preprocess.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.

import os
import numpy
from collections import Counter
import shutil
import pickle


def get_raw_data():
    if not os.path.isdir('raw_data'):
        os.mkdir('raw_data')

    fin = open('train.txt', 'r')
    fout = open('raw_data/part-0', 'w')
    for line_idx, line in enumerate(fin):
        if line_idx % 200000 == 0 and line_idx != 0:
            fout.close()
            cur_part_idx = int(line_idx / 200000)
            fout = open('raw_data/part-' + str(cur_part_idx), 'w')
        fout.write(line)
    fout.close()
    fin.close()


def split_data():
    split_rate_ = 0.9
    dir_train_file_idx_ = 'aid_data/train_file_idx.txt'
    filelist_ = [
        'raw_data/part-%d' % x for x in range(len(os.listdir('raw_data')))
    ]

    if not os.path.exists(dir_train_file_idx_):
        train_file_idx = list(
            numpy.random.choice(
                len(filelist_), int(len(filelist_) * split_rate_), False))
        with open(dir_train_file_idx_, 'w') as fout:
            fout.write(str(train_file_idx))
    else:
        with open(dir_train_file_idx_, 'r') as fin:
            train_file_idx = eval(fin.read())

    for idx in range(len(filelist_)):
        if idx in train_file_idx:
            shutil.move(filelist_[idx], 'train_data')
        else:
            shutil.move(filelist_[idx], 'test_data')


def get_feat_dict():
    freq_ = 10
    dir_feat_dict_ = 'aid_data/feat_dict_' + str(freq_) + '.pkl2'
    continuous_range_ = range(1, 14)
    categorical_range_ = range(14, 40)

    if not os.path.exists(dir_feat_dict_):
        # Count the number of occurrences of discrete features
        feat_cnt = Counter()
        with open('train.txt', 'r') as fin:
            for line_idx, line in enumerate(fin):
                if line_idx % 100000 == 0:
                    print('generating feature dict', line_idx / 45000000)
                features = line.rstrip('\n').split('\t')
                for idx in categorical_range_:
                    if features[idx] == '': continue
                    feat_cnt.update([features[idx]])

        # Only retain discrete features with high frequency 
        dis_feat_set = set()
        for feat, ot in feat_cnt.items():
            if ot >= freq_:
                dis_feat_set.add(feat)

        # Create a dictionary for continuous and discrete features
        feat_dict = {}
        tc = 1
        # Continuous features
        for idx in continuous_range_:
            feat_dict[idx] = tc
            tc += 1
        for feat in dis_feat_set:
            feat_dict[feat] = tc
            tc += 1
        # Save dictionary
        with open(dir_feat_dict_, 'wb') as fout:
            pickle.dump(feat_dict, fout, protocol=2)
        print('args.num_feat ', len(feat_dict) + 1)


if __name__ == '__main__':
    if not os.path.isdir('train_data'):
        os.mkdir('train_data')
    if not os.path.isdir('test_data'):
        os.mkdir('test_data')
    if not os.path.isdir('aid_data'):
        os.mkdir('aid_data')

    get_raw_data()
    split_data()
    get_feat_dict()

    print('Done!')