def test_adaboost_base_estimator(self):
np.random.seed(0)
stump_estimator = TreeLearner(max_depth=1)
tree_estimator = TreeLearner()
stump = SklAdaBoostLearner(base_estimator=stump_estimator)
tree = SklAdaBoostLearner(base_estimator=tree_estimator)
results = CrossValidation(self.iris, [stump, tree], k=4)
ca = CA(results)
self.assertLess(ca[0], ca[1])
def test_adaboost_adequacy(self):
learner = SklAdaBoostLearner()
self.assertRaises(ValueError, learner, self.housing)
def test_predict_numpy(self):
learn = SklAdaBoostLearner()
m = learn(self.iris)
_, _ = m(self.iris.X, m.ValueProbs)
def test_predict_table(self):
learn = SklAdaBoostLearner()
m = learn(self.iris)
m(self.iris)
_, _ = m(self.iris, m.ValueProbs)
def test_predict_single_instance(self):
learn = SklAdaBoostLearner()
m = learn(self.iris)
ins = self.iris[0]
m(ins)
_, _ = m(ins, m.ValueProbs)
def test_adaboost(self):
learn = SklAdaBoostLearner()
results = CrossValidation(self.iris, [learn], k=3)
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 0.99)