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)

df_results = try_5(fraudTrain, 10,11406996,0.8)
df_results = try_5(fraudTrain, 10,11406996,0.9, prev_results=df_results)
df_results = try_5(fraudTrain, 10,11406996,0.7, prev_results=df_results)
df_results = try_5(fraudTrain, 9,11406996,0.9, prev_results=df_results)
df_results = try_5(fraudTrain, 9,11406996,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 9,11406996,0.7, prev_results=df_results)
df_results = try_5(fraudTrain, 8,11406996,0.9, prev_results=df_results)
df_results = try_5(fraudTrain, 8,11406996,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 8,11406996,0.7, prev_results=df_results)
df_results = try_5(fraudTrain, 7,11406996,0.9, prev_results=df_results)
df_results = try_5(fraudTrain, 7,11406996,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 7,11406996,0.7, 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	0.917783	0.016590	0.947368	0.032609	0.968007	True	Proposed	0.131127	9009	amt	0.505051	25980	0.001463	None	11406996	0.8
1	GCN	None	0.938217	0.022547	0.973684	0.044074	0.982845	True	Proposed	0.129820	9009	amt	0.499944	25978	0.001463	None	11406996	0.9
2	GCN	None	0.886999	0.012450	0.973684	0.024585	0.960453	True	Proposed	0.129633	9009	amt	0.499278	25982	0.001463	None	11406996	0.7
3	GCN	None	0.941596	0.024899	0.934783	0.048505	0.984682	True	Proposed	0.120230	9009	amt	0.500611	28885	0.001593	None	11406996	0.9
4	GCN	None	0.915980	0.018661	0.867925	0.036537	0.947152	True	Proposed	0.120450	9009	amt	0.500611	28874	0.001836	None	11406996	0.8
5	GCN	None	0.892921	0.011832	0.948718	0.023373	0.958848	True	Proposed	0.120417	9009	amt	0.502054	28876	0.001351	None	11406996	0.7
6	GCN	None	0.940046	0.022579	0.978261	0.044139	0.984014	True	Proposed	0.109714	9009	amt	0.500500	32508	0.001415	None	11406996	0.9
7	GCN	None	0.921472	0.012408	0.864865	0.024465	0.963446	True	Proposed	0.109427	9009	amt	0.500056	32498	0.001139	None	11406996	0.8
8	GCN	None	0.895498	0.011357	0.975000	0.022453	0.960901	True	Proposed	0.108611	9009	amt	0.495948	32497	0.001231	None	11406996	0.7
9	GCN	None	0.947098	0.032544	0.970588	0.062977	0.985970	True	Proposed	0.098821	9009	amt	0.498501	37125	0.001832	None	11406996	0.9
10	GCN	None	0.911643	0.012940	1.000000	0.025550	0.980952	True	Proposed	0.099066	9009	amt	0.502498	37122	0.001158	None	11406996	0.8
11	GCN	None	0.894713	0.013640	0.931034	0.026886	0.969827	True	Proposed	0.098448	9009	amt	0.497724	37127	0.001562	None	11406996	0.7

df_results = try_5(fraudTrain, 10,1e+7,0.8)
df_results = try_5(fraudTrain, 9,1e+7,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 8,1e+7,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 7,1e+7,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 6,1e+7,0.8, prev_results=df_results)
df_results = try_5(fraudTrain, 5,1e+7,0.8, 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	0.920676	0.019971	1.000000	0.039161	0.977881	True	Proposed	0.129262	9009	amt	0.497391	25982	0.001617	None	10000000.0	0.8
1	GCN	None	0.924151	0.018834	0.954545	0.036939	0.975426	True	Proposed	0.120242	9009	amt	0.500722	28873	0.001524	None	10000000.0	0.8
2	GCN	None	0.921382	0.015046	0.951220	0.029624	0.975071	True	Proposed	0.109015	9009	amt	0.497724	32499	0.001262	None	10000000.0	0.8
3	GCN	None	0.926061	0.018253	0.962264	0.035827	0.972528	True	Proposed	0.098496	9009	amt	0.498501	37125	0.001428	None	10000000.0	0.8
4	GCN	None	0.925250	0.018226	0.909091	0.035736	0.974611	True	Proposed	0.087928	9009	amt	0.503386	43318	0.001524	None	10000000.0	0.8
5	GCN	None	0.916731	0.019714	0.988636	0.038658	0.979137	True	Proposed	0.074902	9009	amt	0.497169	51964	0.001693	None	10000000.0	0.8