63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
import unittest
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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from quapy.method.aggregative import PACC
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from quapy.method.confidence import ConfidenceIntervals, ConfidenceEllipseSimplex, AggregativeBootstrap
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from quapy.tests._synthetic import make_dataset
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def _dirichlet_samples(n_classes=3, n_samples=300, random_state=0):
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rng = np.random.RandomState(random_state)
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return rng.dirichlet(np.ones(n_classes) * 5, size=n_samples)
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class TestConfidenceRegions(unittest.TestCase):
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def test_confidence_intervals_contain_own_mean(self):
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samples = _dirichlet_samples()
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region = ConfidenceIntervals(samples)
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point_estimate = region.point_estimate()
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self.assertEqual(region.coverage(point_estimate), 1.)
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self.assertEqual(region.n_dim, 3)
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def test_confidence_ellipse_simplex_contains_own_mean(self):
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samples = _dirichlet_samples()
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region = ConfidenceEllipseSimplex(samples)
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point_estimate = region.point_estimate()
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self.assertIn(point_estimate, region)
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def test_simplex_portion_is_cached_and_consistent(self):
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# regression test for the @lru_cache-on-bound-method memory leak fix:
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# results must still be memoized per instance, and two instances must not share state
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region1 = ConfidenceEllipseSimplex(_dirichlet_samples(random_state=1))
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region2 = ConfidenceEllipseSimplex(_dirichlet_samples(random_state=2))
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p1_first = region1.simplex_portion()
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p1_second = region1.simplex_portion()
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self.assertEqual(p1_first, p1_second)
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p2 = region2.simplex_portion()
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self.assertTrue(hasattr(region1, '_simplex_portion_cache'))
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self.assertTrue(hasattr(region2, '_simplex_portion_cache'))
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# each instance keeps its own cached value
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self.assertEqual(region1._simplex_portion_cache, p1_first)
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self.assertEqual(region2._simplex_portion_cache, p2)
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def test_aggregative_bootstrap_end_to_end(self):
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dataset = make_dataset(n_train=150, n_test=50, n_classes=3, n_features=12, random_state=5)
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learner = LogisticRegression(max_iter=2000)
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learner.fit(*dataset.training.Xy)
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quantifier = AggregativeBootstrap(
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PACC(learner, fit_classifier=False), n_test_samples=50, confidence_level=0.9
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)
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quantifier.fit(*dataset.training.Xy)
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point_estimate, region = quantifier.predict_conf(dataset.test.X)
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self.assertEqual(len(point_estimate), 3)
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self.assertEqual(region.coverage(point_estimate), 1.)
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if __name__ == '__main__':
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unittest.main()
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