240513 결과 정리

import pandas as pd
import os

24513

folder_path = './results2'

all_data = []

for file_name in os.listdir(folder_path):

    if file_name.endswith('.csv'):

        df = pd.read_csv(os.path.join(folder_path, file_name))

        all_data.append(df)

merged_df = pd.concat(all_data, ignore_index=True)

merged_df

	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	ABOD	17.510304	0.994275	0.000000	0.000000	0.000000	0.500000	False	pyod	0.008171	420500	['amt']	0.010000	314572	0.005725	NaN
1	COPOD	0.084473	0.990832	0.304442	0.468073	0.368928	0.730958	False	pyod	0.008171	420500	['amt']	0.010000	314572	0.005725	NaN
2	ECOD	0.083442	0.988054	0.165241	0.268184	0.204488	0.630192	False	pyod	0.008171	420500	['amt']	0.010000	314572	0.005725	NaN
3	GMM	0.141062	0.991659	0.334139	0.460300	0.387202	0.727509	False	pyod	0.008171	420500	['amt']	0.010000	314572	0.005725	NaN
4	HBOS	0.012526	0.993938	0.000000	0.000000	0.000000	0.499831	False	pyod	0.008171	420500	['amt']	0.010000	314572	0.005725	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2139	NeuralNetFastAI	NaN	0.972080	0.139732	0.751805	0.235663	0.898952	False	Autogluon	0.018278	14017	['amt']	0.299993	314572	0.005725	NaN
2140	XGBoost	NaN	0.961230	0.106459	0.780677	0.187367	0.962270	False	Autogluon	0.018278	14017	['amt']	0.299993	314572	0.005725	NaN
2141	NeuralNetTorch	NaN	0.976031	0.159245	0.744586	0.262375	0.953301	False	Autogluon	0.018278	14017	['amt']	0.299993	314572	0.005725	NaN
2142	LightGBMLarge	NaN	0.973211	0.144070	0.744586	0.241426	0.959773	False	Autogluon	0.018278	14017	['amt']	0.299993	314572	0.005725	NaN
2143	WeightedEnsemble_L2	NaN	0.973211	0.144070	0.744586	0.241426	0.959773	False	Autogluon	0.018278	14017	['amt']	0.299993	314572	0.005725	NaN

2144 rows × 16 columns

merged_df.drop_duplicates()

	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	ABOD	17.510304	0.994275	0.000000	0.000000	0.000000	0.500000	False	pyod	0.008171	420500	['amt']	0.01	314572	0.005725	NaN
1	COPOD	0.084473	0.990832	0.304442	0.468073	0.368928	0.730958	False	pyod	0.008171	420500	['amt']	0.01	314572	0.005725	NaN
2	ECOD	0.083442	0.988054	0.165241	0.268184	0.204488	0.630192	False	pyod	0.008171	420500	['amt']	0.01	314572	0.005725	NaN
3	GMM	0.141062	0.991659	0.334139	0.460300	0.387202	0.727509	False	pyod	0.008171	420500	['amt']	0.01	314572	0.005725	NaN
4	HBOS	0.012526	0.993938	0.000000	0.000000	0.000000	0.499831	False	pyod	0.008171	420500	['amt']	0.01	314572	0.005725	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2111	LOF	0.078528	0.874442	0.006829	0.144919	0.013044	0.511781	False	pyod	0.016841	42050	['amt']	0.10	314572	0.005725	NaN
2112	MAD	0.002493	0.975665	0.156133	0.737923	0.257733	0.857479	False	pyod	0.016841	42050	['amt']	0.10	314572	0.005725	NaN
2113	MCD	0.011081	0.971313	0.136633	0.754026	0.231346	0.863295	False	pyod	0.016841	42050	['amt']	0.10	314572	0.005725	NaN
2114	PCA	0.003764	0.937483	0.041194	0.445308	0.075411	0.692813	False	pyod	0.016841	42050	['amt']	0.10	314572	0.005725	NaN
2115	ROD	3.873942	0.881445	0.000169	0.003331	0.000322	0.444917	False	pyod	0.016841	42050	['amt']	0.10	314572	0.005725	NaN

1359 rows × 16 columns

merged_df.to_csv('./results2/240513_meged.csv', index=False)