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cluster.py
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# encoding: utf-8
'''
@author: Lingcheng Dai
@contact: [email protected]
@file: cluster.py
@time: 2019/4/22 15:30
'''
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import multiprocessing
# 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_preliminary.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'
#
# def save_dataset1(x):
# print('Reading data...')
# train_df = pd.read_csv(DataSet_DIR)
# print('Loading train dataset completed!')
# train_vin_df = pd.read_csv(BTC_VIN_DIR, nrows=1000)
# print('Loading vin completed!')
# train_vout_df = pd.read_csv(BTC_VOUT_DIR, nrows=1000)
# # train_vout_df.to_csv('btc_vout1.csv')
# print("Loading vout completed!")
# ds = pd.DataFrame(columns=['address', 'label'])
# end = int(train_df.shape[0]*0.25)
# for i in range(end):
# if i%1000 != 0: print(1, 'iterations', i)
# address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
# ds = ds.append({'address':address, 'label':label}, ignore_index=True)
# tmp = train_vin_df[train_vin_df['address_origin'].isin([address])]
# if len(tmp)>0:
# for t in tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# out_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id])]
# if len(out_tmp) > 0:
# ds = ds.append({'address':out_tmp['address'], 'label':label}, ignore_index=True)
#
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id_origin])]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
# if i % 1000 == 0: train_df.to_csv('ds1.csv')
#
# def save_dataset2(x):
# print('Reading data...')
# train_df = pd.read_csv(DataSet_DIR)
# print('Loading train dataset completed!')
# train_vin_df = pd.read_csv(BTC_VIN_DIR, nrows=1000)
# print('Loading vin completed!')
# train_vout_df = pd.read_csv(BTC_VOUT_DIR, nrows=1000)
# # train_vout_df.to_csv('btc_vout1.csv')
# print("Loading vout completed!")
# ds = pd.DataFrame(columns=['address', 'label'])
# for i in range(int(train_df.shape[0]*0.25), int(train_df.shape[0]*0.5)):
# if i%1000 != 0: print('iterations', 2, i)
# address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
# ds = ds.append({'address':address, 'label':label}, ignore_index=True)
# tmp = train_vin_df[train_vin_df['address_origin'].isin([address])]
# if len(tmp)>0:
# for t in tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# out_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id])]
# if len(out_tmp) > 0:
# ds = ds.append({'address':out_tmp['address'], 'label':label}, ignore_index=True)
#
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id_origin])]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
# if i % 1000 == 0: train_df.to_csv('ds2.csv')
#
# def save_dataset3(x):
# print('Reading data...')
# train_df = pd.read_csv(DataSet_DIR)
# print('Loading train dataset completed!')
# train_vin_df = pd.read_csv(BTC_VIN_DIR, nrows=1000)
# print('Loading vin completed!')
# train_vout_df = pd.read_csv(BTC_VOUT_DIR, nrows=1000)
# # train_vout_df.to_csv('btc_vout1.csv')
# print("Loading vout completed!")
# ds = pd.DataFrame(columns=['address', 'label'])
# for i in range(int(train_df.shape[0]*0.5), int(train_df.shape[0]*0.75)):
# if i%1000 != 0: print('iterations', 3, i)
# address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
# ds = ds.append({'address':address, 'label':label}, ignore_index=True)
# tmp = train_vin_df[train_vin_df['address_origin'].isin([address])]
# if len(tmp)>0:
# for t in tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# out_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id])]
# if len(out_tmp) > 0:
# ds = ds.append({'address':out_tmp['address'], 'label':label}, ignore_index=True)
#
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id_origin])]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
# if i % 1000 == 0: train_df.to_csv('ds3.csv')
#
# def save_dataset4(x):
# print('Reading data...')
# train_df = pd.read_csv(DataSet_DIR)
# print('Loading train dataset completed!')
# train_vin_df = pd.read_csv(BTC_VIN_DIR, nrows=1000)
# print('Loading vin completed!')
# train_vout_df = pd.read_csv(BTC_VOUT_DIR, nrows=1000)
# # train_vout_df.to_csv('btc_vout1.csv')
# print("Loading vout completed!")
# ds = pd.DataFrame(columns=['address', 'label'])
# for i in range(int(train_df.shape[0]*0.75), train_df.shape[0]):
# if i%1000 != 0: print('iterations', 4, i)
# address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
# ds = ds.append({'address':address, 'label':label}, ignore_index=True)
# tmp = train_vin_df[train_vin_df['address_origin'].isin([address])]
# if len(tmp)>0:
# for t in tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# out_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id])]
# if len(out_tmp) > 0:
# ds = ds.append({'address':out_tmp['address'], 'label':label}, ignore_index=True)
#
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id'].isin([tx_id_origin])]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
# if i % 1000 == 0: train_df.to_csv('ds4.csv')
if __name__ == "__main__":
# pool = multiprocessing.Pool(processes=4)
# #
# # n_samples = 2000
# # 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, train_df.head())
# # print('vin shape', train_vin_df.shape, train_vin_df.head())
# # print('vout shape', train_vout_df.shape, train_vout_df.head())
#
#
# pool.apply_async(save_dataset1, (1,))
# pool.apply_async(save_dataset2, (2,))
# pool.apply_async(save_dataset3, (3,))
# pool.apply_async(save_dataset4, (4,))
#
# pool.close()
# pool.join()
#
# for inputfile in ['ds1.csv','ds2.csv','ds3.csv','ds4.csv']:
# a = pd.read_csv(inputfile) # header=None表示原始文件数据没有列索引，这样的话read_csv会自动加上列索引
# a.to_csv('submission_0502.csv', mode='a', index=False,
# header=False) # header=0表示不保留列名，index=False表示不保留行索引，mode='a'表示附加方式写入，文件原有内容不会被清除
# print('done!')
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!")
ds = pd.DataFrame(columns=['address', 'label'])
# for i in range(train_df.shape[0]):
# print('iterations', i)
# address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
# ds = ds.append({'address': address, 'label': label}, ignore_index=True)
# tmp = train_vin_df[train_vin_df['address_origin']==address]
# if len(tmp) > 0:
# for t in tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# out_tmp = train_vout_df[train_vout_df['tx_id']==tx_id]
# if len(out_tmp) > 0:
# ds = ds.append(out_tmp.loc[:, ['address', 'label']], ignore_index=True)
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id']==tx_id_origin]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
# if i % 100 == 0: train_df.to_csv('DATASET.csv', index=False)
for i in range(train_df.shape[0]):
if i%100 ==0: print('iterations', i)
address, label = train_df.loc[i, 'address'], train_df.loc[i, 'label']
ds = ds.append({'address': address, 'label': label}, ignore_index=True)
tmp = train_vin_df[train_vin_df['address_origin']==address]
if len(tmp) > 0:
for t in tmp.index:
tx_id = tmp.loc[t, 'tx_id']
in_txid = train_vin_df[train_vin_df['tx_id'] == tx_id]
if len(in_txid) > 0:
print('vin', in_txid)
in_txid = in_txid[['address_origin']]
in_txid.rename(columns={'address_origin': 'address'}, inplace=True)
in_txid['label'] = label
ds = ds.append(in_txid, ignore_index=True)
#
# out_tmp = train_vout_df[train_vout_df['address'] == address]
# if len(out_tmp) > 0:
# for t in out_tmp.index:
# tx_id = tmp.loc[t, 'tx_id']
# in_txid = train_vin_df[train_vin_df['tx_id'] == tx_id]
# if len(in_txid) > 0:
# print('vin', in_txid)
# in_txid = in_txid[in_txid['address_origin']]
# in_txid.rename(columns={'address_origin': 'address'}, inplace=True)
# in_txid['label'] = label
# ds = ds.append(in_txid, ignore_index=True)
#
#
# out_tmp = train_vout_df[train_vout_df['tx_id']==tx_id]
# if len(out_tmp) > 0:
# ds = ds.append(out_tmp.loc[:, ['address', 'label']], ignore_index=True)
# tx_id_origin = tmp.loc[t, 'tx_id_origin']
# out_origin_tmp = train_vout_df[train_vout_df['tx_id']==tx_id_origin]
# if len(out_origin_tmp) > 0:
# # print('out_origin', out_origin_tmp)
# out_origin_tmp.loc[:, 'label'] = label
# ds = ds.append(out_origin_tmp.loc[:, ['address', 'label']], ignore_index=True)
if i % 100 == 0:
ds = ds.drop_duplicates()
ds.to_csv('newDATASET.csv', index=False)