def test_ridge_scorer_feature(self): data = Table('housing') learner = RidgeRegressionLearner() scores = learner.score_data(data) for i, attr in enumerate(data.domain.attributes): score = learner.score_data(data, attr) self.assertEqual(score, scores[i])
def test_deprecated_normalize(self): """ When this test starts to fail: - remove normalize=False kwargs from Orange.regression. - remove _remove_deprecated_normalize and its calls - remove this test """ import Orange # pylint: disable=import-outside-toplevel self.assertLess(Orange.__version__, "3.33") with self.assertWarns(OrangeDeprecationWarning): RidgeRegressionLearner(normalize=True)
def test_scorer_feature(self): learners = [LinearRegressionLearner(), RidgeRegressionLearner(), LassoRegressionLearner(alpha=0.01), ElasticNetLearner(alpha=0.01)] for learner in learners: scores = learner.score_data(self.housing) for i, attr in enumerate(self.housing.domain.attributes): score = learner.score_data(self.housing, attr) self.assertEqual(score, scores[i])
def test_scorer(self): learners = [LinearRegressionLearner(), RidgeRegressionLearner(), LassoRegressionLearner(alpha=0.01), ElasticNetLearner(alpha=0.01)] for learner in learners: scores = learner.score_data(self.housing) self.assertEqual('LSTAT', self.housing.domain.attributes[np.argmax(scores)].name) self.assertEqual(len(scores), len(self.housing.domain.attributes))
def test_Regression(self): ridge = RidgeRegressionLearner() lasso = LassoRegressionLearner() elastic = ElasticNetLearner() elasticCV = ElasticNetCVLearner() mean = MeanLearner() learners = [ridge, lasso, elastic, elasticCV, mean] res = CrossValidation(self.housing, learners, k=2) rmse = RMSE(res) for i in range(len(learners) - 1): self.assertLess(rmse[i], rmse[-1])
def test_scorer(self): data = Table('housing') learners = [ LinearRegressionLearner(), RidgeRegressionLearner(), LassoRegressionLearner(alpha=0.01), ElasticNetLearner(alpha=0.01) ] for learner in learners: scores = learner.score_data(data) self.assertEqual('NOX', data.domain.attributes[np.argmax(scores)].name) self.assertEqual(len(scores), len(data.domain.attributes))
def __init__(self, learners, aggregate=RidgeRegressionLearner(), k=5, preprocessors=None): super().__init__(learners, aggregate, k=k, preprocessors=preprocessors)
def test_ridge_scorer(self): data = Table('housing') learner = RidgeRegressionLearner() scores = learner.score_data(data) self.assertEqual(len(scores), len(data.domain.attributes))
def __init__(self, learners, aggregate=RidgeRegressionLearner(), k=5): super().__init__(learners=learners, aggregate=aggregate, k=k)