batcher_sentiment.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications Copyright 2017 Abigail See
#
# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""This file contains code to process data into batches"""

import queue
from random import shuffle
from threading import Thread
import time
import numpy as np
import tensorflow as tf
import data
from nltk.tokenize import sent_tokenize
import glob
import codecs
import json
FLAGS = tf.app.flags.FLAGS
class Example(object):
  """Class representing a train/val/test example for text summarization."""

  def __init__(self, review, weight, score, reward,vocab, hps):
    """Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.

    Args:
      article: source text; a string. each token is separated by a single space.
      abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
      vocab: Vocabulary object
      hps: hyperparameters
    """
    self.hps = hps

    # Get ids of special tokens
    start_decoding = vocab.word2id(data.START_DECODING)
    stop_decoding = vocab.word2id(data.STOP_DECODING)

    '''review_sentence = sent_tokenize(review)

    article = review_sentence[0]'''

    article_words = review.split()
    if len(article_words) > hps.max_enc_steps: #:
        article_words = article_words[:hps.max_enc_steps]
    abs_ids = [vocab.word2id(w) for w in
                      article_words]  # list of word ids; OOVs are represented by the id for UNK token
    self.original_review_input = review
    
    if len(review.split())>20 or reward<0.8:
      self.weight = np.zeros((hps.max_enc_steps), dtype=np.int32)
    else:
      self.weight = weight
    
    
    
    
    self.reward = reward
    self.score = score


    # Get the decoder input sequence and target sequence
    #self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
    #self.dec_len = len(self.dec_input)
    self.original_review = review

    self.enc_len = len(article_words)  # store the length after truncation but before padding
    #self.enc_sen_len = [len(sentence_words) for sentence_words in article_words]
    self.enc_input = [vocab.word2id(w) for w  in
                      article_words]  # list of word ids; OOVs are represented by the id for UNK token
    #self.score = score

  def pad_encoder_input(self, max_len, pad_id):
        """Pad the encoder input sequence with pad_id up to max_len."""
        while len(self.enc_input) < max_len:
            self.enc_input.append(pad_id)


class Batch(object):
  """Class representing a minibatch of train/val/test examples for text summarization."""

  def __init__(self, example_list, hps, vocab):
    """Turns the example_list into a Batch object.

    Args:
       example_list: List of Example objects
       hps: hyperparameters
       vocab: Vocabulary object
    """
    self.pad_id = vocab.word2id(data.PAD_TOKEN) # id of the PAD token used to pad sequences

    self.init_encoder_seq(example_list, hps)  # initialize the input to the encoder'''
    #self.init_decoder_seq(example_list, hps) # initialize the input and targets for the decoder
    self.store_orig_strings(example_list) # store the original strings
    #self.score = score


  def init_encoder_seq(self, example_list, hps):

      # print ([ex.enc_len for ex in example_list])

      #max_enc_seq_len = max([ex.enc_len for ex in example_list])

      # Pad the encoder input sequences up to the length of the longest sequence
      for ex in example_list:
          ex.pad_encoder_input(hps.max_enc_steps, self.pad_id)

      # Initialize the numpy arrays
      # Note: our enc_batch can have different length (second dimension) for each batch because we use dynamic_rnn for the encoder.
      self.enc_batch = np.zeros((hps.batch_size, hps.max_enc_steps), dtype=np.int32)
      self.weight = np.zeros((hps.batch_size, hps.max_enc_steps), dtype=np.int32)
      self.enc_lens = np.zeros((hps.batch_size), dtype=np.int32)
      self.reward = np.zeros((hps.batch_size), dtype=np.float32)
      self.score = np.zeros((hps.batch_size),dtype = np.int32)
      self.enc_padding_mask = np.zeros((hps.batch_size,  hps.max_enc_steps), dtype=np.float32)

      # Fill in the numpy arrays
      for i, ex in enumerate(example_list):
          # print (ex.enc_input)
          self.enc_batch[i, :] = ex.enc_input[:]
          self.enc_lens[i] = ex.enc_len
          self.weight[i,:] = ex.weight[:]
          self.reward[i] = ex.reward
          self.score[i] = ex.score
          for j in range(ex.enc_len):
              self.enc_padding_mask[i][j]=1
          #self.reward[i] = ex.reward
      #self.weight = np.ones((hps.batch_size, hps.max_enc_steps), dtype=np.float32)


  def store_orig_strings(self, example_list):
    """Store the original article and abstract strings in the Batch object"""

    self.original_reviews = [ex.original_review for ex in example_list] # list of lists
    '''if FLAGS.run_method == 'auto-encoder':
        self.original_review_inputs = [ex.original_review_input for ex in example_list]  # list of lists'''



class SenBatcher(object):

    def __init__(self, hps,vocab):
        self._vocab = vocab
        self._hps = hps

        self.train_queue = self.fill_example_queue("train/*", mode ="train", filenumber = 643)
        self.test_queue = self.fill_example_queue("test/*",  mode ="test", filenumber = 5)
        #self.valid_transfer_queue_positive = self.fill_example_queue("valid/*", mode="valid", target_score=1)

        #self.train_queue_negetive = self.fill_example_queue(
        #    "train/*", mode="train", target_score = 0)
        #self.valid_queue_negetive = self.fill_example_queue(
        #   "valid/*", mode="valid", target_score = 0)

        #self.valid_transfer_queue_negetive = self.fill_example_queue(
        #    "valid/*", mode="valid", target_score=0)


        #self.test_queue = self.fill_example_queue("/home/xujingjing/code/review_summary/dataset/review_generation_dataset/test/*")
        self.train_batch = self.create_batch(mode="train")
        self.test_batch = self.create_batch(mode="test", shuffleis=False)
        #self.valid_transfer_batch = self.create_batch(mode="valid-transfer", shuffleis=False)
        #train_batch = self.create_bach(mode="train")

    def create_batch(self, mode="train", shuffleis=True):
        all_batch = []

        if mode == "train":
            num_batches = int(len(self.train_queue) / self._hps.batch_size)
            #num_batches_negetive = int(len(self.train_queue_negetive) / self._hps.batch_size)

        elif mode == 'test':
            num_batches = int(len(self.test_queue) / self._hps.batch_size)
            #num_batches_negetive = int(len(self.valid_queue_negetive) / self._hps.batch_size)

        for i in range(0, num_batches):
            batch = []
            if mode == 'train':
                batch += (self.train_queue[i * self._hps.batch_size:i * self._hps.batch_size + self._hps.batch_size])
            elif mode == 'test':
                batch += (self.test_queue[i * self._hps.batch_size:i * self._hps.batch_size + self._hps.batch_size])


            all_batch.append(Batch(batch, self._hps, self._vocab))

        if mode == "train" and shuffleis:
            shuffle(all_batch)

        return all_batch


    def get_batches(self, mode="train"):


        if mode == "train":
            shuffle(self.train_batch)
            return self.train_batch
        elif mode == 'test':
            return self.test_batch





    def fill_example_queue(self, data_path, mode = "test", filenumber=None):

        new_queue =[]

        filelist = glob.glob(data_path)  # get the list of datafiles
        assert filelist, ('Error: Empty filelist at %s' % data_path)  # check filelist isn't empty
        filelist = sorted(filelist)
        if mode == "train":
            filelist = filelist
        if filenumber !=None:
            filelist = filelist[:filenumber]


        for f in filelist:


            reader = codecs.open(f, 'r', 'utf-8')
            while True:
                string_ = reader.readline()
                if not string_: break
                dict_example = json.loads(string_)
                review = dict_example["review"]
                score = dict_example["score"]
                weight = dict_example["weight"]
                reward = dict_example["reward"]
                
                '''if reward < 0.8:
                    continue
                if len(review.split())>20:
                    continue'''
                
                sum = 0 
                num = 0
                for i in range(len(weight)):
                    if weight[i] >= 1.0:
                      break
                    else:
                      sum += weight[i]
                      num += 1
                if num>1:
                  num -= 1
                  sum -= weight[num-1]
                average = 1.0*sum/num
                for i in range(len(weight)):
                    if weight[i] >= 1.0:
                      break
                    else:
                      if weight[i] >= average:
                        weight[i] = 1
                      else:
                        weight[i] = 0
                #dict_example["weight"] = weight
                #print (dict_example)
                      

                example = Example(review, weight, score, reward, self._vocab, self._hps)
                new_queue.append(example)
        return new_queue