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_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_predict_single_instance_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
ins = self.housing[0]
pred = m(ins)
self.assertGreaterEqual(pred, 0)
def test_adaboost_reg(self):
learn = SklAdaBoostRegressionLearner()
cv = CrossValidation(k=3)
results = cv(self.housing, [learn])
_ = RMSE(results)
def test_adaboost_adequacy_reg(self):
learner = SklAdaBoostRegressionLearner()
self.assertRaises(ValueError, learner, self.iris)
def test_predict_table_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
pred = m(self.housing)
self.assertEqual(len(self.housing), len(pred))
self.assertTrue(all(pred) > 0)
def test_predict_single_instance_reg(self):
learn = SklAdaBoostRegressionLearner()
m = learn(self.housing)
for ins in self.housing:
pred = m(ins)
self.assertTrue(pred > 0)
def test_adaboost_reg(self):
learn = SklAdaBoostRegressionLearner()
results = CrossValidation(self.housing, [learn], k=10)
_ = RMSE(results)
