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Usage of custom eval metric function with Optuna
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diff --git a/examples/pruning/lightgbm_integration.py b/examples/pruning/lightgbm_integration.py | |
index 8e623772..4c0c315c 100644 | |
--- a/examples/pruning/lightgbm_integration.py | |
+++ b/examples/pruning/lightgbm_integration.py | |
@@ -21,6 +21,15 @@ import optuna | |
# FYI: Objective functions can take additional arguments | |
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args). | |
def objective(trial): | |
+ def custom_accuracy_pct(preds, data): | |
+ y_true = data.get_label() | |
+ acc = custom_accuracy_numpy(preds > 0.5, y_true) | |
+ return 'custom_accuracy', acc, True | |
+ | |
+ def custom_accuracy_numpy(y_pred, y_true): | |
+ acc = np.mean(y_true == y_pred) * 100 | |
+ return acc | |
+ | |
data, target = sklearn.datasets.load_breast_cancer(return_X_y=True) | |
train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25) | |
dtrain = lgb.Dataset(train_x, label=train_y) | |
@@ -41,14 +50,16 @@ def objective(trial): | |
} | |
# Add a callback for pruning. | |
- pruning_callback = optuna.integration.LightGBMPruningCallback(trial, 'auc') | |
+ pruning_callback = optuna.integration.LightGBMPruningCallback(trial, 'custom_accuracy') | |
gbm = lgb.train( | |
- param, dtrain, valid_sets=[dtest], verbose_eval=False, callbacks=[pruning_callback]) | |
+ param, dtrain, valid_sets=[dtest], verbose_eval=False, callbacks=[pruning_callback], | |
+ feval=custom_accuracy_pct) | |
preds = gbm.predict(test_x) | |
pred_labels = np.rint(preds) | |
- accuracy = sklearn.metrics.accuracy_score(test_y, pred_labels) | |
- return accuracy | |
+ | |
+ accuracy_pct = custom_accuracy_numpy(test_y, pred_labels) | |
+ return accuracy_pct | |
if __name__ == '__main__': |
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