import zipfile from common import download import re import random import functools __all__ = [ 'train', 'test', 'get_movie_title_dict', 'max_movie_id', 'max_user_id', 'age_table' ] age_table = [1, 18, 25, 35, 45, 50, 56] class MovieInfo(object): def __init__(self, index, categories, title): self.index = int(index) self.categories = categories self.title = title def value(self): return [ self.index, [CATEGORIES_DICT[c] for c in self.categories], [MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()] ] class UserInfo(object): def __init__(self, index, gender, age, job_id): self.index = int(index) self.is_male = gender == 'M' self.age = age_table.index(int(age)) self.job_id = int(job_id) def value(self): return [self.index, 0 if self.is_male else 1, self.age, self.job_id] MOVIE_INFO = None MOVIE_TITLE_DICT = None CATEGORIES_DICT = None USER_INFO = None def __initialize_meta_info__(): fn = download( url='http://files.grouplens.org/datasets/movielens/ml-1m.zip', module_name='movielens', md5sum='c4d9eecfca2ab87c1945afe126590906') global MOVIE_INFO if MOVIE_INFO is None: pattern = re.compile(r'^(.*)\((\d+)\)$') with zipfile.ZipFile(file=fn) as package: for info in package.infolist(): assert isinstance(info, zipfile.ZipInfo) MOVIE_INFO = dict() title_word_set = set() categories_set = set() with package.open('ml-1m/movies.dat') as movie_file: for i, line in enumerate(movie_file): movie_id, title, categories = line.strip().split('::') categories = categories.split('|') for c in categories: categories_set.add(c) title = pattern.match(title).group(1) MOVIE_INFO[int(movie_id)] = MovieInfo( index=movie_id, categories=categories, title=title) for w in title.split(): title_word_set.add(w.lower()) global MOVIE_TITLE_DICT MOVIE_TITLE_DICT = dict() for i, w in enumerate(title_word_set): MOVIE_TITLE_DICT[w] = i global CATEGORIES_DICT CATEGORIES_DICT = dict() for i, c in enumerate(categories_set): CATEGORIES_DICT[c] = i global USER_INFO USER_INFO = dict() with package.open('ml-1m/users.dat') as user_file: for line in user_file: uid, gender, age, job, _ = line.strip().split("::") USER_INFO[int(uid)] = UserInfo( index=uid, gender=gender, age=age, job_id=job) return fn def __reader__(rand_seed=0, test_ratio=0.1, is_test=False): fn = __initialize_meta_info__() rand = random.Random(x=rand_seed) with zipfile.ZipFile(file=fn) as package: with package.open('ml-1m/ratings.dat') as rating: for line in rating: if (rand.random() < test_ratio) == is_test: uid, mov_id, rating, _ = line.strip().split("::") uid = int(uid) mov_id = int(mov_id) rating = float(rating) * 2 - 5.0 mov = MOVIE_INFO[mov_id] usr = USER_INFO[uid] yield usr.value() + mov.value() + [[rating]] def __reader_creator__(**kwargs): return lambda: __reader__(**kwargs) train = functools.partial(__reader_creator__, is_test=False) test = functools.partial(__reader_creator__, is_test=True) def get_movie_title_dict(): __initialize_meta_info__() return MOVIE_TITLE_DICT def __max_index_info__(a, b): if a.index > b.index: return a else: return b def max_movie_id(): __initialize_meta_info__() return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index def max_user_id(): __initialize_meta_info__() return reduce(__max_index_info__, USER_INFO.viewvalues()).index def unittest(): for train_count, _ in enumerate(train()()): pass for test_count, _ in enumerate(test()()): pass print train_count, test_count if __name__ == '__main__': unittest()