forked from moreo/QuaPy
27 lines
876 B
Python
27 lines
876 B
Python
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from data.datasets import LEQUA2022_SAMPLE_SIZE, fetch_lequa2022
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from evaluation import evaluation_report
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from method.aggregative import EMQ
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from model_selection import GridSearchQ
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task = 'T1A'
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qp.environ['SAMPLE_SIZE']=LEQUA2022_SAMPLE_SIZE[task]
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training, val_generator, test_generator = fetch_lequa2022(task=task)
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# define the quantifier
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quantifier = EMQ(learner=LogisticRegression())
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# model selection
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param_grid = {'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]}
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model_selection = GridSearchQ(quantifier, param_grid, protocol=val_generator, n_jobs=-1, refit=False, verbose=True)
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quantifier = model_selection.fit(training)
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# evaluation
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report = evaluation_report(quantifier, protocol=test_generator, error_metrics=['mae', 'mrae'], verbose=True)
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print(report)
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