def test_adaboost_reg_base_estimator(self):
np.random.seed(0)
stump_estimator = TreeRegressionLearner(max_depth=1)
tree_estimator = TreeRegressionLearner()
stump = SklAdaBoostRegressionLearner(base_estimator=stump_estimator)
tree = SklAdaBoostRegressionLearner(base_estimator=tree_estimator)
results = CrossValidation(self.housing, [stump, tree], k=3)
rmse = RMSE(results)
self.assertGreaterEqual(rmse[0], rmse[1])
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_input_learner(self):
"""Check if base learner properly changes with learner on the input"""
max_depth = 2
default_base_est = self.widget.base_estimator
self.assertIsInstance(default_base_est, TreeRegressionLearner)
self.assertIsNone(default_base_est.params.get("max_depth"))
self.send_signal("Learner", TreeRegressionLearner(max_depth=max_depth))
self.assertEqual(self.widget.base_estimator.params.get("max_depth"),
max_depth)
self.widget.apply_button.button.click()
output_base_est = self.get_output("Learner").params.get("base_estimator")
self.assertEqual(output_base_est.max_depth, max_depth)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
# Set up for output tests
tree = TreeClassificationLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Set up for widget tests
titanic_data = Table('titanic')[::50]
cls.titanic = TreeClassificationLearner(max_depth=1)(titanic_data)
cls.titanic.instances = titanic_data
housing_data = Table('housing')[:10]
cls.housing = TreeRegressionLearner(max_depth=1)(housing_data)
cls.housing.instances = housing_data
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])
class OWAdaBoostRegression(owadaboost.OWAdaBoostClassification):
name = "AdaBoost"
description = "An ensemble meta-algorithm that combines weak learners " \
"and adapts to the 'hardness' of each training sample. "
icon = "icons/AdaBoost.svg"
priority = 80
LEARNER = SklAdaBoostRegressionLearner
inputs = [("Learner", LearnerRegression, "set_base_learner")]
losses = ["Linear", "Square", "Exponential"]
loss = Setting(0)
DEFAULT_BASE_ESTIMATOR = TreeRegressionLearner()
def add_specific_parameters(self, box):
self.loss_combo = gui.comboBox(box,
self,
"loss",
label="Loss:",
orientation=Qt.Horizontal,
items=self.losses,
callback=self.settings_changed)
def create_learner(self):
return self.LEARNER(base_estimator=self.base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
preprocessors=self.preprocessors,
loss=self.losses[self.loss].lower())
def get_learner_parameters(self):
return (("Base estimator", self.base_estimator),
("Number of estimators", self.n_estimators),
("Loss", self.losses[self.loss].capitalize()))
def test_regression(self):
table = Table('housing')
learn = TreeRegressionLearner()
model = learn(table)
pred = model(table)
self.assertTrue(np.all(table.Y.flatten() == pred))
def test_get_tree_regression(self):
table = Table('housing')
learn = TreeRegressionLearner()
clf = learn(table)
self.assertIsInstance(clf.tree, Tree)