data_helpers.py

import numpy as np
import re
import itertools
import codecs
from collections import Counter
import jieba

def clean_str(string):
  """
  Tokenization/string cleaning for all datasets except for SST.
  Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
  """
  string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
  string = re.sub(r"\'s", " \'s", string)
  string = re.sub(r"\'ve", " \'ve", string)
  string = re.sub(r"n\'t", " n\'t", string)
  string = re.sub(r"\'re", " \'re", string)
  string = re.sub(r"\'d", " \'d", string)
  string = re.sub(r"\'ll", " \'ll", string)
  string = re.sub(r",", " , ", string)
  string = re.sub(r"!", " ! ", string)
  string = re.sub(r"\(", " \( ", string)
  string = re.sub(r"\)", " \) ", string)
  string = re.sub(r"\?", " \? ", string)
  string = re.sub(r"\s{2,}", " ", string)
  return string.strip().lower()


def load_data_and_labels(pos=None,neg=None):
  """
  Loads MR polarity data from files, splits the data into words and generates labels.
  Returns split sentences and labels.
  """
  # Load data from files
  positive_examples = list(codecs.open("./data/chinese/pos.txt", "r", "utf-8").readlines())
  positive_examples = [[item for item in jieba.cut(s, cut_all=False)] for s in positive_examples]
  negative_examples = list(codecs.open("./data/chinese/neg.txt", "r", "utf-8").readlines())
  negative_examples = [[item for item in jieba.cut(s, cut_all=False)] for s in negative_examples]
  # Split by words
  x_text = positive_examples + negative_examples

  # Generate labels
  positive_labels = [[0, 1] for _ in positive_examples]
  negative_labels = [[1, 0] for _ in negative_examples]
  y = np.concatenate([positive_labels, negative_labels], 0)
  return [x_text, y]




def load_test_data_and_labels(pos=None,neg=None):
  """
  Loads MR polarity data from files, splits the data into words and generates labels.
  Returns split sentences and labels.
  tf.flags.DEFINE_string("positive_data_file", "./data/test_text/pos.txt", "Data source for the positive data.")
  tf.flags.DEFINE_string("negative_data_file", "./data/test_text/neg.txt", "Data source for the negative data.")
  """
  # Load data from files
  positive_examples = list(codecs.open("./data/test_text/pos.txt", "r", "utf-8").readlines())
  positive_examples = [[item for item in jieba.cut(s, cut_all=False)] for s in positive_examples]
  negative_examples = list(codecs.open("./data/test_text/neg.txt", "r", "utf-8").readlines())
  negative_examples = [[item for item in jieba.cut(s, cut_all=False)] for s in negative_examples]
  # Split by words
  x_text = positive_examples + negative_examples

  # Generate labels
  positive_labels = [[0, 1] for _ in positive_examples]
  negative_labels = [[1, 0] for _ in negative_examples]
  y = np.concatenate([positive_labels, negative_labels], 0)
  return [x_text, y]

def pad_sentences(sentences, padding_word="<PAD/>"):
  """
  Pads all sentences to the same length. The length is defined by the longest sentence.
  Returns padded sentences.
  """
  sequence_length = max(len(x) for x in sentences)
  padded_sentences = []
  for i in range(len(sentences)):
    sentence = sentences[i]
    num_padding = sequence_length - len(sentence)
    new_sentence = sentence + [padding_word] * num_padding
    padded_sentences.append(new_sentence)
  return padded_sentences


def build_vocab(sentences):
  """
  Builds a vocabulary mapping from word to index based on the sentences.
  Returns vocabulary mapping and inverse vocabulary mapping.
  """
  # Build vocabulary
  word_counts = Counter(itertools.chain(*sentences))
  # Mapping from index to word
  vocabulary_inv = [x[0] for x in word_counts.most_common()]
  # Mapping from word to index
  vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
  return [vocabulary, vocabulary_inv]


def build_input_data(sentences, labels, vocabulary):
  """
  Maps sentencs and labels to vectors based on a vocabulary.
  """
  x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])
  y = np.array(labels)
  return [x, y]


def load_data():
  """
  Loads and preprocessed data for the MR dataset.
  Returns input vectors, labels, vocabulary, and inverse vocabulary.
  """
  # Load and preprocess data
  sentences, labels = load_data_and_labels()
  sentences_padded = pad_sentences(sentences)
  vocabulary, vocabulary_inv = build_vocab(sentences_padded)
  x, y = build_input_data(sentences_padded, labels, vocabulary)
  return [x, y, vocabulary, vocabulary_inv]


def batch_iter(data, batch_size, num_epochs):
  """
  Generates a batch iterator for a dataset.
  """
  data = np.array(data)
  data_size = len(data)
  num_batches_per_epoch = int(len(data)/batch_size) + 1
  for epoch in range(num_epochs):
    # Shuffle the data at each epoch
    shuffle_indices = np.random.permutation(np.arange(data_size))
    shuffled_data = data[shuffle_indices]
    for batch_num in range(num_batches_per_epoch):
      start_index = batch_num * batch_size
      end_index = min((batch_num + 1) * batch_size, data_size)
      yield shuffled_data[start_index:end_index]