added more metrics to default pipeline

Author: urbanmatthias

autoPyTorch/core/autonet_classes/autonet_feature_classification.py

@@ -18,7 +18,7 @@ def _apply_default_pipeline_settings(pipeline):
 
         import torch.nn as nn
         from sklearn.model_selection import StratifiedKFold
-        from autoPyTorch.components.metrics.standard_metrics import accuracy
+        from autoPyTorch.components.metrics import accuracy, auc_metric, pac_metric, balanced_accuracy
         from autoPyTorch.components.preprocessing.loss_weight_strategies import LossWeightStrategyWeighted
 
         AutoNetFeatureData._apply_default_pipeline_settings(pipeline)
@@ -33,6 +33,9 @@ def _apply_default_pipeline_settings(pipeline):
 
         metric_selector = pipeline[MetricSelector.get_name()]
         metric_selector.add_metric('accuracy', accuracy)
+        metric_selector.add_metric('auc_metric', auc_metric)
+        metric_selector.add_metric('pac_metric', pac_metric)
+        metric_selector.add_metric('balanced_accuracy', balanced_accuracy)
 
         resample_selector = pipeline[ResamplingStrategySelector.get_name()]
         resample_selector.add_over_sampling_method('random', RandomOverSamplingWithReplacement)

autoPyTorch/core/autonet_classes/autonet_feature_multilabel.py

@@ -12,7 +12,7 @@ def _apply_default_pipeline_settings(pipeline):
         from autoPyTorch.pipeline.nodes.cross_validation import CrossValidation
 
         import torch.nn as nn
-        from autoPyTorch.components.metrics.standard_metrics import multilabel_accuracy
+        from autoPyTorch.components.metrics import multilabel_accuracy, auc_metric, pac_metric
         from autoPyTorch.components.preprocessing.loss_weight_strategies import LossWeightStrategyWeightedBinary
 
         AutoNetFeatureData._apply_default_pipeline_settings(pipeline)
@@ -26,6 +26,8 @@ def _apply_default_pipeline_settings(pipeline):
 
         metric_selector = pipeline[MetricSelector.get_name()]
         metric_selector.add_metric('multilabel_accuracy', multilabel_accuracy)
+        metric_selector.add_metric('auc_metric', auc_metric)
+        metric_selector.add_metric('pac_metric', pac_metric)
 
         train_node = pipeline[TrainNode.get_name()]
         train_node.default_minimize_value = False

autoPyTorch/utils/benchmarking/benchmark_pipeline/create_autonet.py

@@ -27,12 +27,6 @@ def fit(self, pipeline_config, data_manager):
             test_logger.__name__, test_logger(autonet, data_manager.X_test, data_manager.Y_test))
 
         metrics = autonet.pipeline[autonet_nodes.MetricSelector.get_name()]
-        metrics.add_metric('pac_metric', autonet_metrics.pac_metric)
-        metrics.add_metric('balanced_accuracy', autonet_metrics.balanced_accuracy)
-        metrics.add_metric('mean_distance', autonet_metrics.mean_distance)
-        metrics.add_metric('multilabel_accuracy', autonet_metrics.multilabel_accuracy)
-        metrics.add_metric('auc_metric', autonet_metrics.auc_metric)
-        metrics.add_metric('accuracy', autonet_metrics.accuracy)
 
         return { 'autonet': autonet }
 

examples/real_data/advanced_classification.py

@@ -55,10 +55,6 @@
 
 # add metrics and test_result to pipeline
 autonet.pipeline[autonet_nodes.LogFunctionsSelector.get_name()].add_log_function('test_result', test_result(autonet, dm.X_test, dm.Y_test))
-metrics = autonet.pipeline[autonet_nodes.MetricSelector.get_name()]
-metrics.add_metric('pac_metric', autonet_metrics.pac_metric)
-metrics.add_metric('auc_metric', autonet_metrics.auc_metric)
-metrics.add_metric('accuracy', autonet_metrics.accuracy)
 
 # Fit autonet using train data
 res = autonet.fit(min_budget=300,