add pytorch code

Author: captain

__pycache__/data_manager.cpython-36.pyc

@@ -1,3 +1,5 @@
+#coding:utf-8
+
 import os
 import sys
 import pickle

data_manager.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 import os
 import time
 

data_manager.pyc

@@ -1,3 +1,5 @@
+#coding:utf-8
+
 import argparse
 import sys
 import os

models.py

@@ -3,16 +3,6 @@ python ./run.py \
 -d ./data/preprocessed/ml-1m/0.2/ \
 -a ./data/preprocessed/ml-1m/ \
 -c True \
--r ./data/rare/movielens/ml-1m_ratings.dat \
--i ./data/rare/movielens/ml_plot.dat \
+-r ./data/movielens/ml-1m_ratings.dat \
+-i ./data/movielens/ml_plot.dat \
 -m 1
-
-
-##!/usr/bin/env bash
-#python ./run.py \
-#-d ./data/preprocessed/aiv/0.2/ \
-#-a ./data/preprocessed/aiv/ \
-#-c True \
-#-r ./data/rare/aiv/Amazon_Instant_Video_ratings.txt \
-#-i ./data/rare/aiv/Amazon_Instant_Video_items.txt \
-#-m 1

run.py

@@ -0,0 +1,43 @@
+# coding:utf-8
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+
+class CNN(nn.Module):
+    batch_size = 128
+    # More than this epoch cause easily over-fitting on our data sets
+    nb_epoch = 5
+
+    def __init__(self, output_dimesion, vocab_size, dropout_rate, emb_dim, max_len, nb_filters, init_W=None):
+        super(CNN, self).__init__()
+
+        self.max_len = max_len
+        max_features = vocab_size
+        vanila_dimension = 200
+        projection_dimension = output_dimesion
+        self.qual_conv_set = {}
+
+        self.embedding = nn.Embedding()
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(1, nb_filters, kernel_size=(3, emb_dim)),
+            nn.MaxPool2d(kernel_size=(max_len - 3 + 1, 1))
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(1, nb_filters, kernel_size=(4, emb_dim)),
+            nn.MaxPool2d(kernel_size=(max_len - 4 + 1, 1))
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(1, nb_filters, kernel_size=(5, emb_dim)),
+            nn.MaxPool2d(kernel_size=(max_len - 5 + 1, 1))
+        )
+
+
+        self.conv_1 =
+        self.conv_2 = nn.Conv2d(1, nb_filters, kernel_size=(4, emb_dim))
+        self.conv_3 = nn.Conv2d(1, nb_filters, kernel_size=(5, emb_dim))
+
+    def forward(self, *input):
+        pass

run_test_preprocess.sh

@@ -1,3 +1,5 @@
+#coding:utf-8
+
 import numpy as np
 np.random.seed(1337)
 
@@ -74,43 +76,6 @@ def __init__(self, output_dimesion, vocab_size, dropout_rate, emb_dim, max_len,
 
         print("Jay::compile completed")
 
-        # '''Embedding Layer'''
-        # self.model.add_input(name='input', input_shape=(max_len,), dtype=int)
-        #
-        # if init_W is None:
-        #     self.model.add_node(Embedding(
-        #         max_features, emb_dim, input_length=max_len), name='sentence_embeddings', input='input')
-        # else:
-        #     self.model.add_node(Embedding(max_features, emb_dim, input_length=max_len, weights=[
-        #                         init_W / 20]), name='sentence_embeddings', input='input')
-        #
-        # '''Convolution Layer & Max Pooling Layer'''
-        # for i in filter_lengths:
-        #     model_internal = Sequential()
-        #     model_internal.add(
-        #         Reshape(dims=(1, self.max_len, emb_dim), input_shape=(self.max_len, emb_dim)))
-        #     model_internal.add(Convolution2D(
-        #         nb_filters, i, emb_dim, activation="relu"))
-        #     model_internal.add(MaxPooling2D(
-        #         pool_size=(self.max_len - i + 1, 1)))
-        #     model_internal.add(Flatten())
-        #
-        #     self.model.add_node(model_internal, name='unit_' +
-        #                         str(i), input='sentence_embeddings')
-        #
-        # '''Dropout Layer'''
-        # self.model.add_node(Dense(vanila_dimension, activation='tanh'),
-        #                     name='fully_connect', inputs=['unit_' + str(i) for i in filter_lengths])
-        # self.model.add_node(Dropout(dropout_rate),
-        #                     name='dropout', input='fully_connect')
-        # '''Projection Layer & Output Layer'''
-        # self.model.add_node(Dense(projection_dimension, activation='tanh'),
-        #                     name='projection', input='dropout')
-        #
-        # # Output Layer
-        # self.model.add_output(name='output', input='projection')
-        # self.model.compile('rmsprop', {'output': 'mse'})
-
     def load_model(self, model_path):
         self.model.load_weights(model_path)
 

text_analysis/cnn_model.py

@@ -0,0 +1,78 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    unset -f pydoc >/dev/null 2>&1
+
+    # reset old environment variables
+    # ! [ -z ${VAR+_} ] returns true if VAR is declared at all
+    if ! [ -z "${_OLD_VIRTUAL_PATH+_}" ] ; then
+        PATH="$_OLD_VIRTUAL_PATH"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then
+        PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+        hash -r 2>/dev/null
+    fi
+
+    if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then
+        PS1="$_OLD_VIRTUAL_PS1"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    if [ ! "${1-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV="/home/captain/git/ConvMF/venv"
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/bin:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+if ! [ -z "${PYTHONHOME+_}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then
+    _OLD_VIRTUAL_PS1="$PS1"
+    if [ "x" != x ] ; then
+        PS1="$PS1"
+    else
+        PS1="(`basename \"$VIRTUAL_ENV\"`) $PS1"
+    fi
+    export PS1
+fi
+
+# Make sure to unalias pydoc if it's already there
+alias pydoc 2>/dev/null >/dev/null && unalias pydoc
+
+pydoc () {
+    python -m pydoc "$@"
+}
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+    hash -r 2>/dev/null
+fi

text_analysis/models.py

@@ -0,0 +1,36 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+# Created by Davide Di Blasi <[email protected]>.
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate && unalias pydoc'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV "/home/captain/git/ConvMF/venv"
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/bin:$PATH"
+
+
+
+if ("" != "") then
+    set env_name = ""
+else
+    set env_name = `basename "$VIRTUAL_ENV"`
+endif
+
+# Could be in a non-interactive environment,
+# in which case, $prompt is undefined and we wouldn't
+# care about the prompt anyway.
+if ( $?prompt ) then
+    set _OLD_VIRTUAL_PROMPT="$prompt"
+    set prompt = "[$env_name] $prompt"
+endif
+
+unset env_name
+
+alias pydoc python -m pydoc
+
+rehash
+