def test_lasso_scorer_feature(self):
data = Table('housing')
learner = LassoRegressionLearner()
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_lasso_scorer_feature(self):
data = Table('housing')
learner = LassoRegressionLearner()
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_comparison_elastic_net(self):
alphas = [0.001, 0.1, 1, 10, 100]
for a in alphas:
lasso = LassoRegressionLearner(alpha=a)
lasso_model = lasso(self.housing)
elastic = ElasticNetLearner(alpha=a, l1_ratio=1)
elastic_model = elastic(self.housing)
d = np.sum(lasso_model.coefficients - elastic_model.coefficients)
self.assertEqual(d, 0)
def test_comparison_elastic_net(self):
alphas = [0.001, 0.1, 1, 10, 100]
for a in alphas:
lasso = LassoRegressionLearner(alpha=a)
lasso_model = lasso(self.housing)
en = ElasticNetLearner(alpha=a, l1_ratio=1)
en_model = en(self.housing)
np.testing.assert_allclose(lasso_model.coefficients,
en_model.coefficients,
atol=1e-07)
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 test_lasso_scorer(self):
data = Table('housing')
learner = LassoRegressionLearner()
scores = learner.score_data(data)
self.assertEqual(len(scores), len(data.domain.attributes))
def test_lasso_scorer(self):
data = Table('housing')
learner = LassoRegressionLearner()
scores = learner.score_data(data)
self.assertEqual(len(scores), len(data.domain.attributes))