Yu Yang
8 years ago
commit
f7a06f17d8
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# /usr/bin/env python
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# -*- coding:utf-8 -*-
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# 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: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
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"""
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import paddle.v2.dataset.common
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import tarfile
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import Queue
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import re
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import string
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import threading
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__all__ = ['build_dict', 'train', 'test']
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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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# Read files that match pattern. Tokenize and yield each file.
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def tokenize(pattern):
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with tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb',
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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("\n\r").translate(
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None, string.punctuation).lower().split()
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tf = tarf.next()
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def build_dict(pattern, cutoff):
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word_freq = {}
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for doc in tokenize(pattern):
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for word in doc:
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paddle.v2.dataset.common.dict_add(word_freq, word)
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# Not sure if we should prune less-frequent words here.
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word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
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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(zip(words, xrange(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, buffer_size):
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UNK = word_idx['<unk>']
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qs = [Queue.Queue(maxsize=buffer_size), Queue.Queue(maxsize=buffer_size)]
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def load(pattern, queue):
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for doc in tokenize(pattern):
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queue.put(doc)
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queue.put(None)
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def reader():
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# Creates two threads that loads positive and negative samples
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# into qs.
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t0 = threading.Thread(
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target=load, args=(
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pos_pattern,
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qs[0], ))
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t0.daemon = True
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t0.start()
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t1 = threading.Thread(
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target=load, args=(
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neg_pattern,
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qs[1], ))
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t1.daemon = True
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t1.start()
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# Read alternatively from qs[0] and qs[1].
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i = 0
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doc = qs[i].get()
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while doc != None:
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yield [word_idx.get(w, UNK) for w in doc], i % 2
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i += 1
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doc = qs[i % 2].get()
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# If any queue is empty, reads from the other queue.
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i += 1
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doc = qs[i % 2].get()
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while doc != None:
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yield [word_idx.get(w, UNK) for w in doc], i % 2
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doc = qs[i % 2].get()
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return reader()
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def train(word_idx):
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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, 1000)
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def test(word_idx):
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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, 1000)
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@ -0,0 +1,79 @@
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"""
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imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
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"""
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import paddle.v2.dataset.common
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import tarfile
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__all__ = ['train', 'test']
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URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz'
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MD5 = '30177ea32e27c525793142b6bf2c8e2d'
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def word_count(f, word_freq=None):
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add = paddle.v2.dataset.common.dict_add
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if word_freq == None:
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word_freq = {}
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for l in f:
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for w in l.strip().split():
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add(word_freq, w)
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add(word_freq, '<s>')
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add(word_freq, '<e>')
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return word_freq
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def build_dict(train_filename, test_filename):
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with tarfile.open(
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paddle.v2.dataset.common.download(
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paddle.v2.dataset.imikolov.URL, 'imikolov',
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paddle.v2.dataset.imikolov.MD5)) as tf:
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trainf = tf.extractfile(train_filename)
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testf = tf.extractfile(test_filename)
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word_freq = word_count(testf, word_count(trainf))
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TYPO_FREQ = 50
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word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
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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(zip(words, xrange(len(words))))
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word_idx['<unk>'] = len(words)
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return word_idx
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word_idx = {}
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def reader_creator(filename, n):
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global word_idx
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if len(word_idx) == 0:
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word_idx = build_dict('./simple-examples/data/ptb.train.txt',
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'./simple-examples/data/ptb.valid.txt')
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def reader():
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with tarfile.open(
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paddle.v2.dataset.common.download(
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paddle.v2.dataset.imikolov.URL, 'imikolov',
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paddle.v2.dataset.imikolov.MD5)) as tf:
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f = tf.extractfile(filename)
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UNK = word_idx['<unk>']
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for l in f:
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l = ['<s>'] + l.strip().split() + ['<e>']
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if len(l) >= n:
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l = [word_idx.get(w, UNK) for w in l]
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for i in range(n, len(l) + 1):
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yield tuple(l[i - n:i])
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return reader
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def train(n):
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return reader_creator('./simple-examples/data/ptb.train.txt', n)
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def test(n):
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return reader_creator('./simple-examples/data/ptb.valid.txt', n)
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@ -0,0 +1,43 @@
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import paddle.v2.dataset.imdb
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import unittest
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import re
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TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$")
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TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$")
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TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$")
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TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$")
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TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$")
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TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$")
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class TestIMDB(unittest.TestCase):
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word_idx = None
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def test_build_dict(self):
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if self.word_idx == None:
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self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
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150)
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self.assertEqual(len(self.word_idx), 7036)
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def check_dataset(self, dataset, expected_size):
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if self.word_idx == None:
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self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
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150)
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sum = 0
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for l in dataset(self.word_idx):
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self.assertEqual(l[1], sum % 2)
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sum += 1
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self.assertEqual(sum, expected_size)
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def test_train(self):
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self.check_dataset(paddle.v2.dataset.imdb.train, 25000)
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def test_test(self):
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self.check_dataset(paddle.v2.dataset.imdb.test, 25000)
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,20 @@
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import paddle.v2.dataset.imikolov
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import unittest
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class TestMikolov(unittest.TestCase):
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def check_reader(self, reader, n):
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for l in reader():
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self.assertEqual(len(l), n)
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def test_train(self):
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n = 5
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self.check_reader(paddle.v2.dataset.imikolov.train(n), n)
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def test_test(self):
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n = 5
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self.check_reader(paddle.v2.dataset.imikolov.test(n), n)
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if __name__ == '__main__':
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unittest.main()
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