added examples for multiple custom metrics (#2021)

examples/python-guide/advanced_example.py

@@ -163,6 +163,27 @@ gbm = lgb.train(params,
 
 print('Finished 40 - 50 rounds with self-defined objective function and eval metric...')
 
+
+# another self-defined eval metric
+# f(preds: array, train_data: Dataset) -> name: string, eval_result: float, is_higher_better: bool
+# accuracy
+def accuracy(preds, train_data):
+    labels = train_data.get_label()
+    return 'accuracy', np.mean(labels == (preds > 0.5)), True
+
+
+gbm = lgb.train(params,
+                lgb_train,
+                num_boost_round=10,
+                init_model=gbm,
+                fobj=loglikelihood,
+                feval=lambda preds, train_data: [binary_error(preds, train_data),
+                                                 accuracy(preds, train_data)],
+                valid_sets=lgb_eval)
+
+print('Finished 50 - 60 rounds with self-defined objective function '
+      'and multiple self-defined eval metrics...')
+
 print('Starting a new training job...')
 
 

examples/python-guide/sklearn_example.py

@@ -51,11 +51,27 @@ gbm.fit(X_train, y_train,
         eval_metric=rmsle,
         early_stopping_rounds=5)
 
+
+# another self-defined eval metric
+# f(y_true: array, y_pred: array) -> name: string, eval_result: float, is_higher_better: bool
+# Relative Absolute Error (RAE)
+def rae(y_true, y_pred):
+    return 'RAE', np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False
+
+
+print('Starting training with multiple custom eval functions...')
+# train
+gbm.fit(X_train, y_train,
+        eval_set=[(X_test, y_test)],
+        eval_metric=lambda y_true, y_pred: [rmsle(y_true, y_pred), rae(y_true, y_pred)],
+        early_stopping_rounds=5)
+
 print('Starting predicting...')
 # predict
 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
 # eval
 print('The rmsle of prediction is:', rmsle(y_test, y_pred)[1])
+print('The rae of prediction is:', rae(y_test, y_pred)[1])
 
 # other scikit-learn modules
 estimator = lgb.LGBMRegressor(num_leaves=31)