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movielens.py
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movielens.py
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# 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.
from __future__ import print_function
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)