-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdataanalysis.py
92 lines (85 loc) · 3.65 KB
/
dataanalysis.py
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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
# btc_vin.csv记录了训练集和测试集地址相关的所有输入（inputs）记录，字段解释：
# tx_id:交易id
# tx_id_origin:引用的输出的交易id
# timestamp:时间戳
# number:交易的第几条输入
# vout_num_origin:引用的第几条输出
# address_origin:引用的输出的地址
# value_origin:引用的输出的原始金额
# btc_vout.csv记录了训练集和测试集地址相关的所有输出（outputs）记录，字段解释：
# tx_id:交易id
# value:金额
# address:地址
# timestamp:时间戳
# number:交易的第几条输出
# is_coinbase:是否是挖矿交易
DataSet_DIR = r'./address_classfication_rematch.jdcloud/test.csv'
# DataSet_DIR = r'./address_classfication-dataset.jdcloud/train.csv'
BTC_VIN_DIR = r'./address_classfication-dataset.jdcloud/btc_vin.csv'
BTC_VOUT_DIR = r'./address_classfication-dataset.jdcloud/btc_vout.csv'
n_samples = 0
if n_samples>0:
print('Reading data...')
train_df = pd.read_csv(DataSet_DIR, nrows=n_samples)
print('Loading train dataset completed!')
train_vin_df = pd.read_csv(BTC_VIN_DIR, nrows=n_samples)
print('Loading vin completed!')
train_vout_df = pd.read_csv(BTC_VOUT_DIR, nrows=n_samples)
train_vout_df.to_csv('btc_vout1.csv')
print("Loading vout completed!")
else:
print('Reading data...')
train_df = pd.read_csv(DataSet_DIR)
print('Loading train dataset completed!')
train_vin_df = pd.read_csv(BTC_VIN_DIR)
print('Loading vin completed!')
train_vout_df = pd.read_csv(BTC_VOUT_DIR)
# train_vout_df.to_csv('btc_vout1.csv')
print("Loading vout completed!")
print('train dataset shape', train_df.shape)
print('vin shape', train_vin_df.shape)
print('vout shape', train_vout_df.shape)
for i in range(265001, train_df.shape[0]):
if i%10 == 0: print('iterations', i)
address = train_df.loc[i, 'address']
tmp = train_vin_df[train_vin_df['address_origin'].isin([address])]
# print(tmp)
if len(tmp)>0:
# print('tmp', tmp)
train_df.loc[i, 'in_len'] = len(tmp)
train_df.loc[i, 'number'] = sum(tmp['number'])
train_df.loc[i, 'vout_num_origin'] = sum(tmp['vout_num_origin'])
train_df.loc[i, 'value_origin'] = sum(tmp['value_origin'])
# train_df.loc[i, 'average'] = sum(tmp['value_origin'])/len(tmp)
vout_tmp = train_vout_df[train_vout_df['address'].isin([address])]
if len(vout_tmp) > 0:
# tx_ids = vout_tmp.loc[:, ['tx_id']]
# out_df = train_vout_df[train_vout_df['tx_id'].isin(tx_ids)]
train_df.loc[i, 'out_len'] = len(vout_tmp)
train_df.loc[i, 'out_value'] = sum(vout_tmp['value'])
train_df.loc[i, 'out_number'] = sum(vout_tmp['number'])
train_df.loc[i, 'is_coinbase'] = sum(vout_tmp['is_coinbase'])
if i%1000==0: train_df.to_csv('rematch_test_all2.csv', na_rep=0)
# for j in range(len(tmp)):
# tx_id = tmp.iloc[j]['tx_id']
# tx_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id])]
# if len(tx_tmp)>0:
# print('tx_tmp', tx_tmp)
# train_df[i, 'out_len'] = len(tx_tmp)
# train_df[i, 'out_value'] = tx_tmp['value']
# train_df[i, 'out_number'] = tx_tmp['number']
# train_df[i, 'is_coinbase'] = tx_tmp['is_coinbase']
# else:
# train_df[i, 'out_number'] = 0
#
#
# else:
# train_df.loc[i, 'number'] = 0
# train_df.loc[i, 'total'] = 0
# train_df.loc[i, 'average'] = 0
# train_df.loc[i, 'out_number'] = 0
# train_df.fillna(0)
# train_df.to_csv('processed_train_dataset.csv',na_rep=0)