# 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 paddle.fluid as fluid from paddlerec.core.reader import ReaderBase from paddlerec.core.utils import envs from collections import defaultdict class Reader(ReaderBase): def init(self): self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab") self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab") self.item_len = envs.get_global_env("hyper_parameters.item_len") self.batch_size = envs.get_global_env("hyper_parameters.batch_size") def reader_creator(self): def reader(): user_slot_name = [] for j in range(self.batch_size): user_slot_name.append( [int(np.random.randint(self.user_vocab))]) item_slot_name = np.random.randint( self.item_vocab, size=(self.batch_size, self.item_len)).tolist() length = [self.item_len] * self.batch_size label = np.random.randint( 2, size=(self.batch_size, self.item_len)).tolist() output = [user_slot_name, item_slot_name, length, label] yield output return reader def generate_batch_from_trainfiles(self, files): return fluid.io.batch( self.reader_creator(), batch_size=self.batch_size) def generate_sample(self, line): """ the file is not used """ def reader(): """ This function needs to be implemented by the user, based on data format """ pass return reader