imdb.py

# Copyright (c) 2016 PaddlePaddle 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.
"""
IMDB dataset.

This module download IMDB dataset from
http://ai.stanford.edu/%7Eamaas/data/sentiment/, which contains a set of 25,000
highly polar movie reviews for training, and 25,000 for testing. Besides, this
module also provides API for build dictionary and parse train set and test set
into paddle reader creators.
"""

import paddle.v2.dataset.common
import collections
import tarfile
import Queue
import re
import string
import threading

__all__ = ['build_dict', 'train', 'test']

URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz'
MD5 = '7c2ac02c03563afcf9b574c7e56c153a'


def tokenize(pattern):
    """
    Read files that match pattern.  Tokenize and yield each file.
    """

    with tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb',
                                                        MD5)) as tarf:
        # Note that we should use tarfile.next(), which does
        # sequential access of member files, other than
        # tarfile.extractfile, which does random access and might
        # destroy hard disks.
        tf = tarf.next()
        while tf != None:
            if bool(pattern.match(tf.name)):
                # newline and punctuations removal and ad-hoc tokenization.
                yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
                    None, string.punctuation).lower().split()
            tf = tarf.next()


def build_dict(pattern, cutoff):
    """
    Build a word dictionary, the key is word, and the value is index.
    """
    word_freq = collections.defaultdict(int)
    for doc in tokenize(pattern):
        for word in doc:
            word_freq[word] += 1

    # Not sure if we should prune less-frequent words here.
    word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())

    dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
    words, _ = list(zip(*dictionary))
    word_idx = dict(zip(words, xrange(len(words))))
    word_idx['<unk>'] = len(words)
    return word_idx


def reader_creator(pos_pattern, neg_pattern, word_idx, buffer_size):
    UNK = word_idx['<unk>']

    qs = [Queue.Queue(maxsize=buffer_size), Queue.Queue(maxsize=buffer_size)]

    def load(pattern, queue):
        for doc in tokenize(pattern):
            queue.put(doc)
        queue.put(None)

    def reader():
        # Creates two threads that loads positive and negative samples
        # into qs.
        t0 = threading.Thread(
            target=load, args=(
                pos_pattern,
                qs[0], ))
        t0.daemon = True
        t0.start()

        t1 = threading.Thread(
            target=load, args=(
                neg_pattern,
                qs[1], ))
        t1.daemon = True
        t1.start()

        # Read alternatively from qs[0] and qs[1].
        i = 0
        doc = qs[i].get()
        while doc != None:
            yield [word_idx.get(w, UNK) for w in doc], i % 2
            i += 1
            doc = qs[i % 2].get()

        # If any queue is empty, reads from the other queue.
        i += 1
        doc = qs[i % 2].get()
        while doc != None:
            yield [word_idx.get(w, UNK) for w in doc], i % 2
            doc = qs[i % 2].get()

    return reader()


def train(word_idx):
    """
    IMDB train set creator.

    It returns a reader creator, each sample in the reader is an index
    sequence and label in [0, 1].

    :param word_idx: word dictionary
    :type word_idx: dict
    :return: Train reader creator
    :rtype: callable
    """
    return reader_creator(
        re.compile("aclImdb/train/pos/.*\.txt$"),
        re.compile("aclImdb/train/neg/.*\.txt$"), word_idx, 1000)


def test(word_idx):
    """
    IMDB test set creator.

    It returns a reader creator, each sample in the reader is an index
    sequence and label in [0, 1].

    :param word_idx: word dictionary
    :type word_idx: dict
    :return: Test reader creator
    :rtype: callable
    """
    return reader_creator(
        re.compile("aclImdb/test/pos/.*\.txt$"),
        re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)


def word_dict():
    """
    Build word dictionary.

    :return: Word dictionary
    :rtype: dict
    """
    return build_dict(
        re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)


def fetch():
    paddle.v2.dataset.common.download(URL, 'imdb', MD5)