations.py @@ -7,9 +7,14 @@ import numpy as np -# This is the version in the book: - +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) @@ -21,9 +26,7 @@ def all_correlations_book_version(bait, target): for c in target]) # This is a faster, but harder to read, implementation: - - -def all_correlations(y, X): +def all_correlations_fast_no_scipy(y, X): ''' Cs = all_correlations(y, X) @@ -42,12 +45,4 @@ def all_correlations(y, X): return (xy - x_ * y_ * n) / n / xs_ / ys_ -# If you have scipy installed, then you can compute correlations with -# scipy.spatial.cdist: -def all_correlations_scipy(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() From b0636c4fe4b9c36adceeede6a3d6fa386f54ec97 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 12 Oct 2014 10:42:51 +0200 Subject: [PATCH 05/87] ENH Use color histograms with SVMs+Grid Search This achieves better classification than the Sobel example, while also being easier to explain. --- ch10/README.rst | 2 +- ch10/simple_classification.py | 55 ++++++++++++++++++++++++++++++----- 2 files changed, 48 insertions(+), 9 deletions(-) diff --git a/ch10/README.rst b/ch10/README.rst index 7137582b..8c146eac 100644 --- a/ch10/README.rst +++ b/ch10/README.rst @@ -33,7 +33,7 @@ features.py Contains the ``edginess_sobel`` function from the book as well as a simple wrapper around ``mahotas.texture.haralick`` simple_classification.py - Classify SimpleImageDataset with texture features + sobel feature + Classify SimpleImageDataset with texture features + color histogram features figure18.py Classify ``AnimTransDistr`` with both texture and SURF features. diff --git a/ch10/simple_classification.py b/ch10/simple_classification.py index 68be203b..33ab4959 100644 --- a/ch10/simple_classification.py +++ b/ch10/simple_classification.py @@ -7,10 +7,15 @@ import mahotas as mh from sklearn import cross_validation -from sklearn.linear_model.logistic import LogisticRegression +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVC +from sklearn.grid_search import GridSearchCV + + import numpy as np from glob import glob -from features import texture, edginess_sobel +from features import texture, edginess_sobel, color_histogram basedir = '../SimpleImageDataset/' @@ -18,6 +23,7 @@ haralicks = [] sobels = [] labels = [] +chists = [] print('This script will test (with cross-validation) classification of the simple 3 class dataset') print('Computing features...') @@ -29,8 +35,10 @@ # ordering that the filesystem uses for fname in sorted(images): im = mh.imread(fname, as_grey=True) + imc = mh.imread(fname) haralicks.append(texture(im)) sobels.append(edginess_sobel(im)) + chists.append(color_histogram(imc)) # Files are named like building00.jpg, scene23.jpg... labels.append(fname[:-len('xx.jpg')]) @@ -40,25 +48,56 @@ haralicks = np.array(haralicks) sobels = np.array(sobels) labels = np.array(labels) +chists = np.array(chists) + +haralick_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) +haralick_plus_chists = np.hstack([chists, haralicks]) +haralick_plus_chists_plus_sobel = np.hstack([chists, haralicks, np.atleast_2d(sobels).T]) -# We use logistic regression because it is very fast. +cv=cross_validation.LeaveOneOut(len(images)) + +# We use SVM because it achieves high accuracy on small(ish) datasets # Feel free to experiment with other classifiers +C_range = 10.0 ** np.arange(-2, 9) +gamma_range = 10.0 ** np.arange(-5, 4) +param_grid = dict(gamma=gamma_range, C=C_range) +grid = GridSearchCV(SVC(), param_grid=param_grid) + +p = Pipeline([('preproc', StandardScaler()), + ('classifier', grid)]) + +scores = cross_validation.cross_val_score( + p, haralicks, labels, cv=cv) +print('Accuracy (5 fold x-val) with Logistic Regrssion [haralick features]: {}%'.format( + 0.1 * round(1000 * scores.mean()))) + +scores = cross_validation.cross_val_score( + p, chists, labels, cv=cv) +print('Accuracy (5 fold x-val) with Logistic Regrssion [color histograms]: {}%'.format( + 0.1 * round(1000 * scores.mean()))) + scores = cross_validation.cross_val_score( - LogisticRegression(), haralicks, labels, cv=5) -print('Accuracy (5 fold x-val) with Logistic Regrssion [std features]: {}%'.format( + p, haralick_plus_chists, labels, cv=cv) +print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + chists]: {}%'.format( 0.1 * round(1000 * scores.mean()))) haralick_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) scores = cross_validation.cross_val_score( - LogisticRegression(), haralick_plus_sobel, labels, cv=5).mean() -print('Accuracy (5 fold x-val) with Logistic Regrssion [std features + sobel]: {}%'.format( + p, haralick_plus_sobel, labels, cv=cv) +print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + sobel]: {}%'.format( + 0.1 * round(1000 * scores.mean()))) + +haralick_plus_chists_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) +scores = cross_validation.cross_val_score( + p, haralick_plus_chists_plus_sobel, labels, cv=cv) +print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + color histograms + sobel]: {}%'.format( 0.1 * round(1000 * scores.mean()))) # We can try to just use the sobel feature. The result is almost completely # random. scores = cross_validation.cross_val_score( - LogisticRegression(), np.atleast_2d(sobels).T, labels, cv=5).mean() + p, np.atleast_2d(sobels).T, labels, cv=cv).mean() print('Accuracy (5 fold x-val) with Logistic Regrssion [only using sobel feature]: {}%'.format( 0.1 * round(1000 * scores.mean()))) From 1421b343b52aa6b919a9a86e60e149aa2c0abf05 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 13 Oct 2014 21:14:56 +0200 Subject: [PATCH 06/87] ENH Partition matrix into train/test --- ch08/load_ml100k.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/ch08/load_ml100k.py b/ch08/load_ml100k.py index da6add37..b3c9556b 100644 --- a/ch08/load_ml100k.py +++ b/ch08/load_ml100k.py @@ -21,3 +21,25 @@ def load(): values = data[:, 2] reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() + + +def get_train_test(): + import numpy as np + reviews = load() + U,M = np.where(reviews) + test_idxs = set() + while len(test_idxs) < len(U)//10: + test_idxs.add(np.random.randint(0,len(U)-1)) + test_idxs = np.array(list(test_idxs)) + 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]] + + valid_U = train.any(0) + valid_M = train.any(1) + train = train[valid_M].T[valid_U].T + test = test [valid_M].T[valid_U].T + return train, test + From de74835ef013c0ac008fa75f8739a2af69886c44 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 17 Oct 2014 13:11:19 +0200 Subject: [PATCH 07/87] RFCT Simplify sampling code --- ch08/load_ml100k.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/ch08/load_ml100k.py b/ch08/load_ml100k.py index b3c9556b..fb9f13df 100644 --- a/ch08/load_ml100k.py +++ b/ch08/load_ml100k.py @@ -22,15 +22,12 @@ def load(): reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() - def get_train_test(): import numpy as np + import random reviews = load() U,M = np.where(reviews) - test_idxs = set() - while len(test_idxs) < len(U)//10: - test_idxs.add(np.random.randint(0,len(U)-1)) - test_idxs = np.array(list(test_idxs)) + test_idxs = np.array(random.sample(range(len(U)), len(U)//10)) train = reviews.copy() train[U[test_idxs], M[test_idxs]] = 0 From 87c322ddbbd15105aa54e320fafbb551531be10c Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 17 Oct 2014 13:19:22 +0200 Subject: [PATCH 08/87] RFCT Cleaner neighbor correlation code --- ch08/corrneighbours.py | 104 ++++++++++++++--------------------------- ch08/norm.py | 30 ++++++++++++ 2 files changed, 66 insertions(+), 68 deletions(-) create mode 100644 ch08/norm.py diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py index eef3881e..e9611294 100644 --- a/ch08/corrneighbours.py +++ b/ch08/corrneighbours.py @@ -6,75 +6,43 @@ # It is made available under the MIT License from __future__ import print_function -from all_correlations import all_correlations import numpy as np -from load_ml100k import load - -def estimate_user(user, rest, num_neighbors=100): - '''Estimate ratings for user based on the binary rating matrix - - Returns - ------- - estimates: ndarray - Returns a rating estimate for each movie - ''' - - # Compute binary matrix correlations: - bu = user > 0 - br = rest > 0 - ws = all_correlations(bu, br) - - # Select top `num_neighbors`: - selected = ws.argsort()[-num_neighbors:] - - # Use these to compute estimates: - estimates = rest[selected].mean(0) - estimates /= (.1 + br[selected].mean(0)) - return estimates - - -def train_test(user, rest): - '''Train & test on a single user - - Returns both the prediction error and the null error - ''' - estimates = estimate_user(user, rest) - bu = user > 0 - br = rest > 0 - err = estimates[bu] - user[bu] - null = rest.mean(0) - null /= (.1 + br.mean(0)) - nerr = null[bu] - user[bu] - return np.dot(err, err), np.dot(nerr, nerr) - - -def all_estimates(reviews): - estimates = np.zeros_like(reviews) - for i in range(reviews.shape[0]): - estimates[i] = estimate_user(reviews[i], np.delete(reviews, i, 0)) - return estimates - -def main(): - reviews = load() - - err = [] - for i in range(reviews.shape[0]): - err.append( - train_test(reviews[i], np.delete(reviews, i, 0)) - ) - revs = (reviews > 0).sum(1) - err = np.array(err) - rmse = np.sqrt(err / revs[:, None]) - - rmse_model, rmse_null = np.mean(rmse, 0) - - print("Average of RMSE / Null-model RMSE") - print("{:.2}\t{:.2} (improvement: {:.1%}".format(rmse_model, rmse_null, (rmse_null-rmse_model)/rmse_null)) - print() - - rmse_model, rmse_null = np.mean(rmse[revs > 60], 0) - print("Average of RMSE / Null-model RMSE (users with more than 60 reviewed movies)") - print("{:.2}\t{:.2} (improvement: {:.1%}".format(rmse_model, rmse_null, (rmse_null-rmse_model)/rmse_null)) +from load_ml100k import get_train_test +from scipy.spatial import distance +from sklearn import metrics + +from norm import NormalizePositive + +def main(transpose_inputs=False): + otrain, test = get_train_test() + if transpose_inputs: + otrain = otrain.T + test = test.T + + binary = (otrain > 0) + norm = NormalizePositive() + train = norm.fit_transform(otrain.T).T + + proximity = distance.pdist(binary, 'correlation') + proximity = distance.squareform(proximity) + + neighbors = proximity.argsort(axis=1) + filled = train.copy() + for u in range(filled.shape[0]): + n_u = neighbors[u, 1:] + t_u = train[n_u].T + b_u = binary[n_u].T + for m in range(filled.shape[1]): + revs = t_u[m] + brevs = b_u[m] + revs = revs[brevs] + if len(revs): + revs = revs[:len(revs)//2+1] + filled[u,m] = revs.mean() + + ifilled = norm.inverse_transform(filled.T).T + r2 = metrics.r2_score(test[test > 0], ifilled[test > 0]) + print('R2 score (binary neighbours): {:.1%}'.format(r2)) if __name__ == '__main__': main() diff --git a/ch08/norm.py b/ch08/norm.py new file mode 100644 index 00000000..5b736f39 --- /dev/null +++ b/ch08/norm.py @@ -0,0 +1,30 @@ + +class NormalizePositive(object): + + def fit(self, features): + binary = (features > 0) + count0 = binary.sum(0) + count0 += (count0 == 0) + self.mean = features.sum(0)/count0 + diff = (features - self.mean) * binary + diff **= 2 + diff += 1e-9 + self.std = 1 + diff.sum(0)/count0 + return self + + def fit_transform(self, features): + return self.fit(features).transform(features) + + def transform(self, features): + binary = (features > 0) + features = features - self.mean + features /= self.std + features *= binary + return features + + def inverse_transform(self, features, copy=True): + if copy: + features = features.copy() + features *= self.std + features += self.mean + return features From b6512203f209cb44da271bad7b9186c219e9e5b8 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 18 Oct 2014 00:35:41 +0200 Subject: [PATCH 09/87] RFCT Simplify user regression code Much simpler code based on the get_train_data function --- ch08/usermodel.py | 67 ++++++++++++++++++----------------------------- 1 file changed, 26 insertions(+), 41 deletions(-) diff --git a/ch08/usermodel.py b/ch08/usermodel.py index 589afca4..1d4baa80 100644 --- a/ch08/usermodel.py +++ b/ch08/usermodel.py @@ -7,48 +7,33 @@ import numpy as np from sklearn.linear_model import ElasticNetCV -from sklearn.cross_validation import KFold +from load_ml100k import get_train_test +from norm import NormalizePositive +from sklearn import metrics -def learn_for(reviews, i): +def main(transpose_inputs=False): + train,test = get_train_test() + if transpose_inputs: + train = train.T + test = test.T + binary = (train > 0) reg = ElasticNetCV(fit_intercept=True, alphas=[ 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) - nusers,nmovies = reviews.shape - u = reviews[i] - us = np.arange(reviews.shape[0]) - us = np.delete(us, i) - ps, = np.where(u.ravel() > 0) - x = reviews[us][:, ps].T - kf = KFold(len(ps), n_folds=4) - predictions = np.zeros(len(ps)) - for train, test in kf: - xc = x[train].copy() - x1 = np.array([xi[xi > 0].mean() for xi in xc]) - x1 = np.nan_to_num(x1) - - for i in range(xc.shape[0]): - xc[i] -= (xc[i] > 0) * x1[i] - - reg.fit(xc, u[train] - x1) - - xc = x[test].copy() - x1 = np.array([xi[xi > 0].mean() for xi in xc]) - x1 = np.nan_to_num(x1) - - for i in range(xc.shape[0]): - xc[i] -= (xc[i] > 0) * x1[i] - - p = reg.predict(xc).ravel() - predictions[test] = p - fill_preds = np.zeros(nmovies) - fill_preds[ps] = predictions - return fill_preds - - -def all_estimates(reviews): - whole_data = [] - for i in range(reviews.shape[0]): - s = learn_for(reviews, i) - whole_data.append(s) - return np.array(whole_data) - + norm = NormalizePositive() + train = norm.fit_transform(train) + + nusers,nmovies = train.shape + + filled = train.copy() + for u in range(nusers): + 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) + ifilled = norm.inverse_transform(filled) + r2 = metrics.r2_score(test[test > 0], ifilled[test > 0]) + print('R2 score (user regression): {:.1%}'.format(r2)) + +if __name__ == '__main__': + main() From 5ece8c01165010edb71227b3bfcabb154cafbf46 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 18 Oct 2014 00:48:08 +0200 Subject: [PATCH 10/87] RFCT Split prediction from evaluation --- ch08/corrneighbours.py | 19 +++++++++++-------- ch08/usermodel.py | 22 ++++++++++++---------- 2 files changed, 23 insertions(+), 18 deletions(-) diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py index e9611294..5a0dc096 100644 --- a/ch08/corrneighbours.py +++ b/ch08/corrneighbours.py @@ -13,12 +13,7 @@ from norm import NormalizePositive -def main(transpose_inputs=False): - otrain, test = get_train_test() - if transpose_inputs: - otrain = otrain.T - test = test.T - +def predict(otrain): binary = (otrain > 0) norm = NormalizePositive() train = norm.fit_transform(otrain.T).T @@ -40,8 +35,16 @@ def main(transpose_inputs=False): revs = revs[:len(revs)//2+1] filled[u,m] = revs.mean() - ifilled = norm.inverse_transform(filled.T).T - r2 = metrics.r2_score(test[test > 0], ifilled[test > 0]) + return norm.inverse_transform(filled.T).T + +def main(transpose_inputs=False): + train, test = get_train_test() + 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(r2)) if __name__ == '__main__': diff --git a/ch08/usermodel.py b/ch08/usermodel.py index 1d4baa80..7f6dc82c 100644 --- a/ch08/usermodel.py +++ b/ch08/usermodel.py @@ -12,27 +12,29 @@ from sklearn import metrics -def main(transpose_inputs=False): - train,test = get_train_test() - if transpose_inputs: - train = train.T - test = test.T +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) - nusers,nmovies = train.shape - filled = train.copy() - for u in range(nusers): + 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) - ifilled = norm.inverse_transform(filled) - r2 = metrics.r2_score(test[test > 0], ifilled[test > 0]) + return norm.inverse_transform(filled) + + +def main(transpose_inputs=False): + train,test = get_train_test() + 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 (user regression): {:.1%}'.format(r2)) if __name__ == '__main__': From b7117f273956bb4958c18e182f61e7041a8663fc Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 21 Oct 2014 21:32:26 +0200 Subject: [PATCH 11/87] MIN Simple explanatory comments --- ch08/usermodel.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ch08/usermodel.py b/ch08/usermodel.py index 7f6dc82c..cd35674f 100644 --- a/ch08/usermodel.py +++ b/ch08/usermodel.py @@ -20,10 +20,14 @@ def predict(train): 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] 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) From a183b7e9663996400d7514bab6d1882da1d54579 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 23 Oct 2014 14:12:42 +0200 Subject: [PATCH 12/87] ENH Set random seed to ensure consistency Also, test models based on user and movie consistently --- ch08/corrneighbours.py | 7 +++++-- ch08/load_ml100k.py | 25 ++++++++++++++++++++++--- ch08/norm.py | 22 ++++++++++++++++++++++ ch08/{usermodel.py => regression.py} | 17 +++++++++++------ 4 files changed, 60 insertions(+), 11 deletions(-) rename ch08/{usermodel.py => regression.py} (70%) diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py index 5a0dc096..9c526daf 100644 --- a/ch08/corrneighbours.py +++ b/ch08/corrneighbours.py @@ -38,14 +38,17 @@ def predict(otrain): return norm.inverse_transform(filled.T).T def main(transpose_inputs=False): - train, test = get_train_test() + 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(r2)) + 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/load_ml100k.py b/ch08/load_ml100k.py index fb9f13df..0920415a 100644 --- a/ch08/load_ml100k.py +++ b/ch08/load_ml100k.py @@ -22,12 +22,29 @@ def load(): reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() -def get_train_test(): +def get_train_test(reviews=None, return_valid=False, random_state=None): + '''Split data into training & testing + + Parameters + ---------- + reviews : ndarray, optional + Input data + + Returns + ------- + train : ndarray + training data + test : ndarray + testing data + ''' import numpy as np import random - reviews = load() + r = random.Random(random_state) + + if reviews is None: + reviews = load() U,M = np.where(reviews) - test_idxs = np.array(random.sample(range(len(U)), len(U)//10)) + test_idxs = np.array(r.sample(range(len(U)), len(U)//10)) train = reviews.copy() train[U[test_idxs], M[test_idxs]] = 0 @@ -38,5 +55,7 @@ def get_train_test(): valid_M = train.any(1) train = train[valid_M].T[valid_U].T test = test [valid_M].T[valid_U].T + if return_valid: + return train, test, valid_M, valid_U return train, test diff --git a/ch08/norm.py b/ch08/norm.py index 5b736f39..3cd6e477 100644 --- a/ch08/norm.py +++ b/ch08/norm.py @@ -28,3 +28,25 @@ def inverse_transform(self, features, copy=True): features *= self.std features += self.mean return 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/usermodel.py b/ch08/regression.py similarity index 70% rename from ch08/usermodel.py rename to ch08/regression.py index cd35674f..693e99a4 100644 --- a/ch08/usermodel.py +++ b/ch08/regression.py @@ -7,7 +7,6 @@ import numpy as np from sklearn.linear_model import ElasticNetCV -from load_ml100k import get_train_test from norm import NormalizePositive from sklearn import metrics @@ -25,21 +24,27 @@ def predict(train): # remove the current user for training curtrain = np.delete(train, u, axis=0) bu = binary[u] - reg.fit(curtrain[:,bu].T, train[u, bu]) + 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) + # 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): - train,test = get_train_test() + 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 (user regression): {:.1%}'.format(r2)) + + print('R2 score ({} regression): {:.1%}'.format( + ('movie' if transpose_inputs else 'user'), + r2)) if __name__ == '__main__': main() + main(transpose_inputs=True) From b89ac7ee029a6251a8691f2b309f276bee7362bd Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 24 Oct 2014 12:03:51 +0200 Subject: [PATCH 13/87] RFCT Remove flawed stacking code --- ch08/stacked.py | 40 ---------------------------------------- ch08/stacked5.py | 44 -------------------------------------------- 2 files changed, 84 deletions(-) delete mode 100644 ch08/stacked.py delete mode 100644 ch08/stacked5.py diff --git a/ch08/stacked.py b/ch08/stacked.py deleted file mode 100644 index 76afe9fc..00000000 --- a/ch08/stacked.py +++ /dev/null @@ -1,40 +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 sklearn.linear_model import LinearRegression -from load_ml100k import load -import numpy as np -import similar_movie -import usermodel -import corrneighbours - -reviews = load() -reg = LinearRegression() -es = np.array([ - usermodel.all_estimates(reviews), - corrneighbours.all_estimates(reviews), - similar_movie.all_estimates(reviews), -]) - -total_error = 0.0 -coefficients = [] -for u in range(reviews.shape[0]): - es0 = np.delete(es, u, 1) - r0 = np.delete(reviews, u, 0) - X, Y = np.where(r0 > 0) - X = es[:, X, Y] - y = r0[r0 > 0] - reg.fit(X.T, y) - coefficients.append(reg.coef_) - - r0 = reviews[u] - X = np.where(r0 > 0) - p0 = reg.predict(es[:, u, X].squeeze().T) - err0 = r0[r0 > 0] - p0 - total_error += np.dot(err0, err0) - print(u) diff --git a/ch08/stacked5.py b/ch08/stacked5.py deleted file mode 100644 index 7ace7d32..00000000 --- a/ch08/stacked5.py +++ /dev/null @@ -1,44 +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 -from load_ml100k import load -import numpy as np -import similar_movie -import usermodel - -reviews = load() -# Collect several estimates -es = np.array([ - usermodel.all_estimates(reviews), - similar_movie.all_estimates(reviews, k=1), - similar_movie.all_estimates(reviews, k=2), - similar_movie.all_estimates(reviews, k=3), - similar_movie.all_estimates(reviews, k=4), - similar_movie.all_estimates(reviews, k=5), -]) - -total_error = 0.0 -coefficients = [] - -reg = LinearRegression() -# Iterate over all users -for u in range(reviews.shape[0]): - es0 = np.delete(es, u, axis=1) - r0 = np.delete(reviews, u, axis=0) - X, Y = np.where(r0 > 0) - X = es[:, X, Y] - y = r0[r0 > 0] - reg.fit(X.T, y) - coefficients.append(reg.coef_) - - r0 = reviews[u] - X = np.where(r0 > 0) - p0 = reg.predict(es[:, u, X].squeeze().T) - err0 = r0[r0 > 0] - p0 - total_error += np.dot(err0, err0) -coefficients = np.array(coefficients) From 362207f4c9b5c7ad8efdb8de685263c08674b7ec Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 24 Oct 2014 12:42:12 +0200 Subject: [PATCH 14/87] RFCT Do not filter matrix This simplifies the rest of code immensily --- ch08/load_ml100k.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/ch08/load_ml100k.py b/ch08/load_ml100k.py index 0920415a..74428a7f 100644 --- a/ch08/load_ml100k.py +++ b/ch08/load_ml100k.py @@ -22,7 +22,7 @@ def load(): reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() -def get_train_test(reviews=None, return_valid=False, random_state=None): +def get_train_test(reviews=None, random_state=None): '''Split data into training & testing Parameters @@ -51,11 +51,5 @@ def get_train_test(reviews=None, return_valid=False, random_state=None): test = np.zeros_like(reviews) test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]] - valid_U = train.any(0) - valid_M = train.any(1) - train = train[valid_M].T[valid_U].T - test = test [valid_M].T[valid_U].T - if return_valid: - return train, test, valid_M, valid_U return train, test From f5d8cc691755fbb176cdb2bcf9d3ddcb31c3af84 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 24 Oct 2014 12:58:33 +0200 Subject: [PATCH 15/87] ENH Stacked learning for combining methods --- ch08/stacked.py | 47 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 ch08/stacked.py diff --git a/ch08/stacked.py b/ch08/stacked.py new file mode 100644 index 00000000..8fa6344e --- /dev/null +++ b/ch08/stacked.py @@ -0,0 +1,47 @@ +import numpy as np +import load_ml100k +import regression +import corrneighbours +from sklearn import linear_model, metrics +import norm + +def predict(train): + tr_train,tr_test = load_ml100k.get_train_test(train, random_state=34) + tr_predicted0 = regression.predict(tr_train) + tr_predicted1 = regression.predict(tr_train.T).T + tr_predicted2 = corrneighbours.predict(tr_train) + tr_predicted3 = corrneighbours.predict(tr_train.T).T + tr_predicted4 = norm.predict(tr_train) + tr_predicted5 = norm.predict(tr_train.T).T + stack_tr = np.array([ + tr_predicted0[tr_test > 0], + tr_predicted1[tr_test > 0], + tr_predicted2[tr_test > 0], + tr_predicted3[tr_test > 0], + tr_predicted4[tr_test > 0], + tr_predicted5[tr_test > 0], + ]).T + + lr = linear_model.LinearRegression() + lr.fit(stack_tr, tr_test[tr_test > 0]) + + stack_te = np.array([ + tr_predicted0.ravel(), + tr_predicted1.ravel(), + tr_predicted2.ravel(), + tr_predicted3.ravel(), + tr_predicted4.ravel(), + tr_predicted5.ravel(), + ]).T + + return lr.predict(stack_te).reshape(train.shape) + + +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 stacked: {:.2%}'.format(r2)) + +if __name__ == '__main__': + main() From d7e2f96f006a12c3934c6ac3125a8c8e6f3143b5 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 24 Oct 2014 13:49:19 +0200 Subject: [PATCH 16/87] DOC Start a README file --- ch08/README.rst | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 ch08/README.rst diff --git a/ch08/README.rst b/ch08/README.rst new file mode 100644 index 00000000..1ce1e9f6 --- /dev/null +++ b/ch08/README.rst @@ -0,0 +1,9 @@ +========= +Chapter 8 +========= + +Support code for *Chapter 8: Recommendations*. + +The code refers to the second edition of the book and this code has been +significantly refactored when compared to the first one. + From e19da48fb79f9207b40c9bc7c7935e41ab0059d5 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 24 Oct 2014 23:52:27 +0200 Subject: [PATCH 17/87] MIN Describe different scripts in README --- ch08/README.rst | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/ch08/README.rst b/ch08/README.rst index 1ce1e9f6..63ac6815 100644 --- a/ch08/README.rst +++ b/ch08/README.rst @@ -7,3 +7,18 @@ Support code for *Chapter 8: Recommendations*. The code refers to the second edition of the book and this code has been significantly refactored when compared to the first one. +Ratings Prediction +------------------ + +load_ml100k.py + Load data & partition into test/train +norm.py + Normalize the data +corrneighbours.py + Neighbour models based on ncrroaltoin +regression.py + Regression models +stacked.py + Stacked predictions +averaged.py + Averaging of predictions From 87d1cfd86ba23437c296ea9b0b8e192466c2606b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Wed, 29 Oct 2014 09:37:08 +0100 Subject: [PATCH 18/87] ENH Take sqrt for stddev Add documentation to code --- ch08/norm.py | 48 ++++++++++++++++++++++++++++-------------------- 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/ch08/norm.py b/ch08/norm.py index 3cd6e477..2ce2b75d 100644 --- a/ch08/norm.py +++ b/ch08/norm.py @@ -1,33 +1,41 @@ +import numpy as np class NormalizePositive(object): - - def fit(self, features): - binary = (features > 0) + + def fit(self, X, y=None): + # count features that are greater than zero in axis 0: + binary = (X > 0) count0 = binary.sum(0) + + # to avoid division by zero, set zero counts to one: count0 += (count0 == 0) - self.mean = features.sum(0)/count0 - diff = (features - self.mean) * binary + + self.mean = X.sum(0)/count0 + + # 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 = (X - self.mean) * binary diff **= 2 - diff += 1e-9 - self.std = 1 + diff.sum(0)/count0 + self.std = np.sqrt(0.1 + diff.sum(0)/count0) return self - def fit_transform(self, features): - return self.fit(features).transform(features) + def fit_transform(self, X): + return self.fit(X).transform(X) - def transform(self, features): - binary = (features > 0) - features = features - self.mean - features /= self.std - features *= binary - return features + def transform(self, X): + binary = (X > 0) + X = X - self.mean + X /= self.std + X *= binary + return X - def inverse_transform(self, features, copy=True): + def inverse_transform(self, X, copy=True): if copy: - features = features.copy() - features *= self.std - features += self.mean - return features + X = X.copy() + X *= self.std + X += self.mean + return X def predict(train): norm = NormalizePositive() From c3226d868a049f9b50f57ac5ad7326e33fae458b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 30 Oct 2014 01:14:09 +0100 Subject: [PATCH 19/87] MIN Remove unnecessary print statement --- ch08/apriori/apriori.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ch08/apriori/apriori.py b/ch08/apriori/apriori.py index 1ebb7e54..953fc3a9 100644 --- a/ch08/apriori/apriori.py +++ b/ch08/apriori/apriori.py @@ -43,7 +43,6 @@ def apriori(dataset, minsupport, maxsize): itemsets = [frozenset([v]) for v in valid] freqsets = [] for i in range(maxsize - 1): - print(len(itemsets)) newsets = [] for i, ell in enumerate(itemsets): ccounts = baskets[ell] From b8dfd13ad9440070939c666e12ed14449fded6af Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 30 Oct 2014 01:14:41 +0100 Subject: [PATCH 20/87] MIN Remove unused import --- ch08/apriori/apriori_naive.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ch08/apriori/apriori_naive.py b/ch08/apriori/apriori_naive.py index cb50a8e1..179d64a9 100644 --- a/ch08/apriori/apriori_naive.py +++ b/ch08/apriori/apriori_naive.py @@ -5,7 +5,6 @@ # # It is made available under the MIT License -import numpy as np from collections import defaultdict from itertools import chain from gzip import GzipFile From e28cc3ae06c9ef861b7258b7ba480442f961c89f Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 31 Oct 2014 13:19:13 +0100 Subject: [PATCH 21/87] ENH Simple & commented code --- ch10/features.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/ch10/features.py b/ch10/features.py index c651e7b9..d5a3b4cc 100644 --- a/ch10/features.py +++ b/ch10/features.py @@ -50,10 +50,15 @@ def color_histogram(im): c : ndarray 1-D array of histogram values ''' - im = mh.stretch_rgb(im) + + # Downsample pixel values: im = im // 64 - pixels = (im * (1, 4, 16)).sum(2).ravel() - hist = np.bincount(pixels, minlength=64) + + # 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) From 51701a023616a69396631ec0cda442a271eb6a47 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 31 Oct 2014 13:59:59 +0100 Subject: [PATCH 22/87] ENH Update URL & make curl call follow redirects Also, ignore downloaded data. --- ch08/data/.gitignore | 3 +++ ch08/data/download.sh | 4 ++-- ch08/load_ml100k.py | 4 ++++ 3 files changed, 9 insertions(+), 2 deletions(-) create mode 100644 ch08/data/.gitignore diff --git a/ch08/data/.gitignore b/ch08/data/.gitignore new file mode 100644 index 00000000..c391d625 --- /dev/null +++ b/ch08/data/.gitignore @@ -0,0 +1,3 @@ +retail.dat.gz +ml-100k.zip +/ml-100k/ diff --git a/ch08/data/download.sh b/ch08/data/download.sh index bc5091d0..b671171d 100755 --- a/ch08/data/download.sh +++ b/ch08/data/download.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -curl -O http://www.grouplens.org/system/files/ml-100k.zip +curl -L -O http://files.grouplens.org/papers/ml-100k.zip unzip ml-100k.zip -curl -O http://fimi.ua.ac.be/data/retail.dat.gz +curl -L -O http://fimi.ua.ac.be/data/retail.dat.gz diff --git a/ch08/load_ml100k.py b/ch08/load_ml100k.py index 74428a7f..7096e75c 100644 --- a/ch08/load_ml100k.py +++ b/ch08/load_ml100k.py @@ -11,6 +11,10 @@ def load(): Returns the review matrix as a numpy array''' import numpy as np from scipy import sparse + from os import path + + if not path.exists('data/ml-100k/u.data'): + raise IOError("Data has not been downloaded.\nTry the following:\n\n\tcd data\n\t./download.sh") # The input is in the form of a CSC sparse matrix, so it's a natural fit to # load the data, but we then convert to a more traditional array before From 9904f16b58776af857ab210590030744c501f0a3 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 31 Oct 2014 14:04:56 +0100 Subject: [PATCH 23/87] ENH Comment code. Use namedtuple for readability --- ch08/apriori/apriori.py | 16 +++++++++++++--- ch08/apriori/apriori_example.py | 13 +++++++++---- 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/ch08/apriori/apriori.py b/ch08/apriori/apriori.py index 953fc3a9..ac272125 100644 --- a/ch08/apriori/apriori.py +++ b/ch08/apriori/apriori.py @@ -5,6 +5,9 @@ # # It is made available under the MIT License +from collections import namedtuple + + def apriori(dataset, minsupport, maxsize): ''' freqsets, baskets = apriori(dataset, minsupport, maxsize) @@ -59,9 +62,12 @@ def apriori(dataset, minsupport, maxsize): return freqsets, baskets +# A namedtuple to collect all values that may be interesting +AssociationRule = namedtuple('AssociationRule', ['antecendent', 'consequent', 'base', 'py_x', 'lift']) + def association_rules(dataset, freqsets, baskets, minlift): ''' - for (antecendent, consequent, base, py_x, lift) in association_rules(dataset, freqsets, baskets, minlift): + for assoc_rule in association_rules(dataset, freqsets, baskets, minlift): ... This function takes the returns from ``apriori``. @@ -74,6 +80,10 @@ def association_rules(dataset, freqsets, baskets, minlift): baskets : dictionary minlift : int minimal lift of yielded rules + + Returns + ------- + assoc_rule : sequence of AssociationRule objects ''' nr_transactions = float(len(dataset)) freqsets = [f for f in freqsets if len(f) > 1] @@ -81,8 +91,8 @@ def association_rules(dataset, freqsets, baskets, minlift): for f in fset: consequent = frozenset([f]) antecendent = fset - consequent - base = len(baskets[consequent]) / nr_transactions py_x = len(baskets[fset]) / float(len(baskets[antecendent])) + base = len(baskets[consequent]) / nr_transactions lift = py_x / base if lift > minlift: - yield (antecendent, consequent, base, py_x, lift) + yield AssociationRule(antecendent, consequent, base, py_x, lift) diff --git a/ch08/apriori/apriori_example.py b/ch08/apriori/apriori_example.py index da77e96c..5a290346 100644 --- a/ch08/apriori/apriori_example.py +++ b/ch08/apriori/apriori_example.py @@ -7,10 +7,15 @@ from apriori import apriori, association_rules from gzip import GzipFile + +# Load dataset dataset = [[int(tok) for tok in line.strip().split()] for line in GzipFile('retail.dat.gz')] + freqsets, baskets = apriori(dataset, 80, maxsize=5) -nr_transactions = float(len(dataset)) -for ant, con, base, pyx, lift in association_rules(dataset, freqsets, baskets, 30): - print('{} | {} | {} ({:%}) | {} | {} | {}' - .format(ant, con, len(baskets[con]), len(baskets[con]) / nr_transactions, len(baskets[ant]), len(baskets[con | ant]), int(lift))) + +for ar in association_rules(dataset, freqsets, baskets, 30): + print('{} -> {} (lift = {:.4})' + .format(set(ar.antecendent), + set(ar.consequent), + ar.lift)) From 13fc4794f887f1f8f8f2edafb656f1aca8c072ca Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 31 Oct 2014 14:14:33 +0100 Subject: [PATCH 24/87] ENH Comment on alternative implementation --- ch10/features.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ch10/features.py b/ch10/features.py index d5a3b4cc..dd9f0a0e 100644 --- a/ch10/features.py +++ b/ch10/features.py @@ -54,6 +54,12 @@ def color_histogram(im): # 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)) From f559c3a2752e994056a9b7af97b6a72e1e28faaa Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 31 Oct 2014 14:19:01 +0100 Subject: [PATCH 25/87] DOC Describe Apriori files --- ch08/README.rst | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/ch08/README.rst b/ch08/README.rst index 63ac6815..d22c6bab 100644 --- a/ch08/README.rst +++ b/ch08/README.rst @@ -22,3 +22,16 @@ stacked.py Stacked predictions averaged.py Averaging of predictions + +Association Rule Mining +----------------------- + +Check the folder ``apriori/`` + +apriori/histogram.py + Print a histogram of how many times each product was bought +apriori/apriori.py + Implementation of Apriori algorithm and association rule building +apriori/apriori_example.py + Example of Apriori algorithm in retail dataset + From a9e4eeac1752f3825c4f031d43e2e035f0b14346 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 2 Nov 2014 15:43:54 +0100 Subject: [PATCH 26/87] ENH Simplify & document code --- ch08/apriori/apriori.py | 36 +++++++++++++++++++++++------------ ch08/apriori/apriori_naive.py | 35 ++++++++++++++++++++++++---------- 2 files changed, 49 insertions(+), 22 deletions(-) diff --git a/ch08/apriori/apriori.py b/ch08/apriori/apriori.py index ac272125..fd20de61 100644 --- a/ch08/apriori/apriori.py +++ b/ch08/apriori/apriori.py @@ -29,31 +29,42 @@ def apriori(dataset, minsupport, maxsize): from collections import defaultdict baskets = defaultdict(list) + pointers = defaultdict(list) for i, ds in enumerate(dataset): for ell in ds: pointers[ell].append(i) baskets[frozenset([ell])].append(i) - pointers = dict([(k, frozenset(v)) for k, v in pointers.items()]) - baskets = dict([(k, frozenset(v)) for k, v in baskets.items()]) - valid = set(list(el)[0] - for el, c in baskets.items() if (len(c) >= minsupport)) - dataset = [[el for el in ds if (el in valid)] for ds in dataset] - dataset = [ds for ds in dataset if len(ds) > 1] - dataset = map(frozenset, dataset) + # Convert pointer items to frozensets to speed up operations later + new_pointers = dict() + for k in pointers: + if len(pointers[k]) >= minsupport: + new_pointers[k] = frozenset(pointers[k]) + pointers = new_pointers + for k in baskets: + baskets[k] = frozenset(baskets[k]) + + + # Valid are all elements whose support is >= minsupport + valid = set() + for el, c in baskets.items(): + if len(c) >= minsupport: + valid.update(el) + # Itemsets at first iteration are simply all singleton with valid elements: itemsets = [frozenset([v]) for v in valid] freqsets = [] for i in range(maxsize - 1): newsets = [] - for i, ell in enumerate(itemsets): - ccounts = baskets[ell] - for v_, pv in pointers.items(): - if v_ not in ell: + for it in itemsets: + ccounts = baskets[it] + + for v, pv in pointers.items(): + if v not in it: csup = (ccounts & pv) if len(csup) >= minsupport: - new = frozenset(ell | set([v_])) + new = frozenset(it | frozenset([v])) if new not in baskets: newsets.append(new) baskets[new] = csup @@ -96,3 +107,4 @@ def association_rules(dataset, freqsets, baskets, minlift): lift = py_x / base if lift > minlift: yield AssociationRule(antecendent, consequent, base, py_x, lift) + diff --git a/ch08/apriori/apriori_naive.py b/ch08/apriori/apriori_naive.py index 179d64a9..742e7205 100644 --- a/ch08/apriori/apriori_naive.py +++ b/ch08/apriori/apriori_naive.py @@ -8,31 +8,46 @@ from collections import defaultdict from itertools import chain from gzip import GzipFile -minsupport = 44 +minsupport = 80 dataset = [[int(tok) for tok in line.strip().split()] for line in GzipFile('retail.dat.gz')] -dataset = dataset[::20] counts = defaultdict(int) for elem in chain(*dataset): counts[elem] += 1 +# Only elements that have at least minsupport should be considered. valid = set(el for el, c in counts.items() if (c >= minsupport)) + +# Filter the dataset to contain only valid elements +# (This step is not strictly necessary, but will make the rest of the code +# faster as the itemsets will be smaller): dataset = [[el for el in ds if (el in valid)] for ds in dataset] +# Convert to frozenset for fast processing dataset = [frozenset(ds) for ds in dataset if len(ds) > 1] itemsets = [frozenset([v]) for v in valid] -allsets = [itemsets] +freqsets = [itemsets] for i in range(16): - print(len(itemsets)) + print("At iteration {}, number of frequent baskets: {}".format( + i, len(itemsets))) nextsets = [] - for i, ell in enumerate(itemsets): - for v_ in valid: - if v_ not in ell: - c = (ell | set([v_])) - if sum(1 for d in dataset if d.issuperset(c)) > minsupport: + 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])) + + # 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) - allsets.append(nextsets) + freqsets.extend(nextsets) itemsets = nextsets + if not len(itemsets): + break +print("Finished!") From 5886bf01049d5f7bbab1655a361958c8d3953278 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 3 Nov 2014 21:34:04 +0100 Subject: [PATCH 27/87] ENH Improve Apriori code examples --- ch08/apriori/apriori.py | 27 ++++++++++++++++++--------- ch08/apriori/apriori_example.py | 6 ++++-- 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/ch08/apriori/apriori.py b/ch08/apriori/apriori.py index fd20de61..eeaf0d46 100644 --- a/ch08/apriori/apriori.py +++ b/ch08/apriori/apriori.py @@ -10,7 +10,7 @@ def apriori(dataset, minsupport, maxsize): ''' - freqsets, baskets = apriori(dataset, minsupport, maxsize) + freqsets, support = apriori(dataset, minsupport, maxsize) Parameters ---------- @@ -24,13 +24,15 @@ def apriori(dataset, minsupport, maxsize): Returns ------- freqsets : sequence of sequences - baskets : dictionary + support : dictionary + This associates each itemset (represented as a frozenset) with a float + (the support of that itemset) ''' from collections import defaultdict baskets = defaultdict(list) - pointers = defaultdict(list) + for i, ds in enumerate(dataset): for ell in ds: pointers[ell].append(i) @@ -56,6 +58,8 @@ def apriori(dataset, minsupport, maxsize): itemsets = [frozenset([v]) for v in valid] freqsets = [] for i in range(maxsize - 1): + print("At iteration {}, number of frequent baskets: {}".format( + i, len(itemsets))) newsets = [] for it in itemsets: ccounts = baskets[it] @@ -70,15 +74,20 @@ def apriori(dataset, minsupport, maxsize): baskets[new] = csup freqsets.extend(itemsets) itemsets = newsets - return freqsets, baskets + if not len(itemsets): + break + support = {} + for k in baskets: + support[k] = float(len(baskets[k])) + return freqsets, support # A namedtuple to collect all values that may be interesting AssociationRule = namedtuple('AssociationRule', ['antecendent', 'consequent', 'base', 'py_x', 'lift']) -def association_rules(dataset, freqsets, baskets, minlift): +def association_rules(dataset, freqsets, support, minlift): ''' - for assoc_rule in association_rules(dataset, freqsets, baskets, minlift): + for assoc_rule in association_rules(dataset, freqsets, support, minlift): ... This function takes the returns from ``apriori``. @@ -88,7 +97,7 @@ def association_rules(dataset, freqsets, baskets, minlift): dataset : sequence of sequences input dataset freqsets : sequence of sequences - baskets : dictionary + support : dictionary minlift : int minimal lift of yielded rules @@ -102,8 +111,8 @@ def association_rules(dataset, freqsets, baskets, minlift): for f in fset: consequent = frozenset([f]) antecendent = fset - consequent - py_x = len(baskets[fset]) / float(len(baskets[antecendent])) - base = len(baskets[consequent]) / nr_transactions + py_x = support[fset] / support[antecendent] + base = support[consequent] / nr_transactions lift = py_x / base if lift > minlift: yield AssociationRule(antecendent, consequent, base, py_x, lift) diff --git a/ch08/apriori/apriori_example.py b/ch08/apriori/apriori_example.py index 5a290346..971ed4d5 100644 --- a/ch08/apriori/apriori_example.py +++ b/ch08/apriori/apriori_example.py @@ -12,9 +12,11 @@ dataset = [[int(tok) for tok in line.strip().split()] for line in GzipFile('retail.dat.gz')] -freqsets, baskets = apriori(dataset, 80, maxsize=5) +freqsets, support = apriori(dataset, 80, maxsize=16) +rules = list(association_rules(dataset, freqsets, support, minlift=30.0)) -for ar in association_rules(dataset, freqsets, baskets, 30): +rules.sort(key=(lambda ar: -ar.lift)) +for ar in rules: print('{} -> {} (lift = {:.4})' .format(set(ar.antecendent), set(ar.consequent), From 268077983fece60f29d5e0a2cecf42866b5dcf9b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 3 Nov 2014 21:34:51 +0100 Subject: [PATCH 28/87] ENH Add association rule code --- ch08/apriori/apriori_naive.py | 35 +++++++++++++++++++++++++++++++++-- 1 file changed, 33 insertions(+), 2 deletions(-) diff --git a/ch08/apriori/apriori_naive.py b/ch08/apriori/apriori_naive.py index 742e7205..0001d48a 100644 --- a/ch08/apriori/apriori_naive.py +++ b/ch08/apriori/apriori_naive.py @@ -26,19 +26,23 @@ dataset = [[el for el in ds if (el in valid)] for ds in dataset] # Convert to frozenset for fast processing -dataset = [frozenset(ds) for ds in dataset if len(ds) > 1] +dataset = [frozenset(ds) for ds in dataset] itemsets = [frozenset([v]) for v in valid] -freqsets = [itemsets] +freqsets = itemsets[:] for i in range(16): print("At iteration {}, number of frequent baskets: {}".format( i, len(itemsets))) 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])) + if c in tested: + continue + tested.add(c) # Count support by looping over dataset # This step is slow. @@ -51,3 +55,30 @@ if not len(itemsets): break print("Finished!") + + +def rules_from_itemset(itemset, dataset, minlift=1.): + nr_transactions = float(len(dataset)) + 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)) + +for itemset in freqsets: + if len(itemset) > 1: + rules_from_itemset(itemset, dataset, minlift=4.) From 5758d97a459956ff0a5fa9ace6889c2471e694b7 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 8 Nov 2014 00:20:52 +0100 Subject: [PATCH 29/87] MIN Output Nr descriptors. Use range --- ch10/figure18.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ch10/figure18.py b/ch10/figure18.py index c0e80d0a..14f08d6d 100644 --- a/ch10/figure18.py +++ b/ch10/figure18.py @@ -80,11 +80,13 @@ def features_labels(groups): # alldescriptors.append(surf.dense(im, spacing=max(im.shape)//32)) alldescriptors.append(surf.surf(im, descriptor_only=True)) -print('Descriptors done') +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) @@ -92,7 +94,7 @@ def features_labels(groups): for d in alldescriptors: c = km.predict(d) features.append( - np.array([np.sum(c == i) for i in xrange(k)]) + np.array([np.sum(c == i) for i in range(k)]) ) features = np.array(features) print('predicting...') From 7c4ad5c62cefc906bb13d5e11842376483e4e529 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 8 Nov 2014 14:41:30 +0100 Subject: [PATCH 30/87] MIN Add explanatory comment --- ch08/apriori/apriori_naive.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ch08/apriori/apriori_naive.py b/ch08/apriori/apriori_naive.py index 0001d48a..03ab9130 100644 --- a/ch08/apriori/apriori_naive.py +++ b/ch08/apriori/apriori_naive.py @@ -34,12 +34,15 @@ print("At iteration {}, number of frequent baskets: {}".format( i, len(itemsets))) 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) From 61b894dce05258c55d18d96dfc4fa01f4827df80 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 8 Nov 2014 15:01:35 +0100 Subject: [PATCH 31/87] ENH Second edition has less image processing --- ch10/figure13.py | 28 ---------------------------- ch10/figure5_6.py | 37 ------------------------------------- ch10/figure9.py | 28 ---------------------------- 3 files changed, 93 deletions(-) delete mode 100644 ch10/figure13.py delete mode 100644 ch10/figure5_6.py delete mode 100644 ch10/figure9.py diff --git a/ch10/figure13.py b/ch10/figure13.py deleted file mode 100644 index 89174e8e..00000000 --- a/ch10/figure13.py +++ /dev/null @@ -1,28 +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 -from mahotas.colors import rgb2grey -import numpy as np - -# Adds a little salt-n-pepper noise to an image - -im = mh.demos.load('lena') -im = rgb2grey(im) - -# Salt & pepper arrays -salt = np.random.random(im.shape) > .975 -pepper = np.random.random(im.shape) > .975 - -# salt is 170 & pepper is 30 -# Some playing around showed that setting these to more extreme values looks -# very artificial. These look nicer - -im = np.maximum(salt * 170, mh.stretch(im)) -im = np.minimum(pepper * 30 + im * (~pepper), im) - -mh.imsave('../1400OS_10_13+.jpg', im.astype(np.uint8)) diff --git a/ch10/figure5_6.py b/ch10/figure5_6.py deleted file mode 100644 index 37d52752..00000000 --- a/ch10/figure5_6.py +++ /dev/null @@ -1,37 +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 -image = mh.imread('../SimpleImageDataset/building05.jpg') -image = mh.colors.rgb2gray(image) - -# Compute Gaussian filtered versions with increasing kernel widths -im8 = mh.gaussian_filter(image, 8) -im16 = mh.gaussian_filter(image, 16) -im32 = mh.gaussian_filter(image, 32) - -# We now build a composite image with three panels: -# -# [ IM8 | | IM16 | | IM32 ] - -h, w = im8.shape -canvas = np.ones((h, 3 * w + 256), np.uint8) -canvas *= 255 -canvas[:, :w] = im8 -canvas[:, w + 128:2 * w + 128] = im16 -canvas[:, -w:] = im32 -mh.imsave('../1400OS_10_05+.jpg', canvas[:, ::2]) - -# Threshold the image -# We need to first stretch it to convert to an integer image -im32 = mh.stretch(im32) -ot32 = mh.otsu(im32) - -# Convert to 255 np.uint8 to match the other images -im255 = 255 * (im32 > ot32).astype(np.uint8) -mh.imsave('../1400OS_10_06+.jpg', im255) diff --git a/ch10/figure9.py b/ch10/figure9.py deleted file mode 100644 index fbaa985f..00000000 --- a/ch10/figure9.py +++ /dev/null @@ -1,28 +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 -image = mh.imread('../SimpleImageDataset/building05.jpg') -image = mh.colors.rgb2gray(image, dtype=np.uint8) - -# We need to downsample ``image`` so that the details are visibly pixelated. -# This exaggerates the effect so that the result is clear -image = image[::4, ::4] -thresh = mh.sobel(image) -filtered = mh.sobel(image, just_filter=True) - -thresh = mh.dilate(thresh, np.ones((7, 7))) -filtered = mh.dilate(mh.stretch(filtered), np.ones((7, 7))) - -# Paste the thresholded and non-thresholded versions of the image side-by-side -h, w = thresh.shape -canvas = 255 * np.ones((h, w * 2 + 64), np.uint8) -canvas[:, :w] = thresh * 255 -canvas[:, -w:] = filtered - -mh.imsave('../1400OS_10_09+.jpg', canvas) From 332400f3256318f4b86204fc7e9fbc5ee74370f0 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 10 Nov 2014 19:52:34 +0100 Subject: [PATCH 32/87] ENH Update to newer Chapter 10 architecture --- ch12/image-classification.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index 6283a588..8d217da3 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -34,12 +34,14 @@ def features_for(im): 1-D array of features ''' im = mh.imread(im, as_grey=True).astype(np.uint8) - return mh.features.haralick(im).mean(0) + return mh.features.haralick(im).ravel() @TaskGenerator -def edginess_sobel_from_fname(fname): - from edginess import edginess_sobel - return edginess_sobel(mh.imread(fname, as_grey=True)) +def chist(fname): + from features import color_histogram + im = mh.imread(fname) + return color_histogram(im) + @TaskGenerator def accuracy(features, labels): @@ -51,8 +53,8 @@ def accuracy(features, labels): @TaskGenerator -def stack_features(sobels, haralicks): - return np.hstack([np.atleast_2d(sobels).T, haralicks]) +def stack_features(chists, haralicks): + return np.hstack([np.atleast_2d(chists), haralicks]) @TaskGenerator def print_results(scores_base, scores_combined): @@ -67,22 +69,22 @@ def print_results(scores_base, scores_combined): to_array = TaskGenerator(np.array) haralicks = [] -sobels = [] +chists = [] labels = [] # Use glob to get all the images images = glob('{}/*.jpg'.format(basedir)) for fname in sorted(images): haralicks.append(features_for(fname)) - sobels.append(edginess_sobel_from_fname(fname)) + chists.append(chist(fname)) labels.append(fname[:-len('00.jpg')]) # The class is encoded in the filename as xxxx00.jpg haralicks = to_array(haralicks) -sobels = to_array(sobels) +chists = to_array(chists) labels = to_array(labels) scores_base = accuracy(haralicks, labels) -haralick_plus_sobel = stack_features(sobels, haralicks) +haralick_plus_sobel = stack_features(chists, haralicks) scores_combined = accuracy(haralick_plus_sobel, labels) print_results(scores_base, scores_combined) From 55b9fa9e4ec6f091e9b0a9fb4ebc3cc26820815e Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 10 Nov 2014 19:54:35 +0100 Subject: [PATCH 33/87] BLD Ignore output files --- ch12/.gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ch12/.gitignore b/ch12/.gitignore index fb47e2ef..fa8fbcc2 100644 --- a/ch12/.gitignore +++ b/ch12/.gitignore @@ -1 +1,3 @@ *.jugdata/ +output.txt +results.image.txt From ac0afd90c58ac42b996d5e275893c40e97962882 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 16 Nov 2014 15:31:14 +0100 Subject: [PATCH 34/87] MIN Use all Haralick features --- ch10/features.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch10/features.py b/ch10/features.py index dd9f0a0e..c183d909 100644 --- a/ch10/features.py +++ b/ch10/features.py @@ -34,7 +34,7 @@ def texture(im): 1-D array of features ''' im = im.astype(np.uint8) - return mh.features.haralick(im).mean(0) + return mh.features.haralick(im).ravel() def color_histogram(im): From 0fbc909e2393fa6c30e5910730d3da552c88f0d4 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 16 Nov 2014 16:37:12 +0100 Subject: [PATCH 35/87] ENH Final updated version of simple_classification Logistic regression is simple and works well, so go back to using it. --- ch10/simple_classification.py | 73 ++++++++++------------------------- 1 file changed, 20 insertions(+), 53 deletions(-) diff --git a/ch10/simple_classification.py b/ch10/simple_classification.py index 33ab4959..0e3ab347 100644 --- a/ch10/simple_classification.py +++ b/ch10/simple_classification.py @@ -6,22 +6,18 @@ # It is made available under the MIT License import mahotas as mh -from sklearn import cross_validation -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.svm import SVC -from sklearn.grid_search import GridSearchCV - - import numpy as np from glob import glob -from features import texture, edginess_sobel, color_histogram + +from features import texture, color_histogram +from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler basedir = '../SimpleImageDataset/' haralicks = [] -sobels = [] labels = [] chists = [] @@ -34,10 +30,8 @@ # Otherwise, this would introduce some variation just based on the random # ordering that the filesystem uses for fname in sorted(images): - im = mh.imread(fname, as_grey=True) imc = mh.imread(fname) - haralicks.append(texture(im)) - sobels.append(edginess_sobel(im)) + haralicks.append(texture(mh.colors.rgb2grey(imc))) chists.append(color_histogram(imc)) # Files are named like building00.jpg, scene23.jpg... @@ -46,58 +40,31 @@ print('Finished computing features.') haralicks = np.array(haralicks) -sobels = np.array(sobels) labels = np.array(labels) chists = np.array(chists) -haralick_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) haralick_plus_chists = np.hstack([chists, haralicks]) -haralick_plus_chists_plus_sobel = np.hstack([chists, haralicks, np.atleast_2d(sobels).T]) -cv=cross_validation.LeaveOneOut(len(images)) -# We use SVM because it achieves high accuracy on small(ish) datasets +# We use Logistic Regression because it achieves high accuracy on small(ish) datasets # Feel free to experiment with other classifiers -C_range = 10.0 ** np.arange(-2, 9) -gamma_range = 10.0 ** np.arange(-5, 4) -param_grid = dict(gamma=gamma_range, C=C_range) -grid = GridSearchCV(SVC(), param_grid=param_grid) - -p = Pipeline([('preproc', StandardScaler()), - ('classifier', grid)]) +clf = Pipeline([('preproc', StandardScaler()), + ('classifier', LogisticRegression())]) +from sklearn import cross_validation +cv = cross_validation.LeaveOneOut(len(images)) scores = cross_validation.cross_val_score( - p, haralicks, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regrssion [haralick features]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) - -scores = cross_validation.cross_val_score( - p, chists, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regrssion [color histograms]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) - -scores = cross_validation.cross_val_score( - p, haralick_plus_chists, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + chists]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) - -haralick_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) -scores = cross_validation.cross_val_score( - p, haralick_plus_sobel, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + sobel]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) + clf, haralicks, labels, cv=cv) +print('Accuracy (Leave-one-out) with Logistic Regression [haralick features]: {:.1%}'.format( + scores.mean())) -haralick_plus_chists_plus_sobel = np.hstack([np.atleast_2d(sobels).T, haralicks]) scores = cross_validation.cross_val_score( - p, haralick_plus_chists_plus_sobel, labels, cv=cv) -print('Accuracy (5 fold x-val) with Logistic Regrssion [texture features + color histograms + sobel]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) - + clf, chists, labels, cv=cv) +print('Accuracy (Leave-one-out) with Logistic Regression [color histograms]: {:.1%}'.format( + scores.mean())) -# We can try to just use the sobel feature. The result is almost completely -# random. scores = cross_validation.cross_val_score( - p, np.atleast_2d(sobels).T, labels, cv=cv).mean() -print('Accuracy (5 fold x-val) with Logistic Regrssion [only using sobel feature]: {}%'.format( - 0.1 * round(1000 * scores.mean()))) + clf, haralick_plus_chists, labels, cv=cv) +print('Accuracy (Leave-one-out) with Logistic Regression [texture features + color histograms]: {:.1%}'.format( + scores.mean())) From e4403a9ac79cd0acff4f28519da046250ede2884 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 16 Nov 2014 16:44:13 +0100 Subject: [PATCH 36/87] FIG Show building text & building The scene is now shown first in the text --- ch10/figure10.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ch10/figure10.py b/ch10/figure10.py index 058b1069..6cb45e7a 100644 --- a/ch10/figure10.py +++ b/ch10/figure10.py @@ -11,10 +11,10 @@ # This little script just builds an image with two examples, side-by-side: text = mh.imread("../SimpleImageDataset/text21.jpg") -scene = mh.imread("../SimpleImageDataset/scene00.jpg") +building = mh.imread("../SimpleImageDataset/building00.jpg") h, w, _ = text.shape canvas = np.zeros((h, 2 * w + 128, 3), np.uint8) -canvas[:, -w:] = scene +canvas[:, -w:] = building canvas[:, :w] = text canvas = canvas[::4, ::4] -mh.imsave('../1400OS_10_10+.jpg', canvas) +mh.imsave('figure10.jpg', canvas) From 9d3ea50530797cbe5d98dcb52ac8d85731733a7b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 17 Nov 2014 11:25:18 +0100 Subject: [PATCH 37/87] ENH Finalize large classification method No longer output a figure as the results as the results will instead be discussed at greater length in the text. --- ch10/figure18.py | 123 ----------------------------------- ch10/large_classification.py | 110 +++++++++++++++++++++++++++++++ 2 files changed, 110 insertions(+), 123 deletions(-) delete mode 100644 ch10/figure18.py create mode 100644 ch10/large_classification.py diff --git a/ch10/figure18.py b/ch10/figure18.py deleted file mode 100644 index 14f08d6d..00000000 --- a/ch10/figure18.py +++ /dev/null @@ -1,123 +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 sklearn import cross_validation -from sklearn.linear_model.logistic import LogisticRegression -from matplotlib import pyplot as plt -import numpy as np -from glob import glob - -basedir = 'AnimTransDistr' -print('This script will test classification of the AnimTransDistr dataset') - - -def features_for(images): - fs = [] - for im in images: - im = mh.imread(im, as_grey=True).astype(np.uint8) - fs.append(mh.features.haralick(im).mean(0)) - return np.array(fs) - - -def features_labels(groups): - labels = np.zeros(sum(map(len, groups))) - st = 0 - for i, g in enumerate(groups): - labels[st:st + len(g)] = i - st += len(g) - return np.vstack(groups), labels - -classes = [ - 'Anims', - 'Cars', - 'Distras', - 'Trans', -] - -print('Computing whole-image texture features...') -features = [] -labels = [] -for ci, cl in enumerate(classes): - images = glob('{}/{}/*.jpg'.format(basedir, cl)) - features.extend(features_for(images)) - labels.extend([ci for _ in images]) - -features = np.array(features) -labels = np.array(labels) - -scores0 = cross_validation.cross_val_score( - LogisticRegression(), features, labels, cv=10) -print('Accuracy (5 fold x-val) with Logistic Regrssion [std features]: %s%%' % ( - 0.1 * round(1000 * scores0.mean()))) - -tfeatures = features - -from sklearn.cluster import KMeans -from mahotas.features import surf - -images = [] -labels = [] - -for ci, cl in enumerate(classes): - curimages = glob('{}/{}/*.jpg'.format(basedir, cl)) - images.extend(curimages) - labels.extend([ci for _ in curimages]) -labels = np.array(labels) - -print('Computing SURF descriptors...') -alldescriptors = [] -for im in images: - im = mh.imread(im, as_grey=1) - im = im.astype(np.uint8) - - # To use dense sampling, you can try the following line: - # alldescriptors.append(surf.dense(im, spacing=max(im.shape)//32)) - 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) -features = [] -for d in alldescriptors: - c = km.predict(d) - features.append( - np.array([np.sum(c == i) for i in range(k)]) - ) -features = np.array(features) -print('predicting...') -scoreSURFlr = cross_validation.cross_val_score( - LogisticRegression(), features, labels, cv=5).mean() -print('Accuracy (5 fold x-val) with Log. Reg [SURF features]: %s%%' % ( - 0.1 * round(1000 * scoreSURFlr.mean()))) - -print('Attemping classification with all features combined...') -allfeatures = np.hstack([features, tfeatures]) -scoreSURFplr = cross_validation.cross_val_score( - LogisticRegression(), allfeatures, labels, cv=5).mean() - -print('Accuracy (5 fold x-val) with Log. Reg [All features]: %s%%' % ( - 0.1 * round(1000 * scoreSURFplr.mean()))) - -plt.plot([0, 1, 2], 100 * - np.array([scores0.mean(), scoreSURFlr, scoreSURFplr]), 'k-', lw=8) -plt.plot( - [0, 1, 2], 100 * np.array([scores0.mean(), scoreSURFlr, scoreSURFplr]), - 'o', mec='#cccccc', mew=12, mfc='white') -plt.xlim(-.5, 2.5) -plt.ylim(scores0.mean() * 90., scoreSURFplr * 110) -plt.xticks([0, 1, 2], ["Texture", "SURF", "combined"]) -plt.ylabel('Accuracy (%)') -plt.savefig('../1400OS_10_18+.png') diff --git a/ch10/large_classification.py b/ch10/large_classification.py new file mode 100644 index 00000000..b8f4f3fc --- /dev/null +++ b/ch10/large_classification.py @@ -0,0 +1,110 @@ +# 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 color_histogram + im = mh.imread(im) + img = mh.colors.rgb2grey(im).astype(np.uint8) + return np.concatenate([mh.features.haralick(img).ravel(), + color_histogram(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.array([np.sum(c == i) for i in range(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())) From 5bdba98dcc6b04580d1d7e8d5257239a36ea8d41 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 17 Nov 2014 12:05:28 +0100 Subject: [PATCH 38/87] ENH Add code for image neighbors This is a new section in the second edition, but it makes sense to demonstrate the possibilities of images features in some other task than classification. --- ch10/README.rst | 16 +++--------- ch10/neighbors.py | 62 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 12 deletions(-) create mode 100644 ch10/neighbors.py diff --git a/ch10/README.rst b/ch10/README.rst index 8c146eac..c53c3d05 100644 --- a/ch10/README.rst +++ b/ch10/README.rst @@ -17,23 +17,15 @@ Running ``download.sh`` will retrieve the other dataset into a directory Scripts ------- -threshold.py - Threshold building image with both Otsu and Ridley-Calvard threshold -lena-ring.py - Show Lena in a sharp ring around a soft focus image -figure5_6.py - Computes figures 5 and 6 in the book -figure9.py - Compute Sobel images figure10.py Just paste two images next to each others -figure13.py - Demonstrate salt&pepper effect features.py - Contains the ``edginess_sobel`` function from the book as well as a simple + Contains the color histogram function from the book as well as a simple wrapper around ``mahotas.texture.haralick`` simple_classification.py Classify SimpleImageDataset with texture features + color histogram features -figure18.py +large_classification.py Classify ``AnimTransDistr`` with both texture and SURF features. +neighbors.py + Computes image neighbors as well as the neighbor figure from the book. diff --git a/ch10/neighbors.py b/ch10/neighbors.py new file mode 100644 index 00000000..495bbd77 --- /dev/null +++ b/ch10/neighbors.py @@ -0,0 +1,62 @@ +# 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, color_histogram +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(color_histogram(imc)) + +haralicks = np.array(haralicks) +chists = np.array(chists) +features = np.hstack([chists, haralicks]) + +print('Computing neighbors...') +sc = StandardScaler() +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 = 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) From f7645361dc72d4dd5a293e723db84f06af28d3cc Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 21 Nov 2014 17:35:07 +0100 Subject: [PATCH 39/87] MIN Scale features --- ch10/neighbors.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ch10/neighbors.py b/ch10/neighbors.py index 495bbd77..c62a0e2c 100644 --- a/ch10/neighbors.py +++ b/ch10/neighbors.py @@ -37,6 +37,7 @@ print('Computing neighbors...') sc = StandardScaler() +features = sc.fit_transform(features) dists = distance.squareform(distance.pdist(features)) print('Plotting...') @@ -51,6 +52,7 @@ 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) From ba4e379e3f0c59347b5897c6b7d87eb3e87d0c0d Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 21 Nov 2014 17:35:31 +0100 Subject: [PATCH 40/87] MIN Use np.bincount instead of loop Version 1.6 added `minlength` argument --- ch10/large_classification.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/ch10/large_classification.py b/ch10/large_classification.py index b8f4f3fc..9ae8232a 100644 --- a/ch10/large_classification.py +++ b/ch10/large_classification.py @@ -91,9 +91,7 @@ def images(): sfeatures = [] for d in alldescriptors: c = km.predict(d) - sfeatures.append( - np.array([np.sum(c == i) for i in range(k)]) - ) + sfeatures.append(np.bincount(c, minlength=k)) sfeatures = np.array(sfeatures, dtype=float) print('predicting...') score_SURF = cross_validation.cross_val_score( From d2525574168c514526b607f774d2cf1dc3478187 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 21 Nov 2014 17:56:08 +0100 Subject: [PATCH 41/87] ENH Add AWS code from the book --- ch12/README.rst | 26 ++++++++++++++++++++++++++ ch12/run-image-classification.sh | 3 +++ ch12/run-jugfile.sh | 4 ++++ ch12/setup-aws.txt | 7 +++++++ 4 files changed, 40 insertions(+) create mode 100644 ch12/README.rst create mode 100755 ch12/run-image-classification.sh create mode 100755 ch12/run-jugfile.sh create mode 100644 ch12/setup-aws.txt diff --git a/ch12/README.rst b/ch12/README.rst new file mode 100644 index 00000000..ecf688f1 --- /dev/null +++ b/ch12/README.rst @@ -0,0 +1,26 @@ +========== +Chapter 12 +========== + +Support code for *Chapter 12: Big(ger) Data* + +Data +---- + +This chapter relies only on the image dataset that is packaged with the +repository at ``../SimpleImageDataset/``. + +Scripts +------- + +jugfile.py + Example jugfile +image-classification.py + Jugfile implementation of image classification from Chapter 10 + +setup-aws.txt + Commands to setup Amazon WebServices machine +run-jugfile.sh + Wrapper script to run jug file on jugfile.py +run-image-classification.sh + Wrapper script to run jug file on image-classification.py diff --git a/ch12/run-image-classification.sh b/ch12/run-image-classification.sh new file mode 100755 index 00000000..868d07fa --- /dev/null +++ b/ch12/run-image-classification.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +jug execute image-classification.py diff --git a/ch12/run-jugfile.sh b/ch12/run-jugfile.sh new file mode 100755 index 00000000..0ff59131 --- /dev/null +++ b/ch12/run-jugfile.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash + +jug execute + diff --git a/ch12/setup-aws.txt b/ch12/setup-aws.txt new file mode 100644 index 00000000..292654eb --- /dev/null +++ b/ch12/setup-aws.txt @@ -0,0 +1,7 @@ +sudo yum update +sudo yum -y install python-devel python-pip numpy scipy python-matplotlib +sudo yum -y install gcc-c++ +sudo yum -y install git +sudo pip-python install -U pip +sudo pip install scikit-learn jug mahotas + From a24de07b70e91d9fc60234788f61e26168cd76a6 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 21 Nov 2014 23:13:33 +0100 Subject: [PATCH 42/87] ENH Update jug file to match Chapter 10 --- ch12/image-classification.py | 39 +++++++++++++++++++++++------------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index 8d217da3..0a84d6b2 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -6,11 +6,11 @@ # It is made available under the MIT License import mahotas as mh -from sklearn import cross_validation -from sklearn.linear_model.logistic import LogisticRegression import numpy as np from glob import glob from jug import TaskGenerator + +# We need to use the `features` module from chapter 10. from sys import path path.append('../ch10') @@ -45,25 +45,30 @@ def chist(fname): @TaskGenerator def accuracy(features, labels): + from sklearn.linear_model import LogisticRegression + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + from sklearn import cross_validation # We use logistic regression because it is very fast. # Feel free to experiment with other classifiers + clf = Pipeline([('preproc', StandardScaler()), + ('classifier', LogisticRegression())]) + cv = cross_validation.LeaveOneOut(len(features)) scores = cross_validation.cross_val_score( - LogisticRegression(), features, labels, cv=5) + clf, features, labels, cv=cv) return scores.mean() @TaskGenerator def stack_features(chists, haralicks): - return np.hstack([np.atleast_2d(chists), haralicks]) + return np.hstack([chists, haralicks]) @TaskGenerator -def print_results(scores_base, scores_combined): - output = open('results.image.txt', 'w') - output.write('Accuracy (5 fold x-val) with Logistic Regrssion [std features]: {}%\n'.format( - 0.1 * round(1000 * scores_base.mean()))) - output.write('Accuracy (5 fold x-val) with Logistic Regrssion [std features + sobel]: {}%\n'.format( - 0.1 * round(1000 * scores_combined.mean()))) - output.close() +def print_results(scores): + with open('results.image.txt', 'w') as output: + for k in scores: + output.write('Accuracy (LOO x-val) with Logistic Regression [{}]: {:.1%}\n'.format( + k, scores[k].mean())) to_array = TaskGenerator(np.array) @@ -84,8 +89,14 @@ def print_results(scores_base, scores_combined): labels = to_array(labels) scores_base = accuracy(haralicks, labels) -haralick_plus_sobel = stack_features(chists, haralicks) -scores_combined = accuracy(haralick_plus_sobel, labels) +scores_chist = accuracy(chists, labels) + +combined = stack_features(chists, haralicks) +scores_combined = accuracy(combined, labels) -print_results(scores_base, scores_combined) +print_results({ + 'base': scores_base, + 'chists': scores_chist, + 'combined' : scores_combined, + }) From 940baf50f690d23532c02634cd3c123f6610d360 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 22 Nov 2014 17:05:38 +0100 Subject: [PATCH 43/87] ENH Code that is simpler to explain --- ch12/image-classification.py | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index 0a84d6b2..3a0d2414 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -20,7 +20,7 @@ basedir = '../SimpleImageDataset/' @TaskGenerator -def features_for(im): +def compute_texture(im): '''Compute features for an image Parameters @@ -33,8 +33,9 @@ def features_for(im): fs : ndarray 1-D array of features ''' - im = mh.imread(im, as_grey=True).astype(np.uint8) - return mh.features.haralick(im).ravel() + from features import texture + imc = mh.imread(im) + return texture(mh.colors.rgb2grey(imc)) @TaskGenerator def chist(fname): @@ -59,19 +60,16 @@ def accuracy(features, labels): return scores.mean() -@TaskGenerator -def stack_features(chists, haralicks): - return np.hstack([chists, haralicks]) - @TaskGenerator def print_results(scores): with open('results.image.txt', 'w') as output: - for k in scores: + for k,v in scores: output.write('Accuracy (LOO x-val) with Logistic Regression [{}]: {:.1%}\n'.format( - k, scores[k].mean())) + k, v.mean())) to_array = TaskGenerator(np.array) +hstack = TaskGenerator(np.hstack) haralicks = [] chists = [] @@ -80,7 +78,7 @@ def print_results(scores): # Use glob to get all the images images = glob('{}/*.jpg'.format(basedir)) for fname in sorted(images): - haralicks.append(features_for(fname)) + haralicks.append(compute_texture(fname)) chists.append(chist(fname)) labels.append(fname[:-len('00.jpg')]) # The class is encoded in the filename as xxxx00.jpg @@ -91,12 +89,12 @@ def print_results(scores): scores_base = accuracy(haralicks, labels) scores_chist = accuracy(chists, labels) -combined = stack_features(chists, haralicks) +combined = hstack([chists, haralicks]) scores_combined = accuracy(combined, labels) -print_results({ - 'base': scores_base, - 'chists': scores_chist, - 'combined' : scores_combined, - }) +print_results([ + ('base', scores_base), + ('chists', scores_chist), + ('combined' , scores_combined), + ]) From 40a59ec440167c095b5f00eba392905e324e3292 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 23 Nov 2014 00:28:09 +0100 Subject: [PATCH 44/87] ENH Add LBP to image classification script --- ch12/image-classification.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index 3a0d2414..cd3dd4dd 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -43,6 +43,13 @@ def chist(fname): im = mh.imread(fname) return color_histogram(im) +@TaskGenerator +def compute_lbp(fname): + from mahotas.features import lbp + imc = mh.imread(fname) + im = mh.colors.rgb2grey(imc) + return lbp(im, radius=8, points=6) + @TaskGenerator def accuracy(features, labels): @@ -73,6 +80,7 @@ def print_results(scores): haralicks = [] chists = [] +lbps = [] labels = [] # Use glob to get all the images @@ -80,21 +88,29 @@ def print_results(scores): for fname in sorted(images): haralicks.append(compute_texture(fname)) chists.append(chist(fname)) + lbps.append(compute_lbp(fname)) labels.append(fname[:-len('00.jpg')]) # The class is encoded in the filename as xxxx00.jpg haralicks = to_array(haralicks) chists = to_array(chists) +lbps = to_array(lbps) labels = to_array(labels) scores_base = accuracy(haralicks, labels) scores_chist = accuracy(chists, labels) +scores_lbps = accuracy(lbps, labels) combined = hstack([chists, haralicks]) -scores_combined = accuracy(combined, labels) +scores_combined = accuracy(combined, labels) + +combined_all = hstack([chists, haralicks, lbps]) +scores_combined_all = accuracy(combined_all, labels) print_results([ ('base', scores_base), ('chists', scores_chist), + ('lbps', scores_lbps), ('combined' , scores_combined), + ('combined_all' , scores_combined_all), ]) From 34302d2d4dd2089342ff51b639e0a71d2884aae3 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 23 Nov 2014 09:28:35 +0100 Subject: [PATCH 45/87] ENH Work with Python 2.6 This is what AWS machines have by default --- ch12/image-classification.py | 4 ++-- ch12/jugfile.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index cd3dd4dd..09dbd5b4 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -71,7 +71,7 @@ def accuracy(features, labels): def print_results(scores): with open('results.image.txt', 'w') as output: for k,v in scores: - output.write('Accuracy (LOO x-val) with Logistic Regression [{}]: {:.1%}\n'.format( + output.write('Accuracy (LOO x-val) with Logistic Regression [{0}]: {1:.1%}\n'.format( k, v.mean())) @@ -84,7 +84,7 @@ def print_results(scores): labels = [] # Use glob to get all the images -images = glob('{}/*.jpg'.format(basedir)) +images = glob('{0}/*.jpg'.format(basedir)) for fname in sorted(images): haralicks.append(compute_texture(fname)) chists.append(chist(fname)) diff --git a/ch12/jugfile.py b/ch12/jugfile.py index 858c64f4..9d7e2b7a 100644 --- a/ch12/jugfile.py +++ b/ch12/jugfile.py @@ -23,7 +23,7 @@ def add(a, b): @TaskGenerator def print_final_result(oname, value): with open(oname, 'w') as output: - output.write("Final result: {}\n".format(value)) + output.write("Final result: {0}\n".format(value)) input = 2 y = double(input) From 79857c0649cbbd0254550b022ae70e7fbce02750 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 6 Jan 2015 15:42:11 +0100 Subject: [PATCH 46/87] MIN Use curl instead of wget --- ch10/download.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch10/download.sh b/ch10/download.sh index 93121a7f..fb623f3d 100755 --- a/ch10/download.sh +++ b/ch10/download.sh @@ -2,7 +2,7 @@ mkdir -p AnimTransDistr cd AnimTransDistr -wget http://vision.stanford.edu/Datasets/AnimTransDistr.rar +curl -O http://vision.stanford.edu/Datasets/AnimTransDistr.rar unrar x AnimTransDistr.rar # The following file is a weird file: rm Anims/104034.jpg From 691de56155145edc4ed426a0254811b93bf318d2 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 6 Jan 2015 16:05:58 +0100 Subject: [PATCH 47/87] ENH Add thresholded figure This is based on the scene image, now. This is a visually more pleasant image. --- ch10/README.rst | 4 ++++ ch10/thresholded_figure.py | 31 +++++++++++++++++++++++++++++++ 2 files changed, 35 insertions(+) create mode 100644 ch10/thresholded_figure.py diff --git a/ch10/README.rst b/ch10/README.rst index c53c3d05..e409ec36 100644 --- a/ch10/README.rst +++ b/ch10/README.rst @@ -17,6 +17,10 @@ Running ``download.sh`` will retrieve the other dataset into a directory Scripts ------- +thresholded_figure.py + Computes the thresholded figures, including after Gaussian blurring +lena-ring.py + Lena image with center in focus and blurred edges figure10.py Just paste two images next to each others features.py diff --git a/ch10/thresholded_figure.py b/ch10/thresholded_figure.py new file mode 100644 index 00000000..947762e8 --- /dev/null +++ b/ch10/thresholded_figure.py @@ -0,0 +1,31 @@ +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() From d535cfc1279669e7a41a1e7cee611b22e269a2d7 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 26 Jan 2015 10:00:18 +0100 Subject: [PATCH 48/87] FIG Improve figures --- ch02/figure1.py | 14 +++++++++++--- ch02/figure4_5_sklearn.py | 4 ++-- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/ch02/figure1.py b/ch02/figure1.py index 2bbdfb9c..4ec6fff8 100644 --- a/ch02/figure1.py +++ b/ch02/figure1.py @@ -19,13 +19,21 @@ 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] - # Use a different marker/color for each class `t` - for t, marker, c in zip(range(3), ">ox", "rgb"): + 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, s=40) + target == t, p1], marker=marker, c=c) ax.set_xlabel(feature_names[p0]) ax.set_ylabel(feature_names[p1]) ax.set_xticks([]) diff --git a/ch02/figure4_5_sklearn.py b/ch02/figure4_5_sklearn.py index 9d20c7cb..55ac0c80 100644 --- a/ch02/figure4_5_sklearn.py +++ b/ch02/figure4_5_sklearn.py @@ -58,11 +58,11 @@ def plot_decision(features, labels, num_neighbors=1): 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, s=40) + 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=8) + labels == lab, 2], ma, c=(1., 1., 1.), ms=6) return fig,ax From fa00f07400bdfad094b1ff6055703781b7ec2b97 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 26 Jan 2015 17:46:26 +0100 Subject: [PATCH 49/87] BUG Use threshold obtained from current code --- ch02/figure2.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ch02/figure2.py b/ch02/figure2.py index 29e6d96e..0b69d395 100644 --- a/ch02/figure2.py +++ b/ch02/figure2.py @@ -23,8 +23,9 @@ labels = labels[~is_setosa] is_virginica = (labels == 'virginica') -# Hand fixed threshold: -t = 1.75 +# Hand fixed thresholds: +t = 1.65 +t2 = 1.75 # Features to use: 3 & 2 f0, f1 = 3, 2 @@ -49,7 +50,7 @@ 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([t - .1, t - .1], [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], From 7fd23a31a8e2af4330cf51dfbf3c5af39c1f7177 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 26 Jan 2015 17:52:57 +0100 Subject: [PATCH 50/87] RFCT Use model as first argument This makes a slightly smoother transition to using an object --- ch02/knn.py | 4 ++-- ch02/threshold.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ch02/knn.py b/ch02/knn.py index e1d48ce0..89ebfdb4 100644 --- a/ch02/knn.py +++ b/ch02/knn.py @@ -26,7 +26,7 @@ def plurality(xs): return k # This function was called ``apply_model`` in the first edition -def predict(features, model): +def predict(model, features): '''Apply k-nn model''' k, train_feats, labels = model results = [] @@ -42,5 +42,5 @@ def predict(features, model): def accuracy(features, labels, model): - preds = predict(features, model) + preds = predict(model, features) return np.mean(preds == labels) diff --git a/ch02/threshold.py b/ch02/threshold.py index efbd942b..d621a350 100644 --- a/ch02/threshold.py +++ b/ch02/threshold.py @@ -40,7 +40,7 @@ def fit_model(features, labels): # This function was called ``apply_model`` in the first edition -def predict(features, model): +def predict(model, features): '''Apply a learned model''' # A model is a pair as returned by fit_model t, fi, reverse = model @@ -51,5 +51,5 @@ def predict(features, model): def accuracy(features, labels, model): '''Compute the accuracy of the model''' - preds = predict(features, model) + preds = predict(model, features) return np.mean(preds == labels) From 91da35988fd796a057da2d6eacc91cbd1e68f35e Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 27 Jan 2015 20:46:36 +0100 Subject: [PATCH 51/87] ENH Update to Python 3. Better figures --- ch04/blei_lda.py | 34 +++++++++++++++------------------- ch04/build_lda.py | 2 +- 2 files changed, 16 insertions(+), 20 deletions(-) diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py index d6cc37b7..a9de3b41 100644 --- a/ch04/blei_lda.py +++ b/ch04/blei_lda.py @@ -15,12 +15,6 @@ print("Please install it") raise -try: - from mpltools import style - style.use('ggplot') -except: - print("Could not import mpltools: plots will not be styled correctly") - import matplotlib.pyplot as plt import numpy as np from os import path @@ -39,9 +33,8 @@ model = models.ldamodel.LdaModel( corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=None) -ti = 0 # Iterate over all the topics in the model -for ti in xrange(model.num_topics): +for ti in range(model.num_topics): words = model.show_topic(ti, 64) tf = sum(f for f, w in words) with open('topics.txt', 'w') as output: @@ -68,11 +61,12 @@ create_cloud('cloud_blei_lda.png', words) num_topics_used = [len(model[doc]) for doc in corpus] -plt.hist(num_topics_used, np.arange(42)) -plt.ylabel('Nr of documents') -plt.xlabel('Nr of topics') -plt.savefig('Figure_04_01.png') -plt.clf() +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 @@ -83,12 +77,14 @@ corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=ALPHA) num_topics_used1 = [len(model1[doc]) for doc in corpus] -plt.hist([num_topics_used, num_topics_used1], np.arange(42)) -plt.ylabel('Nr of documents') -plt.xlabel('Nr of topics') +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 -plt.text(9, 223, r'default alpha') -plt.text(26, 156, 'alpha=1.0') -plt.savefig('Figure_04_02.png') +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 index d4dcb7f2..a0ee9c5f 100644 --- a/ch04/build_lda.py +++ b/ch04/build_lda.py @@ -79,7 +79,7 @@ def __len__(self): distances = distance.squareform(distance.pdist(thetas)) large = distances.max() + 1 -for i in xrange(len(distances)): +for i in range(len(distances)): distances[i, i] = large print(otexts[1]) From 9682f472afa5abbf095c1d16886ebfa06def07d1 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 29 Jan 2015 21:02:09 +0100 Subject: [PATCH 52/87] RFCT Update figure generation code --- ch07/boston1.py | 14 ++++++++------ ch07/figure1_2.py | 23 ++++++++++++----------- ch07/figure3.py | 15 ++++++++------- ch07/figure4.py | 13 +++++++------ ch07/lasso_path_plot.py | 2 +- ch07/predict10k_en.py | 3 ++- 6 files changed, 38 insertions(+), 32 deletions(-) diff --git a/ch07/boston1.py b/ch07/boston1.py index 1f82409f..d0b30447 100644 --- a/ch07/boston1.py +++ b/ch07/boston1.py @@ -27,11 +27,13 @@ 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: -plt.scatter(lr.predict(x), boston.target) +ax.scatter(lr.predict(x), boston.target) -# Plot a diagonal (for reference): -plt.plot([0, 50], [0, 50], '-', color=(.9,.3,.3), lw=4) -plt.xlabel('predicted') -plt.ylabel('real') -plt.savefig('Figure_07_08.png') +ax.set_xlabel('predicted') +ax.set_ylabel('real') +fig.savefig('Figure_07_08.png') diff --git a/ch07/figure1_2.py b/ch07/figure1_2.py index ec1d31da..e40bbfcb 100644 --- a/ch07/figure1_2.py +++ b/ch07/figure1_2.py @@ -14,9 +14,10 @@ boston = load_boston() # Index number five in the number of rooms -plt.scatter(boston.data[:, 5], boston.target) -plt.xlabel("Number of rooms (RM)") -plt.ylabel("House Price") +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] # fit (used below) takes a two-dimensional array as input. We use np.atleast_2d @@ -29,9 +30,9 @@ lr = LinearRegression(fit_intercept=False) lr.fit(x, y) -plt.plot([0, boston.data[:, 5].max() + 1], +ax.plot([0, boston.data[:, 5].max() + 1], [0, lr.predict(boston.data[:, 5].max() + 1)], '-', lw=4) -plt.savefig('Figure1.png', dpi=150) +fig.savefig('Figure1.png') mse = mean_squared_error(y, lr.predict(x)) rmse = np.sqrt(mse) @@ -42,14 +43,14 @@ lr.fit(x, y) -plt.clf() -plt.xlabel("Number of rooms (RM)") -plt.ylabel("House Price") -plt.scatter(boston.data[:, 5], boston.target) +fig,ax = plt.subplots() +ax.set_xlabel("Number of rooms (RM)") +ax.set_ylabel("House Price") +ax.scatter(boston.data[:, 5], boston.target) xmin = x.min() xmax = x.max() -plt.plot([xmin, xmax], lr.predict([[xmin], [xmax]]) , '-', lw=4) -plt.savefig('Figure2.png', dpi=150) +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)) diff --git a/ch07/figure3.py b/ch07/figure3.py index d8c10ddc..7543c1ec 100644 --- a/ch07/figure3.py +++ b/ch07/figure3.py @@ -8,12 +8,13 @@ from sklearn.linear_model import LinearRegression, Lasso import numpy as np from sklearn.datasets import load_boston -import pylab as plt +from matplotlib import pyplot as plt boston = load_boston() -plt.scatter(boston.data[:, 5], boston.target) -plt.xlabel("Number of rooms (RM)") -plt.ylabel("House Price") +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] @@ -24,9 +25,9 @@ lr = LinearRegression() lr.fit(x, y) -plt.plot([xmin, xmax], lr.predict([[xmin], [xmax]]), ':', lw=4, label='OLS model') +ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]), ':', lw=4, label='OLS model') las = Lasso() las.fit(x, y) -plt.plot([xmin, xmax], las.predict([ [xmin], [xmax] ]), '-', lw=4, label='Lasso model') -plt.savefig('Figure3.png', dpi=150) +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 index 70e61c16..a24d48be 100644 --- a/ch07/figure4.py +++ b/ch07/figure4.py @@ -12,7 +12,7 @@ from sklearn.linear_model import LinearRegression from sklearn.datasets import load_boston from sklearn.metrics import mean_squared_error -import pylab as plt +from matplotlib import pyplot as plt boston = load_boston() @@ -24,9 +24,10 @@ p = lr.predict(x) print("RMSE: {:.2}.".format(np.sqrt(mean_squared_error(y, p)))) print("R2: {:.2}.".format(lr.score(x, y))) -plt.scatter(p, y) -plt.xlabel('Predicted price') -plt.ylabel('Actual price') -plt.plot([y.min(), y.max()], [y.min(), y.max()], lw=4) +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) -plt.savefig('Figure4.png', dpi=150) +fig.savefig('Figure4.png') diff --git a/ch07/lasso_path_plot.py b/ch07/lasso_path_plot.py index f0a6f3f5..eab64c26 100644 --- a/ch07/lasso_path_plot.py +++ b/ch07/lasso_path_plot.py @@ -25,5 +25,5 @@ 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', dpi=150) +fig.savefig('Figure_LassoPath.png') diff --git a/ch07/predict10k_en.py b/ch07/predict10k_en.py index bcadcef3..a7dd960a 100644 --- a/ch07/predict10k_en.py +++ b/ch07/predict10k_en.py @@ -69,4 +69,5 @@ 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', dpi=150) +fig.savefig('Figure_10k_scatter_EN_l1_ratio.png') + From 90776764f7da2c5f5decc87abcae30debbffa957 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 29 Jan 2015 21:05:05 +0100 Subject: [PATCH 53/87] RFCT Remove old script In the second edition, this material has moved completely to Chapter 8. --- ch07/usermodel.py | 61 ----------------------------------------------- 1 file changed, 61 deletions(-) delete mode 100644 ch07/usermodel.py diff --git a/ch07/usermodel.py b/ch07/usermodel.py deleted file mode 100644 index 7b309455..00000000 --- a/ch07/usermodel.py +++ /dev/null @@ -1,61 +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 scipy import sparse -from sklearn.linear_model import LassoCV, RidgeCV, ElasticNetCV -from sklearn.cross_validation import KFold - -data = np.array([[int(tok) for tok in line.split('\t')[:3]] - for line in open('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) - -reg = ElasticNetCV(fit_intercept=True, alphas=[ - 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) - - -def movie_norm(xc): - '''Normalize per movie''' - xc = xc.copy().toarray() - # xpos is the mean of the positive items - xpos = np.array([xi[xi > 0].mean() for xi in xc]) - xpos = np.nan_to_num(xpos) - - for i in range(xc.shape[0]): - xc[i] -= (xc[i] > 0) * xpos[i] - return xc, xpos - - -def learn_for(i): - u = reviews[i] - us = np.delete(np.arange(reviews.shape[0]), i) - ps, = np.where(u.toarray().ravel() > 0) - x = reviews[us][:, ps].T - y = u.data - err = 0 - eb = 0 - kf = KFold(len(y), n_folds=5) - for train, test in kf: - xc, xpos = movie_norm(x[train]) - reg.fit(xc, y[train] - xpos) - - xc, xpos = movie_norm(x[test]) - p = reg.predict(xc).ravel() - e = (p + xpos) - y[test] - err += np.sum(e * e) - eb += np.sum((y[train].mean() - y[test]) ** 2) - return np.sqrt(err / float(len(y))), np.sqrt(eb / float(len(y))) - -whole_data = [] -for i in range(reviews.shape[0]): - s = learn_for(i) - print(s[0] < s[1]) - print(s) - whole_data.append(s) From 822f6acfdff3c4d943a0c4732d73a0c80267ec9d Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 6 Feb 2015 08:45:37 +0100 Subject: [PATCH 54/87] MIN Better axis label --- ch07/figure1_2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ch07/figure1_2.py b/ch07/figure1_2.py index e40bbfcb..3f11a0c7 100644 --- a/ch07/figure1_2.py +++ b/ch07/figure1_2.py @@ -16,7 +16,7 @@ # Index number five in the number of rooms fig,ax = plt.subplots() ax.scatter(boston.data[:, 5], boston.target) -ax.set_xlabel("Number of rooms (RM)") +ax.set_xlabel("Average number of rooms (RM)") ax.set_ylabel("House Price") x = boston.data[:, 5] @@ -44,7 +44,7 @@ lr.fit(x, y) fig,ax = plt.subplots() -ax.set_xlabel("Number of rooms (RM)") +ax.set_xlabel("Average number of rooms (RM)") ax.set_ylabel("House Price") ax.scatter(boston.data[:, 5], boston.target) xmin = x.min() From 83d426c2ab78db288f2680e5300df327f8738bda Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 9 Feb 2015 14:38:45 +0100 Subject: [PATCH 55/87] MIN Add axis= for explicitness --- ch08/norm.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ch08/norm.py b/ch08/norm.py index 2ce2b75d..81fa876a 100644 --- a/ch08/norm.py +++ b/ch08/norm.py @@ -5,19 +5,19 @@ class NormalizePositive(object): def fit(self, X, y=None): # count features that are greater than zero in axis 0: binary = (X > 0) - count0 = binary.sum(0) + count0 = binary.sum(axis=0) # to avoid division by zero, set zero counts to one: count0 += (count0 == 0) - self.mean = X.sum(0)/count0 + self.mean = X.sum(axis=0)/count0 # 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 = (X - self.mean) * binary diff **= 2 - self.std = np.sqrt(0.1 + diff.sum(0)/count0) + self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0) return self def fit_transform(self, X): From d192bee488717763791e8b94eb064a6e7c9092b7 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 12 Feb 2015 22:55:47 +0100 Subject: [PATCH 56/87] ENH Improve code readability This is likely to be slightly slower, but it's easier to explain and this code is to teach the concepts, not raw speed. --- ch08/corrneighbours.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py index 9c526daf..f9738195 100644 --- a/ch08/corrneighbours.py +++ b/ch08/corrneighbours.py @@ -18,21 +18,25 @@ def predict(otrain): norm = NormalizePositive() train = norm.fit_transform(otrain.T).T - proximity = distance.pdist(binary, 'correlation') - proximity = distance.squareform(proximity) + dists = distance.pdist(binary, 'correlation') + dists = distance.squareform(dists) - neighbors = proximity.argsort(axis=1) + 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:] - t_u = train[n_u].T - b_u = binary[n_u].T for m in range(filled.shape[1]): - revs = t_u[m] - brevs = b_u[m] - revs = revs[brevs] + # 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): - revs = revs[:len(revs)//2+1] + n = len(revs) + n //= 2 + n += 1 + revs = revs[:n] filled[u,m] = revs.mean() return norm.inverse_transform(filled.T).T From f2b5bdf0ba3944052ffd16b6793e89084e0f7192 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 14 Feb 2015 13:00:16 +0100 Subject: [PATCH 57/87] MIN Better filename for output --- ch08/figure3.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch08/figure3.py b/ch08/figure3.py index ac6961f3..daafc300 100644 --- a/ch08/figure3.py +++ b/ch08/figure3.py @@ -12,4 +12,4 @@ plt.imshow(data[:200, :200], interpolation='nearest') plt.xlabel('User ID') plt.ylabel('Film ID') -plt.savefig('../1400_08_03+.png') +plt.savefig('Figure_08_03_DataMatrix.png') From cd69e60ecdf61edc13102e9945d697cd66ed910b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 20 Mar 2015 13:02:53 +0100 Subject: [PATCH 58/87] ENH More readable code --- ch08/norm.py | 40 +++++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/ch08/norm.py b/ch08/norm.py index 81fa876a..24715322 100644 --- a/ch08/norm.py +++ b/ch08/norm.py @@ -2,40 +2,42 @@ class NormalizePositive(object): - def fit(self, X, y=None): + def fit(self, features, y=None): # count features that are greater than zero in axis 0: - binary = (X > 0) + binary = (features > 0) count0 = binary.sum(axis=0) # to avoid division by zero, set zero counts to one: - count0 += (count0 == 0) + count0[count0 == 0] = 1. - self.mean = X.sum(axis=0)/count0 + self.mean = features.sum(axis=0)/count0 # 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 = (X - self.mean) * binary + 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 fit_transform(self, X): - return self.fit(X).transform(X) + def transform(self, features): + binary = (features > 0) + features = features - self.mean + features /= self.std + features *= binary + return features - def transform(self, X): - binary = (X > 0) - X = X - self.mean - X /= self.std - X *= binary - return X - - def inverse_transform(self, X, copy=True): + def inverse_transform(self, features, copy=True): if copy: - X = X.copy() - X *= self.std - X += self.mean - return X + features = features.copy() + features *= self.std + features += self.mean + return features + + def fit_transform(self, features): + return self.fit(features).transform(features) + def predict(train): norm = NormalizePositive() From 30a1512cd0c1acfd4046ec76426cb01654261841 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 20 Mar 2015 13:07:43 +0100 Subject: [PATCH 59/87] ENH Use matutils.corpus2dense instead of looping --- ch04/blei_lda.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py index a9de3b41..bbad9d1f 100644 --- a/ch04/blei_lda.py +++ b/ch04/blei_lda.py @@ -8,7 +8,7 @@ from __future__ import print_function from wordcloud import create_cloud try: - from gensim import corpora, models + from gensim import corpora, models, matutils except: print("import gensim failed.") print() @@ -44,15 +44,11 @@ # We first identify the most discussed topic, i.e., the one with the # highest total weight -# First, we need to sum up the weights across all the documents -weight = np.zeros(model.num_topics) -for doc in corpus: - for col, val in model[doc]: - weight[col] += val - # As a reasonable alternative, we could have used the log of val: - # weight[col] += np.log(val) +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) From 2c530e1b623a6cf3ada68a042577182b9c71885b Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 20 Mar 2015 13:19:11 +0100 Subject: [PATCH 60/87] MIN Use same function name as in book --- ch10/features.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch10/features.py b/ch10/features.py index c183d909..42847b30 100644 --- a/ch10/features.py +++ b/ch10/features.py @@ -37,7 +37,7 @@ def texture(im): return mh.features.haralick(im).ravel() -def color_histogram(im): +def chist(im): '''Compute color histogram of input image Parameters From 694856399378a72fe421bf966004f60328c5eedc Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 20 Mar 2015 13:50:10 +0100 Subject: [PATCH 61/87] RFCT Simpler code --- ch08/corrneighbours.py | 8 ++++---- ch08/norm.py | 23 ++++++++++++++++++----- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/ch08/corrneighbours.py b/ch08/corrneighbours.py index f9738195..eb30e685 100644 --- a/ch08/corrneighbours.py +++ b/ch08/corrneighbours.py @@ -15,8 +15,8 @@ def predict(otrain): binary = (otrain > 0) - norm = NormalizePositive() - train = norm.fit_transform(otrain.T).T + norm = NormalizePositive(axis=1) + train = norm.fit_transform(otrain) dists = distance.pdist(binary, 'correlation') dists = distance.squareform(dists) @@ -37,9 +37,9 @@ def predict(otrain): n //= 2 n += 1 revs = revs[:n] - filled[u,m] = revs.mean() + filled[u,m] = np.mean(revs) - return norm.inverse_transform(filled.T).T + return norm.inverse_transform(filled) def main(transpose_inputs=False): train, test = get_train_test(random_state=12) diff --git a/ch08/norm.py b/ch08/norm.py index 24715322..2925bbca 100644 --- a/ch08/norm.py +++ b/ch08/norm.py @@ -2,15 +2,20 @@ 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 0: + # count features that are greater than zero in axis `self.axis`: + if self.axis == 1: + features = features.T binary = (features > 0) - count0 = binary.sum(axis=0) + count = binary.sum(axis=0) # to avoid division by zero, set zero counts to one: - count0[count0 == 0] = 1. + count[count == 0] = 1. - self.mean = features.sum(axis=0)/count0 + 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 @@ -18,21 +23,29 @@ def fit(self, features, y=None): 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) + 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): From c03c2f3caf67fdb7533f6540d59a4b53b598a9f4 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 20 Mar 2015 13:54:09 +0100 Subject: [PATCH 62/87] DOC Add note on randomness --- ch08/README.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ch08/README.rst b/ch08/README.rst index d22c6bab..4cc08bc5 100644 --- a/ch08/README.rst +++ b/ch08/README.rst @@ -10,6 +10,10 @@ significantly refactored when compared to the first one. Ratings Prediction ------------------ +Note that since the partition of the data into training and testing is random, +everytime you run the code, the results will be different. + + load_ml100k.py Load data & partition into test/train norm.py From c1881c9f86c7c95337d047734f67715fd6d8e0ba Mon Sep 17 00:00:00 2001 From: Willi Richert Date: Wed, 5 Nov 2014 22:50:55 +0100 Subject: [PATCH 63/87] Switching to py3-complaint twitter library; improving handling of rate limiting. --- ch06/install.py | 130 ++++++++++++++++++++---------------------------- 1 file changed, 55 insertions(+), 75 deletions(-) diff --git a/ch06/install.py b/ch06/install.py index c07a195f..2c1ea31c 100644 --- a/ch06/install.py +++ b/ch06/install.py @@ -11,23 +11,19 @@ # # Pulls tweet data from Twitter because ToS prevents distributing it directly. # -# Right now we use unauthenticated requests, which are rate-limited to 150/hr. -# We use 125/hr to stay safe. -# -# # - Niek Sanders # njs@sananalytics.com # October 20, 2011 # # -# Excuse the ugly code. I threw this together as quickly as possible and I -# don't normally code in Python. -# # In Sanders' original form, the code was using Twitter API 1.0. # Now that Twitter moved to 1.1, we had to make a few changes. # Cf. twitterauth.py for the details. +# Regarding rate limiting, please check +# https://dev.twitter.com/rest/public/rate-limiting + import sys import csv import json @@ -38,24 +34,21 @@ import twitter except ImportError: print("""\ -You need to install python-twitter: - pip install python-twitter +You need to ... + pip install twitter If pip is not found you might have to install it using easy_install. If it does not work on your system, you might want to follow instructions -at https://github.com/bear/python-twitter, most likely: - $ git clone https://github.com/bear/python-twitter - $ cd python-twitter +at https://github.com/sixohsix/twitter, most likely: + $ git clone https://github.com/sixohsix/twitter + $ cd twitter $ sudo python setup.py install """) sys.exit(1) from twitterauth import CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN_KEY, ACCESS_TOKEN_SECRET -api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET, - access_token_key=ACCESS_TOKEN_KEY, access_token_secret=ACCESS_TOKEN_SECRET) - - -MAX_TWEETS_PER_HR = 350 +api = twitter.Twitter(auth=twitter.OAuth(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET, + token=ACCESS_TOKEN_KEY, token_secret=ACCESS_TOKEN_SECRET)) DATA_PATH = "data" @@ -87,16 +80,15 @@ def get_user_params(DATA_PATH): def dump_user_params(user_params): # dump user params for confirmation - print 'Input: ' + user_params['inList'] - print 'Output: ' + user_params['outList'] - print 'Raw data: ' + user_params['rawDir'] - return + print('Input: ' + user_params['inList']) + print('Output: ' + user_params['outList']) + print('Raw data: ' + user_params['rawDir']) def read_total_list(in_filename): # read total fetch list csv - fp = open(in_filename, 'rb') + fp = open(in_filename, 'rt') reader = csv.reader(fp, delimiter=',', quotechar='"') if os.path.exists(MISSING_ID_FILE): @@ -111,10 +103,12 @@ def read_total_list(in_filename): else: not_authed_ids = [] - print "We will skip %i tweets that are not available/visible any more on twitter" % (len(missing_ids) + len(not_authed_ids)) + print("We will skip %i tweets that are not available or visible any more on twitter" % ( + len(missing_ids) + len(not_authed_ids))) ignore_ids = set(missing_ids + not_authed_ids) total_list = [] + for row in reader: if row[2] not in ignore_ids: total_list.append(row) @@ -140,12 +134,12 @@ def purge_already_fetched(fetch_list, raw_dir): parse_tweet_json(tweet_file) count_done += 1 except RuntimeError: - print "Error parsing", item + print("Error parsing", item) rem_list.append(item) else: rem_list.append(item) - print "We have already downloaded %i tweets." % count_done + print("We have already downloaded %i tweets." % count_done) return rem_list @@ -156,66 +150,50 @@ def download_tweets(fetch_list, raw_dir): if not os.path.exists(raw_dir): os.mkdir(raw_dir) - # stay within rate limits - download_pause_sec = 3600 / MAX_TWEETS_PER_HR - # download tweets for idx in range(0, len(fetch_list)): - # stay in Twitter API rate limits - print 'Pausing %d sec to obey Twitter API rate limits' % \ - (download_pause_sec) - time.sleep(download_pause_sec) - # current item item = fetch_list[idx] - print item - - # print status - print '--> downloading tweet #%s (%d of %d)' % \ - (item[2], idx + 1, len(fetch_list)) + print(item) - # Old Twitter API 1.0 - # pull data - # url = 'https://api.twitter.com/1/statuses/show.json?id=' + item[2] - # print url - # urllib.urlretrieve(url, raw_dir + item[2] + '.json') + print('--> downloading tweet #%s (%d of %d)' % + (item[2], idx + 1, len(fetch_list))) - # New Twitter API 1.1 try: - sec = api.GetSleepTime('/statuses/show/:id') - if sec > 0: - print "Sleeping %i seconds to conform to Twitter's rate limiting" % sec - time.sleep(sec) + #import pdb;pdb.set_trace() + response = api.statuses.show(_id=item[2]) - result = api.GetStatus(item[2]) - json_data = result.AsJsonString() + if response.rate_limit_remaining <= 0: + wait_seconds = response.rate_limit_reset - time.time() + print("Rate limiting requests us to wait %f seconds" % + wait_seconds) + time.sleep(wait_seconds) - except twitter.TwitterError, e: + except twitter.TwitterError as e: fatal = True - for m in e.message: + print(e) + for m in json.loads(e.response_data.decode())['errors']: if m['code'] == 34: - print "Tweet missing: ", item - # [{u'message': u'Sorry, that page does not exist', u'code': 34}] - with open(MISSING_ID_FILE, "a") as f: + print("Tweet missing: ", item) + with open(MISSING_ID_FILE, "at") as f: f.write(item[2] + "\n") fatal = False break elif m['code'] == 63: - print "User of tweet '%s' has been suspended." % item - # [{u'message': u'Sorry, that page does not exist', u'code': 34}] - with open(MISSING_ID_FILE, "a") as f: + print("User of tweet '%s' has been suspended." % item) + with open(MISSING_ID_FILE, "at") as f: f.write(item[2] + "\n") fatal = False break elif m['code'] == 88: - print "Rate limit exceeded. Please lower max_tweets_per_hr." + print("Rate limit exceeded.") fatal = True break elif m['code'] == 179: - print "Not authorized to view this tweet." - with open(NOT_AUTHORIZED_ID_FILE, "a") as f: + print("Not authorized to view this tweet.") + with open(NOT_AUTHORIZED_ID_FILE, "at") as f: f.write(item[2] + "\n") fatal = False break @@ -225,8 +203,8 @@ def download_tweets(fetch_list, raw_dir): else: continue - with open(raw_dir + item[2] + '.json', "w") as f: - f.write(json_data + "\n") + with open(raw_dir + item[2] + '.json', "wt") as f: + f.write(json.dumps(dict(response)) + "\n") return @@ -234,12 +212,13 @@ def download_tweets(fetch_list, raw_dir): def parse_tweet_json(filename): # read tweet - fp = open(filename, 'rb') + fp = open(filename, 'r') # parse json try: tweet_json = json.load(fp) - except ValueError: + except ValueError as e: + print(e) raise RuntimeError('error parsing json') # look for twitter api error msgs @@ -281,20 +260,20 @@ def build_output_corpus(out_filename, raw_dir, total_list): writer.writerow(full_row) except RuntimeError: - print '--> bad data in tweet #' + item[2] + print('--> bad data in tweet #' + item[2]) missing_count += 1 else: - print '--> missing tweet #' + item[2] + print('--> missing tweet #' + item[2]) missing_count += 1 # indicate success if missing_count == 0: - print '\nSuccessfully downloaded corpus!' - print 'Output in: ' + out_filename + '\n' + print('\nSuccessfully downloaded corpus!') + print('Output in: ' + out_filename + '\n') else: - print '\nMissing %d of %d tweets!' % (missing_count, len(total_list)) - print 'Partial output in: ' + out_filename + '\n' + print('\nMissing %d of %d tweets!' % (missing_count, len(total_list))) + print('Partial output in: ' + out_filename + '\n') return @@ -302,7 +281,7 @@ def build_output_corpus(out_filename, raw_dir, total_list): def main(): # get user parameters user_params = get_user_params(DATA_PATH) - print user_params + print(user_params) dump_user_params(user_params) # get fetch list @@ -310,7 +289,7 @@ def main(): # remove already fetched or missing tweets fetch_list = purge_already_fetched(total_list, user_params['rawDir']) - print "Fetching %i tweets..." % len(fetch_list) + print("Fetching %i tweets..." % len(fetch_list)) if fetch_list: # start fetching data from twitter @@ -319,10 +298,11 @@ def main(): # second pass for any failed downloads fetch_list = purge_already_fetched(total_list, user_params['rawDir']) if fetch_list: - print '\nStarting second pass to retry %i failed downloads...' % len(fetch_list) + print('\nStarting second pass to retry %i failed downloads...' % + len(fetch_list)) download_tweets(fetch_list, user_params['rawDir']) else: - print "Nothing to fetch any more." + print("Nothing to fetch any more.") # build output corpus build_output_corpus(user_params['outList'], user_params['rawDir'], From 2f8ee9e5a62a386acf3262db6b21d88fc087dfe5 Mon Sep 17 00:00:00 2001 From: Willi Richert Date: Wed, 5 Nov 2014 22:58:52 +0100 Subject: [PATCH 64/87] py3 compliant --- ch06/01_start.py | 20 ++++++++++---------- ch06/02_tuning.py | 22 +++++++++++----------- ch06/03_clean.py | 24 ++++++++++++------------ ch06/04_sent.py | 26 +++++++++++++------------- 4 files changed, 46 insertions(+), 46 deletions(-) diff --git a/ch06/01_start.py b/ch06/01_start.py index 2940b0fa..a7ed2633 100644 --- a/ch06/01_start.py +++ b/ch06/01_start.py @@ -83,7 +83,7 @@ def train_model(clf_factory, X, Y, name="NB ngram", plot=False): summary = (np.mean(scores), np.std(scores), np.mean(pr_scores), np.std(pr_scores)) - print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary + print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary) return np.mean(train_errors), np.mean(test_errors) @@ -94,18 +94,18 @@ def print_incorrect(clf, X, Y): X_wrong = X[wrong_idx] Y_wrong = Y[wrong_idx] Y_hat_wrong = Y_hat[wrong_idx] - for idx in xrange(len(X_wrong)): - print "clf.predict('%s')=%i instead of %i" %\ - (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx]) + for idx in range(len(X_wrong)): + print("clf.predict('%s')=%i instead of %i" % + (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx])) if __name__ == "__main__": X_orig, Y_orig = load_sanders_data() classes = np.unique(Y_orig) for c in classes: - print "#%s: %i" % (c, sum(Y_orig == c)) + print("#%s: %i" % (c, sum(Y_orig == c))) - print "== Pos vs. neg ==" + print("== Pos vs. neg ==") pos_neg = np.logical_or(Y_orig == "positive", Y_orig == "negative") X = X_orig[pos_neg] Y = Y_orig[pos_neg] @@ -113,19 +113,19 @@ def print_incorrect(clf, X, Y): train_model(create_ngram_model, X, Y, name="pos vs neg", plot=True) - print "== Pos/neg vs. irrelevant/neutral ==" + print("== Pos/neg vs. irrelevant/neutral ==") X = X_orig Y = tweak_labels(Y_orig, ["positive", "negative"]) train_model(create_ngram_model, X, Y, name="sent vs rest", plot=True) - print "== Pos vs. rest ==" + print("== Pos vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["positive"]) train_model(create_ngram_model, X, Y, name="pos vs rest", plot=True) - print "== Neg vs. rest ==" + print("== Neg vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["negative"]) train_model(create_ngram_model, X, Y, name="neg vs rest", plot=True) - print "time spent:", time.time() - start_time + print("time spent:", time.time() - start_time) diff --git a/ch06/02_tuning.py b/ch06/02_tuning.py index a48d3edc..7061c435 100644 --- a/ch06/02_tuning.py +++ b/ch06/02_tuning.py @@ -64,7 +64,7 @@ def grid_search_model(clf_factory, X, Y): verbose=10) grid_search.fit(X, Y) clf = grid_search.best_estimator_ - print clf + print(clf) return clf @@ -114,7 +114,7 @@ def train_model(clf, X, Y, name="NB ngram", plot=False): summary = (np.mean(scores), np.std(scores), np.mean(pr_scores), np.std(pr_scores)) - print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary + print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary) return np.mean(train_errors), np.mean(test_errors) @@ -125,9 +125,9 @@ def print_incorrect(clf, X, Y): X_wrong = X[wrong_idx] Y_wrong = Y[wrong_idx] Y_hat_wrong = Y_hat[wrong_idx] - for idx in xrange(len(X_wrong)): - print "clf.predict('%s')=%i instead of %i" %\ - (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx]) + for idx in range(len(X_wrong)): + print("clf.predict('%s')=%i instead of %i" % + (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx])) def get_best_model(): @@ -149,16 +149,16 @@ def get_best_model(): X_orig, Y_orig = load_sanders_data() classes = np.unique(Y_orig) for c in classes: - print "#%s: %i" % (c, sum(Y_orig == c)) + print("#%s: %i" % (c, sum(Y_orig == c))) - print "== Pos vs. neg ==" + print("== Pos vs. neg ==") pos_neg = np.logical_or(Y_orig == "positive", Y_orig == "negative") X = X_orig[pos_neg] Y = Y_orig[pos_neg] Y = tweak_labels(Y, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs neg", plot=True) - print "== Pos/neg vs. irrelevant/neutral ==" + print("== Pos/neg vs. irrelevant/neutral ==") X = X_orig Y = tweak_labels(Y_orig, ["positive", "negative"]) @@ -166,16 +166,16 @@ def get_best_model(): # rest", plot=True) train_model(get_best_model(), X, Y, name="pos vs neg", plot=True) - print "== Pos vs. rest ==" + print("== Pos vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs rest", plot=True) - print "== Neg vs. rest ==" + print("== Neg vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["negative"]) train_model(get_best_model(), X, Y, name="neg vs rest", plot=True) - print "time spent:", time.time() - start_time + print("time spent:", time.time() - start_time) diff --git a/ch06/03_clean.py b/ch06/03_clean.py index f00c2c44..09a94c48 100644 --- a/ch06/03_clean.py +++ b/ch06/03_clean.py @@ -57,7 +57,7 @@ } emo_repl_order = [k for (k_len, k) in reversed( - sorted([(len(k), k) for k in emo_repl.keys()]))] + sorted([(len(k), k) for k in list(emo_repl.keys())]))] re_repl = { r"\br\b": "are", @@ -84,7 +84,7 @@ def preprocessor(tweet): for k in emo_repl_order: tweet = tweet.replace(k, emo_repl[k]) - for r, repl in re_repl.iteritems(): + for r, repl in re_repl.items(): tweet = re.sub(r, repl, tweet) return tweet @@ -150,7 +150,7 @@ def train_model(clf, X, Y, name="NB ngram", plot=False): summary = (np.mean(scores), np.std(scores), np.mean(pr_scores), np.std(pr_scores)) - print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary + print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary) return np.mean(train_errors), np.mean(test_errors) @@ -161,9 +161,9 @@ def print_incorrect(clf, X, Y): X_wrong = X[wrong_idx] Y_wrong = Y[wrong_idx] Y_hat_wrong = Y_hat[wrong_idx] - for idx in xrange(len(X_wrong)): - print "clf.predict('%s')=%i instead of %i" %\ - (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx]) + for idx in range(len(X_wrong)): + print("clf.predict('%s')=%i instead of %i" % + (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx])) def get_best_model(): @@ -185,16 +185,16 @@ def get_best_model(): X_orig, Y_orig = load_sanders_data() classes = np.unique(Y_orig) for c in classes: - print "#%s: %i" % (c, sum(Y_orig == c)) + print("#%s: %i" % (c, sum(Y_orig == c))) - print "== Pos vs. neg ==" + print("== Pos vs. neg ==") pos_neg = np.logical_or(Y_orig == "positive", Y_orig == "negative") X = X_orig[pos_neg] Y = Y_orig[pos_neg] Y = tweak_labels(Y, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs neg", plot=True) - print "== Pos/neg vs. irrelevant/neutral ==" + print("== Pos/neg vs. irrelevant/neutral ==") X = X_orig Y = tweak_labels(Y_orig, ["positive", "negative"]) @@ -202,16 +202,16 @@ def get_best_model(): # rest", plot=True) train_model(get_best_model(), X, Y, name="pos+neg vs rest", plot=True) - print "== Pos vs. rest ==" + print("== Pos vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs rest", plot=True) - print "== Neg vs. rest ==" + print("== Neg vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["negative"]) train_model(get_best_model(), X, Y, name="neg vs rest", plot=True) - print "time spent:", time.time() - start_time + print("time spent:", time.time() - start_time) diff --git a/ch06/04_sent.py b/ch06/04_sent.py index 87fbafc9..07d570b0 100644 --- a/ch06/04_sent.py +++ b/ch06/04_sent.py @@ -153,7 +153,7 @@ def transform(self, documents): } emo_repl_order = [k for (k_len, k) in reversed( - sorted([(len(k), k) for k in emo_repl.keys()]))] + sorted([(len(k), k) for k in list(emo_repl.keys())]))] re_repl = { r"\br\b": "are", @@ -179,7 +179,7 @@ def preprocessor(tweet): for k in emo_repl_order: tweet = tweet.replace(k, emo_repl[k]) - for r, repl in re_repl.iteritems(): + for r, repl in re_repl.items(): tweet = re.sub(r, repl, tweet) return tweet.replace("-", " ").replace("_", " ") @@ -220,7 +220,7 @@ def __grid_search_model(clf_factory, X, Y): verbose=10) grid_search.fit(X, Y) clf = grid_search.best_estimator_ - print clf + print(clf) return clf @@ -275,7 +275,7 @@ def train_model(clf, X, Y, name="NB ngram", plot=False): summary = (np.mean(scores), np.std(scores), np.mean(pr_scores), np.std(pr_scores)) - print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary + print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary) return np.mean(train_errors), np.mean(test_errors) @@ -286,9 +286,9 @@ def print_incorrect(clf, X, Y): X_wrong = X[wrong_idx] Y_wrong = Y[wrong_idx] Y_hat_wrong = Y_hat[wrong_idx] - for idx in xrange(len(X_wrong)): - print "clf.predict('%s')=%i instead of %i" %\ - (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx]) + for idx in range(len(X_wrong)): + print("clf.predict('%s')=%i instead of %i" % + (X_wrong[idx], Y_hat_wrong[idx], Y_wrong[idx])) def get_best_model(): @@ -315,16 +315,16 @@ def get_best_model(): #Y_orig = Y_orig[:100,] classes = np.unique(Y_orig) for c in classes: - print "#%s: %i" % (c, sum(Y_orig == c)) + print("#%s: %i" % (c, sum(Y_orig == c))) - print "== Pos vs. neg ==" + print("== Pos vs. neg ==") pos_neg = np.logical_or(Y_orig == "positive", Y_orig == "negative") X = X_orig[pos_neg] Y = Y_orig[pos_neg] Y = tweak_labels(Y, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs neg", plot=True) - print "== Pos/neg vs. irrelevant/neutral ==" + print("== Pos/neg vs. irrelevant/neutral ==") X = X_orig Y = tweak_labels(Y_orig, ["positive", "negative"]) @@ -332,18 +332,18 @@ def get_best_model(): # rest", plot=True) train_model(get_best_model(), X, Y, name="pos+neg vs rest", plot=True) - print "== Pos vs. rest ==" + print("== Pos vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["positive"]) train_model(get_best_model(), X, Y, name="pos vs rest", plot=True) - print "== Neg vs. rest ==" + print("== Neg vs. rest ==") X = X_orig Y = tweak_labels(Y_orig, ["negative"]) train_model(get_best_model(), X, Y, name="neg vs rest", plot=True) - print "time spent:", time.time() - start_time + print("time spent:", time.time() - start_time) json.dump(poscache, open(poscache_filename, "w")) From 37920fbcfff0b98c3ec7d2e3dbcdce727daa81c4 Mon Sep 17 00:00:00 2001 From: Willi Richert Date: Thu, 6 Nov 2014 11:29:25 +0100 Subject: [PATCH 65/87] py3-compliant; help message for missing SentiWordNet --- ch06/utils.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/ch06/utils.py b/ch06/utils.py index ca8b2357..6757354f 100644 --- a/ch06/utils.py +++ b/ch06/utils.py @@ -6,6 +6,7 @@ # It is made available under the MIT License import os +import sys import collections import csv import json @@ -57,7 +58,7 @@ def load_sanders_data(dirname=".", line_count=-1): try: tweet = json.load(open(tweet_fn, "r")) except IOError: - print("Tweet '%s' not found. Skip."%tweet_fn) + print(("Tweet '%s' not found. Skip." % tweet_fn)) continue if 'text' in tweet and tweet['user']['lang'] == "en": @@ -84,14 +85,14 @@ def plot_pr(auc_score, name, phase, precision, recall, label=None): pylab.title('P/R curve (AUC=%0.2f) / %s' % (auc_score, label)) filename = name.replace(" ", "_") pylab.savefig(os.path.join(CHART_DIR, "pr_%s_%s.png" % - (filename, phase)), bbox_inches="tight") + (filename, phase)), bbox_inches="tight") def show_most_informative_features(vectorizer, clf, n=20): c_f = sorted(zip(clf.coef_[0], vectorizer.get_feature_names())) - top = zip(c_f[:n], c_f[:-(n + 1):-1]) + top = list(zip(c_f[:n], c_f[:-(n + 1):-1])) for (c1, f1), (c2, f2) in top: - print "\t%.4f\t%-15s\t\t%.4f\t%-15s" % (c1, f1, c2, f2) + print("\t%.4f\t%-15s\t\t%.4f\t%-15s" % (c1, f1, c2, f2)) def plot_log(): @@ -119,7 +120,7 @@ def plot_feat_importance(feature_names, clf, name): inds = np.argsort(coef) f_imp = f_imp[inds] coef = coef[inds] - xpos = np.array(range(len(coef))) + xpos = np.array(list(range(len(coef)))) pylab.bar(xpos, coef, width=1) pylab.title('Feature importance for %s' % (name)) @@ -181,8 +182,13 @@ def plot_bias_variance(data_sizes, train_errors, test_errors, name): def load_sent_word_net(): sent_scores = collections.defaultdict(list) + sentiwordnet_path = os.path.join(DATA_DIR, "SentiWordNet_3.0.0_20130122.txt") - with open(os.path.join(DATA_DIR, "SentiWordNet_3.0.0_20130122.txt"), "r") as csvfile: + if not os.path.exists(sentiwordnet_path): + print("Please download SentiWordNet_3.0.0 from http://sentiwordnet.isti.cnr.it/download.php, extract it and put it into the data directory") + sys.exit(1) + + with open(sentiwordnet_path, 'r') as csvfile: reader = csv.reader(csvfile, delimiter='\t', quotechar='"') for line in reader: if line[0].startswith("#"): @@ -200,7 +206,7 @@ def load_sent_word_net(): term = term.replace("-", " ").replace("_", " ") key = "%s/%s" % (POS, term.split("#")[0]) sent_scores[key].append((float(PosScore), float(NegScore))) - for key, value in sent_scores.iteritems(): + for key, value in sent_scores.items(): sent_scores[key] = np.mean(value, axis=0) return sent_scores From fbd6a3d8ef3b36b1fcc18c922697e94239ffa5f7 Mon Sep 17 00:00:00 2001 From: Willi Richert Date: Thu, 6 Nov 2014 11:31:49 +0100 Subject: [PATCH 66/87] grace wait time --- ch06/install.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch06/install.py b/ch06/install.py index 2c1ea31c..be61f485 100644 --- a/ch06/install.py +++ b/ch06/install.py @@ -167,7 +167,7 @@ def download_tweets(fetch_list, raw_dir): wait_seconds = response.rate_limit_reset - time.time() print("Rate limiting requests us to wait %f seconds" % wait_seconds) - time.sleep(wait_seconds) + time.sleep(wait_seconds+5) except twitter.TwitterError as e: fatal = True From 9b8b80ac97bdcfcd63b52fa7b78c96d74ffa9af9 Mon Sep 17 00:00:00 2001 From: Willi Richert Date: Thu, 6 Nov 2014 22:59:55 +0100 Subject: [PATCH 67/87] Remove deprecated parameter 'indices' --- ch06/01_start.py | 2 +- ch06/02_tuning.py | 2 +- ch06/03_clean.py | 2 +- ch06/04_sent.py | 4 ++-- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/ch06/01_start.py b/ch06/01_start.py index a7ed2633..0bdbdd12 100644 --- a/ch06/01_start.py +++ b/ch06/01_start.py @@ -40,7 +40,7 @@ def create_ngram_model(): def train_model(clf_factory, X, Y, name="NB ngram", plot=False): cv = ShuffleSplit( - n=len(X), n_iter=10, test_size=0.3, indices=True, random_state=0) + n=len(X), n_iter=10, test_size=0.3, random_state=0) train_errors = [] test_errors = [] diff --git a/ch06/02_tuning.py b/ch06/02_tuning.py index 7061c435..5b6a835f 100644 --- a/ch06/02_tuning.py +++ b/ch06/02_tuning.py @@ -45,7 +45,7 @@ def create_ngram_model(params=None): def grid_search_model(clf_factory, X, Y): cv = ShuffleSplit( - n=len(X), n_iter=10, test_size=0.3, indices=True, random_state=0) + n=len(X), n_iter=10, test_size=0.3, random_state=0) param_grid = dict(vect__ngram_range=[(1, 1), (1, 2), (1, 3)], vect__min_df=[1, 2], diff --git a/ch06/03_clean.py b/ch06/03_clean.py index 09a94c48..0170ed82 100644 --- a/ch06/03_clean.py +++ b/ch06/03_clean.py @@ -103,7 +103,7 @@ def preprocessor(tweet): def train_model(clf, X, Y, name="NB ngram", plot=False): # create it again for plotting cv = ShuffleSplit( - n=len(X), n_iter=10, test_size=0.3, indices=True, random_state=0) + n=len(X), n_iter=10, test_size=0.3, random_state=0) train_errors = [] test_errors = [] diff --git a/ch06/04_sent.py b/ch06/04_sent.py index 07d570b0..c09a435f 100644 --- a/ch06/04_sent.py +++ b/ch06/04_sent.py @@ -202,7 +202,7 @@ def preprocessor(tweet): def __grid_search_model(clf_factory, X, Y): cv = ShuffleSplit( - n=len(X), n_iter=10, test_size=0.3, indices=True, random_state=0) + n=len(X), n_iter=10, test_size=0.3, random_state=0) param_grid = dict(vect__ngram_range=[(1, 1), (1, 2), (1, 3)], vect__min_df=[1, 2], @@ -228,7 +228,7 @@ def __grid_search_model(clf_factory, X, Y): def train_model(clf, X, Y, name="NB ngram", plot=False): # create it again for plotting cv = ShuffleSplit( - n=len(X), n_iter=10, test_size=0.3, indices=True, random_state=0) + n=len(X), n_iter=10, test_size=0.3, random_state=0) train_errors = [] test_errors = [] From 22bc1405ec5c5b3f9351233880390d044b48c625 Mon Sep 17 00:00:00 2001 From: re4lfl0w Date: Tue, 27 Jan 2015 10:05:21 +0900 Subject: [PATCH 68/87] Modify UnicodeDecodeError text. You'll use utf-8 --- ch05/classify.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ch05/classify.py b/ch05/classify.py index ef5af419..26f3731a 100644 --- a/ch05/classify.py +++ b/ch05/classify.py @@ -54,6 +54,7 @@ def prepare_sent_features(): if not text: meta[pid]['AvgSentLen'] = meta[pid]['AvgWordLen'] = 0 else: + text = text.decode('utf-8') sent_lens = [len(nltk.word_tokenize( sent)) for sent in nltk.sent_tokenize(text)] meta[pid]['AvgSentLen'] = np.mean(sent_lens) From 1d8fd230cda177d916d3e73eaeae12a49ef6a587 Mon Sep 17 00:00:00 2001 From: re4lfl0w Date: Wed, 28 Jan 2015 06:21:37 +0900 Subject: [PATCH 69/87] Modify UnicodeDecodeError text in Python 2.x --- ch05/classify.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ch05/classify.py b/ch05/classify.py index 26f3731a..76699819 100644 --- a/ch05/classify.py +++ b/ch05/classify.py @@ -54,7 +54,9 @@ def prepare_sent_features(): if not text: meta[pid]['AvgSentLen'] = meta[pid]['AvgWordLen'] = 0 else: - text = text.decode('utf-8') + from platform import python_version + if python_version().startswith('2'): + text = text.decode('utf-8') sent_lens = [len(nltk.word_tokenize( sent)) for sent in nltk.sent_tokenize(text)] meta[pid]['AvgSentLen'] = np.mean(sent_lens) From 42052080ceabd373844d45a5ce460b2d3d44f290 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Wed, 25 Mar 2015 22:21:37 +0100 Subject: [PATCH 70/87] ENH Single file with the code as in book --- ch02/README.rst | 3 + ch02/chapter.py | 167 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 170 insertions(+) create mode 100644 ch02/chapter.py diff --git a/ch02/README.rst b/ch02/README.rst index 4dc0b211..e2cb729a 100644 --- a/ch02/README.rst +++ b/ch02/README.rst @@ -6,6 +6,9 @@ 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 diff --git a/ch02/chapter.py b/ch02/chapter.py new file mode 100644 index 00000000..712cefb5 --- /dev/null +++ b/ch02/chapter.py @@ -0,0 +1,167 @@ +# 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]) + predict(model, features[testing]) + 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))) From bb9b8de532a5250b7b1842314d5a975148aea9b9 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Wed, 25 Mar 2015 23:15:35 +0100 Subject: [PATCH 71/87] ENH Average methods (instead of stacked learning) This is mentioned in the book, but the code is only shown here. --- ch08/README.rst | 2 +- ch08/averaged.py | 33 +++++++++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 1 deletion(-) create mode 100644 ch08/averaged.py diff --git a/ch08/README.rst b/ch08/README.rst index 4cc08bc5..488844e6 100644 --- a/ch08/README.rst +++ b/ch08/README.rst @@ -25,7 +25,7 @@ regression.py stacked.py Stacked predictions averaged.py - Averaging of predictions + Averaging of predictions (mentioned in book, but code is not shown there). Association Rule Mining ----------------------- diff --git a/ch08/averaged.py b/ch08/averaged.py new file mode 100644 index 00000000..5b19bba7 --- /dev/null +++ b/ch08/averaged.py @@ -0,0 +1,33 @@ +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() From afcdc0bd9fdb8d1e12a0786b42833664c551901e Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 27 Mar 2015 09:46:12 +0100 Subject: [PATCH 72/87] ENH Add chapter.py files These contain the code as typed in the text --- ch08/chapter.py | 208 +++++++++++++++++++++++++++++++++++++++++++++++ ch10/README.rst | 2 + ch10/chapter.py | 186 ++++++++++++++++++++++++++++++++++++++++++ ch12/README.rst | 2 + ch12/chapter.py | 95 ++++++++++++++++++++++ ch12/features.py | 1 + 6 files changed, 494 insertions(+) create mode 100644 ch08/chapter.py create mode 100644 ch10/chapter.py create mode 100644 ch12/chapter.py create mode 120000 ch12/features.py diff --git a/ch08/chapter.py b/ch08/chapter.py new file mode 100644 index 00000000..d039d93f --- /dev/null +++ b/ch08/chapter.py @@ -0,0 +1,208 @@ +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/ch10/README.rst b/ch10/README.rst index e409ec36..91e32756 100644 --- a/ch10/README.rst +++ b/ch10/README.rst @@ -17,6 +17,8 @@ Running ``download.sh`` will retrieve the other dataset into a directory Scripts ------- +chapter.py + Code as written in the book. thresholded_figure.py Computes the thresholded figures, including after Gaussian blurring lena-ring.py diff --git a/ch10/chapter.py b/ch10/chapter.py new file mode 100644 index 00000000..233720bb --- /dev/null +++ b/ch10/chapter.py @@ -0,0 +1,186 @@ +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/ch12/README.rst b/ch12/README.rst index ecf688f1..fba88d4a 100644 --- a/ch12/README.rst +++ b/ch12/README.rst @@ -13,6 +13,8 @@ repository at ``../SimpleImageDataset/``. Scripts ------- +chapter.py + Code as written in the book jugfile.py Example jugfile image-classification.py diff --git a/ch12/chapter.py b/ch12/chapter.py new file mode 100644 index 00000000..15326680 --- /dev/null +++ b/ch12/chapter.py @@ -0,0 +1,95 @@ +from jug import TaskGenerator +from glob import glob +import mahotas as mh +@TaskGenerator +def compute_texture(im): + from features import texture + imc = mh.imread(im) + return texture(mh.colors.rgb2gray(imc)) + +@TaskGenerator +def chist_file(fname): + from features import chist + im = mh.imread(fname) + return chist(im) + +import numpy as np +to_array = TaskGenerator(np.array) +hstack = TaskGenerator(np.hstack) + +haralicks = [] +chists = [] +labels = [] + +# Change this variable to point to +# the location of the dataset is on disk +basedir = '../SimpleImageDataset/' +# Use glob to get all the images +images = glob('{}/*.jpg'.format(basedir)) + +for fname in sorted(images): + haralicks.append(compute_texture(fname)) + chists.append(chist_file(fname)) + # The class is encoded in the filename as xxxx00.jpg + labels.append(fname[:-len('00.jpg')]) + +haralicks = to_array(haralicks) +chists = to_array(chists) +labels = to_array(labels) + +@TaskGenerator +def accuracy(features, labels): + from sklearn.linear_model import LogisticRegression + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + from sklearn import cross_validation + + clf = Pipeline([('preproc', StandardScaler()), + ('classifier', LogisticRegression())]) + cv = cross_validation.LeaveOneOut(len(features)) + scores = cross_validation.cross_val_score( + clf, features, labels, cv=cv) + return scores.mean() +scores_base = accuracy(haralicks, labels) +scores_chist = accuracy(chists, labels) + +combined = hstack([chists, haralicks]) +scores_combined = accuracy(combined, labels) + +@TaskGenerator +def print_results(scores): + with open('results.image.txt', 'w') as output: + for k,v in scores: + output.write('Accuracy [{}]: {:.1%}\n'.format( + k, v.mean())) + +print_results([ + ('base', scores_base), + ('chists', scores_chist), + ('combined' , scores_combined), + ]) + +@TaskGenerator +def compute_lbp(fname): + from mahotas.features import lbp + imc = mh.imread(fname) + im = mh.colors.rgb2grey(imc) + return lbp(im, radius=8, points=6) + +lbps = [] +for fname in sorted(images): + # the rest of the loop as before + lbps.append(compute_lbp(fname)) +lbps = to_array(lbps) + +scores_lbps = accuracy(lbps, labels) +combined_all = hstack([chists, haralicks, lbps]) +scores_combined_all = accuracy(combined_all, labels) + +print_results([ + ('base', scores_base), + ('chists', scores_chist), + ('lbps', scores_lbps), + ('combined' , scores_combined), + ('combined_all' , scores_combined_all), + ]) diff --git a/ch12/features.py b/ch12/features.py new file mode 120000 index 00000000..142a324d --- /dev/null +++ b/ch12/features.py @@ -0,0 +1 @@ +../ch10/features.py \ No newline at end of file From 3ae4c9daf6eb31ab1579c7dee4d07298734b1c24 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 27 Mar 2015 09:49:54 +0100 Subject: [PATCH 73/87] BLD Update gitignore files Ignore downloaded data & output files --- ch04/.gitignore | 4 ++++ ch04/data/.gitignore | 2 ++ ch07/.gitignore | 2 +- ch07/data/.gitignore | 1 + ch08/apriori/.gitignore | 2 +- ch10/.gitignore | 1 + 6 files changed, 10 insertions(+), 2 deletions(-) create mode 100644 ch07/data/.gitignore create mode 100644 ch10/.gitignore diff --git a/ch04/.gitignore b/ch04/.gitignore index 0a7a205c..c4c0b18a 100644 --- a/ch04/.gitignore +++ b/ch04/.gitignore @@ -1,2 +1,6 @@ wiki_lda.pkl wiki_lda.pkl.state +*.png +*.npy +*.pkl +topics.txt diff --git a/ch04/data/.gitignore b/ch04/data/.gitignore index d71ac12b..91f24bbc 100644 --- a/ch04/data/.gitignore +++ b/ch04/data/.gitignore @@ -4,7 +4,9 @@ dataset-379-20news-18828_HJRZF.zip 379/ enwiki-latest-pages-articles.xml.bz2 wiki_en_output_bow.mm +wiki_en_output_bow.mm.gz wiki_en_output_bow.mm.index wiki_en_output_tfidf.mm +wiki_en_output_tfidf.mm.gz wiki_en_output_tfidf.mm.index wiki_en_output_wordids.txt.bz2 diff --git a/ch07/.gitignore b/ch07/.gitignore index d21bb3d1..e33609d2 100644 --- a/ch07/.gitignore +++ b/ch07/.gitignore @@ -1 +1 @@ -.formula_*/ +*.png diff --git a/ch07/data/.gitignore b/ch07/data/.gitignore new file mode 100644 index 00000000..3286ba0f --- /dev/null +++ b/ch07/data/.gitignore @@ -0,0 +1 @@ +E2006.train diff --git a/ch08/apriori/.gitignore b/ch08/apriori/.gitignore index 05697a95..6379e20f 100644 --- a/ch08/apriori/.gitignore +++ b/ch08/apriori/.gitignore @@ -1 +1 @@ -retail.dat +retail.dat.gz diff --git a/ch10/.gitignore b/ch10/.gitignore new file mode 100644 index 00000000..2f266195 --- /dev/null +++ b/ch10/.gitignore @@ -0,0 +1 @@ +AnimTransDistr/ From 24ac39d08259a1fc06a8749767f91eb5f6eef68e Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 27 Mar 2015 09:52:38 +0100 Subject: [PATCH 74/87] ENH Add script to process Wikipedia with HDP --- ch04/README.rst | 4 ++++ ch04/wikitopics_create_hdp.py | 39 +++++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+) create mode 100644 ch04/wikitopics_create_hdp.py diff --git a/ch04/README.rst b/ch04/README.rst index 7582ba54..7fe0a92f 100644 --- a/ch04/README.rst +++ b/ch04/README.rst @@ -49,3 +49,7 @@ Scripts blei_lda.py Computes LDA using the AP Corpus. +wikitopics_create.py + Create the topic model for Wikipedia using LDA (must download wikipedia database first) +wikitopics_create_hdp.py + Create the topic model for Wikipedia using HDP (must download wikipedia database first) diff --git a/ch04/wikitopics_create_hdp.py b/ch04/wikitopics_create_hdp.py new file mode 100644 index 00000000..951d1850 --- /dev/null +++ b/ch04/wikitopics_create_hdp.py @@ -0,0 +1,39 @@ +# 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 logging +import gensim +import numpy as np + +# Set up logging in order to get progress information as the model is being built: +logging.basicConfig( + format='%(asctime)s : %(levelname)s : %(message)s', + level=logging.INFO) + +# Load the preprocessed corpus (id2word & 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') + +# Calling the constructor is enough to build the model +# This call will take a few hours! +model = gensim.models.hdpmodel.HdpModel( + corpus=mm, + id2word=id2word, + chunksize=10000) + +# Save the model so we do not need to learn it again. +model.save('wiki_hdp.pkl') + +# Compute the document/topic matrix +topics = np.zeros((len(mm), model.num_topics)) +for di,doc in enumerate(mm): + doc_top = model[doc] + for ti,tv in doc_top: + topics[di,ti] += tv +np.save('topics_hdp.npy', topics) From 309ab17ba0feec52719a4805409d56dceeeaf795 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 27 Mar 2015 09:53:27 +0100 Subject: [PATCH 75/87] DAT Add example image This is a copy from the image with the same name in SimpleDataSet, but by having it here, it can be loaded in an easier way. --- ch10/scene00.jpg | Bin 0 -> 1080630 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ch10/scene00.jpg diff --git a/ch10/scene00.jpg b/ch10/scene00.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ed727a50bccb2eaac2562722e35517ec05238d8a GIT binary patch literal 1080630 zcmeFZcT`hb`!1T$n{*VUNC~}cdPe~%p+hLrRJwqG6h**BR}hheA{|0{LLecb;}%r9 z2+~AAMXCjH3+n!`Y|g~Jzy15h9lty7J!hPM&NzEv)_T`mbItY4=Y8IH&dinJ-rU|R zWr}S|R0IU#;sQ~FKp%5<%tcksy8v;N!WEH7gHw_DjVirD_POEW9~ zzL%6)^S4dEA^+F2fRJt||FnU{)`@~bcmNd8Ew+28|9b8qiuLdEaTMF%_EQx5-)+7` zar|v>pg8|(9|#G>^_R^JZ0^5o@DLQw-}OH<@cwQ8-XQk3z1MK)ulhi=m_Ipz?sJa) 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zzQ`7NZ)aXsbv%)6z5j`|R#yx}wywFt_`E!o6WJcbJ4typ!4=uA^&8_l?ql9BmF?YJ e&CDh|CyH$MySHSDsk*MnR;W#(OX&aSKmP}!P_onj literal 0 HcmV?d00001 From e28dfc48be757e24dcc7631861ebbbcd60042e78 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Fri, 3 Apr 2015 15:02:12 +0200 Subject: [PATCH 76/87] DOC Clarify that this is the second edition code Also, link to first_edition branch for readers looking for that information. --- README.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 05218427..cbcd3155 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,14 @@ Building Machine Learning Systems with Python ============================================= -Source Code for the book Building Machine Learning Systems with Python by -[Willi Richert](http://twotoreal.com) and [Luis Pedro -Coelho](http://luispedro.org). +Source Code for the book Building Machine Learning Systems with Python by [Luis +Pedro Coelho](http://luispedro.org) and [Willi Richert](http://twotoreal.com). -The book was published in 2013 by Packt Publishing and is available [from their +The book was published in 2013 (second edition in 2015) by Packt Publishing and +is available [from their website](http://www.packtpub.com/building-machine-learning-systems-with-python/book). +The code in the repository corresponds to the second edition. Code for the +first edition is available in [first\_edition +branch](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython/tree/first_edition). From 79581d2c19ceb91614a3dd06da4a85176059c783 Mon Sep 17 00:00:00 2001 From: iory Date: Mon, 20 Apr 2015 12:29:01 +0900 Subject: [PATCH 77/87] rename missed name --- ch10/large_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ch10/large_classification.py b/ch10/large_classification.py index 9ae8232a..8db3571b 100644 --- a/ch10/large_classification.py +++ b/ch10/large_classification.py @@ -24,11 +24,11 @@ ('classifier', grid)]) def features_for(im): - from features import color_histogram + from features import chist im = mh.imread(im) img = mh.colors.rgb2grey(im).astype(np.uint8) return np.concatenate([mh.features.haralick(img).ravel(), - color_histogram(im)]) + chist(im)]) def images(): '''Iterate over all (image,label) pairs From 8386e856c562d9ddd8f0d7f59fc7d9150c73acbe Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 16 Jun 2015 14:54:10 +0200 Subject: [PATCH 78/87] ENH Better spacing & comments --- ch02/chapter.py | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/ch02/chapter.py b/ch02/chapter.py index 712cefb5..ac887650 100644 --- a/ch02/chapter.py +++ b/ch02/chapter.py @@ -29,8 +29,8 @@ elif t == 2: c = 'b' marker = 'x' - plt.scatter(features[target == t,0], - features[target == t,1], + 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: @@ -151,17 +151,15 @@ def is_virginica_test(fi, t, reverse, example): 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) + # 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 6536b1b5d4f54a6e45014fc589e5819b1582b9bb Mon Sep 17 00:00:00 2001 From: JuanPablo Date: Sun, 3 Jan 2016 18:28:20 -0300 Subject: [PATCH 79/87] analyze_webstats with PEP8 --- ch01/analyze_webstats.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ch01/analyze_webstats.py b/ch01/analyze_webstats.py index 8d3c4c41..5da892e2 100644 --- a/ch01/analyze_webstats.py +++ b/ch01/analyze_webstats.py @@ -26,8 +26,9 @@ x = x[~sp.isnan(y)] y = y[~sp.isnan(y)] -# plot input data + def plot_models(x, y, models, fname, mx=None, ymax=None, xmin=None): + ''' plot input data ''' plt.figure(num=None, figsize=(8, 6)) plt.clf() @@ -138,8 +139,8 @@ def error(f, x, y): train = sorted(shuffled[split_idx:]) fbt1 = sp.poly1d(sp.polyfit(xb[train], yb[train], 1)) fbt2 = sp.poly1d(sp.polyfit(xb[train], yb[train], 2)) -print("fbt2(x)= \n%s"%fbt2) -print("fbt2(x)-100,000= \n%s"%(fbt2-100000)) +print("fbt2(x)= \n%s" % fbt2) +print("fbt2(x)-100,000= \n%s" % (fbt2-100000)) fbt3 = sp.poly1d(sp.polyfit(xb[train], yb[train], 3)) fbt10 = sp.poly1d(sp.polyfit(xb[train], yb[train], 10)) fbt100 = sp.poly1d(sp.polyfit(xb[train], yb[train], 100)) From db795e08dc720fce5a1f327f1ce3de154cf2303c Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 8 Mar 2016 14:57:27 +0100 Subject: [PATCH 80/87] BUG Fix name of function chist The function for computing color histograms is chist. Use it throughout. closes #15 --- ch10/neighbors.py | 4 ++-- ch10/simple_classification.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ch10/neighbors.py b/ch10/neighbors.py index c62a0e2c..1f71d0de 100644 --- a/ch10/neighbors.py +++ b/ch10/neighbors.py @@ -6,7 +6,7 @@ import numpy as np import mahotas as mh from glob import glob -from features import texture, color_histogram +from features import texture, chist from matplotlib import pyplot as plt from sklearn.preprocessing import StandardScaler from scipy.spatial import distance @@ -29,7 +29,7 @@ imc = mh.imread(fname) imc = imc[200:-200,200:-200] haralicks.append(texture(mh.colors.rgb2grey(imc))) - chists.append(color_histogram(imc)) + chists.append(chist(imc)) haralicks = np.array(haralicks) chists = np.array(chists) diff --git a/ch10/simple_classification.py b/ch10/simple_classification.py index 0e3ab347..a5a448d2 100644 --- a/ch10/simple_classification.py +++ b/ch10/simple_classification.py @@ -9,7 +9,7 @@ import numpy as np from glob import glob -from features import texture, color_histogram +from features import texture, chist from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler @@ -32,7 +32,7 @@ for fname in sorted(images): imc = mh.imread(fname) haralicks.append(texture(mh.colors.rgb2grey(imc))) - chists.append(color_histogram(imc)) + chists.append(chist(imc)) # Files are named like building00.jpg, scene23.jpg... labels.append(fname[:-len('xx.jpg')]) From c71430360ececfe2bbe7dbc79add793dab85e5d9 Mon Sep 17 00:00:00 2001 From: tomahawk28 Date: Mon, 2 Nov 2015 15:37:33 +0900 Subject: [PATCH 81/87] Fix predict method called --- ch02/figure4_5_no_sklearn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch02/figure4_5_no_sklearn.py b/ch02/figure4_5_no_sklearn.py index 5f67e0d7..adc83d73 100644 --- a/ch02/figure4_5_no_sklearn.py +++ b/ch02/figure4_5_no_sklearn.py @@ -45,7 +45,7 @@ def plot_decision(features, labels): model = fit_model(1, features[:, (0, 2)], np.array(labels)) C = predict( - np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape) + 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: From b97a0be7a45ca977442f195b3be1dbc42c12621d Mon Sep 17 00:00:00 2001 From: pi Date: Thu, 28 Apr 2016 17:00:16 +0800 Subject: [PATCH 82/87] format this file --- ch01/gen_webstats.py | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/ch01/gen_webstats.py b/ch01/gen_webstats.py index fa133d76..61d0b738 100644 --- a/ch01/gen_webstats.py +++ b/ch01/gen_webstats.py @@ -17,26 +17,22 @@ 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) +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)) +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 [0, 1, 2, 3, 4]], ['week %i' % (w + 1) for w in - [0, 1, 2, 3, 4]]) - +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("..", "web_traffic.tsv"), -# zip(x[~y.mask],y[~y.mask]), delimiter="\t", fmt="%i") - -sp.savetxt(os.path.join( - DATA_DIR, "web_traffic.tsv"), list(zip(x, y)), delimiter="\t", fmt="%s") +sp.savetxt(os.path.join(DATA_DIR, "web_traffic.tsv"), + list(zip(x, y)), delimiter="\t", fmt="%s") From 98d66ea5b9775bda9efc25d0ce8710fb576f2be7 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Thu, 21 Jul 2016 23:38:44 +0200 Subject: [PATCH 83/87] MIN Remove extraneous function call --- ch02/chapter.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ch02/chapter.py b/ch02/chapter.py index ac887650..c68b45ab 100644 --- a/ch02/chapter.py +++ b/ch02/chapter.py @@ -100,7 +100,6 @@ def is_virginica_test(fi, t, reverse, example): training[ei] = False testing = ~training model = fit_model(features[training], is_virginica[training]) - predict(model, features[testing]) predictions = predict(model, features[testing]) correct += np.sum(predictions == is_virginica[testing]) acc = correct/float(len(features)) From c0a3b3a397d22c6941012c78d24c0fcfefe740fc Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 27 Nov 2016 11:05:39 +0100 Subject: [PATCH 84/87] DOC Make explicit how to get AP data --- ch04/README.rst | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/ch04/README.rst b/ch04/README.rst index 7fe0a92f..99a3c186 100644 --- a/ch04/README.rst +++ b/ch04/README.rst @@ -4,6 +4,16 @@ Chapter 4 Support code for *Chapter 4: Topic Modeling* + +AP Data +------- + +To download the AP data, use the ``download_ap.sh`` script inside the ``data`` +directory:: + + cd data + ./download_ap.sh + Word cloud creation ------------------- From c4c71a59644d6f9c80993aed3bae5eb6f453c00c Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Tue, 28 Mar 2017 18:47:48 +0200 Subject: [PATCH 85/87] MIN Update link to AP data --- ch04/data/download_ap.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch04/data/download_ap.sh b/ch04/data/download_ap.sh index 6de8ded8..da27814a 100755 --- a/ch04/data/download_ap.sh +++ b/ch04/data/download_ap.sh @@ -1,3 +1,3 @@ #!/bin/sh -wget http://www.cs.princeton.edu/~blei/lda-c/ap.tgz +wget http://www.cs.columbia.edu/~blei/lda-c/ap.tgz tar xzf ap.tgz From a237d75979fe47d6505ea0c3bc731050503064e8 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sun, 25 Jun 2017 13:55:34 +0200 Subject: [PATCH 86/87] BUG Fixes API usage For newer versions of gensim at least, topics are represented as `(word, frequency)`. closes #21 --- ch04/blei_lda.py | 4 ++-- ch04/wordcloud.py | 4 +--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py index bbad9d1f..7f6ac2b3 100644 --- a/ch04/blei_lda.py +++ b/ch04/blei_lda.py @@ -36,9 +36,9 @@ # 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, w in words) + 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 f, w in words)) + 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 diff --git a/ch04/wordcloud.py b/ch04/wordcloud.py index 6c5302ea..accca2d6 100644 --- a/ch04/wordcloud.py +++ b/ch04/wordcloud.py @@ -24,8 +24,6 @@ def create_cloud(oname, words,maxsize=120, fontname='Lobster'): # 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. - # We also need to flip the order as gensim returns (value, word), whilst - # pytagcloud expects (word, value): - words = [(w,int(v*10000)) for v,w in words] + 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) From 52891e6bac00213bf94ab1a3b1f2d8d5ed04a774 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 21 May 2018 15:45:33 +0200 Subject: [PATCH 87/87] BUG Fix function import --- ch12/image-classification.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch12/image-classification.py b/ch12/image-classification.py index 09dbd5b4..6f76d26d 100644 --- a/ch12/image-classification.py +++ b/ch12/image-classification.py @@ -39,7 +39,7 @@ def compute_texture(im): @TaskGenerator def chist(fname): - from features import color_histogram + from features import chist as color_histogram im = mh.imread(fname) return color_histogram(im)

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