def test_sparse_pps(self):
with self.data.unlocked():
self.data.X = csr_matrix(self.data.X)
out = AdaptiveNormalize()(self.data)
true_out = Scale(center=Scale.NoCentering, scale=Scale.Span)(self.data)
np.testing.assert_array_equal(out, true_out)
self.data = self.data.X.toarray()
def test_reconstruct_domain(self):
data = Table("heart_disease")
cls = LogisticRegressionLearner()(data)
domain = OWNomogram.reconstruct_domain(cls, cls.domain)
transformed_data = cls.original_data.transform(domain)
self.assertEqual(transformed_data.X.shape, data.X.shape)
self.assertFalse(np.isnan(transformed_data.X[0]).any())
scaled_data = Scale()(data)
cls = LogisticRegressionLearner()(scaled_data)
domain = OWNomogram.reconstruct_domain(cls, cls.domain)
transformed_data = cls.original_data.transform(domain)
self.assertEqual(transformed_data.X.shape, scaled_data.X.shape)
self.assertFalse(np.isnan(transformed_data.X[0]).any())
disc_data = Continuize()(data)
cls = LogisticRegressionLearner()(disc_data)
domain = OWNomogram.reconstruct_domain(cls, cls.domain)
transformed_data = cls.original_data.transform(domain)
self.assertEqual(transformed_data.X.shape, disc_data.X.shape)
self.assertFalse(np.isnan(transformed_data.X[0]).any())
def test_scaling_pickling(self):
c = Scale(center=Scale.Median, scale=Scale.Span)
s = pickle.dumps(c, -1)
c1 = pickle.loads(s)
self.assertIs(c1.center, c.center)
self.assertIs(c1.scale, c.scale)
def test_scaling_median_stddev(self):
table = Scale(center=Scale.Median, scale=Scale.Std)(self.table)
np.testing.assert_almost_equal(np.std(table, 0), 1)
def test_scaling_mean_span(self):
table = Scale(center=Scale.Mean, scale=Scale.Span)(self.table)
np.testing.assert_almost_equal(np.mean(table, 0), 0)
np.testing.assert_almost_equal(np.ptp(table, 0), 1)
