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 = fraudTrain[::10]

df = df.reset_index()

df.is_fraud.mean()

# df_train, df_test = sklearn.model_selection.train_test_split(df)

0.005807854431707643

df_results = try_2(df, 0.005807854431707643, 0.0058363532328819375, 8.028000e+04, 0.3)

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	0.995805	0.0	0.0	0.0	0.856951	True	Proposed	0.005808	7864	amt	0.004578	2622	0.005836	None	80280.0	0.3

df_results = try_2(fraudTrain, 0.005808, 0.005836, 8.028000e+04, 0.3)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 8.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 7.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.0058366, 528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 5.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 4.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 3.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 2.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 1.528000e+04, 0.3, prev_results=df_results)
df_results = try_2(fraudTrain, 0.005808, 0.005836, 0.528000e+04, 0.3, 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