BayesianOptimization 과정 중에서 'module' object is not callable 오류가 발생합니다.
김별희
10일 전
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
def lgb_eval(num_leaves,feature_fraction,bagging_fraction,max_depth,min_data_in_leaf):
params = {'num_leaves':int(round(num_leaves)),
'min_data_in_leaf':min_data_in_leaf,
'objective':'regression',
'max_depth':int(round(max_depth)),
'learning_rate':0.02,
"boosting":'gbdt',
"feature_fraction":feature_fraction,
"bagging_freq":1,
"bagging_fraction": bagging_fraction,
"bagging_seed":11,
"metric":'rmse',
"random_state":2019}
print("params:", params)
lgb_model = lgb(**params)
lgb_model.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=30, eval_metric="rmse", verbose=100 )
best_iter = lgb_model.best_iteration_
print('best_iter:', best_iter)
valid_proba = lgb_model.predict_proba(X_test, num_iteration=best_iter)[:, 1]
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print('rmse:',rmse)
return rmse
#####################################
bayes_params = {
'num_leaves': (24, 45),
'feature_fraction':(0.5, 1),
'bagging_fraction': (0.5, 1),
'max_depth': (4, 12),
'min_data_in_leaf':(5, 50)
}
#################################
from bayes_opt import BayesianOptimization
BO_lgb = BayesianOptimization(lgb_eval, bayes_params, random_state=0)
###############################################
BO_lgb.maximize(init_points=5, n_iter=10)
안녕하세요 언제나 좋은 강의 감사합니다.
제가 lightgbm 모델을 BayesianOptimization을 통해 최적의 하이퍼 파라미터를 구하고자하는데,
param이 print되는 것 까지는 진행이 되고나서, 아래 오류와 같이 'module' object is not callable 라는 안내문이 출력되엇습니다. lgb 모듈이 불러와지지 않앗다는 의미인것 같은데 import로 호출한 것과는 별개의 문제인가요?
| iter | target | baggin... | featur... | max_depth | min_da... | num_le... |
-------------------------------------------------------------------------------------
params: {'num_leaves': 33, 'min_data_in_leaf': 29.51974323486036, 'objective': 'regression', 'max_depth': 9, 'learning_rate': 0.02, 'boosting': 'gbdt', 'feature_fraction': 0.8575946831862098, 'bagging_freq': 1, 'bagging_fraction': 0.7744067519636624, 'bagging_seed': 11, 'metric': 'rmse', 'random_state': 2019}
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
E:\anaconda\lib\site-packages\bayes_opt\target_space.py in probe(self, params)
190 try:
--> 191 target = self._cache[_hashable(x)]
192 except KeyError:
KeyError: (0.7744067519636624, 0.8575946831862098, 8.822107008573152, 29.51974323486036, 32.896750786116996)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-103-adbdc94e6584> in <module>
----> 1 BO_lgb.maximize(init_points=5, n_iter=10)
E:\anaconda\lib\site-packages\bayes_opt\bayesian_optimization.py in maximize(self, init_points, n_iter, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)
183 iteration += 1
184
--> 185 self.probe(x_probe, lazy=False)
186
187 if self._bounds_transformer:
E:\anaconda\lib\site-packages\bayes_opt\bayesian_optimization.py in probe(self, params, lazy)
114 self._queue.add(params)
115 else:
--> 116 self._space.probe(params)
117 self.dispatch(Events.OPTIMIZATION_STEP)
118
E:\anaconda\lib\site-packages\bayes_opt\target_space.py in probe(self, params)
192 except KeyError:
193 params = dict(zip(self._keys, x))
--> 194 target = self.target_func(**params)
195 self.register(x, target)
196 return target
<ipython-input-101-a7b26b7824de> in lgb_eval(num_leaves, feature_fraction, bagging_fraction, max_depth, min_data_in_leaf)
22
23 # 모델 훈련
---> 24 lgb_model = lgb(**params)
25 lgb_model.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=30, eval_metric="rmse", verbose=100 )
26 best_iter = lgb_model.best_iteration_
TypeError: 'module' object is not callable
