84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
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# OtterTune - test_preprocessing.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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import unittest
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import numpy as np
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from analysis.preprocessing import DummyEncoder, consolidate_columnlabels
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class TestDummyEncoder(unittest.TestCase):
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def test_no_categoricals(self):
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X = [[1, 2, 3], [4, 5, 6]]
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n_values = []
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categorical_features = []
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cat_columnlabels = []
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noncat_columnlabels = ['a', 'b', 'c']
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enc = DummyEncoder(n_values, categorical_features,
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cat_columnlabels, noncat_columnlabels)
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X_encoded = enc.fit_transform(X)
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new_labels = enc.new_labels
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self.assertTrue(np.all(X == X_encoded))
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self.assertEqual(noncat_columnlabels, new_labels)
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def test_simple_categorical(self):
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X = [[0, 1, 2], [1, 1, 2], [2, 1, 2]]
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n_values = [3]
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categorical_features = [0]
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cat_columnlabels = ['label']
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noncat_columnlabels = ['a', 'b']
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X_expected = [[1, 0, 0, 1, 2], [0, 1, 0, 1, 2], [0, 0, 1, 1, 2]]
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new_labels_expected = ['label____0', 'label____1', 'label____2', 'a', 'b']
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enc = DummyEncoder(n_values, categorical_features,
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cat_columnlabels, noncat_columnlabels)
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X_encoded = enc.fit_transform(X)
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new_labels = enc.new_labels
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self.assertTrue(np.all(X_expected == X_encoded))
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self.assertEqual(new_labels_expected, new_labels)
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def test_mixed_categorical(self):
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X = [[1, 0, 2], [1, 1, 2], [1, 2, 2]]
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n_values = [3]
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categorical_features = [1]
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cat_columnlabels = ['label']
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noncat_columnlabels = ['a', 'b']
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X_expected = [[1, 0, 0, 1, 2], [0, 1, 0, 1, 2], [0, 0, 1, 1, 2]]
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new_labels_expected = ['label____0', 'label____1', 'label____2', 'a', 'b']
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enc = DummyEncoder(n_values, categorical_features,
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cat_columnlabels, noncat_columnlabels)
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X_encoded = enc.fit_transform(X)
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new_labels = enc.new_labels
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self.assertTrue(np.all(X_expected == X_encoded))
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self.assertEqual(new_labels_expected, new_labels)
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def test_consolidate(self):
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labels = ['label1____0', 'label1____1', 'label2____0', 'label2____1', 'noncat']
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consolidated = consolidate_columnlabels(labels)
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expected = ['label1', 'label2', 'noncat']
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self.assertEqual(expected, consolidated)
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def test_inverse_transform(self):
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X = [[1, 0, 2], [1, 1, 2], [1, 2, 2]]
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n_values = [3]
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categorical_features = [1]
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cat_columnlabels = ['label']
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noncat_columnlabels = ['a', 'b']
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X_expected = [[1, 0, 0, 1, 2], [0, 1, 0, 1, 2], [0, 0, 1, 1, 2]]
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enc = DummyEncoder(n_values, categorical_features,
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cat_columnlabels, noncat_columnlabels)
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X_encoded = enc.fit_transform(X)
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self.assertTrue(np.all(X_encoded == X_expected))
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X_decoded = enc.inverse_transform(X_encoded)
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self.assertTrue(np.all(X == X_decoded))
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if __name__ == '__main__':
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unittest.main()
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