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150 lines
4.3 KiB
150 lines
4.3 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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IMDB dataset.
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This module downloads IMDB dataset from
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http://ai.stanford.edu/%7Eamaas/data/sentiment/. This dataset contains a set
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of 25,000 highly polar movie reviews for training, and 25,000 for testing.
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Besides, this module also provides API for building dictionary.
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"""
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import paddle.dataset.common
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import collections
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import tarfile
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import re
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import string
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import six
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__all__ = ['build_dict', 'train', 'test', 'convert']
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URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz'
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MD5 = '7c2ac02c03563afcf9b574c7e56c153a'
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def tokenize(pattern):
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"""
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Read files that match the given pattern. Tokenize and yield each file.
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"""
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with tarfile.open(paddle.dataset.common.download(URL, 'imdb', MD5)) as tarf:
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# Note that we should use tarfile.next(), which does
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# sequential access of member files, other than
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# tarfile.extractfile, which does random access and might
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# destroy hard disks.
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tf = tarf.next()
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while tf != None:
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if bool(pattern.match(tf.name)):
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# newline and punctuations removal and ad-hoc tokenization.
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yield tarf.extractfile(tf).read().rstrip(six.b(
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"\n\r")).translate(
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None, six.b(string.punctuation)).lower().split()
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tf = tarf.next()
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def build_dict(pattern, cutoff):
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"""
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Build a word dictionary from the corpus. Keys of the dictionary are words,
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and values are zero-based IDs of these words.
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"""
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word_freq = collections.defaultdict(int)
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for doc in tokenize(pattern):
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for word in doc:
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word_freq[word] += 1
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# Not sure if we should prune less-frequent words here.
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word_freq = [x for x in six.iteritems(word_freq) if x[1] > cutoff]
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dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
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words, _ = list(zip(*dictionary))
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word_idx = dict(list(zip(words, six.moves.range(len(words)))))
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word_idx['<unk>'] = len(words)
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return word_idx
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def reader_creator(pos_pattern, neg_pattern, word_idx):
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UNK = word_idx['<unk>']
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INS = []
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def load(pattern, out, label):
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for doc in tokenize(pattern):
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out.append(([word_idx.get(w, UNK) for w in doc], label))
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load(pos_pattern, INS, 0)
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load(neg_pattern, INS, 1)
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def reader():
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for doc, label in INS:
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yield doc, label
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return reader
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def train(word_idx):
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"""
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IMDB training set creator.
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It returns a reader creator, each sample in the reader is an zero-based ID
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sequence and label in [0, 1].
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:param word_idx: word dictionary
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:type word_idx: dict
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:return: Training reader creator
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:rtype: callable
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"""
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return reader_creator(
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re.compile("aclImdb/train/pos/.*\.txt$"),
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re.compile("aclImdb/train/neg/.*\.txt$"), word_idx)
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def test(word_idx):
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"""
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IMDB test set creator.
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It returns a reader creator, each sample in the reader is an zero-based ID
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sequence and label in [0, 1].
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:param word_idx: word dictionary
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:type word_idx: dict
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:return: Test reader creator
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:rtype: callable
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"""
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return reader_creator(
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re.compile("aclImdb/test/pos/.*\.txt$"),
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re.compile("aclImdb/test/neg/.*\.txt$"), word_idx)
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def word_dict():
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"""
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Build a word dictionary from the corpus.
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:return: Word dictionary
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:rtype: dict
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"""
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return build_dict(
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re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)
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def fetch():
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paddle.dataset.common.download(URL, 'imdb', MD5)
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def convert(path):
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"""
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Converts dataset to recordio format
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"""
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w = word_dict()
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paddle.dataset.common.convert(path, lambda: train(w), 1000, "imdb_train")
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paddle.dataset.common.convert(path, lambda: test(w), 1000, "imdb_test")
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