54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
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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import quapy.functional as F
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from quapy.data.datasets import LEQUA2022_SAMPLE_SIZE, fetch_lequa2022
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from quapy.evaluation import evaluation_report
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from quapy.method.aggregative import EMQ
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from quapy.model_selection import GridSearchQ
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import pandas as pd
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"""
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This example shows hoy to use the LeQua datasets (new in v0.1.7). For more information about the datasets, and the
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LeQua competition itself, check:
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https://lequa2022.github.io/index (the site of the competition)
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https://ceur-ws.org/Vol-3180/paper-146.pdf (the overview paper)
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"""
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# there are 4 tasks (T1A, T1B, T2A, T2B)
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task = 'T1A'
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# set the sample size in the environment. The sample size is task-dendendent and can be consulted by doing:
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qp.environ['SAMPLE_SIZE'] = LEQUA2022_SAMPLE_SIZE[task]
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qp.environ['N_JOBS'] = -1
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# the fetch method returns a training set (an instance of LabelledCollection) and two generators: one for the
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# validation set and another for the test sets. These generators are both instances of classes that extend
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# AbstractProtocol (i.e., classes that implement sampling generation procedures) and, in particular, are instances
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# of SamplesFromDir, a protocol that simply iterates over pre-generated samples (those provided for the competition)
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# stored in a directory.
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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(classifier=LogisticRegression())
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# model selection
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param_grid = {
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'classifier__C': np.logspace(-3, 3, 7), # classifier-dependent: inverse of regularization strength
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'classifier__class_weight': ['balanced', None], # classifier-dependent: weights of each class
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'recalib': ['bcts', 'platt', None] # quantifier-dependent: recalibration method (new in v0.1.7)
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}
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model_selection = GridSearchQ(quantifier, param_grid, protocol=val_generator, error='mrae', 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', 'mkld'], verbose=True)
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# printing results
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pd.set_option('display.expand_frame_repr', False)
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report['estim-prev'] = report['estim-prev'].map(F.strprev)
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print(report)
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print('Averaged values:')
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print(report.mean())
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