movielens.py

import zipfile
from common import download
import re
import random
import functools

__all__ = ['train_creator', 'test_creator']


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 = [1, 18, 25, 35, 45, 50, 56].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',
        md5='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_creator = functools.partial(__reader_creator__, is_test=False)
test_creator = functools.partial(__reader_creator__, is_test=True)


def unittest():
    for train_count, _ in enumerate(train_creator()()):
        pass
    for test_count, _ in enumerate(test_creator()()):
        pass

    print train_count, test_count


if __name__ == '__main__':
    unittest()