criteo_lr_reader.py

#   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

from paddle.io import IterableDataset


class RecDataset(IterableDataset):
    def __init__(self, file_list, config):
        super(RecDataset, self).__init__()
        self.file_list = file_list
        self.init()

    def init(self):
        padding = 0
        self.sparse_slots = ["label", "feat_idx"]
        self.dense_slots = ["feat_value"]
        self.dense_slots_shape = [39]
        self.slots = self.sparse_slots + self.dense_slots
        self.slot2index = {}
        self.visit = {}
        for i in range(len(self.slots)):
            self.slot2index[self.slots[i]] = i
            self.visit[self.slots[i]] = False
        self.padding = padding

    def __iter__(self):
        full_lines = []
        self.data = []
        for file in self.file_list:
            with open(file, "r") as rf:
                for l in rf:
                    line = l.strip().split(" ")

                    output = [(i, []) for i in self.slots]
                    for i in line:
                        slot_feasign = i.split(":")
                        slot = slot_feasign[0]
                        if slot not in self.slots:
                            continue
                        if slot in self.sparse_slots:
                            feasign = int(slot_feasign[1])
                        else:
                            feasign = float(slot_feasign[1])
                        output[self.slot2index[slot]][1].append(feasign)
                        self.visit[slot] = True
                    for i in self.visit:
                        slot = i
                        if not self.visit[slot]:
                            if i in self.dense_slots:
                                output[self.slot2index[i]][1].extend(
                                    [self.padding] *
                                    self.dense_slots_shape[self.slot2index[i]])
                            else:
                                output[self.slot2index[i]][1].extend(
                                    [self.padding])
                        else:
                            self.visit[slot] = False
                    # label, feat_idx, feat_value
                    yield np.array(output[0][1]).astype('int64'), np.array(
                        output[1][1]).astype('int64'), np.array(output[2][
                            1]).astype('float32')