Optuna LightGBM LightGBMPruningCallback
Optuna LightGBM LightGBMPruningCallback
我在搜索最佳 auc 的 lightgbm 建模中遇到错误。任何帮助将不胜感激。
import optuna
from sklearn.model_selection import StratifiedKFold
from optuna.integration import LightGBMPruningCallback
def objective(trial, X, y):
param = {
"objective": "binary",
"metric": "auc",
"verbosity": -1,
"boosting_type": "gbdt",
"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0),
"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0),
"num_leaves": trial.suggest_int("num_leaves", 2, 256),
"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
"bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1121218)
cv_scores = np.empty(5)
for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc")
model = lgb.LGBMClassifier(**param)
model.fit(
X_train,
y_train,
eval_set=[(X_test, y_test)],
early_stopping_rounds=100,
callbacks=[pruning_callback])
preds = model.predict_proba(X_test)
cv_scores[idx] = log_loss(y_test, preds)
auc_scores[idx] = roc_auc_score(y_test, preds)
return np.mean(cv_scores), np.mean(auc_scores)
study = optuna.create_study(direction="minimize", study_name="LGBM Classifier")
func = lambda trial: objective(trial, sample_df[cols_to_keep], sample_df[target])
study.optimize(func, n_trials=1)
Trial 0 failed because of the following error: ValueError('The
intermediate values are inconsistent with the objective values in
terms of study directions. Please specify a metric to be minimized for
LightGBMPruningCallback.',)*
您的 objective 函数 returns 两个值,但您在创建研究时仅指定了一个方向。试试这个:
study = optuna.create_study(directions=["minimize", "maximize"], study_name="LGBM Classifier")
我在搜索最佳 auc 的 lightgbm 建模中遇到错误。任何帮助将不胜感激。
import optuna
from sklearn.model_selection import StratifiedKFold
from optuna.integration import LightGBMPruningCallback
def objective(trial, X, y):
param = {
"objective": "binary",
"metric": "auc",
"verbosity": -1,
"boosting_type": "gbdt",
"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0),
"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0),
"num_leaves": trial.suggest_int("num_leaves", 2, 256),
"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
"bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1121218)
cv_scores = np.empty(5)
for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc")
model = lgb.LGBMClassifier(**param)
model.fit(
X_train,
y_train,
eval_set=[(X_test, y_test)],
early_stopping_rounds=100,
callbacks=[pruning_callback])
preds = model.predict_proba(X_test)
cv_scores[idx] = log_loss(y_test, preds)
auc_scores[idx] = roc_auc_score(y_test, preds)
return np.mean(cv_scores), np.mean(auc_scores)
study = optuna.create_study(direction="minimize", study_name="LGBM Classifier")
func = lambda trial: objective(trial, sample_df[cols_to_keep], sample_df[target])
study.optimize(func, n_trials=1)
Trial 0 failed because of the following error: ValueError('The intermediate values are inconsistent with the objective values in terms of study directions. Please specify a metric to be minimized for LightGBMPruningCallback.',)*
您的 objective 函数 returns 两个值,但您在创建研究时仅指定了一个方向。试试这个:
study = optuna.create_study(directions=["minimize", "maximize"], study_name="LGBM Classifier")