1. Imports

import pandas as pd
import numpy as np
import sklearn
import pickle 
import time 
import datetime

import warnings
warnings.filterwarnings('ignore')

%run ../functions_pyod2.py

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

2.

pyod(*pyod_preprocess4(fraudTrain, 0.04))

	model	time	acc	pre	rec	f1	auc	graph_based	method	throw_rate	train_size	train_cols	train_frate	test_size	test_frate	hyper_params
0	ECOD	0.023506	0.951995	0.407220	0.426110	0.416451	0.700065	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
1	GMM	0.035906	0.967260	0.589629	0.610338	0.599805	0.796273	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
2	HBOS	0.006652	0.959401	0.000000	0.000000	0.000000	0.499792	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
3	IForest	2.369873	0.967100	0.588387	0.604374	0.596273	0.793333	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
4	INNE	2.631451	0.964729	0.560261	0.569914	0.565046	0.775589	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
5	KNN	0.148018	0.965049	0.563263	0.581180	0.572081	0.781153	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
6	LODA	0.182127	0.959401	0.000000	0.000000	0.000000	0.499792	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
7	LOF	0.265123	0.914114	0.025457	0.030484	0.027744	0.490803	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
8	MCD	0.033004	0.967260	0.589629	0.610338	0.599805	0.796273	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
9	PCA	0.010788	0.956551	0.462346	0.496355	0.478747	0.736090	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None
10	ROD	7.429507	0.918030	0.000637	0.000663	0.000650	0.478557	False	pyod	0.04	112612	[amt]	0.039934	37538	0.040199	None