83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
import os
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from pathlib import Path
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from sklearn.base import BaseEstimator
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import quapy as qp
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import numpy as np
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from method.aggregative import KDEyML
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from quapy.functional import l1_norm, ILRtransformation
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from scipy.stats import entropy
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def fetch_UCI_multiclass(data_name):
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return qp.datasets.fetch_UCIMulticlassDataset(data_name, min_class_support=0.01)
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def fetch_UCI_binary(data_name):
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return qp.datasets.fetch_UCIBinaryDataset(data_name)
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# global configurations
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binary = {
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'datasets': qp.datasets.UCI_BINARY_DATASETS,
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'fetch_fn': fetch_UCI_binary,
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'sample_size': 500
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}
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multiclass = {
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'datasets': qp.datasets.UCI_MULTICLASS_DATASETS,
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'fetch_fn': fetch_UCI_multiclass,
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'sample_size': 1000
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}
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try:
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multiclass['datasets'].remove('poker_hand') # random performance
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multiclass['datasets'].remove('hcv') # random performance
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multiclass['datasets'].remove('letter') # many classes
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multiclass['datasets'].remove('isolet') # many classes
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except ValueError:
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pass
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# utils
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def experiment_path(dir:Path, dataset_name:str, method_name:str):
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os.makedirs(dir, exist_ok=True)
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return dir/f'{dataset_name}__{method_name}.pkl'
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def normalized_entropy(p):
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"""
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Normalized Shannon entropy in [0, 1]
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p: array-like, prevalence vector (sums to 1)
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"""
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p = np.asarray(p)
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H = entropy(p) # Shannon entropy
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H_max = np.log(len(p))
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return np.clip(H / H_max, 0, 1)
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def antagonistic_prevalence(p, strength=1):
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ilr = ILRtransformation()
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z = ilr(p)
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z_ant = - strength * z
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p_ant = ilr.inverse(z_ant)
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return p_ant
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class KDEyCLR(KDEyML):
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def __init__(self, classifier: BaseEstimator=None, fit_classifier=True, val_split=5, bandwidth=1., random_state=None):
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super().__init__(
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classifier=classifier, fit_classifier=fit_classifier, val_split=val_split, bandwidth=bandwidth,
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random_state=random_state, kernel='aitchison'
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)
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class KDEyILR(KDEyML):
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def __init__(self, classifier: BaseEstimator=None, fit_classifier=True, val_split=5, bandwidth=1., random_state=None):
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super().__init__(
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classifier=classifier, fit_classifier=fit_classifier, val_split=val_split, bandwidth=bandwidth,
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random_state=random_state, kernel='ilr'
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)
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