code: # 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.
  
  """
  
  from __future__ import print_function
  
  import numpy as np
  import zipfile
  import paddle.dataset.common
  import paddle.utils.deprecated as deprecated
  import re
  import random
  import functools
  import six
  import paddle.compat as cpt
  
  __all__ = [
      'train', 'test', 'get_movie_title_dict', 'max_movie_id', 'max_user_id',
      'age_table', 'movie_categories', 'max_job_id', 'user_info', 'movie_info'
  ]
  
  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",
      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",
      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",
      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",
      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",
      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",
      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",
      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",
      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",
      reason="Please use new dataset API which supports paddle.io.DataLoader")
  def fetch():
      paddle.dataset.common.download(URL, "movielens", MD5)
  
  
  if __name__ == '__main__':
      unittest()
  
apis: numpy.random.random numpy.random.seed