Example #1
0
 def test_can_preserve_more_components(self):
     selector = fs.GMMSelector("mean", n_candidates=-1).fit(self.data)
     assert accuracy_score(self.labels >= 2, selector.selected_) >= 0.99
     selector = fs.GMMSelector("mean", n_candidates=2).fit(self.data)
     assert accuracy_score(self.labels >= 2, selector.selected_) >= 0.99
     selector = fs.GMMSelector("mean", n_candidates=1).fit(self.data)
     assert accuracy_score(self.labels >= 1, selector.selected_) >= 0.99
     selector = fs.GMMSelector("mean", n_candidates=1, preserve_high=False)
     selector.fit(self.data)
     assert accuracy_score(self.labels < 3, selector.selected_) >= 0.99
 def test_can_preserve_more_components(self):
     selector = fs.GMMSelector('mean', n_candidates=-1).fit(self.data)
     self.assertGreaterEqual(
         accuracy_score(self.labels >= 2, selector.selected_), 0.99)
     selector = fs.GMMSelector('mean', n_candidates=2).fit(self.data)
     self.assertGreaterEqual(
         accuracy_score(self.labels >= 2, selector.selected_), 0.99)
     selector = fs.GMMSelector('mean', n_candidates=1).fit(self.data)
     self.assertGreaterEqual(
         accuracy_score(self.labels >= 1, selector.selected_), 0.99)
     selector = fs.GMMSelector('mean', n_candidates=1, preserve_high=False)
     selector.fit(self.data)
     self.assertGreaterEqual(
         accuracy_score(self.labels < 3, selector.selected_), 0.99)
 def test_preserves_min_features_precisely_or_rate(self):
     selector = fs.GMMSelector('mean', min_features=50).fit(self.data)
     self.assertGreaterEqual(selector.selected_.sum(), 50)
     selector = fs.GMMSelector('mean', min_features_rate=0.5).fit(self.data)
     self.assertGreaterEqual(selector.selected_.sum(),
                             0.5 * self.labels.size)
 def test_works_for_mean_and_var_only(self):
     fs.GMMSelector('mean')
     fs.GMMSelector('var')
     with self.assertRaises(ValueError):
         fs.GMMSelector('yolo')
 def test_fails_log_with_negative_features(self):
     self.data[0, 1] = -1
     selector = fs.GMMSelector('mean', use_log=True)
     with self.assertRaises(ValueError):
         selector.fit(self.data)
 def test_that_0_candidates_preserves_all_the_features(self):
     selector = fs.GMMSelector('mean', n_candidates=0).fit(self.data)
     npt.assert_array_equal(selector.selected_, True)
 def test_selects_low_component_when_set(self):
     selector = fs.GMMSelector('mean', preserve_high=False).fit(self.data)
     npt.assert_array_equal(selector.selected_,
                            self.labels == self.labels.min())
 def test_selects_high_component_by_default(self):
     selector = fs.GMMSelector('mean').fit(self.data)
     npt.assert_array_equal(selector.selected_,
                            self.labels == self.labels.max())
Example #9
0
 def test_preserves_min_features_precisely_or_rate(self):
     selector = fs.GMMSelector("mean", min_features=50).fit(self.data)
     assert selector.selected_.sum() >= 50
     selector = fs.GMMSelector("mean", min_features_rate=0.5).fit(self.data)
     assert selector.selected_.sum() >= 0.5 * self.labels.size
Example #10
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 def test_skips_neutral(self):
     data = self.labels.reshape(-1, 1).astype(float)
     m1 = fs.GMMSelector("mean", neutral=0.0).fit(data).vals_
     m2 = data[30:].mean()
     assert m1 == m2
Example #11
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 def test_works_for_selected_metrics_only(self):
     fs.GMMSelector("mean")
     fs.GMMSelector("var")
     fs.GMMSelector("cv")
     with pytest.raises(ValueError):
         fs.GMMSelector("yolo")
Example #12
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 def test_fails_log_with_negative_features(self):
     self.data[0, 1] = -1
     selector = fs.GMMSelector("mean", use_log=True)
     with pytest.raises(ValueError):
         selector.fit(self.data)