reader.py

# Copyright 2015 The TensorFlow 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
#
#     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.
# ==============================================================================
"""Utilities for parsing PTB text files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import os
import sys
import numpy as np

Py3 = sys.version_info[0] == 3


def _read_words(filename):
    data = []
    with open(filename, "r") as f:
        return f.read().decode("utf-8").replace("\n", "<eos>").split()


def _build_vocab(filename):
    data = _read_words(filename)

    counter = collections.Counter(data)
    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

    words, _ = list(zip(*count_pairs))

    print("vocab word num", len(words))
    word_to_id = dict(zip(words, range(len(words))))

    return word_to_id


def _file_to_word_ids(filename, word_to_id):
    data = _read_words(filename)
    return [word_to_id[word] for word in data if word in word_to_id]


def ptb_raw_data(data_path=None):
    """Load PTB raw data from data directory "data_path".

  Reads PTB text files, converts strings to integer ids,
  and performs mini-batching of the inputs.

  The PTB dataset comes from Tomas Mikolov's webpage:

  http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz

  Args:
    data_path: string path to the directory where simple-examples.tgz has
      been extracted.

  Returns:
    tuple (train_data, valid_data, test_data, vocabulary)
    where each of the data objects can be passed to PTBIterator.
  """

    train_path = os.path.join(data_path, "ptb.train.txt")
    #train_path = os.path.join(data_path, "train.fake")
    valid_path = os.path.join(data_path, "ptb.valid.txt")
    test_path = os.path.join(data_path, "ptb.test.txt")

    word_to_id = _build_vocab(train_path)
    train_data = _file_to_word_ids(train_path, word_to_id)
    valid_data = _file_to_word_ids(valid_path, word_to_id)
    test_data = _file_to_word_ids(test_path, word_to_id)
    vocabulary = len(word_to_id)
    return train_data, valid_data, test_data, vocabulary


def get_data_iter(raw_data, batch_size, num_steps):
    data_len = len(raw_data)
    raw_data = np.asarray(raw_data, dtype="int64")

    #print( "raw", raw_data[:20] )

    batch_len = data_len // batch_size

    data = raw_data[0:batch_size * batch_len].reshape((batch_size, batch_len))

    #h = data.reshape( (-1))
    #print( "h", h[:20])

    epoch_size = (batch_len - 1) // num_steps
    for i in range(epoch_size):
        start = i * num_steps
        #print( i * num_steps )
        x = np.copy(data[:, i * num_steps:(i + 1) * num_steps])
        y = np.copy(data[:, i * num_steps + 1:(i + 1) * num_steps + 1])

        yield (x, y)