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