movielens.py

#   Copyright (c) 2020 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.

import numpy as np
import zipfile
import re
import random
import functools
import six

import paddle
from paddle.io import Dataset
import paddle.compat as cpt
from paddle.dataset.common import _check_exists_and_download

__all__ = []

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

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, categories_dict, movie_title_dict):
        """
        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)


class Movielens(Dataset):
    """
    Implementation of `Movielens 1-M <https://grouplens.org/datasets/movielens/1m/>`_ dataset.

    Args:
        data_file(str): path to data tar file, can be set None if
            :attr:`download` is True. Default None
        mode(str): 'train' or 'test' mode. Default 'train'.
        test_ratio(float): split ratio for test sample. Default 0.1.
        rand_seed(int): random seed. Default 0.
        download(bool): whether to download dataset automatically if
            :attr:`data_file` is not set. Default True

    Returns:
        Dataset: instance of Movielens 1-M dataset

    Examples:

        .. code-block:: python

            import paddle
            from paddle.text.datasets import Movielens

            class SimpleNet(paddle.nn.Layer):
                def __init__(self):
                    super(SimpleNet, self).__init__()

                def forward(self, category, title, rating):
                    return paddle.sum(category), paddle.sum(title), paddle.sum(rating)


            movielens = Movielens(mode='train')

            for i in range(10):
                category, title, rating = movielens[i][-3:]
                category = paddle.to_tensor(category)
                title = paddle.to_tensor(title)
                rating = paddle.to_tensor(rating)

                model = SimpleNet()
                category, title, rating = model(category, title, rating)
                print(category.numpy().shape, title.numpy().shape, rating.numpy().shape)

    """

    def __init__(self,
                 data_file=None,
                 mode='train',
                 test_ratio=0.1,
                 rand_seed=0,
                 download=True):
        assert mode.lower() in ['train', 'test'], \
            "mode should be 'train', 'test', but got {}".format(mode)
        self.mode = mode.lower()

        self.data_file = data_file
        if self.data_file is None:
            assert download, "data_file is not set and downloading automatically is disabled"
            self.data_file = _check_exists_and_download(data_file, URL, MD5,
                                                        'sentiment', download)

        self.test_ratio = test_ratio
        self.rand_seed = rand_seed

        np.random.seed(rand_seed)
        self._load_meta_info()
        self._load_data()

    def _load_meta_info(self):
        pattern = re.compile(r'^(.*)\((\d+)\)$')
        self.movie_info = dict()
        self.movie_title_dict = dict()
        self.categories_dict = dict()
        self.user_info = dict()
        with zipfile.ZipFile(self.data_file) as package:
            for info in package.infolist():
                assert isinstance(info, zipfile.ZipInfo)
                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)
                        self.movie_info[int(movie_id)] = MovieInfo(
                            index=movie_id, categories=categories, title=title)
                        for w in title.split():
                            title_word_set.add(w.lower())

                for i, w in enumerate(title_word_set):
                    self.movie_title_dict[w] = i

                for i, c in enumerate(categories_set):
                    self.categories_dict[c] = i

                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("::")
                        self.user_info[int(uid)] = UserInfo(index=uid,
                                                            gender=gender,
                                                            age=age,
                                                            job_id=job)

    def _load_data(self):
        self.data = []
        is_test = self.mode == 'test'
        with zipfile.ZipFile(self.data_file) 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() < self.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 = self.movie_info[mov_id]
                        usr = self.user_info[uid]
                        self.data.append(usr.value() + \
                                         mov.value(self.categories_dict, self.movie_title_dict) + \
                                         [[rating]])

    def __getitem__(self, idx):
        data = self.data[idx]
        return tuple([np.array(d) for d in data])

    def __len__(self):
        return len(self.data)