#  Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
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#   http://www.apache.org/licenses/LICENSE-2.0
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#  distributed under the License is distributed on an "AS IS" BASIS,
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"""
This is an example of using convolutional networks over characters
for DBpedia dataset to predict class from description of an entity.

This model is similar to one described in this paper:
   "Character-level Convolutional Networks for Text Classification"
   http://arxiv.org/abs/1509.01626

and is somewhat alternative to the Lua code from here:
   https://github.com/zhangxiangxiao/Crepe
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from sklearn import metrics
import pandas

import tensorflow as tf
from tensorflow.contrib import learn

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_bool('test_with_fake_data', False,
                         'Test the example code with fake data.')

MAX_DOCUMENT_LENGTH = 100
N_FILTERS = 10
FILTER_SHAPE1 = [20, 256]
FILTER_SHAPE2 = [20, N_FILTERS]
POOLING_WINDOW = 4
POOLING_STRIDE = 2


def char_cnn_model(x, y):
  """Character level convolutional neural network model to predict classes."""
  y = tf.one_hot(y, 15, 1, 0)
  byte_list = tf.reshape(learn.ops.one_hot_matrix(x, 256),
                         [-1, MAX_DOCUMENT_LENGTH, 256, 1])
  with tf.variable_scope('CNN_Layer1'):
    # Apply Convolution filtering on input sequence.
    conv1 = learn.ops.conv2d(byte_list, N_FILTERS,
                             FILTER_SHAPE1, padding='VALID')
    # Add a RELU for non linearity.
    conv1 = tf.nn.relu(conv1)
    # Max pooling across output of Convolution+Relu.
    pool1 = tf.nn.max_pool(conv1, ksize=[1, POOLING_WINDOW, 1, 1],
                           strides=[1, POOLING_STRIDE, 1, 1], padding='SAME')
    # Transpose matrix so that n_filters from convolution becomes width.
    pool1 = tf.transpose(pool1, [0, 1, 3, 2])
  with tf.variable_scope('CNN_Layer2'):
    # Second level of convolution filtering.
    conv2 = learn.ops.conv2d(pool1, N_FILTERS, FILTER_SHAPE2,
        padding='VALID')
    # Max across each filter to get useful features for classification.
    pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])

  # Apply regular WX + B and classification.
  prediction, loss = learn.models.logistic_regression(pool2, y)

  train_op = tf.contrib.layers.optimize_loss(
      loss, tf.contrib.framework.get_global_step(),
      optimizer='Adam', learning_rate=0.01)

  return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op


def main(unused_argv):
  # Prepare training and testing data
  dbpedia = learn.datasets.load_dataset(
      'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data, size='large')
  x_train = pandas.DataFrame(dbpedia.train.data)[1]
  y_train = pandas.Series(dbpedia.train.target)
  x_test = pandas.DataFrame(dbpedia.test.data)[1]
  y_test = pandas.Series(dbpedia.test.target)

  # Process vocabulary
  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
  x_train = np.array(list(char_processor.fit_transform(x_train)))
  x_test = np.array(list(char_processor.transform(x_test)))

  # Build model
  classifier = learn.Estimator(model_fn=char_cnn_model)

  # Train and predict
  classifier.fit(x_train, y_train, steps=100)
  y_predicted = classifier.predict(x_test)
  score = metrics.accuracy_score(y_test, y_predicted['class'])
  print('Accuracy: {0:f}'.format(score))


if __name__ == '__main__':
  tf.app.run()