Source code for paddle.dataset.sentiment

# /usr/bin/env python
# -*- coding:utf-8 -*-

# 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.
"""
The script fetch and preprocess movie_reviews data set that provided by NLTK

TODO(yuyang18): Complete dataset.
"""

from __future__ import print_function

import six
import collections
from itertools import chain

import nltk
from nltk.corpus import movie_reviews

import paddle.dataset.common

__all__ = ['train', 'test', 'get_word_dict', 'convert']
NUM_TRAINING_INSTANCES = 1600
NUM_TOTAL_INSTANCES = 2000


def download_data_if_not_yet():
    """
    Download the data set, if the data set is not download.
    """
    try:
        # make sure that nltk can find the data
        if paddle.dataset.common.DATA_HOME not in nltk.data.path:
            nltk.data.path.append(paddle.dataset.common.DATA_HOME)
        movie_reviews.categories()
    except LookupError:
        print("Downloading movie_reviews data set, please wait.....")
        nltk.download(
            'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
        print("Download data set success.....")
        print("Path is " + nltk.data.find('corpora/movie_reviews').path)


[docs]def get_word_dict(): """ Sorted the words by the frequency of words which occur in sample :return: words_freq_sorted """ words_freq_sorted = list() word_freq_dict = collections.defaultdict(int) download_data_if_not_yet() for category in movie_reviews.categories(): for field in movie_reviews.fileids(category): for words in movie_reviews.words(field): word_freq_dict[words] += 1 words_sort_list = list(six.iteritems(word_freq_dict)) words_sort_list.sort(cmp=lambda a, b: b[1] - a[1]) for index, word in enumerate(words_sort_list): words_freq_sorted.append((word[0], index)) return words_freq_sorted
def sort_files(): """ Sorted the sample for cross reading the sample :return: files_list """ files_list = list() neg_file_list = movie_reviews.fileids('neg') pos_file_list = movie_reviews.fileids('pos') files_list = list( chain.from_iterable(list(zip(neg_file_list, pos_file_list)))) return files_list def load_sentiment_data(): """ Load the data set :return: data_set """ data_set = list() download_data_if_not_yet() words_ids = dict(get_word_dict()) for sample_file in sort_files(): words_list = list() category = 0 if 'neg' in sample_file else 1 for word in movie_reviews.words(sample_file): words_list.append(words_ids[word.lower()]) data_set.append((words_list, category)) return data_set def reader_creator(data): """ Reader creator, generate an iterator for data set :param data: train data set or test data set """ for each in data: yield each[0], each[1]
[docs]def train(): """ Default training set reader creator """ data_set = load_sentiment_data() return reader_creator(data_set[0:NUM_TRAINING_INSTANCES])
[docs]def test(): """ Default test set reader creator """ data_set = load_sentiment_data() return reader_creator(data_set[NUM_TRAINING_INSTANCES:])
def fetch(): nltk.download('movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
[docs]def convert(path): """ Converts dataset to recordio format """ paddle.dataset.common.convert(path, train, 1000, "sentiment_train") paddle.dataset.common.convert(path, test, 1000, "sentiment_test")