imports

import pandas as pd
import numpy as np
import sklearn
import pickle 
import time 
import datetime
import warnings
warnings.filterwarnings('ignore')

%run ../function_proposed_gcn.py

with open('../fraudTrain.pkl', 'rb') as file:
    fraudTrain = pickle.load(file)

try_4(fraudTrain, 10, 11406996.079461852,0.8)

	model	time	acc	pre	rec	f1	auc	graph_based	method	throw_rate	train_size	train_cols	train_frate	test_size	test_frate	hyper_params	theta	gamma
0	GCN	None	0.908907	0.055864	0.965753	0.105618	0.973903	True	Proposed	0.132097	9009	amt	0.500278	26215	0.005569	None	1.140700e+07	0.8

11406996오잉?

11406996

11406996

1e7 < 11406996

True

12000000 == 12e6

df_results = try_4(fraudTrain, 10,11406996,0.8)
df_results = try_4(fraudTrain, 10,11406996,0.9, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.7, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.6, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.5, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.4, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.3, prev_results=df_results)
df_results = try_4(fraudTrain, 10,11406996,0.2, prev_results=df_results)

NameError: name 'ymdhms' is not defined


ymdhms = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H%M%S') 
df_results.to_csv(f'../results/{ymdhms}-proposed.csv',index=False)

df_results

	model	time	acc	pre	rec	f1	auc	graph_based	method	throw_rate	train_size	train_cols	train_frate	test_size	test_frate	hyper_params	theta	gamma
0	GCN	None	0.919550	0.062137	0.926667	0.116464	0.964368	True	Proposed	0.132041	9009	amt	0.499611	26215	0.005722	None	11406996	0.8
1	GCN	None	0.944574	0.088903	0.946309	0.162536	0.980860	True	Proposed	0.131615	9009	amt	0.498057	26215	0.005684	None	11406996	0.9
2	GCN	None	0.891818	0.044317	0.922535	0.084571	0.961205	True	Proposed	0.131984	9009	amt	0.500278	26215	0.005417	None	11406996	0.7
3	GCN	None	0.864009	0.038399	0.940397	0.073785	0.951729	True	Proposed	0.133119	9009	amt	0.503719	26215	0.005760	None	11406996	0.6
4	GCN	None	0.843525	0.033318	0.927632	0.064325	0.937046	True	Proposed	0.132580	9009	amt	0.501499	26215	0.005798	None	11406996	0.5
5	GCN	None	0.822544	0.029547	0.870370	0.057154	0.919623	True	Proposed	0.131047	9009	amt	0.494394	26215	0.006180	None	11406996	0.4
6	GCN	None	0.810757	0.022822	0.756579	0.044307	0.870339	True	Proposed	0.131189	9009	amt	0.496059	26215	0.005798	None	11406996	0.3
7	GCN	None	0.764753	0.018865	0.797297	0.036858	0.868959	True	Proposed	0.132069	9009	amt	0.499944	26215	0.005646	None	11406996	0.2

df_results = try_4(fraudTrain, 10,1e7,0.8)
df_results = try_4(fraudTrain, 10,1e8,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e9,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e10,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e7,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e7,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e7,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,1e7,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,12e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,13e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,14e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,15e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,16e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,17e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,18e6,0.8, prev_results=df_results)
df_results = try_4(fraudTrain, 10,19e6,0.8, prev_results=df_results)


ymdhms = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H%M%S') 
df_results.to_csv(f'../results/{ymdhms}-proposed.csv',index=False)

df_results

	model	time	acc	pre	rec	f1	auc	graph_based	method	throw_rate	train_size	train_cols	train_frate	test_size	test_frate	hyper_params	theta	gamma
0	GCN	None	0.923670	0.065350	0.914474	0.121983	0.967187	True	Proposed	0.132012	9009	amt	0.499278	26215	0.005798	None	1.000000e+07	0.8
1	GCN	None	0.757467	0.020737	0.817073	0.040447	0.864097	True	Proposed	0.133176	9009	amt	0.502498	26215	0.006256	None	1.000000e+08	0.8
2	GCN	None	0.708068	0.013990	0.724832	0.027449	0.790459	True	Proposed	0.131189	9009	amt	0.496392	26215	0.005684	None	1.000000e+09	0.8
3	GCN	None	0.662865	0.012895	0.766667	0.025364	0.796297	True	Proposed	0.133347	9009	amt	0.504718	26215	0.005722	None	1.000000e+10	0.8
4	GCN	None	0.918749	0.065992	0.955414	0.123457	0.975293	True	Proposed	0.130735	9009	amt	0.493728	26215	0.005989	None	1.000000e+07	0.8
5	GCN	None	0.926378	0.073218	0.962025	0.136079	0.973912	True	Proposed	0.132523	9009	amt	0.500611	26215	0.006027	None	1.000000e+07	0.8
6	GCN	None	0.917681	0.057819	0.949640	0.109001	0.969563	True	Proposed	0.132665	9009	amt	0.503275	26215	0.005302	None	1.000000e+07	0.8
7	GCN	None	0.924814	0.061244	0.934307	0.114953	0.971099	True	Proposed	0.132069	9009	amt	0.501166	26215	0.005226	None	1.000000e+07	0.8
8	GCN	None	0.914553	0.056914	0.978261	0.107570	0.973465	True	Proposed	0.132268	9009	amt	0.501832	26215	0.005264	None	1.200000e+07	0.8
9	GCN	None	0.916498	0.062258	0.966667	0.116983	0.966392	True	Proposed	0.132892	9009	amt	0.502942	26215	0.005722	None	1.300000e+07	0.8
10	GCN	None	0.907191	0.052755	0.937500	0.099889	0.964317	True	Proposed	0.131842	9009	amt	0.499500	26215	0.005493	None	1.400000e+07	0.8
11	GCN	None	0.898074	0.045520	0.933824	0.086808	0.963176	True	Proposed	0.132410	9009	amt	0.502609	26215	0.005188	None	1.500000e+07	0.8
12	GCN	None	0.901430	0.057854	0.934911	0.108966	0.963166	True	Proposed	0.132580	9009	amt	0.499611	26215	0.006447	None	1.600000e+07	0.8
13	GCN	None	0.896548	0.046643	0.904110	0.088710	0.950958	True	Proposed	0.131984	9009	amt	0.499833	26215	0.005569	None	1.700000e+07	0.8
14	GCN	None	0.890254	0.054659	0.965116	0.103459	0.966236	True	Proposed	0.132608	9009	amt	0.499389	26215	0.006561	None	1.800000e+07	0.8
15	GCN	None	0.890902	0.045927	0.907285	0.087428	0.957087	True	Proposed	0.131473	9009	amt	0.497280	26215	0.005760	None	1.900000e+07	0.8