'''
  code for textCNN
'''
import tensorflow as tf
import numpy as np

tf.reset_default_graph()

# Text-CNN Parameter
embedding_size = 2  # n-gram
sequence_length = 3
num_classes = 2  # 0 or 1
filter_sizes = [2, 2, 2]  # n-gram window
num_filters = 3

# 3 words sentences (=sequence_length is 3)
sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
labels = [1, 1, 1, 0, 0, 0]  # 1 is good, 0 is not good.

word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
vocab_size = len(word_dict)


class TextCNN(object):
    def __init__(self):
        self.sequence_length = sequence_length
        self.vocab_size = vocab_size
        self.embedding_size = embedding_size

        inputs = []
        for sen in sentences:
            inputs.append(np.asarray([word_dict[n] for n in sen.split()]))

        outputs = []
        for out in labels:
            outputs.append(np.eye(num_classes)[out])  # ONE-HOT : To using Tensor Softmax Loss function

    def train(self):
        # Model
        # add_placeholders
        X = tf.placeholder(tf.int32, [None, sequence_length])
        Y = tf.placeholder(tf.int32, [None, num_classes])

        #inference
        W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
        embedded_chars = tf.nn.embedding_lookup(W, X)  # [batch_size, sequence_length, embedding_size]
        embedded_chars = tf.expand_dims(embedded_chars,
                                        -1)  # add channel(=1) [batch_size, sequence_length, embedding_size, 1]

        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            filter_shape = [filter_size, embedding_size, 1, num_filters]
            W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1))
            b = tf.Variable(tf.constant(0.1, shape=[num_filters]))

            conv = tf.nn.conv2d(embedded_chars,  # [batch_size, sequence_length, embedding_size, 1]
                                W,  # [filter_size(n-gram window), embedding_size, 1, num_filters(=3)]
                                strides=[1, 1, 1, 1],
                                padding='VALID')
            h = tf.nn.relu(tf.nn.bias_add(conv, b))
            pooled = tf.nn.max_pool(h,
                                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                                    # [batch_size, filter_height, filter_width, channel]
                                    strides=[1, 1, 1, 1],
                                    padding='VALID')
            pooled_outputs.append(
                pooled)  # dim of pooled : [batch_size(=6), output_height(=1), output_width(=1), channel(=1)]

        num_filters_total = num_filters * len(filter_sizes)
        h_pool = tf.concat(pooled_outputs,
                           num_filters)  # h_pool : [batch_size(=6), output_height(=1), output_width(=1), channel(=1) * 3]
        h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])  # [batch_size, ]

        # Model-Training
        Weight = tf.get_variable('W', shape=[num_filters_total, num_classes],
                                 initializer=tf.contrib.layers.xavier_initializer())
        Bias = tf.Variable(tf.constant(0.1, shape=[num_classes]))
        model = tf.nn.xw_plus_b(h_pool_flat, Weight, Bias)
        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
        optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)

        # Model-Predict
        hypothesis = tf.nn.softmax(model)
        predictions = tf.argmax(hypothesis, 1)
        # Training
        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)

        for epoch in range(5000):
            _, loss = sess.run([optimizer, cost], feed_dict={X: inputs, Y: outputs})
            if (epoch + 1) % 1000 == 0:
                print('Epoch:', '%06d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

        # Test
        test_text = 'sorry hate you'
        tests = []
        tests.append(np.asarray([word_dict[n] for n in test_text.split()]))

        predict = sess.run([predictions], feed_dict={X: tests})
        result = predict[0][0]
        if result == 0:
            print(test_text, "is Bad Mean...")
        else:
            print(test_text, "is Good Mean!!")
