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)

df50_train, df[::10]: test

df_results = try_3(fraudTrain, 10, 0, 0, 10973.519989002007, 0.501)

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	0.964562	0.115884	0.725	0.199828	0.864214	True	Proposed	0	9009	amt	0.498945	3003	0	None	10973.519989	0.501

df50_tr, df50_tst

df50 = throw(fraudTrain,0.5)

try_1(df50, 0.5, 0.5, 10973.519989002007, 0.501)

	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	0.886483	0.962151	0.804553	0.876323	0.92527	True	Proposed	0.5	8409	amt	0.500059	3603	0.5	None	10973.519989	0.501