def test_unknown(self):
t = self.random_table
t = self.add_meta_fold(t, 3)
fat = t.domain.metas[0]
t[0][fat] = float("nan")
res = CrossValidationFeature(feature=fat)(t, [NaiveBayesLearner()])
self.assertNotIn(0, res.row_indices)
def test_bad_feature(self):
feat = DiscreteVariable(name="fold", values="abc")
domain = Domain([DiscreteVariable("x", values="ab")],
DiscreteVariable("y", values="cd"),
metas=[feat])
t = Table.from_numpy(domain, np.zeros((10, 1)), np.ones((10, 1)),
np.full((10, 1), np.nan))
self.assertRaises(ValueError, CrossValidationFeature(feature=feat), t,
[NaiveBayesLearner()])
def test_call(self):
t = self.random_table
t = self.add_meta_fold(t, 3)
res = CrossValidationFeature(feature=t.domain.metas[0])(t, [NaiveBayesLearner()])
y = t.Y
np.testing.assert_equal(res.actual, y[res.row_indices].reshape(len(t)))
np.testing.assert_equal(res.predicted[0],
y[res.row_indices].reshape(len(t)))
np.testing.assert_equal(np.argmax(res.probabilities[0], axis=1),
y[res.row_indices].reshape(len(t)))
def test_init(self):
var = DiscreteVariable(name="fold", values="abc")
res = CrossValidationFeature(feature=var)
self.assertIs(res.feature, var)
