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)