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reader.py
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# Copyright (c) 2018 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 random
class Dataset:
def __init__(self):
pass
class SyntheticDataset(Dataset):
def __init__(self, sparse_feature_dim, query_slot_num, title_slot_num):
# ids are randomly generated
self.ids_per_slot = 10
self.sparse_feature_dim = sparse_feature_dim
self.query_slot_num = query_slot_num
self.title_slot_num = title_slot_num
self.dataset_size = 10000
def _reader_creator(self, is_train):
def generate_ids(num, space):
return [random.randint(0, space - 1) for i in range(num)]
def reader():
for i in range(self.dataset_size):
query_slots = []
pos_title_slots = []
neg_title_slots = []
for i in range(self.query_slot_num):
qslot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
query_slots.append(qslot)
for i in range(self.title_slot_num):
pt_slot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
pos_title_slots.append(pt_slot)
if is_train:
for i in range(self.title_slot_num):
nt_slot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
neg_title_slots.append(nt_slot)
yield query_slots + pos_title_slots + neg_title_slots
else:
yield query_slots + pos_title_slots
return reader
def train(self):
return self._reader_creator(True)
def valid(self):
return self._reader_creator(True)
def test(self):
return self._reader_creator(False)