[딥러닝/AI] TabNet으로 학습한 딥러닝 모델을 SHAP으로 설명력 구현하기 (풀코드구현)

2022-11-18 7 분 소요

정형 데이터에 최적화된 딥러닝 모델이라고 알려져 있는 TabNet 으로 학습한 TabNet Regression 모델을 생성하고, 이 모델을 SHAP(Shapely Value) 을 통해 모델 예측 결과를 최대한 잘 설명해줄 수 방법을 찾아보고, 결과물을 한 데이터프레임 안에 저장해보자

TabNet 논문과 SHAP 과 같은 explainable AI 에 관한 내용은 다른 포스트에서 좀더 다룰 예정

install package

Colab 환경에서 작성되었음

! pip install pytorch-tabnet ## 설치된 경우에는 실행 안해도 됨

! pip install shap ## 설치된 경우에는 실행 안해도 됨 

env setting

### drive mount

from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

### gpu mapping
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
print("Using {}".format(DEVICE))

Using cpu

### colab pro + 메모리 사양 확인 
# MemTotal:       13298580 kB
# MemFree:         9764400 kB
# MemAvailable:   12273036 kB

! head -n 3 /proc/meminfo  ### 위의 주석과 다르면.. 실험환경 메모리가 미세하게 다른 것임 ㅠㅜㅠ 

MemTotal:       13297228 kB
MemFree:         8930012 kB
MemAvailable:   12369728 kB

Generate Data & Split Train/Test

### import model 

from sklearn.datasets import load_iris, load_boston
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score, f1_score

from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor
from sklearn.metrics import r2_score
import shap
import os
import time

import warnings
warnings.filterwarnings(action='ignore') ### nsample수 20으로 할경우뜨는 warning 메시지 지우기 위해 

Hyperparameter 설정

X_SIZE = 10000 ## 고정 
TEST_SIZE = 20 ### 1000, 2000
COL_SIZE = 15 ### 30, 60
SEED = 2022
N_sample = 'auto'  ## 'auto' or 2048

데이터 생성

random 데이터를 뿌려줌

### make data 
np.random.seed(SEED)
x_data = np.random.rand(X_SIZE, COL_SIZE)
df = pd.DataFrame(x_data)
df['target'] = np.random.randint(1000, 50000, size=(X_SIZE, 1))
df

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	target
0	0.009359	0.499058	0.113384	0.049974	0.685408	0.486988	0.897657	0.647452	0.896963	0.721135	0.831353	0.827568	0.833580	0.957044	0.368044	30906
1	0.494838	0.339509	0.619429	0.977530	0.096433	0.744206	0.292499	0.298675	0.752473	0.018664	0.523737	0.864436	0.388843	0.212192	0.475181	8187
2	0.564672	0.349429	0.975909	0.037820	0.794270	0.357883	0.747964	0.914509	0.372662	0.964883	0.081386	0.042451	0.296796	0.363704	0.490255	23806
3	0.668519	0.673415	0.572101	0.080592	0.898331	0.038389	0.782194	0.036656	0.267184	0.205224	0.258894	0.932615	0.008125	0.403473	0.894102	10204
4	0.204209	0.021776	0.697167	0.191023	0.546433	0.603225	0.988794	0.092446	0.064287	0.987952	0.452108	0.853911	0.401445	0.388206	0.884407	27620
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	0.017416	0.824987	0.246643	0.141063	0.184951	0.384777	0.722438	0.279597	0.194048	0.816772	0.070302	0.708632	0.497547	0.113425	0.302923	20149
9996	0.434106	0.286644	0.964673	0.237779	0.093510	0.788614	0.645321	0.475191	0.551407	0.438434	0.801701	0.698005	0.065917	0.594159	0.664846	9288
9997	0.951556	0.573942	0.489135	0.139136	0.991655	0.563769	0.347741	0.782542	0.520789	0.944053	0.820197	0.364698	0.538379	0.761037	0.904788	34624
9998	0.800968	0.732879	0.651727	0.610226	0.644994	0.756211	0.247786	0.620484	0.464670	0.879303	0.108468	0.580453	0.742119	0.414510	0.988418	24557
9999	0.082800	0.654022	0.453132	0.713547	0.766718	0.452666	0.910464	0.052970	0.132754	0.090441	0.807935	0.648001	0.722958	0.820611	0.093902	9184

10000 rows × 16 columns

train test split - 위에서 설정한 테스트 개수만큼 split

X, y = df.drop('target', axis = 1).values, df['target'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = TEST_SIZE, random_state = 2022)
y_train, y_test = y_train.reshape(-1,1), y_test.reshape(-1,1)

Save & Load Model

model_path = f"/content/drive/MyDrive/tabnet_reg_10000/tabnet_reg_{X_SIZE}_{TEST_SIZE}" ### 우선 구글 드라이브에 바로 저장
regressor = TabNetRegressor(verbose=1, seed=2022)

if os.path.isfile(model_path+".zip"):
    print(f"LOAD SAVED MODEL -- {model_path}")
    regressor.load_model(model_path+".zip")
else: 
    print(f"TRANING MODEL & SAVE")
    regressor = TabNetRegressor(verbose=1, seed=2022)
    regressor.fit(X_train=X_train, y_train=y_train, 
                eval_metric=['rmsle'], 
                batch_size = 64, 
                max_epochs=3) ### default epoch # 100 
    regressor.save_model(model_path)

print(regressor)

TRANING MODEL & SAVE
epoch 0  | loss: 863465970.37419|  0:00:02s
epoch 1  | loss: 835548792.98065|  0:00:07s
epoch 2  | loss: 775246533.57419|  0:00:12s
Successfully saved model at /content/drive/MyDrive/tabnet_reg_10000/tabnet_reg_10000_20.zip
TabNetRegressor(n_d=8, n_a=8, n_steps=3, gamma=1.3, cat_idxs=[], cat_dims=[], cat_emb_dim=1, n_independent=2, n_shared=2, epsilon=1e-15, momentum=0.02, lambda_sparse=0.001, seed=2022, clip_value=1, verbose=1, optimizer_fn=<class 'torch.optim.adam.Adam'>, optimizer_params={'lr': 0.02}, scheduler_fn=None, scheduler_params={}, mask_type='sparsemax', input_dim=15, output_dim=1, device_name='auto', n_shared_decoder=1, n_indep_decoder=1)

model_path = f"/content/drive/MyDrive/tabnet_reg_10000/tabnet_reg_{X_SIZE}_{TEST_SIZE}" 
regressor = TabNetRegressor(verbose=1, seed=2022)

loaded_tabnetregressor = TabNetRegressor()
loaded_tabnetregressor.load_model(model_path+".zip") # 저장한 모델 불러오기

loaded_tabnetregressor

TabNetRegressor(n_d=8, n_a=8, n_steps=3, gamma=1.3, cat_idxs=[], cat_dims=[], cat_emb_dim=1, n_independent=2, n_shared=2, epsilon=1e-15, momentum=0.02, lambda_sparse=0.001, seed=2022, clip_value=1, verbose=1, optimizer_fn=<class 'torch.optim.adam.Adam'>, optimizer_params={'lr': 0.02}, scheduler_fn=None, scheduler_params={}, mask_type='sparsemax', input_dim=15, output_dim=1, device_name='auto', n_shared_decoder=1, n_indep_decoder=1)

Calculate SHAP Value

KernelExplainer 사용 – Deep Learning model 에는 Permutation 또는 Kernel Explainer가 적합하다

### SHAP value 추출  
print("현재 shap value 확인하는 테스트 데이터 사이즈 -", X_test.shape)
explainer = shap.KernelExplainer(loaded_tabnetregressor.predict, X_test)  # 저장한 모델 넣기

start = time.time() 
shap_values = explainer.shap_values(X_test, nsamples = N_sample)
cell_run_time = time.time() - start
print(cell_run_time)

현재 shap value 확인하는 테스트 데이터 사이즈 - (20, 15)

  0%|          | 0/20 [00:00<?, ?it/s]

21.929385900497437

### cross check 
base_value = explainer.expected_value[0]
print("base value", base_value)

base value 2976.890240478515

Merge Predicted Value, Shap Value into Test Dataframe

shap_importance = pd.DataFrame(shap_values[0])
shap_importance.columns = [str(i)+'_SHAPVAL' for i in shap_importance.columns]
shap_importance.loc[:,'TOTAL_SHAPVAL'] = shap_importance.sum(axis=1)
final_report_df = pd.concat([pd.DataFrame(X_test), shap_importance], axis = 1)

y_pred = loaded_tabnetregressor.predict(X_test)
final_report_df['y_pred'] = y_pred
final_report_df['y_pred']  = final_report_df['y_pred'].astype('float64')
final_report_df['base_value']  =  pd.Series([base_value] * X_test.shape[0])
final_report_df['TOTAL_SHAPVAL + base_value'] =  final_report_df['base_value']  + final_report_df['TOTAL_SHAPVAL']
final_report_df

display(final_report_df)

	0	1	2	3	4	5	6	7	8	9	...	9_SHAPVAL	10_SHAPVAL	11_SHAPVAL	12_SHAPVAL	13_SHAPVAL	14_SHAPVAL	TOTAL_SHAPVAL	y_pred	base_value	TOTAL_SHAPVAL + base_value
0	0.665340	0.305386	0.160187	0.608090	0.876134	0.577168	0.409957	0.951125	0.078057	0.580620	...	13.989868	58.825361	0.000000	24.958757	5.865831	207.362089	582.052631	3558.942871	2976.89024	3558.942871
1	0.896871	0.016248	0.043515	0.174851	0.286535	0.426831	0.938803	0.245879	0.652431	0.358082	...	-5.117060	13.446534	1.012135	-7.112571	0.000000	-299.820383	-229.182477	2747.707520	2976.89024	2747.707764
2	0.195551	0.534170	0.054984	0.067725	0.899910	0.864291	0.928148	0.663764	0.529692	0.069272	...	-13.821463	16.158704	0.000000	0.000000	0.000000	-797.671184	-1612.761945	1364.128174	2976.89024	1364.128296
3	0.780031	0.958210	0.060079	0.127752	0.121757	0.998124	0.619833	0.771568	0.357500	0.250555	...	-8.957114	2.896907	0.000000	-2.727257	1.221694	-259.071928	139.967181	3116.857422	2976.89024	3116.857422
4	0.833555	0.818795	0.188312	0.866473	0.029219	0.869027	0.231443	0.502989	0.734818	0.029553	...	-46.340093	97.512771	0.000000	-40.298334	-10.574096	14.495060	128.472308	3105.362305	2976.89024	3105.362549
5	0.387548	0.608834	0.373447	0.163071	0.871580	0.123094	0.695192	0.221676	0.473412	0.179041	...	-5.709209	-32.263871	0.000000	-1.806092	2.042695	519.058980	438.634662	3415.524658	2976.89024	3415.524902
6	0.051641	0.885105	0.505377	0.459587	0.717950	0.021153	0.329931	0.348929	0.030949	0.122640	...	-16.405837	21.901906	-2.739079	0.000000	0.000000	-619.011598	-1625.876813	1351.013306	2976.89024	1351.013428
7	0.622831	0.676264	0.349113	0.840550	0.952585	0.700288	0.910378	0.059485	0.070335	0.392437	...	2.886479	-40.003732	0.000000	7.700811	0.000000	174.672702	53.446185	3030.336914	2976.89024	3030.336426
8	0.540494	0.498669	0.432838	0.635259	0.044647	0.157382	0.870429	0.626106	0.463865	0.389576	...	0.000000	-22.330007	0.000000	-3.186513	0.000000	200.520145	297.537982	3274.428223	2976.89024	3274.428223
9	0.292736	0.394259	0.501476	0.755071	0.963806	0.097967	0.544224	0.091328	0.650178	0.943432	...	17.487106	-37.783851	0.000000	0.000000	-5.146068	578.151493	538.363422	3515.253174	2976.89024	3515.253662
10	0.807821	0.546079	0.826843	0.606432	0.196059	0.102094	0.280362	0.134659	0.952526	0.791225	...	23.460712	23.505667	0.000000	20.740959	3.940929	169.248973	579.097552	3555.987793	2976.89024	3555.987793
11	0.553643	0.405755	0.070046	0.709272	0.852507	0.653668	0.500025	0.306107	0.014521	0.307509	...	-3.137991	8.399765	0.000000	0.000000	2.952945	222.107463	587.687885	3564.578369	2976.89024	3564.578125
12	0.269201	0.886692	0.236271	0.228237	0.790845	0.247772	0.858724	0.103983	0.214732	0.714835	...	33.948094	0.000000	0.000000	-4.854956	-2.832984	-642.829525	-1401.765851	1575.124268	2976.89024	1575.124390
13	0.511182	0.153969	0.987458	0.664299	0.489893	0.038254	0.708350	0.582648	0.444579	0.579134	...	8.152289	3.099521	0.000000	4.080772	6.835309	167.510164	599.708881	3576.599121	2976.89024	3576.599121
14	0.035127	0.145992	0.092245	0.939648	0.698673	0.515788	0.870113	0.728663	0.240244	0.733680	...	32.049755	-13.377773	0.000000	-11.680317	-6.447017	-558.299584	-1539.971051	1436.918945	2976.89024	1436.919189
15	0.458821	0.365300	0.913334	0.213902	0.705668	0.444712	0.655041	0.711478	0.109087	0.047111	...	0.000000	-47.550775	0.000000	-8.974647	-14.749852	212.535800	522.897113	3499.787109	2976.89024	3499.787354
16	0.586684	0.721589	0.554982	0.728450	0.987413	0.365390	0.149811	0.879073	0.132804	0.158433	...	-10.001263	-16.914937	0.000000	4.579777	0.000000	243.402676	539.024310	3515.915283	2976.89024	3515.914551
17	0.447370	0.856104	0.490726	0.512460	0.901070	0.723599	0.946287	0.362711	0.341831	0.226329	...	-4.604177	-30.644028	0.000000	20.202807	10.523801	161.331920	237.945941	3214.836182	2976.89024	3214.836182
18	0.602242	0.488993	0.776788	0.334477	0.417354	0.396566	0.848444	0.130109	0.666934	0.080961	...	-5.500653	5.991046	0.000000	0.000000	5.574099	253.524126	580.183002	3557.073242	2976.89024	3557.073242
19	0.459065	0.059892	0.831910	0.959609	0.553826	0.954812	0.142540	0.463129	0.410726	0.211993	...	-8.669551	-23.851995	3.546563	0.000000	0.000000	52.351894	584.539447	3561.429932	2976.89024	3561.429688

20 rows × 34 columns

Optional) 시간 소요 그래프 만들기

Explainer 를 바꿔가면서 시간 소요 테스트를 해보고 싶을 때, log dataframe 을 만들어 추적하면 편하다

cell_run_time

21.929385900497437

log = pd.DataFrame([[TEST_SIZE, COL_SIZE, N_sample, cell_run_time]], columns=['test_size','col_size','nsamples', 'run_time'])
log
# log.to_excel("/content/drive/MyDrive/tabnet_reg_10000/log.xlsx", index=False) # 최초의 log 파일 생성시
# pd.read_excel("/content/drive/MyDrive/tabnet_reg_10000/log.xlsx")

	test_size	col_size	nsamples	run_time
0	20	15	auto	21.929386

import os
from openpyxl import load_workbook

cache_path = '/content/drive/MyDrive/tabnet_reg_10000/log.xlsx'

if not os.path.exists(cache_path):  # 파일 없으면 최초 log 파일 만들기
  initial_log = pd.DataFrame([[TEST_SIZE, COL_SIZE, N_sample, cell_run_time]],columns=['test_size','col_size','nsamples', 'run_time'])
  initial_log.to_excel(cache_path, index=False)

else:
  book = load_workbook(cache_path)    # 기존 log 파일 불러오기
  writer = pd.ExcelWriter(cache_path, engine='openpyxl')
  writer.book = book
  writer.sheets = {ws.title: ws for ws in book.worksheets}

  for sheetname in writer.sheets:
      log.to_excel(writer,sheet_name=sheetname, startrow=writer.sheets[sheetname].max_row, index = False,header= False) # append rows to existing excel cache file

  writer.save()

pd.read_excel('/content/drive/MyDrive/tabnet_reg_10000/log.xlsx')

	test_size	col_size	nsamples	run_time
0	500	15	20	322.463880
1	200	15	20	62.606001
2	1000	15	auto	43426.773710
3	100	15	auto	1695.991297
4	300	15	auto	18396.789017
5	100	15	auto	492.172346
6	100	15	auto	498.190434
7	200	15	auto	1911.865682
8	400	15	auto	7451.173161
9	300	15	auto	3855.254270
10	500	15	auto	10974.687255
11	400	15	auto	6170.332176
12	100	15	auto	418.124938
13	200	15	auto	1512.140173
14	300	15	auto	3359.933324
15	400	15	auto	6090.160212
16	500	15	auto	9173.967759
17	1000	15	auto	36812.828763
18	20	15	auto	21.929386

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[딥러닝/AI] TabNet으로 학습한 딥러닝 모델을 SHAP으로 설명력 구현하기 (풀코드구현)

install package

env setting

Generate Data & Split Train/Test

Hyperparameter 설정

데이터 생성

train test split - 위에서 설정한 테스트 개수만큼 split

Save & Load Model

Calculate SHAP Value

Merge Predicted Value, Shap Value into Test Dataframe

Optional) 시간 소요 그래프 만들기

공유하기

댓글남기기

참고

[OCR/AI] 네이버 클로바(Clova) OCR 모델 API 신청부터 직접 사용해보기(feat.코랩)

[에러 해결] Python urlopen error [SSL: CERTIFICATE_VERIFY_FAILED]

[AI 소식] 제미나이(Gemini)란? GPT-4보다 뛰어난 구글의 AI 모델 소개 및 간단 사용 후기 & 논란

[OCR/AI] Upstage OCR 모델 API 신청부터 직접 사용해보자(코랩)