def test_regression(self):
sf = StackedFitter([TreeLearner(), KNNLearner()])
cv = CrossValidation(k=3, random_state=0)
results = cv(self.housing[:50], [sf, TreeLearner(), KNNLearner()])
mse = MSE()(results)
self.assertLess(mse[0], mse[1])
self.assertLess(mse[0], mse[2])
def test_NN_model(self):
results = CrossValidation(self.iris, [self.learner], k=3)
self.assertGreater(CA(results), 0.90)
results = CrossValidation(self.housing, [self.learner], k=3)
mse = MSE()
res = mse(results)
self.assertLess(res[0], 35)
def test_KNN_regression(self):
learners = [KNNRegressionLearner(),
KNNRegressionLearner(metric="mahalanobis")]
cv = CrossValidation(k=3)
results = cv(self.housing, learners)
mse = MSE(results)
self.assertLess(mse[1], mse[0])
def test_NN_regression(self):
const = ConstantLearner()
results = CrossValidation(self.housing, [NNRegressionLearner(), const],
k=3)
mse = MSE()
res = mse(results)
self.assertLess(res[0], 35)
self.assertLess(res[0], res[1])
def test_continuous_class(self):
data = self.housing
model = RandomForestRegressionLearner(random_state=0)(data)
res = permutation_feature_importance(model, data, MSE(),
self.n_repeats)
shape = len(data.domain.attributes), self.n_repeats
self.assertEqual(res[0].shape, shape)
self.assertEqual(res[1], [a.name for a in data.domain.attributes])
def test_regression(self):
sf = StackedFitter([TreeLearner(), KNNLearner()])
results = CrossValidation(
self.housing[:50],
[sf, TreeLearner(), KNNLearner()], k=3)
mse = MSE(results)
self.assertLess(mse[0], mse[1])
self.assertLess(mse[0], mse[2])
def test_wrap_score_reg(self):
data = self.housing_missing
model = RandomForestRegressionLearner(random_state=0)(data)
scorer = _wrap_score(MSE(), _check_model(model, data))
mocked_model = Mock(wraps=model)
baseline_score = scorer(mocked_model, data)
mocked_model.assert_called_once()
self.assertAlmostEqual(baseline_score, 2, 0)
def test_wrap_score_predict_reg(self):
data = self.housing
model = TreeRegressionLearner()(data)
scorer = _wrap_score(MSE(), _check_model(model, data))
mocked_model = Mock(wraps=model)
baseline_score = scorer(mocked_model, data)
# mocked_model.assert_not_called()
# mocked_model.predict.assert_called_once()
self.assertAlmostEqual(baseline_score, 0, 3)
def test_orange_models(self):
data = self.heart
n_repeats = self.n_repeats
model = NaiveBayesLearner()(data)
res = permutation_feature_importance(model, data, CA(), n_repeats)
shape = len(data.domain.attributes), n_repeats
self.assertEqual(res[0].shape, shape)
self.assertEqual(res[1], [a.name for a in data.domain.attributes])
data = self.iris
model = TreeLearner()(data)
res = permutation_feature_importance(model, data, AUC(), n_repeats)
shape = len(data.domain.attributes), n_repeats
self.assertEqual(res[0].shape, shape)
self.assertEqual(res[1], [a.name for a in data.domain.attributes])
data = self.housing
model = TreeRegressionLearner()(data)
res = permutation_feature_importance(model, data, MSE(), n_repeats)
shape = len(data.domain.attributes), n_repeats
self.assertEqual(res[0].shape, (shape))
self.assertEqual(res[1], [a.name for a in data.domain.attributes])
def test_NN_model(self):
results = CrossValidation(self.iris, [NNLearner()], k=3)
self.assertGreater(CA(results), 0.90)
results = CrossValidation(self.housing, [NNLearner()], k=3)
scorer = MSE()
self.assertLess(scorer(results)[0], 35)
def test_NN_regression(self):
results = CrossValidation(self.housing, [NNRegressionLearner()], k=3)
scorer = MSE()
self.assertLess(scorer(results)[0], 35)