Textual entailment training using TensorFlow.

## entailment_train.py
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import urllib
import sys
import os
import zipfile

glove_vectors_file = "glove.6B.50d.txt"

snli_dev_file = "snli_1.0_dev.txt"
snli_full_dataset_file = "snli_1.0_train.txt"

glove_wordmap = {}
with open(glove_vectors_file, "r", encoding="utf-8") as glove:
    for line in glove:
        name, vector = tuple(line.split(" ", 1))
        glove_wordmap[name] = np.fromstring(vector, sep=" ")

def sentence2sequence(sentence):
    tokens = sentence.lower().split(" ")
    rows = []
    words = []
    #Greedy search for tokens
    for token in tokens:
        i = len(token)
        while len(token) > 0 and i > 0:
            word = token[:i]
            if word in glove_wordmap:
                rows.append(glove_wordmap[word])
                words.append(word)
                token = token[i:]
                i = len(token)
            else:
                i = i-1
    return rows, words

rnn_size = 64
rnn = tf.contrib.rnn.BasicRNNCell(rnn_size)

#Constants setup
max_hypothesis_length, max_evidence_length = 30, 30
batch_size, vector_size, hidden_size = 128, 50, 64

lstm_size = hidden_size

weight_decay = 0.0001

learning_rate = 1

input_p, output_p = 0.5, 0.5

training_iterations_count = 100000

display_step = 10

def score_setup(row):
    convert_dict = {
      'entailment': 0,
      'neutral': 1,
      'contradiction': 2
    }
    score = np.zeros((3,))
    for x in range(1,6):
        tag = row["label"+str(x)]
        if tag in convert_dict: score[convert_dict[tag]] += 1
    return score / (1.0*np.sum(score))

def fit_to_size(matrix, shape):
    res = np.zeros(shape)
    slices = [slice(0,min(dim,shape[e])) for e, dim in enumerate(matrix.shape)]
    res[slices] = matrix[slices]
    return res

def split_data_into_scores():
    import csv
    with open(snli_dev_file,"r") as data:
        train = csv.DictReader(data, delimiter='\t')
        evi_sentences = []
        hyp_sentences = []
        labels = []
        scores = []
        for row in train:
            hyp_sentences.append(np.vstack(
                    sentence2sequence(row["sentence1"].lower())[0]))
            evi_sentences.append(np.vstack(
                    sentence2sequence(row["sentence2"].lower())[0]))
            labels.append(row["gold_label"])
            scores.append(score_setup(row))

        hyp_sentences = np.stack([fit_to_size(x, (max_hypothesis_length, vector_size))
                          for x in hyp_sentences])
        evi_sentences = np.stack([fit_to_size(x, (max_evidence_length, vector_size))
                          for x in evi_sentences])

        return (hyp_sentences, evi_sentences), labels, np.array(scores)

data_feature_list, correct_values, correct_scores = split_data_into_scores()

l_h, l_e = max_hypothesis_length, max_evidence_length
N, D, H = batch_size, vector_size, hidden_size
l_seq = l_h + l_e

tf.reset_default_graph()

lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)

lstm_drop =  tf.contrib.rnn.DropoutWrapper(lstm, input_p, output_p)

# N: The number of elements in each of our batches,
#   which we use to train subsets of data for efficiency's sake.
# l_h: The maximum length of a hypothesis, or the second sentence.  This is
#   used because training an RNN is extraordinarily difficult without
#   rolling it out to a fixed length.
# l_e: The maximum length of evidence, the first sentence.  This is used
#   because training an RNN is extraordinarily difficult without
#   rolling it out to a fixed length.
# D: The size of our used GloVe or other vectors.
hyp = tf.placeholder(tf.float32, [N, l_h, D], 'hypothesis')
evi = tf.placeholder(tf.float32, [N, l_e, D], 'evidence')
y = tf.placeholder(tf.float32, [N, 3], 'label')
# hyp: Where the hypotheses will be stored during training.
# evi: Where the evidences will be stored during training.
# y: Where correct scores will be stored during training.

# lstm_size: the size of the gates in the LSTM,
#    as in the first LSTM layer's initialization.
lstm_back = tf.contrib.rnn.BasicLSTMCell(lstm_size)
# lstm_back:  The LSTM used for looking backwards
#   through the sentences, similar to lstm.

# input_p: the probability that inputs to the LSTM will be retained at each
#   iteration of dropout.
# output_p: the probability that outputs from the LSTM will be retained at
#   each iteration of dropout.
lstm_drop_back = tf.contrib.rnn.DropoutWrapper(lstm_back, input_p, output_p)
# lstm_drop_back:  A dropout wrapper for lstm_back, like lstm_drop.


fc_initializer = tf.random_normal_initializer(stddev=0.1)
# fc_initializer: initial values for the fully connected layer's weights.
# hidden_size: the size of the outputs from each lstm layer.
#   Multiplied by 2 to account for the two LSTMs.
fc_weight = tf.get_variable('fc_weight', [2*hidden_size, 3],
                            initializer = fc_initializer)
# fc_weight: Storage for the fully connected layer's weights.
fc_bias = tf.get_variable('bias', [3])
# fc_bias: Storage for the fully connected layer's bias.

# tf.GraphKeys.REGULARIZATION_LOSSES:  A key to a collection in the graph
#   designated for losses due to regularization.
#   In this case, this portion of loss is regularization on the weights
#   for the fully connected layer.
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
                     tf.nn.l2_loss(fc_weight))

x = tf.concat([hyp, evi], 1) # N, (Lh+Le), d
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2]) # (Le+Lh), N, d
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(x, [-1, vector_size]) # (Le+Lh)*N, d
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(x, l_seq,)

# x: the inputs to the bidirectional_rnn


# tf.contrib.rnn.static_bidirectional_rnn: Runs the input through
#   two recurrent networks, one that runs the inputs forward and one
#   that runs the inputs in reversed order, combining the outputs.
rnn_outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm, lstm_back,
                                                            x, dtype=tf.float32)
# rnn_outputs: the list of LSTM outputs, as a list.
#   What we want is the latest output, rnn_outputs[-1]

classification_scores = tf.matmul(rnn_outputs[-1], fc_weight) + fc_bias
# The scores are relative certainties for how likely the output matches
#   a certain entailment:
#     0: Positive entailment
#     1: Neutral entailment
#     2: Negative entailment

with tf.variable_scope('Accuracy'):
    predicts = tf.cast(tf.argmax(classification_scores, 1), 'int32')
    y_label = tf.cast(tf.argmax(y, 1), 'int32')
    corrects = tf.equal(predicts, y_label)
    num_corrects = tf.reduce_sum(tf.cast(corrects, tf.float32))
    accuracy = tf.reduce_mean(tf.cast(corrects, tf.float32))

with tf.variable_scope("loss"):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
        logits = classification_scores, labels = y)
    loss = tf.reduce_mean(cross_entropy)
    total_loss = loss + weight_decay * tf.add_n(
        tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))

optimizer = tf.train.GradientDescentOptimizer(learning_rate)

opt_op = optimizer.minimize(total_loss)

# Initialize variables
init = tf.global_variables_initializer()

# Use TQDM if installed
tqdm_installed = False
try:
    from tqdm import tqdm
    tqdm_installed = True
except:
    pass

# Launch the Tensorflow session
sess = tf.Session()

writer = tf.summary.FileWriter('/log', sess.graph)     # write to file
merge_op = tf.summary.merge_all()                       # operation to merge all summary

sess.run(init)

# training_iterations_count: The number of data pieces to train on in total
# batch_size: The number of data pieces per batch
training_iterations = range(0,training_iterations_count,batch_size)
if tqdm_installed:
    # Add a progress bar if TQDM is installed
    training_iterations = tqdm(training_iterations)

for i in training_iterations:

    # Select indices for a random data subset
    batch = np.random.randint(data_feature_list[0].shape[0], size=batch_size)

    # Use the selected subset indices to initialize the graph's
    #   placeholder values
    hyps, evis, ys = (data_feature_list[0][batch,:],
                      data_feature_list[1][batch,:],
                      correct_scores[batch])

    # Run the optimization with these initialized values
    r = sess.run([opt_op], feed_dict={hyp: hyps, evi: evis, y: ys})
    # display_step: how often the accuracy and loss should
    #   be tested and displayed.
    if (i/batch_size) % display_step == 0:
        # Calculate batch accuracy
        acc = sess.run(accuracy, feed_dict={hyp: hyps, evi: evis, y: ys})
        # Calculate batch loss
        tmp_loss = sess.run(loss, feed_dict={hyp: hyps, evi: evis, y: ys})
        # Display results
        print("Iter " + str(i/batch_size) + ", Minibatch Loss= " + \
              "{:.6f}".format(tmp_loss) + ", Training Accuracy= " + \
              "{:.5f}".format(acc))
    summary = tf.Summary(value=[tf.Summary.Value(tag="Accuracy",
                                                     simple_value=acc)])
    writer.add_summary(summary, i)

evidences = ["Janos and Jade both were at the scene of the car crash."]

hypotheses = ["Multiple people saw the accident."]

sentence1 = [fit_to_size(np.vstack(sentence2sequence(evidence)[0]),
                         (30, 50)) for evidence in evidences]

sentence2 = [fit_to_size(np.vstack(sentence2sequence(hypothesis)[0]),
                         (30,50)) for hypothesis in hypotheses]

prediction = sess.run(classification_scores, feed_dict={hyp: (sentence1 * N),
                                                        evi: (sentence2 * N),
                                                        y: [[0,0,0]]*N})
print(["Positive", "Neutral", "Negative"][np.argmax(prediction[0])]+
      " entailment")

sess.close()