[회귀] 자동차 가격 예측 AI 경진대회 (2) - AutoML 라이브러리 AutoGluon

• 1편: https://jaayy.tistory.com/84

[회귀] 자동차 가격 예측 AI 경진대회 (1) - 데이터 불러오기, 간단 EDA

 

 

AutoGluon 활용해보자

<참고 자료>

• https://auto.gluon.ai/stable/index.html
• https://dacon.io/competitions/official/236114/codeshare/8456?page=1&dtype=recent
• https://dacon.io/codeshare/7764
• https://dacon.io/competitions/official/236114/codeshare/8454?page=1&dtype=recent
• https://sarah0518.tistory.com/89

• https://data-newbie.tistory.com/915

 

 

• AutoGluon은 다양한 알고리즘으로, customized parameter 범위 내에서 최적의 알고리즘을 select해주는 기법이다
• 각 알고리즘별 tuning parameter들을 세팅할 수 있는데, 하이퍼 파라미터에 대한 customize는 GBM, NN, RF, CAT에서만 가능하다

 

In [17]:

!pip install autogluon

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In [18]:

#TabularData(정형데이터)를 다루기 때문에 아래 라이브러리 호출
from autogluon.tabular import TabularDataset, TabularPredictor
import autogluon.core as ag

 

 

학습하기

• autogluon 내부에는 여러가지 모델을 알아서 비교해주고 코드로 구현하기 쉬움

 

In [19]:

train = train.drop('ID', axis=1)
test = test.drop('ID', axis=1)

 

In [20]:

#autogluon 학습을 위한 데이터 형태로 변환
train = TabularDataset(train)
test = TabularDataset(test)

 

In [21]:

predictor = TabularPredictor(label='가격',
                             problem_type='regression',
                             eval_metric='mae').fit(train)

No path specified. Models will be saved in: "AutogluonModels/ag-20230703_041124/"
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20230703_041124/"
AutoGluon Version:  0.8.2
Python Version:     3.10.12
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Sat Apr 29 09:15:28 UTC 2023
Disk Space Avail:   82.05 GB / 115.66 GB (70.9%)
Train Data Rows:    57920
Train Data Columns: 13
Label Column: 가격
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    12128.82 MB
	Train Data (Original)  Memory Usage: 18.35 MB (0.2% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 5 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Stage 5 Generators:
		Fitting DropDuplicatesFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('int', [])    : 9 | ['생산년도', '모델출시년도', '주행거리', '배기량', '압축천연가스(CNG)', ...]
		('object', []) : 4 | ['브랜드', '차량모델명', '판매도시', '판매구역']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])  : 4 | ['브랜드', '차량모델명', '판매도시', '판매구역']
		('int', [])       : 4 | ['생산년도', '모델출시년도', '주행거리', '배기량']
		('int', ['bool']) : 5 | ['압축천연가스(CNG)', '경유', '가솔린', '하이브리드', '액화석유가스(LPG)']
	1.8s = Fit runtime
	13 features in original data used to generate 13 features in processed data.
	Train Data (Processed) Memory Usage: 2.49 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 1.95s ...
AutoGluon will gauge predictive performance using evaluation metric: 'mean_absolute_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.043162983425414365, Train Rows: 55420, Val Rows: 2500
User-specified model hyperparameters to be fit:
{
	'NN_TORCH': {},
	'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
	'CAT': {},
	'XGB': {},
	'FASTAI': {},
	'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif ...
	-17.3847	 = Validation score   (-mean_absolute_error)
	6.35s	 = Training   runtime
	0.07s	 = Validation runtime
Fitting model: KNeighborsDist ...
	-16.7037	 = Validation score   (-mean_absolute_error)
	0.18s	 = Training   runtime
	0.08s	 = Validation runtime
Fitting model: LightGBMXT ...

[1000]	valid_set's l1: 5.90157
[2000]	valid_set's l1: 5.84195

	-5.8317	 = Validation score   (-mean_absolute_error)
	26.23s	 = Training   runtime
	1.05s	 = Validation runtime
Fitting model: LightGBM ...

[1000]	valid_set's l1: 5.84017

	-5.8322	 = Validation score   (-mean_absolute_error)
	7.02s	 = Training   runtime
	0.41s	 = Validation runtime
Fitting model: RandomForestMSE ...
	-6.0118	 = Validation score   (-mean_absolute_error)
	97.68s	 = Training   runtime
	0.78s	 = Validation runtime
Fitting model: CatBoost ...
	-5.8151	 = Validation score   (-mean_absolute_error)
	1128.54s	 = Training   runtime
	0.85s	 = Validation runtime
Fitting model: ExtraTreesMSE ...
	-6.0009	 = Validation score   (-mean_absolute_error)
	41.0s	 = Training   runtime
	0.73s	 = Validation runtime
Fitting model: NeuralNetFastAI ...
	-6.0808	 = Validation score   (-mean_absolute_error)
	138.01s	 = Training   runtime
	0.08s	 = Validation runtime
Fitting model: XGBoost ...
	-5.9248	 = Validation score   (-mean_absolute_error)
	90.91s	 = Training   runtime
	0.95s	 = Validation runtime
Fitting model: NeuralNetTorch ...
	-6.369	 = Validation score   (-mean_absolute_error)
	124.29s	 = Training   runtime
	0.05s	 = Validation runtime
Fitting model: LightGBMLarge ...

[1000]	valid_set's l1: 5.65789

	-5.6526	 = Validation score   (-mean_absolute_error)
	14.77s	 = Training   runtime
	1.22s	 = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
	-5.4953	 = Validation score   (-mean_absolute_error)
	0.78s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 1703.68s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230703_041124/")

 

In [27]:

#predictor의 summary값 확인
results = predictor.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                  model  score_val  pred_time_val     fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L2  -5.495345       4.660031  1635.963700                0.001131           0.779660            2       True         12
1         LightGBMLarge  -5.652586       1.224401    14.770763                1.224401          14.770763            1       True         11
2              CatBoost  -5.815062       0.850227  1128.538806                0.850227        1128.538806            1       True          6
3            LightGBMXT  -5.831700       1.049686    26.232757                1.049686          26.232757            1       True          3
4              LightGBM  -5.832224       0.414197     7.016816                0.414197           7.016816            1       True          4
5               XGBoost  -5.924753       0.952236    90.906208                0.952236          90.906208            1       True          9
6         ExtraTreesMSE  -6.000862       0.730689    40.998093                0.730689          40.998093            1       True          7
7       RandomForestMSE  -6.011757       0.775243    97.677653                0.775243          97.677653            1       True          5
8       NeuralNetFastAI  -6.080845       0.079530   138.006784                0.079530         138.006784            1       True          8
9        NeuralNetTorch  -6.368998       0.046575   124.285734                0.046575         124.285734            1       True         10
10       KNeighborsDist -16.703670       0.078764     0.180185                0.078764           0.180185            1       True          2
11       KNeighborsUnif -17.384714       0.072684     6.350637                0.072684           6.350637            1       True          1
Number of models trained: 12
Types of models trained:
{'WeightedEnsembleModel', 'LGBModel', 'XTModel', 'TabularNeuralNetTorchModel', 'XGBoostModel', 'RFModel', 'KNNModel', 'NNFastAiTabularModel', 'CatBoostModel'}
Bagging used: False 
Multi-layer stack-ensembling used: False 
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])  : 4 | ['브랜드', '차량모델명', '판매도시', '판매구역']
('int', [])       : 4 | ['생산년도', '모델출시년도', '주행거리', '배기량']
('int', ['bool']) : 5 | ['압축천연가스(CNG)', '경유', '가솔린', '하이브리드', '액화석유가스(LPG)']
Plot summary of models saved to file: AutogluonModels/ag-20230703_041124/SummaryOfModels.html
*** End of fit() summary ***

 

In [22]:

#위와 같은 정보를 리더보드로 확인
ld_board = predictor.leaderboard(train, silent=True)
ld_board

 

Out[22]:

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist	-1.113688	-16.703670	0.709999	0.078764	0.180185	0.709999	0.078764	0.180185	1	True	2
1	RandomForestMSE	-2.750212	-6.011757	8.421520	0.775243	97.677653	8.421520	0.775243	97.677653	1	True	5
2	ExtraTreesMSE	-3.049630	-6.000862	9.992340	0.730689	40.998093	9.992340	0.730689	40.998093	1	True	7
3	LightGBMLarge	-3.466650	-5.652586	23.486255	1.224401	14.770763	23.486255	1.224401	14.770763	1	True	11
4	WeightedEnsemble_L2	-3.510822	-5.495345	87.940834	4.660031	1635.963700	0.013026	0.001131	0.779660	2	True	12
5	XGBoost	-3.931571	-5.924753	25.156268	0.952236	90.906208	25.156268	0.952236	90.906208	1	True	9
6	LightGBM	-4.395887	-5.832224	12.172485	0.414197	7.016816	12.172485	0.414197	7.016816	1	True	4
7	NeuralNetFastAI	-4.513795	-6.080845	2.335398	0.079530	138.006784	2.335398	0.079530	138.006784	1	True	8
8	LightGBMXT	-4.815487	-5.831700	31.435905	1.049686	26.232757	31.435905	1.049686	26.232757	1	True	3
9	CatBoost	-5.007776	-5.815062	18.176280	0.850227	1128.538806	18.176280	0.850227	1128.538806	1	True	6
10	NeuralNetTorch	-5.701318	-6.368998	0.359748	0.046575	124.285734	0.359748	0.046575	124.285734	1	True	10
11	KNeighborsUnif	-13.880928	-17.384714	0.752758	0.072684	6.350637	0.752758	0.072684	6.350637	1	True	1

 

 

예측

 

In [29]:

#어떤 모델이 가장 성능이 좋았는지
model_to_use = predictor.get_model_best()
model_to_use

 

Out[29]:

'WeightedEnsemble_L2'

 

In [23]:

#가장 성능 좋은 모델로 예측
predictor = TabularPredictor.load("AutogluonModels/ag-20230703_041124/")
pred = predictor.predict(test, model=model_to_use)

 

In [24]:

pred

 

Out[24]:

0         92.595566
1         26.624863
2         87.392128
3        125.270287
4         46.227715
            ...    
14475     40.767590
14476     23.771589
14477    107.951439
14478     51.633385
14479     43.190853
Name: 가격, Length: 14480, dtype: float32

 

In [26]:

submission = pd.read_csv('/content/sample_submission.csv')
submission['가격'] = pred
submission.to_csv('AutoGluon_submit.csv', index=False)

 

 

기간 끝난 대회로 연습해서 순위에 큰 의미는 없지만 public 21등을 할 정도로 성능이 뛰어남을 알 수 있다.

전처리 작업을 따로 하지 않아도 이 정도라 AutoML 라이브러리들을 앞으로 많이 쓰게 될 것 같다.