import pandas as pd

from keras.models import Model

from keras.layers import Dense, Input, Dropout, MaxPooling1D, Conv1D, GlobalMaxPool1D

from keras.layers import LSTM, Lambda, Bidirectional, concatenate, BatchNormalization

from keras.layers import TimeDistributed

from keras.optimizers import Adam

import keras.backend as K

import numpy as np

import tensorflow as tf

import re

import keras.callbacks

import sys

import os

def binarize(x, sz=71):

return tf.to_float(tf.one_hot(x, sz, on_value=1, off_value=0, axis=-1))

def binarize_outshape(in_shape):

return in_shape[0], in_shape[1], 71

def striphtml(s):

p = re.compile(r'<.*?>')

return p.sub('', s)

def clean(s):

return re.sub(r'[^\x00-\x7f]', r'', s)

total = len(sys.argv)

cmdargs = str(sys.argv)

print ("Script name: %s" % str(sys.argv[0]))

checkpoint = None

if len(sys.argv) == 2:

if os.path.exists(str(sys.argv[1])):

print ("Checkpoint : %s" % str(sys.argv[1]))

checkpoint = str(sys.argv[1])

data = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)

txt = ''

docs = []

sentences = []

sentiments = []

for cont, sentiment in zip(data.review, data.sentiment):

sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', clean(striphtml(cont)))

sentences = [sent.lower() for sent in sentences]

docs.append(sentences)

sentiments.append(sentiment)

num_sent = []

for doc in docs:

num_sent.append(len(doc))

for s in doc:

txt += s

chars = set(txt)

print('total chars:', len(chars))

char_indices = dict((c, i) for i, c in enumerate(chars))

indices_char = dict((i, c) for i, c in enumerate(chars))

print('Sample doc{}'.format(docs[1200]))

maxlen = 512

max_sentences = 15

X = np.ones((len(docs), max_sentences, maxlen), dtype=np.int64) * -1

y = np.array(sentiments)

for i, doc in enumerate(docs):

for j, sentence in enumerate(doc):

if j < max_sentences:

for t, char in enumerate(sentence[-maxlen:]):

X[i, j, (maxlen - 1 - t)] = char_indices[char]

print('Sample X:{}'.format(X[1200, 2]))

print('y:{}'.format(y[1200]))

ids = np.arange(len(X))

np.random.shuffle(ids)

# shuffle

X = X[ids]

y = y[ids]

X_train = X[:20000]

X_test = X[22500:]

y_train = y[:20000]

y_test = y[22500:]

def char_block(in_layer, nb_filter=(64, 100), filter_length=(3, 3), subsample=(2, 1), pool_length=(2, 2)):

return block

max_features = len(chars) + 1

char_embedding = 40

document = Input(shape=(max_sentences, maxlen), dtype='int64')

in_sentence = Input(shape=(maxlen,), dtype='int64')

embedded = Lambda(binarize, output_shape=binarize_outshape)(in_sentence)

block2 = char_block(embedded, (128, 256), filter_length=(5, 5), subsample=(1, 1), pool_length=(2, 2))

block3 = char_block(embedded, (192, 320), filter_length=(7, 5), subsample=(1, 1), pool_length=(2, 2))

sent_encode = concatenate([block2, block3], axis=-1)

# sent_encode = Dropout(0.2)(sent_encode)

encoder = Model(inputs=in_sentence, outputs=sent_encode)

encoder.summary()

encoded = TimeDistributed(encoder)(document)

lstm_h = 92

lstm_layer = LSTM(lstm_h, return_sequences=True, dropout=0.1, recurrent_dropout=0.1, implementation=0)(encoded)

lstm_layer2 = LSTM(lstm_h, return_sequences=False, dropout=0.1, recurrent_dropout=0.1, implementation=0)(lstm_layer)

# output = Dropout(0.2)(bi_lstm)

output = Dense(1, activation='sigmoid')(lstm_layer2)

model = Model(outputs=output, inputs=document)

model.summary()

if checkpoint:

model.load_weights(checkpoint)

file_name = os.path.basename(sys.argv[0]).split('.')[0]

check_cb = keras.callbacks.ModelCheckpoint('checkpoints/' + file_name + '.{epoch:02d}-{val_loss:.2f}.hdf5',

monitor='val_loss',

verbose=0, save_best_only=True, mode='min')

earlystop_cb = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')

tb_cb = keras.callbacks.TensorBoard(log_dir='./logs', hitogram_freq=10)

optimizer = 'rmsprop'

model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=10, epochs=30, shuffle=True, callbacks=[check_cb, earlystop_cb, tb_cb])