first example

KDEy/experiments.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+import quapy as qp
+from protocol import UPP
+from quapy.method.aggregative import KDEyML
+
+qp.environ["SAMPLE_SIZE"]=500
+
+
+def datasets():
+    for dataset_name in qp.datasets.UCI_MULTICLASS_DATASETS:
+        yield qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
+
+
+for dataset in datasets():
+    train, test = dataset.train_test
+    test_gen = UPP(test, repeats=500)
+
+    print(f"testing KDEy in {dataset.name}")
+    for b in np.linspace(0.01, 20, 20):
+        kdey = KDEyML(bandwidth=b, random_state=0)
+        kdey.fit(train)
+
+        report = qp.evaluation.evaluation_report(kdey, protocol=test_gen, error_metrics=['ae', 'rae', 'kld'], verbose=True)
+        print(f'bandwidth={b}')
+        print(f'MAE={report["ae"].mean()}')
+        print(f'MRAE={report["ae"].mean()}')
+
+
+

quapy/data/datasets.py

@@ -637,7 +637,7 @@ def fetch_UCIMulticlassDataset(
         if n_train > max_train_instances:
             train_prop = (max_train_instances / n)
 
-    data = Dataset(*data.split_stratified(train_prop, random_state=0))
+    data = Dataset(*data.split_stratified(train_prop, random_state=0), name=dataset_name)
     
     if standardize:
         data = standardizer(data)