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140 lines
4.6 KiB
140 lines
4.6 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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Movielens 1-M dataset.
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TODO(yuyang18): Complete comments.
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"""
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import zipfile
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from common import download
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import re
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import random
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import functools
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__all__ = ['train_creator', 'test_creator']
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class MovieInfo(object):
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def __init__(self, index, categories, title):
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self.index = int(index)
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self.categories = categories
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self.title = title
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def value(self):
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return [
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self.index, [CATEGORIES_DICT[c] for c in self.categories],
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[MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
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]
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class UserInfo(object):
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def __init__(self, index, gender, age, job_id):
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self.index = int(index)
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self.is_male = gender == 'M'
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self.age = [1, 18, 25, 35, 45, 50, 56].index(int(age))
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self.job_id = int(job_id)
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def value(self):
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return [self.index, 0 if self.is_male else 1, self.age, self.job_id]
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MOVIE_INFO = None
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MOVIE_TITLE_DICT = None
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CATEGORIES_DICT = None
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USER_INFO = None
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def __initialize_meta_info__():
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fn = download(
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url='http://files.grouplens.org/datasets/movielens/ml-1m.zip',
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md5='c4d9eecfca2ab87c1945afe126590906')
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global MOVIE_INFO
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if MOVIE_INFO is None:
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pattern = re.compile(r'^(.*)\((\d+)\)$')
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with zipfile.ZipFile(file=fn) as package:
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for info in package.infolist():
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assert isinstance(info, zipfile.ZipInfo)
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MOVIE_INFO = dict()
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title_word_set = set()
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categories_set = set()
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with package.open('ml-1m/movies.dat') as movie_file:
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for i, line in enumerate(movie_file):
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movie_id, title, categories = line.strip().split('::')
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categories = categories.split('|')
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for c in categories:
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categories_set.add(c)
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title = pattern.match(title).group(1)
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MOVIE_INFO[int(movie_id)] = MovieInfo(
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index=movie_id, categories=categories, title=title)
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for w in title.split():
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title_word_set.add(w.lower())
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global MOVIE_TITLE_DICT
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MOVIE_TITLE_DICT = dict()
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for i, w in enumerate(title_word_set):
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MOVIE_TITLE_DICT[w] = i
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global CATEGORIES_DICT
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CATEGORIES_DICT = dict()
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for i, c in enumerate(categories_set):
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CATEGORIES_DICT[c] = i
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global USER_INFO
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USER_INFO = dict()
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with package.open('ml-1m/users.dat') as user_file:
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for line in user_file:
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uid, gender, age, job, _ = line.strip().split("::")
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USER_INFO[int(uid)] = UserInfo(
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index=uid, gender=gender, age=age, job_id=job)
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return fn
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def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
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fn = __initialize_meta_info__()
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rand = random.Random(x=rand_seed)
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with zipfile.ZipFile(file=fn) as package:
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with package.open('ml-1m/ratings.dat') as rating:
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for line in rating:
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if (rand.random() < test_ratio) == is_test:
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uid, mov_id, rating, _ = line.strip().split("::")
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uid = int(uid)
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mov_id = int(mov_id)
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rating = float(rating) * 2 - 5.0
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mov = MOVIE_INFO[mov_id]
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usr = USER_INFO[uid]
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yield usr.value() + mov.value() + [[rating]]
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def __reader_creator__(**kwargs):
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return lambda: __reader__(**kwargs)
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train_creator = functools.partial(__reader_creator__, is_test=False)
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test_creator = functools.partial(__reader_creator__, is_test=True)
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def unittest():
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for train_count, _ in enumerate(train_creator()()):
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pass
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for test_count, _ in enumerate(test_creator()()):
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pass
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print train_count, test_count
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if __name__ == '__main__':
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unittest()
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