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test.py
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test.py
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from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import pandas as pd
import tensorflow as tf
import pydash as _
import nltk
import sys
import re
nltk.download('stopwords')
class Dataset():
raw_data = []
sanitized = []
tokenized = []
padded = []
x = []
valid_x = []
y = []
valid_y = []
def main():
train_dataset = pd.read_csv('train.tsv', delimiter='\t')
test_dataset = pd.read_csv('test.tsv', delimiter='\t')
train, test = load_datasets(
train_dataset.review,
train_dataset.sentiment,
test_dataset.review
)
stats('train', train)
stats('test', test)
def load_datasets(train_dataset_data, train_dataset_scores, test_dataset_data):
train = Dataset()
test = Dataset()
train.raw_data = train_dataset_data
test.raw_data = test_dataset_data
train.sanitized, test.sanitized = sanitize(train_dataset_data, test_dataset_data)
train.tokenized, test.tokenized = tokenize(train.sanitized, test.sanitized)
train.padded, test.padded = pad(train.tokenized, test.tokenized)
train.x, train.valid_x, train.y, train.valid_y = split(train.padded, train_dataset_scores)
return (train, test)
def sanitize(train_data, test_data):
train_sanitized = [sanitize_text(text) for text in train_data]
test_sanitized = [sanitize_text(text) for text in test_data]
print('::: SANITIZED :::')
sys.stdout.flush()
return (train_sanitized, test_sanitized)
def tokenize(train_sanitized, test_sanitized):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(train_sanitized + test_sanitized)
train_tokenized = tokenizer.texts_to_sequences(train_sanitized)
test_tokenized = tokenizer.texts_to_sequences(test_sanitized)
print('::: TOKENIZED :::')
sys.stdout.flush()
return (train_tokenized, test_tokenized)
def pad(train_tokenized, test_tokenized, maxlen=200):
train_padded = pad_sequences(train_tokenized, maxlen=maxlen)
test_padded = pad_sequences(test_tokenized, maxlen=maxlen)
print('::: PADDED :::')
sys.stdout.flush()
return (train_padded, test_padded)
def split(train_padded, train_scores, test_size=0.15, random_state=2):
train_x, train_valid_x, train_y, train_valid_y = train_test_split(
train_padded,
train_scores,
test_size=test_size,
random_state=random_state
)
print('::: SPLIT :::')
sys.stdout.flush()
return (train_x, train_valid_x, train_y, train_valid_y)
def stats(name, dataset):
if len(dataset.raw_data) > 0:
print('\n')
print(name + ' raw_data: ' + str(len(dataset.raw_data)))
print('example length: ' + str(len(dataset.raw_data[0].split(' '))))
print('******* EXAMPLE ********')
print(dataset.raw_data[0])
if len(dataset.sanitized) > 0:
print('\n')
print(name + ' sanitized: ' + str(len(dataset.sanitized)))
print('example length: ' + str(len(dataset.sanitized[0].split(' '))))
print('******* EXAMPLE ********')
print(dataset.sanitized[0])
if len(dataset.tokenized) > 0:
print('\n')
print(name + ' tokenized: ' + str(len(dataset.tokenized)))
print('example length: ' + str(len(dataset.tokenized[0])))
print('******* EXAMPLE ********')
print(dataset.tokenized[0])
if len(dataset.padded) > 0:
print('\n')
print(name + ' padded: ' + str(len(dataset.padded)))
print('example length: ' + str(len(dataset.padded[0])))
print('******* EXAMPLE ********')
print(dataset.padded[0])
if len(dataset.x) > 0:
print('\n')
print(name + ' x: ' + str(len(dataset.x)))
print('example length: ' + str(len(dataset.x[_.keys(dataset.x)[0]])))
print('******* EXAMPLE ********')
print(dataset.x[_.keys(dataset.x)[0]])
if len(dataset.valid_x) > 0:
print('\n')
print(name + ' valid_x: ' + str(len(dataset.valid_x)))
print('example length: ' + str(len(dataset.valid_x[_.keys(dataset.valid_x)[0]])))
print('******* EXAMPLE ********')
print(dataset.valid_x[_.keys(dataset.valid_x)[0]])
if len(dataset.y) > 0:
print('\n')
print(name + ' y: ' + str(len(dataset.y)))
print('******* EXAMPLE ********')
print(dataset.y[_.keys(dataset.y)[0]])
if len(dataset.valid_y) > 0:
print('\n')
print(name + ' valid_y: ' + str(len(dataset.valid_y)))
print('******* EXAMPLE ********')
print(dataset.valid_y[_.keys(dataset.valid_y)[0]])
def sanitize_text(text):
stops = set(nltk.corpus.stopwords.words('english'))
text = text.lower()
text = re.sub(r'<br />', ' ', text)
text = re.sub(r'[^a-z]', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = ' '.join([w for w in text.split() if not w in stops])
return text
def build_rnn(n_words, embed_size, batch_size, lstm_size, num_layers, dropout, learning_rate, multiple_fc, fc_units):
print('building rnn')
tf.reset_default_graph()
with tf.name_scope('inputs'):
inputs = tf.placeholder(tf.int32, [None, None], name='inputs')
with tf.name_scope('labels'):
labels = tf.placeholder(tf.int32, [None, None], name='labels')
keep_prob = tf.placeholder(tf.float32, [None, None], name='keep_prob')
with tf.name_scope('embeddings'):
embedding = tf.Variable(tf.random_uniform((n_words, embed_size), -1, 1))
embed = tf.nn.embedding_lookup(embedding, inputs)
with tf.name_scope('RNN_layers'):
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
with tf.name_scope('RNN_init_state'):
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.name_scope('RNN_forward'):
outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state)
with tf.name_scope('fully_connected'):
weights = tf.truncated_normal_initializer(stddev=0.1)
biases = tf.zeros_initializer()
dense = tf.contrib.layers.fully_connected(
outputs[:, -1],
num_outputs = fc_units,
activation_fn = tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases
)
dense = tf.contrib.layers.dropout(dense, keep_prob)
if multiple_fc == True:
dense = tf.contrib.layers.fully_connected(
dense,
num_outputs = fc_units,
activation_fn = tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases
)
dense = tf.contrib.layers.dropout(dense, keep_prob)
with tf.name_scope('predictions'):
predictions = tf.contrib.layers.fully_connected(
dense,
num_outputs = 1,
activation_fn=tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases
)
tf.summary.histogram('predictions', predictions)
with tf.name_scope('cost'):
cost = tf.losses.mean_squared_error(labels, predictions)
tf.summary.scalar('cost', cost)
with tf.name_scope('train'):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
with tf.name_scope('accuracy'):
correct_pred = tf.equal(
tf.cast(tf.round(predictions), tf.int32),
labels
)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
export_nodes = [
'inputs', 'labels', 'keep_prob','initial_state',
'final_state','accuracy', 'predictions', 'cost',
'optimizer', 'merged'
]
Graph = namedtuple('Graph', export_nodes)
local_dict = locals()
graph = Graph(*[local_dict[each] for each in export_nodes])
return graph
main()