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# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import numpy as np
from packaging.version import Version
import Orange
from Orange.data import Table
from Orange.classification import SklTreeLearner
from Orange.regression import SklTreeRegressionLearner
from Orange.ensembles import (
SklAdaBoostClassificationLearner,
SklAdaBoostRegressionLearner,
)
from Orange.evaluation import CrossValidation, CA, RMSE
from Orange.util import OrangeDeprecationWarning
class TestSklAdaBoostLearner(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.iris = Table("iris")
cls.housing = Table("housing")
def test_adaboost(self):
learn = SklAdaBoostClassificationLearner()
cv = CrossValidation(k=3)
results = cv(self.iris, [learn])
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 0.99)
def test_adaboost_estimator(self):
np.random.seed(4)
stump_estimator = SklTreeLearner(max_depth=1)
tree_estimator = SklTreeLearner()
stump = SklAdaBoostClassificationLearner(
estimator=stump_estimator, n_estimators=5)
tree = SklAdaBoostClassificationLearner(
estimator=tree_estimator, n_estimators=5)
cv = CrossValidation(k=4)
results = cv(self.iris, [stump, tree])
ca = CA(results)
self.assertLessEqual(ca[0], ca[1])
def test_predict_single_instance(self):
learn = SklAdaBoostClassificationLearner()
m = learn(self.iris)
ins = self.iris[0]
m(ins)
_, _ = m(ins, m.ValueProbs)
def test_predict_table(self):
learn = SklAdaBoostClassificationLearner()
m = learn(self.iris)
m(self.iris)
_, _ = m(self.iris, m.ValueProbs)
def test_predict_numpy(self):
learn = SklAdaBoostClassificationLearner()
m = learn(self.iris)
_, _ = m(self.iris.X, m.ValueProbs)
def test_adaboost_adequacy(self):
learner = SklAdaBoostClassificationLearner()
self.assertRaises(ValueError, learner, self.housing)
def test_adaboost_reg(self):
learn = SklAdaBoostRegressionLearner()
cv = CrossValidation(k=3)
results = cv(self.housing, [learn])
_ = RMSE(results)
def test_adaboost_reg_estimator(self):
np.random.seed(0)
stump_estimator = SklTreeRegressionLearner(max_depth=1)
tree_estimator = SklTreeRegressionLearner()
stump = SklAdaBoostRegressionLearner(estimator=stump_estimator)
tree = SklAdaBoostRegressionLearner(estimator=tree_estimator)
cv = CrossValidation(k=3)
results = cv(self.housing, [stump, tree])
rmse = RMSE(results)
self.assertGreaterEqual(rmse[0], rmse[1])
def test_predict_single_instance_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
ins = self.housing[0]
pred = m(ins)
self.assertGreaterEqual(pred, 0)
def test_predict_table_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
pred = m(self.housing)
self.assertEqual(len(self.housing), len(pred))
self.assertGreater(all(pred), 0)
def test_predict_numpy_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
pred = m(self.housing.X)
self.assertEqual(len(self.housing), len(pred))
self.assertGreater(all(pred), 0)
def test_adaboost_adequacy_reg(self):
learner = SklAdaBoostRegressionLearner()
self.assertRaises(ValueError, learner, self.iris)
def test_remove_deprecation(self):
if (Version(Orange.__version__).is_prerelease
and Version(Orange.__version__) >= Version("3.42")):
self.fail(
"SklAdaBoostClassificationLearner: `algorithm` was deprecated in "
"version 3.40. Please remove everything related to it."
)
with self.assertWarns(OrangeDeprecationWarning):
SklAdaBoostClassificationLearner(algorithm="invalid")(self.iris)
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