50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import numpy as np
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import quapy as qp
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from sklearn.linear_model import LogisticRegression
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from quapy.model_selection import GridSearchQ
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from quapy.evaluation import evaluation_report
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print('Quantifying the IFCB dataset with PACC\n')
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# model selection
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print('loading dataset for model selection...', end='')
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train, val_gen = qp.datasets.fetch_IFCB(for_model_selection=True, single_sample_train=True)
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print('[done]')
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print(f'\ttraining size={len(train)}, features={train.X.shape[1]}, classes={train.n_classes}')
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print(f'\tvalidation samples={val_gen.total()}')
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print('model selection starts')
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quantifier = qp.method.aggregative.PACC(LogisticRegression())
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mod_sel = GridSearchQ(
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quantifier,
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param_grid={
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'classifier__C': np.logspace(-3,3,7),
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'classifier__class_weight': [None, 'balanced']
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},
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protocol=val_gen,
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refit=False,
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n_jobs=-1,
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verbose=True,
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raise_errors=True
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).fit(train)
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print(f'model selection chose hyperparameters: {mod_sel.best_params_}')
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quantifier = mod_sel.best_model_
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print('loading dataset for test...', end='')
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train, test_gen = qp.datasets.fetch_IFCB(for_model_selection=False, single_sample_train=True)
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print('[done]')
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print(f'\ttraining size={len(train)}, features={train.X.shape[1]}, classes={train.n_classes}')
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print(f'\ttest samples={test_gen.total()}')
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print('training on the whole dataset before test')
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quantifier.fit(train)
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print('testing...')
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report = evaluation_report(quantifier, protocol=test_gen, error_metrics=['mae'], verbose=True)
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print(report.mean())
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