testing kde normal
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@ -5,7 +5,7 @@ from quapy.method.aggregative import EMQ, KDEyML
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import LogisticRegression
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METHODS = ['EMQ',
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METHODS = ['EMQ',
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# 'KDEy-ML',
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'KDEy-ML',
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'KDEy-MLA'
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'KDEy-MLA'
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]
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]
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@ -17,7 +17,7 @@ hyper_LR = {
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}
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}
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hyper_kde = {
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hyper_kde = {
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'bandwidth': np.linspace(0.01, 0.2, 20)
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'bandwidth': np.linspace(0.001, 0.5, 100)
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}
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}
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hyper_kde_aitchison = {
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hyper_kde_aitchison = {
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@ -24,7 +24,7 @@ if __name__ == '__main__':
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global_result_path = f'{result_dir}/{method}'
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global_result_path = f'{result_dir}/{method}'
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print(f'Method\tDataset\tMAE\tMRAE\tKLD')
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print(f'Method\tDataset\tMAE\tMRAE\tKLD')
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for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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print(dataset)
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# print(dataset)
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local_result_path = global_result_path + '_' + dataset
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local_result_path = global_result_path + '_' + dataset
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if os.path.exists(local_result_path + '.dataframe'):
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if os.path.exists(local_result_path + '.dataframe'):
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report = pd.read_csv(local_result_path+'.dataframe')
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report = pd.read_csv(local_result_path+'.dataframe')
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