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137 lines
3.8 KiB
137 lines
3.8 KiB
# /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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The script fetch and preprocess movie_reviews data set that provided by NLTK
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TODO(yuyang18): Complete dataset.
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"""
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from __future__ import print_function
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import six
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import collections
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from itertools import chain
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import nltk
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from nltk.corpus import movie_reviews
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import paddle.dataset.common
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__all__ = ['train', 'test', 'get_word_dict']
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NUM_TRAINING_INSTANCES = 1600
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NUM_TOTAL_INSTANCES = 2000
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def download_data_if_not_yet():
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"""
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Download the data set, if the data set is not download.
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"""
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try:
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# make sure that nltk can find the data
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if paddle.dataset.common.DATA_HOME not in nltk.data.path:
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nltk.data.path.append(paddle.dataset.common.DATA_HOME)
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movie_reviews.categories()
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except LookupError:
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print("Downloading movie_reviews data set, please wait.....")
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nltk.download(
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'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
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print("Download data set success.....")
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print("Path is " + nltk.data.find('corpora/movie_reviews').path)
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def get_word_dict():
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"""
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Sorted the words by the frequency of words which occur in sample
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:return:
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words_freq_sorted
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"""
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words_freq_sorted = list()
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word_freq_dict = collections.defaultdict(int)
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download_data_if_not_yet()
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for category in movie_reviews.categories():
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for field in movie_reviews.fileids(category):
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for words in movie_reviews.words(field):
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word_freq_dict[words] += 1
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words_sort_list = list(six.iteritems(word_freq_dict))
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words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
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for index, word in enumerate(words_sort_list):
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words_freq_sorted.append((word[0], index))
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return words_freq_sorted
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def sort_files():
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"""
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Sorted the sample for cross reading the sample
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:return:
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files_list
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"""
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files_list = list()
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neg_file_list = movie_reviews.fileids('neg')
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pos_file_list = movie_reviews.fileids('pos')
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files_list = list(
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chain.from_iterable(list(zip(neg_file_list, pos_file_list))))
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return files_list
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def load_sentiment_data():
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"""
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Load the data set
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:return:
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data_set
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"""
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data_set = list()
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download_data_if_not_yet()
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words_ids = dict(get_word_dict())
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for sample_file in sort_files():
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words_list = list()
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category = 0 if 'neg' in sample_file else 1
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for word in movie_reviews.words(sample_file):
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words_list.append(words_ids[word.lower()])
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data_set.append((words_list, category))
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return data_set
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def reader_creator(data):
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"""
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Reader creator, generate an iterator for data set
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:param data:
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train data set or test data set
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"""
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for each in data:
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yield each[0], each[1]
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def train():
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"""
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Default training set reader creator
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"""
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data_set = load_sentiment_data()
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return reader_creator(data_set[0:NUM_TRAINING_INSTANCES])
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def test():
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"""
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Default test set reader creator
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"""
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data_set = load_sentiment_data()
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return reader_creator(data_set[NUM_TRAINING_INSTANCES:])
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def fetch():
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nltk.download('movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
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