Usage of custom eval metric function with Optuna

lightgbm_integratio.patch

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__':