textClassifierConv.py

import numpy as np
import pandas as pd
import pickle
from collections import defaultdict
import re

from bs4 import BeautifulSoup

import sys
import os

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical

from keras.layers import Embedding
from keras.layers import Dense, Input, Flatten
from keras.layers import Conv1D, MaxPooling1D, Embedding, Merge, Dropout
from keras.models import Model

MAX_SEQUENCE_LENGTH = 1000
MAX_NB_WORDS = 20000
EMBEDDING_DIM = 100
VALIDATION_SPLIT = 0.2

def clean_str(string):
    """
    Tokenization/string cleaning for dataset
    Every dataset is lower cased except
    """
    string = re.sub(r"\\", "", string)    
    string = re.sub(r"\'", "", string)    
    string = re.sub(r"\"", "", string)    
    return string.strip().lower()

data_train = pd.read_csv('labeledTrainData.tsv', sep='\t')
print(data_train.shape)

texts = []
labels = []

for idx in range(data_train.review.shape[0]):
    text = BeautifulSoup(data_train.review[idx], "lxml")
    texts.append(clean_str(text.get_text()))
    labels.append(data_train.sentiment[idx])
    

tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)

word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))

data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)

labels = to_categorical(np.asarray(labels))
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', labels.shape)

indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])

x_train = data[:-nb_validation_samples]
y_train = labels[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels[-nb_validation_samples:]

print('Number of positive and negative reviews in traing and validation set ')
print(y_train.sum(axis=0))
print(y_val.sum(axis=0))

GLOVE_DIR = "glove/glove.6B/"
embeddings_index = {}
f = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'))
for line in f:
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype='float32')
    embeddings_index[word] = coefs
f.close()

print('Total %s word vectors in Glove 6B 100d.' % len(embeddings_index))

embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        # words not found in embedding index will be all-zeros.
        embedding_matrix[i] = embedding_vector
        
embedding_layer = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=True)

sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
l_cov1= Conv1D(128, 5, activation='relu')(embedded_sequences)
l_pool1 = MaxPooling1D(5)(l_cov1)
l_cov2 = Conv1D(128, 5, activation='relu')(l_pool1)
l_pool2 = MaxPooling1D(5)(l_cov2)
l_cov3 = Conv1D(128, 5, activation='relu')(l_pool2)
l_pool3 = MaxPooling1D(35)(l_cov3)  # global max pooling
l_flat = Flatten()(l_pool3)
l_dense = Dense(128, activation='relu')(l_flat)
preds = Dense(2, activation='softmax')(l_dense)

model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['acc'])

print("model fitting - simplified convolutional neural network")
model.summary()
model.fit(x_train, y_train, validation_data=(x_val, y_val),
          epochs=10, batch_size=128)

embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        # words not found in embedding index will be all-zeros.
        embedding_matrix[i] = embedding_vector
        
embedding_layer = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=True)

# applying a more complex convolutional approach
convs = []
filter_sizes = [3,4,5]

sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)

for fsz in filter_sizes:
    l_conv = Conv1D(nb_filter=128,filter_length=fsz,activation='relu')(embedded_sequences)
    l_pool = MaxPooling1D(5)(l_conv)
    convs.append(l_pool)
    
l_merge = Merge(mode='concat', concat_axis=1)(convs)
l_cov1= Conv1D(128, 5, activation='relu')(l_merge)
l_pool1 = MaxPooling1D(5)(l_cov1)
l_cov2 = Conv1D(128, 5, activation='relu')(l_pool1)
l_pool2 = MaxPooling1D(30)(l_cov2)
l_flat = Flatten()(l_pool2)
l_dense = Dense(128, activation='relu')(l_flat)
preds = Dense(2, activation='softmax')(l_dense)

model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['acc'])

print("model fitting - more complex convolutional neural network")
model.summary()
model.fit(x_train, y_train, validation_data=(x_val, y_val),
          nb_epoch=20, batch_size=50)