movielens.py

# 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.
"""
Movielens 1-M dataset.

Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
movies, which was collected by GroupLens Research. This module will download
Movielens 1-M dataset from
http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training
set and test set into paddle reader creators.

"""

import numpy as np
import zipfile
import paddle.dataset.common
import paddle.utils.deprecated as deprecated
import re
import functools
import six
import paddle.compat as cpt

__all__ = []

age_table = [1, 18, 25, 35, 45, 50, 56]

#URL = 'http://files.grouplens.org/datasets/movielens/ml-1m.zip'
URL = 'https://dataset.bj.bcebos.com/movielens%2Fml-1m.zip'
MD5 = 'c4d9eecfca2ab87c1945afe126590906'


class MovieInfo(object):
    """
    Movie id, title and categories information are stored in MovieInfo.
    """

    def __init__(self, index, categories, title):
        self.index = int(index)
        self.categories = categories
        self.title = title

    def value(self):
        """
        Get information from a movie.
        """
        return [
            self.index, [CATEGORIES_DICT[c] for c in self.categories],
            [MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
        ]

    def __str__(self):
        return "<MovieInfo id(%d), title(%s), categories(%s)>" % (
            self.index, self.title, self.categories)

    def __repr__(self):
        return self.__str__()


class UserInfo(object):
    """
    User id, gender, age, and job information are stored in UserInfo.
    """

    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):
        """
        Get information from a user.
        """
        return [self.index, 0 if self.is_male else 1, self.age, self.job_id]

    def __str__(self):
        return "<UserInfo id(%d), gender(%s), age(%d), job(%d)>" % (
            self.index, "M" if self.is_male else "F", age_table[self.age],
            self.job_id)

    def __repr__(self):
        return str(self)


MOVIE_INFO = None
MOVIE_TITLE_DICT = None
CATEGORIES_DICT = None
USER_INFO = None


def __initialize_meta_info__():
    fn = paddle.dataset.common.download(URL, "movielens", MD5)
    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):
                        line = cpt.to_text(line, encoding='latin')
                        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:
                        line = cpt.to_text(line, encoding='latin')
                        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__()
    np.random.seed(rand_seed)
    with zipfile.ZipFile(file=fn) as package:
        with package.open('ml-1m/ratings.dat') as rating:
            for line in rating:
                line = cpt.to_text(line, encoding='latin')
                if (np.random.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]]


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def __reader_creator__(**kwargs):
    return lambda: __reader__(**kwargs)


train = functools.partial(__reader_creator__, is_test=False)
test = functools.partial(__reader_creator__, is_test=True)


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def get_movie_title_dict():
    """
    Get movie title dictionary.
    """
    __initialize_meta_info__()
    return MOVIE_TITLE_DICT


def __max_index_info__(a, b):
    if a.index > b.index:
        return a
    else:
        return b


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def max_movie_id():
    """
    Get the maximum value of movie id.
    """
    __initialize_meta_info__()
    return six.moves.reduce(__max_index_info__, list(MOVIE_INFO.values())).index


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def max_user_id():
    """
    Get the maximum value of user id.
    """
    __initialize_meta_info__()
    return six.moves.reduce(__max_index_info__, list(USER_INFO.values())).index


def __max_job_id_impl__(a, b):
    if a.job_id > b.job_id:
        return a
    else:
        return b


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def max_job_id():
    """
    Get the maximum value of job id.
    """
    __initialize_meta_info__()
    return six.moves.reduce(__max_job_id_impl__,
                            list(USER_INFO.values())).job_id


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def movie_categories():
    """
    Get movie categories dictionary.
    """
    __initialize_meta_info__()
    return CATEGORIES_DICT


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def user_info():
    """
    Get user info dictionary.
    """
    __initialize_meta_info__()
    return USER_INFO


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def movie_info():
    """
    Get movie info dictionary.
    """
    __initialize_meta_info__()
    return MOVIE_INFO


def unittest():
    for train_count, _ in enumerate(train()()):
        pass
    for test_count, _ in enumerate(test()()):
        pass

    print(train_count, test_count)


@deprecated(
    since="2.0.0",
    update_to="paddle.text.datasets.Movielens",
    level=1,
    reason="Please use new dataset API which supports paddle.io.DataLoader")
def fetch():
    paddle.dataset.common.download(URL, "movielens", MD5)


if __name__ == '__main__':
    unittest()