wehub-resource-sync
51 lines
1.7 KiB
Python
Executable File
51 lines
1.7 KiB
Python
Executable File
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.distributed import fleet
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cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
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cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
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hash_dim_ = 1000001
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continuous_range_ = range(1, 14)
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categorical_range_ = range(14, 40)
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class CriteoDataset(fleet.MultiSlotDataGenerator):
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def generate_sample(self, line):
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"""
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Read the data line by line and process it as a dictionary
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"""
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def reader():
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"""
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This function needs to be implemented by the user, based on data format
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"""
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features = line.rstrip('\n').split('\t')
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feature_name = []
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sparse_feature = []
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for idx in categorical_range_:
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sparse_feature.append(
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[hash(str(idx) + features[idx]) % hash_dim_]
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)
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for idx in categorical_range_:
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feature_name.append("C" + str(idx - 13))
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yield list(zip(feature_name, sparse_feature))
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return reader
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d = CriteoDataset()
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d.run_from_stdin()
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