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_1(fraudTrain, 0.5, 1e7, 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	0.933067	0.919005	0.951155	0.934804	0.966693	True	Proposed	0.5	9009	amt	0.498501	3003	0.504496	None	10000000.0	0.8

df_results = try_11(fraudTrain, 0.5, 1e7, 0.7)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.72, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.74, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.76, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.78, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.8, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.82, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.84, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.86, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.88, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.9, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.92, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.94, prev_results=df_results)
df_results = try_11(fraudTrain, 0.5, 1e7, 0.96, 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	0.922411	0.891892	0.962227	0.925725	0.954604	True	Proposed	0.5	9009	amt	0.499167	3003	0.502498	None	10000000.0	0.70
1	GCN	None	0.918748	0.890909	0.958279	0.923367	0.957870	True	Proposed	0.5	9009	amt	0.496392	3003	0.510823	None	10000000.0	0.72
2	GCN	None	0.925075	0.894510	0.964096	0.928000	0.958778	True	Proposed	0.5	9009	amt	0.499722	3003	0.500833	None	10000000.0	0.74
3	GCN	None	0.938395	0.920382	0.960133	0.939837	0.966664	True	Proposed	0.5	9009	amt	0.499611	3003	0.501166	None	10000000.0	0.76
4	GCN	None	0.931735	0.907987	0.958839	0.932721	0.966264	True	Proposed	0.5	9009	amt	0.502165	3003	0.493506	None	10000000.0	0.78
5	GCN	None	0.932734	0.915601	0.953395	0.934116	0.964429	True	Proposed	0.5	9009	amt	0.499944	3003	0.500167	None	10000000.0	0.80
6	GCN	None	0.939727	0.914801	0.970957	0.942043	0.969211	True	Proposed	0.5	9009	amt	0.498501	3003	0.504496	None	10000000.0	0.82
7	GCN	None	0.947386	0.930142	0.966398	0.947924	0.973762	True	Proposed	0.5	9009	amt	0.501499	3003	0.495504	None	10000000.0	0.84
8	GCN	None	0.946054	0.931717	0.963696	0.947437	0.970296	True	Proposed	0.5	9009	amt	0.498501	3003	0.504496	None	10000000.0	0.86
9	GCN	None	0.950716	0.937139	0.966909	0.951792	0.975631	True	Proposed	0.5	9009	amt	0.498945	3003	0.503164	None	10000000.0	0.88
10	GCN	None	0.952381	0.939633	0.965610	0.952444	0.976409	True	Proposed	0.5	9009	amt	0.502054	3003	0.493839	None	10000000.0	0.90
11	GCN	None	0.958375	0.954045	0.964660	0.959323	0.978292	True	Proposed	0.5	9009	amt	0.497058	3003	0.508825	None	10000000.0	0.92
12	GCN	None	0.955045	0.953083	0.956288	0.954683	0.978829	True	Proposed	0.5	9009	amt	0.501610	3003	0.495171	None	10000000.0	0.94
13	GCN	None	0.966034	0.963887	0.968977	0.966425	0.985536	True	Proposed	0.5	9009	amt	0.498501	3003	0.504496	None	10000000.0	0.96