/
process_public_dataset.py
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/
process_public_dataset.py
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'''
process the Ali-CCP (Alibaba Click and Conversion Prediction) dataset.
https://tianchi.aliyun.com/datalab/dataSet.html?dataId=408
@The author:
Dongbo Xi (xidongbo@meituan.com)
'''
import numpy as np
import joblib
import re
import random
random.seed(2020)
np.random.seed(2020)
data_path = 'data/sample_skeleton_{}.csv'
common_feat_path = 'data/common_features_{}.csv'
enum_path = 'data/ctrcvr_enum.pkl'
write_path = 'data/ctr_cvr'
use_columns = [
'101',
'121',
'122',
'124',
'125',
'126',
'127',
'128',
'129',
'205',
'206',
'207',
'216',
'508',
'509',
'702',
'853',
'301']
class process(object):
def __init__(self):
pass
def process_train(self):
c = 0
common_feat_dict = {}
with open(common_feat_path.format('train'), 'r') as fr:
for line in fr:
line_list = line.strip().split(',')
kv = np.array(re.split('\x01|\x02|\x03', line_list[2]))
key = kv[range(0, len(kv), 3)]
value = kv[range(1, len(kv), 3)]
feat_dict = dict(zip(key, value))
common_feat_dict[line_list[0]] = feat_dict
c += 1
if c % 100000 == 0:
print(c)
print('join feats...')
c = 0
vocabulary = dict(zip(use_columns, [{} for _ in range(len(use_columns))]))
with open(data_path.format('train') + '.tmp', 'w') as fw:
fw.write('click,purchase,' + ','.join(use_columns) + '\n')
with open(data_path.format('train'), 'r') as fr:
for line in fr:
line_list = line.strip().split(',')
if line_list[1] == '0' and line_list[2] == '1':
continue
kv = np.array(re.split('\x01|\x02|\x03', line_list[5]))
key = kv[range(0, len(kv), 3)]
value = kv[range(1, len(kv), 3)]
feat_dict = dict(zip(key, value))
feat_dict.update(common_feat_dict[line_list[3]])
feats = line_list[1:3]
for k in use_columns:
feats.append(feat_dict.get(k, '0'))
fw.write(','.join(feats) + '\n')
for k, v in feat_dict.items():
if k in use_columns:
if v in vocabulary[k]:
vocabulary[k][v] += 1
else:
vocabulary[k][v] = 0
c += 1
if c % 100000 == 0:
print(c)
print('before filter low freq:')
for k, v in vocabulary.items():
print(k + ':' + str(len(v)))
new_vocabulary = dict(
zip(use_columns, [set() for _ in range(len(use_columns))]))
for k, v in vocabulary.items():
for k1, v1 in v.items():
if v1 > 10:
new_vocabulary[k].add(k1)
vocabulary = new_vocabulary
print('after filter low freq:')
for k, v in vocabulary.items():
print(k + ':' + str(len(v)))
joblib.dump(vocabulary, enum_path, compress=3)
print('encode feats...')
vocabulary = joblib.load(enum_path)
feat_map = {}
for feat in use_columns:
feat_map[feat] = dict(
zip(vocabulary[feat], range(1, len(vocabulary[feat]) + 1)))
c = 0
with open(write_path + '.train', 'w') as fw1:
with open(write_path + '.dev', 'w') as fw2:
fw1.write('click,purchase,' + ','.join(use_columns) + '\n')
fw2.write('click,purchase,' + ','.join(use_columns) + '\n')
with open(data_path.format('train') + '.tmp', 'r') as fr:
fr.readline() # remove header
for line in fr:
line_list = line.strip().split(',')
new_line = line_list[:2]
for value, feat in zip(line_list[2:], use_columns):
new_line.append(
str(feat_map[feat].get(value, '0')))
if random.random() >= 0.9:
fw2.write(','.join(new_line) + '\n')
else:
fw1.write(','.join(new_line) + '\n')
c += 1
if c % 100000 == 0:
print(c)
def process_test(self):
c = 0
common_feat_dict = {}
with open(common_feat_path.format('test'), 'r') as fr:
for line in fr:
line_list = line.strip().split(',')
kv = np.array(re.split('\x01|\x02|\x03', line_list[2]))
key = kv[range(0, len(kv), 3)]
value = kv[range(1, len(kv), 3)]
feat_dict = dict(zip(key, value))
common_feat_dict[line_list[0]] = feat_dict
c += 1
if c % 100000 == 0:
print(c)
print('join feats...')
c = 0
with open(data_path.format('test') + '.tmp', 'w') as fw:
fw.write('click,purchase,' + ','.join(use_columns) + '\n')
with open(data_path.format('test'), 'r') as fr:
for line in fr:
line_list = line.strip().split(',')
if line_list[1] == '0' and line_list[2] == '1':
continue
kv = np.array(re.split('\x01|\x02|\x03', line_list[5]))
key = kv[range(0, len(kv), 3)]
value = kv[range(1, len(kv), 3)]
feat_dict = dict(zip(key, value))
feat_dict.update(common_feat_dict[line_list[3]])
feats = line_list[1:3]
for k in use_columns:
feats.append(str(feat_dict.get(k, '0')))
fw.write(','.join(feats) + '\n')
c += 1
if c % 100000 == 0:
print(c)
print('encode feats...')
vocabulary = joblib.load(enum_path)
feat_map = {}
for feat in use_columns:
feat_map[feat] = dict(
zip(vocabulary[feat], range(1, len(vocabulary[feat]) + 1)))
c = 0
with open(write_path + '.test', 'w') as fw:
fw.write('click,purchase,' + ','.join(use_columns) + '\n')
with open(data_path.format('test') + '.tmp', 'r') as fr:
fr.readline() # remove header
for line in fr:
line_list = line.strip().split(',')
new_line = line_list[:2]
for value, feat in zip(line_list[2:], use_columns):
new_line.append(str(feat_map[feat].get(value, '0')))
fw.write(','.join(new_line) + '\n')
c += 1
if c % 100000 == 0:
print(c)
if __name__ == "__main__":
pros = process()
pros.process_train()
pros.process_test()