forked from moreo/QuaPy
193 lines
6.6 KiB
Python
193 lines
6.6 KiB
Python
import unittest
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import numpy as np
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import quapy.functional
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from quapy.data import LabelledCollection
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from quapy.protocol import APP, NPP, UPP, DomainMixer, AbstractStochasticSeededProtocol
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def mock_labelled_collection(prefix=''):
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y = [0] * 250 + [1] * 250 + [2] * 250 + [3] * 250
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X = [prefix + str(i) + '-' + str(yi) for i, yi in enumerate(y)]
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return LabelledCollection(X, y, classes=sorted(np.unique(y)))
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def samples_to_str(protocol):
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samples_str = ""
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for instances, prev in protocol():
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samples_str += f'{instances}\t{prev}\n'
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return samples_str
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class TestProtocols(unittest.TestCase):
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def test_app_sanity_check(self):
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data = mock_labelled_collection()
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n_prevpoints = 101
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repeats = 10
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with self.assertRaises(RuntimeError):
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p = APP(data, sample_size=5, n_prevalences=n_prevpoints, repeats=repeats, random_state=42)
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n_combinations = \
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quapy.functional.num_prevalence_combinations(n_prevpoints, n_classes=data.n_classes, n_repeats=repeats)
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p = APP(data, sample_size=5, n_prevalences=n_prevpoints, random_state=42, sanity_check=n_combinations)
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p = APP(data, sample_size=5, n_prevalences=n_prevpoints, random_state=42, sanity_check=None)
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def test_app_replicate(self):
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data = mock_labelled_collection()
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p = APP(data, sample_size=5, n_prevalences=11, random_state=42)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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p = APP(data, sample_size=5, n_prevalences=11) # <- random_state is by default set to 0
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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def test_app_not_replicate(self):
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data = mock_labelled_collection()
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p = APP(data, sample_size=5, n_prevalences=11, random_state=None)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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p = APP(data, sample_size=5, n_prevalences=11, random_state=42)
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samples1 = samples_to_str(p)
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p = APP(data, sample_size=5, n_prevalences=11, random_state=0)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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def test_app_number(self):
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data = mock_labelled_collection()
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p = APP(data, sample_size=100, n_prevalences=10, repeats=1)
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# surprisingly enough, for some n_prevalences the test fails, notwithstanding
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# everything is correct. The problem is that in function APP.prevalence_grid()
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# there is sometimes one rounding error that gets cumulated and
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# surpasses 1.0 (by a very small float value, 0.0000000000002 or sthe like)
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# so these tuples are mistakenly removed... I have tried with np.close, and
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# other workarounds, but eventually happens that there is some negative probability
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# in the sampling function...
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count = 0
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for _ in p():
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count+=1
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self.assertEqual(count, p.total())
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def test_npp_replicate(self):
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data = mock_labelled_collection()
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p = NPP(data, sample_size=5, repeats=5, random_state=42)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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p = NPP(data, sample_size=5, repeats=5) # <- random_state is by default set to 0
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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def test_npp_not_replicate(self):
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data = mock_labelled_collection()
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p = NPP(data, sample_size=5, repeats=5, random_state=None)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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p = NPP(data, sample_size=5, repeats=5, random_state=42)
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samples1 = samples_to_str(p)
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p = NPP(data, sample_size=5, repeats=5, random_state=0)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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def test_kraemer_replicate(self):
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data = mock_labelled_collection()
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p = UPP(data, sample_size=5, repeats=10, random_state=42)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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p = UPP(data, sample_size=5, repeats=10) # <- random_state is by default set to 0
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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def test_kraemer_not_replicate(self):
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data = mock_labelled_collection()
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p = UPP(data, sample_size=5, repeats=10, random_state=None)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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def test_covariate_shift_replicate(self):
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dataA = mock_labelled_collection('domA')
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dataB = mock_labelled_collection('domB')
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p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11, random_state=1)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11) # <- random_state is by default set to 0
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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def test_covariate_shift_not_replicate(self):
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dataA = mock_labelled_collection('domA')
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dataB = mock_labelled_collection('domB')
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p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11, random_state=None)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertNotEqual(samples1, samples2)
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def test_no_seed_init(self):
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class NoSeedInit(AbstractStochasticSeededProtocol):
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def __init__(self):
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self.data = mock_labelled_collection()
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def samples_parameters(self):
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# return a matrix containing sampling indexes in the rows
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return np.random.randint(0, len(self.data), 10*10).reshape(10, 10)
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def sample(self, params):
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index = np.unique(params)
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return self.data.sampling_from_index(index)
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p = NoSeedInit()
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# this should raise a ValueError, since the class is said to be AbstractStochasticSeededProtocol but the
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# random_seed has never been passed to super(NoSeedInit, self).__init__(random_seed)
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with self.assertRaises(ValueError):
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for sample in p():
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pass
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print('done')
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if __name__ == '__main__':
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unittest.main()
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