112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
import unittest
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import quapy as qp
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from quapy.data import LabelledCollection
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from quapy.functional import strprev
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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from quapy.method.aggregative import PACC
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import quapy.functional as F
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class MyTestCase(unittest.TestCase):
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def test_prediction_replicability(self):
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dataset = qp.datasets.fetch_UCIBinaryDataset('yeast')
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with qp.util.temp_seed(0):
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lr = LogisticRegression(random_state=0, max_iter=10000)
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pacc = PACC(lr)
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prev = pacc.fit(dataset.training).quantify(dataset.test.X)
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str_prev1 = strprev(prev, prec=5)
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with qp.util.temp_seed(0):
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lr = LogisticRegression(random_state=0, max_iter=10000)
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pacc = PACC(lr)
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prev2 = pacc.fit(dataset.training).quantify(dataset.test.X)
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str_prev2 = strprev(prev2, prec=5)
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self.assertEqual(str_prev1, str_prev2) # add assertion here
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def test_samping_replicability(self):
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def equal_collections(c1, c2, value=True):
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self.assertEqual(np.all(c1.X == c2.X), value)
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self.assertEqual(np.all(c1.y == c2.y), value)
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if value:
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self.assertEqual(np.all(c1.classes_ == c2.classes_), value)
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X = list(map(str, range(100)))
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y = np.random.randint(0, 2, 100)
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data = LabelledCollection(instances=X, labels=y)
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sample1 = data.sampling(50)
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sample2 = data.sampling(50)
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equal_collections(sample1, sample2, False)
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sample1 = data.sampling(50, random_state=0)
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sample2 = data.sampling(50, random_state=0)
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equal_collections(sample1, sample2, True)
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sample1 = data.sampling(50, *[0.7, 0.3], random_state=0)
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sample2 = data.sampling(50, *[0.7, 0.3], random_state=0)
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equal_collections(sample1, sample2, True)
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with qp.util.temp_seed(0):
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sample1 = data.sampling(50, *[0.7, 0.3])
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with qp.util.temp_seed(0):
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sample2 = data.sampling(50, *[0.7, 0.3])
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equal_collections(sample1, sample2, True)
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sample1 = data.sampling(50, *[0.7, 0.3], random_state=0)
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sample2 = data.sampling(50, *[0.7, 0.3], random_state=0)
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equal_collections(sample1, sample2, True)
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sample1_tr, sample1_te = data.split_stratified(train_prop=0.7, random_state=0)
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sample2_tr, sample2_te = data.split_stratified(train_prop=0.7, random_state=0)
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equal_collections(sample1_tr, sample2_tr, True)
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equal_collections(sample1_te, sample2_te, True)
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with qp.util.temp_seed(0):
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sample1_tr, sample1_te = data.split_stratified(train_prop=0.7)
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with qp.util.temp_seed(0):
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sample2_tr, sample2_te = data.split_stratified(train_prop=0.7)
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equal_collections(sample1_tr, sample2_tr, True)
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equal_collections(sample1_te, sample2_te, True)
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def test_parallel_replicability(self):
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train, test = qp.datasets.fetch_UCIMulticlassDataset('dry-bean').train_test
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test = test.sampling(500, *[0.1, 0.0, 0.1, 0.1, 0.2, 0.5, 0.0])
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with qp.util.temp_seed(10):
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pacc = PACC(LogisticRegression(), val_split=2, n_jobs=2)
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pacc.fit(train, val_split=0.5)
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prev1 = F.strprev(pacc.quantify(test.instances))
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with qp.util.temp_seed(0):
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pacc = PACC(LogisticRegression(), val_split=2, n_jobs=2)
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pacc.fit(train, val_split=0.5)
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prev2 = F.strprev(pacc.quantify(test.instances))
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with qp.util.temp_seed(0):
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pacc = PACC(LogisticRegression(), val_split=2, n_jobs=2)
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pacc.fit(train, val_split=0.5)
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prev3 = F.strprev(pacc.quantify(test.instances))
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print(prev1)
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print(prev2)
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print(prev3)
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self.assertNotEqual(prev1, prev2)
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self.assertEqual(prev2, prev3)
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if __name__ == '__main__':
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unittest.main()
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