wehub-resource-sync
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright (c) 2019 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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import logging
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from paddle.distributed import fleet
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logging.basicConfig(
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format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO
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)
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logger = logging.getLogger(__name__)
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class Reader(fleet.MultiSlotDataGenerator):
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def init(self):
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padding = 0
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sparse_slots = "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
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self.sparse_slots = sparse_slots.strip().split(" ")
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self.dense_slots = ["dense_feature"]
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self.dense_slots_shape = [13]
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self.slots = self.sparse_slots + self.dense_slots
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self.slot2index = {}
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self.visit = {}
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for i in range(len(self.slots)):
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self.slot2index[self.slots[i]] = i
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self.visit[self.slots[i]] = False
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self.padding = padding
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logger.info("pipe init success")
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def line_process(self, line):
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line = line.strip().split(" ")
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output = [(i, []) for i in self.slots]
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for i in line:
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slot_feasign = i.split(":")
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slot = slot_feasign[0]
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if slot not in self.slots:
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continue
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if slot in self.sparse_slots:
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feasign = int(slot_feasign[1])
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else:
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feasign = float(slot_feasign[1])
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output[self.slot2index[slot]][1].append(feasign)
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self.visit[slot] = True
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for i in self.visit:
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slot = i
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if not self.visit[slot]:
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if i in self.dense_slots:
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output[self.slot2index[i]][1].extend(
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[self.padding]
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* self.dense_slots_shape[self.slot2index[i]]
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)
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else:
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output[self.slot2index[i]][1].extend([self.padding])
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else:
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self.visit[slot] = False
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return output
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# return [label] + sparse_feature + [dense_feature]
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def generate_sample(self, line):
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r"Dataset Generator"
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def reader():
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output_dict = self.line_process(line)
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# {key, value} dict format: {'labels': [1], 'sparse_slot1': [2, 3], 'sparse_slot2': [4, 5, 6, 8], 'dense_slot': [1,2,3,4]}
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# dict must match static_model.create_feed()
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yield output_dict
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return reader
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if __name__ == "__main__":
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r = Reader()
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r.init()
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r.run_from_stdin()
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