diff --git a/autosklearn/pipeline/components/regression/ridge_regression.py b/autosklearn/pipeline/components/regression/ridge_regression.py index 7a39178205..005b279926 100644 --- a/autosklearn/pipeline/components/regression/ridge_regression.py +++ b/autosklearn/pipeline/components/regression/ridge_regression.py @@ -22,7 +22,8 @@ def fit(self, X, Y): fit_intercept=self.fit_intercept, tol=self.tol, copy_X=False, - normalize=False) + normalize=False, + random_state=self.random_state) self.estimator.fit(X, Y) return self diff --git a/test/test_pipeline/components/regression/test_adaboost.py b/test/test_pipeline/components/regression/test_adaboost.py index 2f9d469c23..56731fae0f 100644 --- a/test/test_pipeline/components/regression/test_adaboost.py +++ b/test/test_pipeline/components/regression/test_adaboost.py @@ -1,25 +1,24 @@ -import unittest +import sklearn.ensemble from autosklearn.pipeline.components.regression.adaboost import \ AdaboostRegressor -from autosklearn.pipeline.util import _test_regressor +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class AdaBoostComponentTest(BaseRegressionComponentTest): -class AdaBoostComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = \ - _test_regressor(AdaboostRegressor, dataset='boston') - self.assertAlmostEqual(0.60525743737887405, - sklearn.metrics.r2_score(targets, - predictions)) + __test__ = True - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = \ - _test_regressor(AdaboostRegressor, sparse=True, dataset='boston') - self.assertAlmostEqual(0.22111559712318207, - sklearn.metrics.r2_score(targets, - predictions)) + res = dict() + res["default_boston"] = 0.60525743737887405 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = 0.22111559712318207 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.25129853514492517 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = 0.090755670764629537 + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.ensemble.AdaBoostRegressor + + module = AdaboostRegressor diff --git a/test/test_pipeline/components/regression/test_ard_regression.py b/test/test_pipeline/components/regression/test_ard_regression.py index 29973eb8a4..b13319a26d 100644 --- a/test/test_pipeline/components/regression/test_ard_regression.py +++ b/test/test_pipeline/components/regression/test_ard_regression.py @@ -1,17 +1,24 @@ -import unittest +import sklearn.linear_model from autosklearn.pipeline.components.regression.ard_regression import \ ARDRegression -from autosklearn.pipeline.util import _test_regressor +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class ARDRegressionComponentTest(BaseRegressionComponentTest): -class ARDRegressionComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = \ - _test_regressor(ARDRegression, dataset='boston') - self.assertAlmostEqual(0.70316694175513961, - sklearn.metrics.r2_score(targets, - predictions)) + __test__ = True + + res = dict() + res["default_boston"] = 0.70316694175513961 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = None + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.4172236487551515 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = None + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.linear_model.ARDRegression + + module = ARDRegression diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py new file mode 100644 index 0000000000..f33ed5ecd0 --- /dev/null +++ b/test/test_pipeline/components/regression/test_base.py @@ -0,0 +1,146 @@ +import unittest + +from autosklearn.pipeline.util import _test_regressor, \ + _test_regressor_iterative_fit + +from autosklearn.pipeline.constants import * + +import sklearn.metrics + + +class BaseRegressionComponentTest(unittest.TestCase): + + res = None + + module = None + sk_module = None + + # Magic command to not run tests on base class + __test__ = False + + def test_default_boston(self): + for i in range(2): + predictions, targets = \ + _test_regressor(dataset="boston", + Regressor=self.module) + + if "default_boston_le_ge" in self.res: + # Special treatment for Gaussian Process Regression + self.assertLessEqual( + sklearn.metrics.r2_score(y_true=targets, + y_pred=predictions), + self.res["default_boston_le_ge"][0]) + self.assertGreaterEqual( + sklearn.metrics.r2_score(y_true=targets, + y_pred=predictions), + self.res["default_boston_le_ge"][1]) + else: + self.assertAlmostEqual(self.res["default_boston"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_boston_places", 7)) + + def test_default_boston_iterative_fit(self): + if not hasattr(self.module, 'iterative_fit'): + return + + for i in range(2): + predictions, targets = \ + _test_regressor_iterative_fit(dataset="boston", + Regressor=self.module) + self.assertAlmostEqual(self.res["default_boston_iterative"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_boston_iterative_places", 7)) + + def test_default_boston_iterative_sparse_fit(self): + if not hasattr(self.module, 'iterative_fit'): + return + if SPARSE not in self.module.get_properties()["input"]: + return + + for i in range(2): + predictions, targets = \ + _test_regressor_iterative_fit(dataset="boston", + Regressor=self.module, + sparse=True) + self.assertAlmostEqual(self.res["default_boston_iterative_sparse"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_boston_iterative_sparse_places", 7)) + + def test_default_boston_sparse(self): + if SPARSE not in self.module.get_properties()["input"]: + return + + for i in range(2): + predictions, targets = \ + _test_regressor(dataset="boston", + Regressor=self.module, + sparse=True) + self.assertAlmostEqual(self.res["default_boston_sparse"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_boston_sparse_places", 7)) + + def test_default_diabetes(self): + for i in range(2): + predictions, targets = \ + _test_regressor(dataset="diabetes", + Regressor=self.module) + + self.assertAlmostEqual(self.res["default_diabetes"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_diabetes_places", 7)) + + def test_default_diabetes_iterative_fit(self): + if not hasattr(self.module, 'iterative_fit'): + return + + for i in range(2): + predictions, targets = \ + _test_regressor_iterative_fit(dataset="diabetes", + Regressor=self.module) + self.assertAlmostEqual(self.res["default_diabetes_iterative"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_diabetes_iterative_places", 7)) + + def test_default_diabetes_iterative_sparse_fit(self): + if not hasattr(self.module, 'iterative_fit'): + return + if SPARSE not in self.module.get_properties()["input"]: + return + + for i in range(2): + predictions, targets = \ + _test_regressor_iterative_fit(dataset="diabetes", + Regressor=self.module, + sparse=True) + self.assertAlmostEqual(self.res["default_diabetes_iterative_sparse"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_diabetes_iterative_sparse_places", 7)) + + def test_default_diabetes_sparse(self): + if SPARSE not in self.module.get_properties()["input"]: + return + + for i in range(2): + predictions, targets = \ + _test_regressor(dataset="diabetes", + Regressor=self.module, + sparse=True) + self.assertAlmostEqual(self.res["default_diabetes_sparse"], + sklearn.metrics.r2_score(targets, + predictions), + places=self.res.get( + "default_diabetes_sparse_places", 7)) \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_decision_tree.py b/test/test_pipeline/components/regression/test_decision_tree.py index 9cb7aae7a0..b48a54b317 100644 --- a/test/test_pipeline/components/regression/test_decision_tree.py +++ b/test/test_pipeline/components/regression/test_decision_tree.py @@ -1,22 +1,24 @@ -import unittest +import sklearn.tree -from autosklearn.pipeline.components.regression.decision_tree import DecisionTree -from autosklearn.pipeline.util import _test_regressor +from autosklearn.pipeline.components.regression.decision_tree import \ + DecisionTree +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class DecisionTreeComponentTest(BaseRegressionComponentTest): -class DecisionTreetComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = _test_regressor(DecisionTree,) - self.assertAlmostEqual(0.1564592449511697, - sklearn.metrics.r2_score(targets, - predictions)) + __test__ = True - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = _test_regressor(DecisionTree, sparse=True) - self.assertAlmostEqual(-0.020818312539637507, - sklearn.metrics.r2_score(targets, - predictions)) + res = dict() + res["default_boston"] = 0.35616796434879905 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = 0.18031669797027394 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.1564592449511697 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = -0.020818312539637507 + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.tree.DecisionTreeRegressor + + module = DecisionTree \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_extra_trees.py b/test/test_pipeline/components/regression/test_extra_trees.py index 9b9ab6f91a..1cf3cfb72c 100644 --- a/test/test_pipeline/components/regression/test_extra_trees.py +++ b/test/test_pipeline/components/regression/test_extra_trees.py @@ -1,33 +1,24 @@ -import unittest +import sklearn.ensemble from autosklearn.pipeline.components.regression.extra_trees import \ ExtraTreesRegressor -from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class ExtraTreesComponentTest(BaseRegressionComponentTest): -class ExtraTreesComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = \ - _test_regressor(ExtraTreesRegressor) - self.assertAlmostEqual(0.43258995365114405, - sklearn.metrics.r2_score(targets, - predictions)) + __test__ = True - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = \ - _test_regressor(ExtraTreesRegressor, sparse=True) - self.assertAlmostEqual(0.28016012771570553, - sklearn.metrics.r2_score(targets, - predictions)) + res = dict() + res["default_boston"] = 0.77744792837581866 + res["default_boston_iterative"] = 0.77744792837581866 + res["default_boston_sparse"] = 0.32936702992375644 + res["default_boston_iterative_sparse"] = 0.32936702992375644 + res["default_diabetes"] = 0.43258995365114405 + res["default_diabetes_iterative"] = 0.43258995365114405 + res["default_diabetes_sparse"] = 0.28016012771570553 + res["default_diabetes_iterative_sparse"] = 0.28016012771570553 - def test_default_configuration_iterative_fit(self): - for i in range(2): - predictions, targets = \ - _test_regressor_iterative_fit(ExtraTreesRegressor) - self.assertAlmostEqual(0.43258995365114405, - sklearn.metrics.r2_score(targets, - predictions)) \ No newline at end of file + sk_mod = sklearn.ensemble.ExtraTreesRegressor + + module = ExtraTreesRegressor \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_gaussian_process.py b/test/test_pipeline/components/regression/test_gaussian_process.py index 6871e4c225..07a9ad9183 100644 --- a/test/test_pipeline/components/regression/test_gaussian_process.py +++ b/test/test_pipeline/components/regression/test_gaussian_process.py @@ -1,24 +1,38 @@ -import unittest - -from autosklearn.pipeline.components.regression.gaussian_process import GaussianProcess -from autosklearn.pipeline.util import _test_regressor - -import sklearn.metrics - - -class GaussianProcessComponentTest(unittest.TestCase): - def test_default_configuration(self): - # Only twice to reduce the number of warning printed to the command line - for i in range(2): - # Float32 leads to numeric instabilities - predictions, targets = _test_regressor(GaussianProcess, - dataset='boston') - # My machine: 0.574913739659292 - # travis-ci: 0.49562471963524557 - self.assertLessEqual( - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), - 0.6) - self.assertGreaterEqual( - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), - 0.4) +import sklearn.gaussian_process +from autosklearn.pipeline.components.regression.gaussian_process import \ + GaussianProcess + +from .test_base import BaseRegressionComponentTest + + +class GaussianProcessComponentTest(BaseRegressionComponentTest): + + __test__ = True + + res = dict() + res["default_boston_le_ge"] = [0.6, 0.4] + res["default_boston_places"] = 1 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = None + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = -7.4131230585194885e-06 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = None + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.gaussian_process.GaussianProcessRegressor + + module = GaussianProcess + + """ + # Leave this here for future reference + # My machine: 0.574913739659292 + # travis-ci: 0.49562471963524557 + self.assertLessEqual( + sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), + 0.6) + self.assertGreaterEqual( + sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), + 0.4) + """ diff --git a/test/test_pipeline/components/regression/test_gradient_boosting.py b/test/test_pipeline/components/regression/test_gradient_boosting.py index f6c7100768..a70e5ebb2c 100644 --- a/test/test_pipeline/components/regression/test_gradient_boosting.py +++ b/test/test_pipeline/components/regression/test_gradient_boosting.py @@ -1,21 +1,25 @@ -import unittest +import sklearn.ensemble -from autosklearn.pipeline.components.regression.gradient_boosting import GradientBoosting -from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit +from autosklearn.pipeline.components.regression.gradient_boosting import \ + GradientBoosting -import sklearn.metrics +from .test_base import BaseRegressionComponentTest -class GradientBoostingComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): +class GradientBoostingComponentTest(BaseRegressionComponentTest): - predictions, targets = _test_regressor(GradientBoosting) - self.assertAlmostEqual(0.37192663934006487, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + __test__ = True - def test_default_configuration_iterative_fit(self): - for i in range(2): - predictions, targets = _test_regressor_iterative_fit(GradientBoosting) - self.assertAlmostEqual(0.37192663934006487, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + res = dict() + res["default_boston"] = 0.83961954550470863 + res["default_boston_iterative"] = 0.83961954550470863 + res["default_boston_sparse"] = None + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.37192663934006487 + res["default_diabetes_iterative"] = 0.37192663934006487 + res["default_diabetes_sparse"] = None + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.ensemble.GradientBoostingRegressor + + module = GradientBoosting \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_k_nearest_neighbors.py b/test/test_pipeline/components/regression/test_k_nearest_neighbors.py index 455045bf59..7b8b63b829 100644 --- a/test/test_pipeline/components/regression/test_k_nearest_neighbors.py +++ b/test/test_pipeline/components/regression/test_k_nearest_neighbors.py @@ -1,25 +1,24 @@ -import unittest +import sklearn.neighbors from autosklearn.pipeline.components.regression.k_nearest_neighbors import \ KNearestNeighborsRegressor -from autosklearn.pipeline.util import _test_regressor +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class KNearestNeighborsComponentTest(BaseRegressionComponentTest): -class KNearestNeighborsComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = \ - _test_regressor(KNearestNeighborsRegressor) - self.assertAlmostEqual(0.068600456340847438, - sklearn.metrics.r2_score(targets, - predictions)) + __test__ = True - def test_default_configuration_sparse_data(self): - for i in range(2): - predictions, targets = \ - _test_regressor(KNearestNeighborsRegressor, sparse=True) - self.assertAlmostEqual(-0.16321841460809972, - sklearn.metrics.r2_score(targets, - predictions)) + res = dict() + res["default_boston"] = 0.18393287980040374 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = -0.23029229186279609 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.068600456340847438 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = -0.16321841460809972 + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.neighbors.KNeighborsRegressor + + module = KNearestNeighborsRegressor \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_liblinear_svr.py b/test/test_pipeline/components/regression/test_liblinear_svr.py index 69eab79b4c..55608c8f04 100644 --- a/test/test_pipeline/components/regression/test_liblinear_svr.py +++ b/test/test_pipeline/components/regression/test_liblinear_svr.py @@ -1,20 +1,23 @@ -import unittest +import sklearn.svm from autosklearn.pipeline.components.regression.liblinear_svr import \ LibLinear_SVR -from autosklearn.pipeline.util import _test_regressor +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class SupportVectorComponentTest(BaseRegressionComponentTest): + __test__ = True -class SupportVectorComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = _test_regressor(LibLinear_SVR, - dataset='boston') - # Lenient test because of travis-ci which gets quite different - # results here! - self.assertAlmostEqual(0.68, - sklearn.metrics.r2_score(y_true=targets, - y_pred=predictions), - places=2) + res = dict() + res["default_boston"] = 0.6768297818275556 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = 0.12626519114138912 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.39152218711865661 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = 0.18704323088631891 + res["default_diabetes_iterative_sparse"] = None + + sk_mod = sklearn.svm.LinearSVR + + module = LibLinear_SVR diff --git a/test/test_pipeline/components/regression/test_random_forests.py b/test/test_pipeline/components/regression/test_random_forests.py index b235acc603..1bbd5bd173 100644 --- a/test/test_pipeline/components/regression/test_random_forests.py +++ b/test/test_pipeline/components/regression/test_random_forests.py @@ -1,35 +1,23 @@ -import unittest +import sklearn.ensemble -from autosklearn.pipeline.components.regression.random_forest import RandomForest -from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit +from autosklearn.pipeline.components.regression.random_forest import \ + RandomForest +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class RandomForestComponentTest(BaseRegressionComponentTest): + __test__ = True -class RandomForestComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): + res = dict() + res["default_boston"] = 0.78435242169129116 + res["default_boston_iterative"] = 0.78435242169129116 + res["default_boston_sparse"] = 0.42374643794982714 + res["default_boston_iterative_sparse"] = 0.42374643794982714 + res["default_diabetes"] = 0.41795829411621988 + res["default_diabetes_iterative"] = 0.41795829411621988 + res["default_diabetes_sparse"] = 0.24225685933770469 + res["default_diabetes_iterative_sparse"] = 0.24225685933770469 - predictions, targets = _test_regressor(RandomForest) - self.assertAlmostEqual(0.41795829411621988, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + sk_mod = sklearn.ensemble.RandomForestRegressor - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = _test_regressor(RandomForest, sparse=True) - self.assertAlmostEqual(0.24225685933770469, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - def test_default_configuration_iterative_fit(self): - for i in range(2): - predictions, targets = \ - _test_regressor_iterative_fit(RandomForest) - self.assertAlmostEqual(0.41795829411621988, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - def test_default_configuration_iterative_fit_sparse(self): - for i in range(2): - predictions, targets = \ - _test_regressor_iterative_fit(RandomForest, sparse=True) - self.assertAlmostEqual(0.24225685933770469, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + module = RandomForest \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_ridge_regression.py b/test/test_pipeline/components/regression/test_ridge_regression.py index 97050380b8..4d98e299bd 100644 --- a/test/test_pipeline/components/regression/test_ridge_regression.py +++ b/test/test_pipeline/components/regression/test_ridge_regression.py @@ -1,43 +1,23 @@ -import unittest +import sklearn.linear_model -from autosklearn.pipeline.components.regression.ridge_regression import RidgeRegression -from autosklearn.pipeline.components.feature_preprocessing.kitchen_sinks import RandomKitchenSinks -from autosklearn.pipeline.util import _test_regressor, get_dataset +from autosklearn.pipeline.components.regression.ridge_regression import \ + RidgeRegression +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class RidgeComponentTest(BaseRegressionComponentTest): + __test__ = True -class RidgeComponentTest(unittest.TestCase): - def test_default_configuration(self): - configuration_space = RidgeRegression.get_hyperparameter_search_space() - default = configuration_space.get_default_configuration() - configuration_space_preproc = RandomKitchenSinks.get_hyperparameter_search_space() - default_preproc = configuration_space_preproc.get_default_configuration() + res = dict() + res["default_boston"] = 0.70337988453496891 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = 0.11243478302989141 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.32614416980439365 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = 0.12989713186102791 + res["default_diabetes_iterative_sparse"] = None - for i in range(2): - # This should be a bad results - predictions, targets = _test_regressor(RidgeRegression,) - self.assertAlmostEqual(0.32614416980439365, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + sk_mod = sklearn.linear_model.Ridge - # This should be much more better - X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes', - make_sparse=False) - preprocessor = RandomKitchenSinks( - random_state=1, - **{hp_name: default_preproc[hp_name] for hp_name in - default_preproc if default_preproc[hp_name] is not None}) - - transformer = preprocessor.fit(X_train, Y_train) - X_train_transformed = transformer.transform(X_train) - X_test_transformed = transformer.transform(X_test) - - regressor = RidgeRegression( - random_state=1, - **{hp_name: default[hp_name] for hp_name in - default if default[hp_name] is not None}) - predictor = regressor.fit(X_train_transformed, Y_train) - predictions = predictor.predict(X_test_transformed) - - self.assertAlmostEqual(0.37183512452087852, - sklearn.metrics.r2_score(y_true=Y_test, y_pred=predictions)) \ No newline at end of file + module = RidgeRegression diff --git a/test/test_pipeline/components/regression/test_sgd.py b/test/test_pipeline/components/regression/test_sgd.py index ea7191703c..bacbb61797 100644 --- a/test/test_pipeline/components/regression/test_sgd.py +++ b/test/test_pipeline/components/regression/test_sgd.py @@ -1,22 +1,22 @@ -import unittest +import sklearn.linear_model from autosklearn.pipeline.components.regression.sgd import SGD -from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class SGDComponentTest(BaseRegressionComponentTest): + __test__ = True -class SGDComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = _test_regressor(SGD) - self.assertAlmostEqual(0.066576586105546731, - sklearn.metrics.r2_score(y_true=targets, - y_pred=predictions)) + res = dict() + res["default_boston"] = -5.4808512936980714e+31 + res["default_boston_iterative"] = -5.4808512936980714e+31 + res["default_boston_sparse"] = -9.432255366952963e+29 + res["default_boston_iterative_sparse"] = -9.432255366952963e+29 + res["default_diabetes"] = 0.066576586105546731 + res["default_diabetes_iterative"] = 0.066576586105546731 + res["default_diabetes_sparse"] = 0.098980579505685062 + res["default_diabetes_iterative_sparse"] = 0.098980579505685062 - def test_default_configuration_iterative_fit(self): - for i in range(2): - predictions, targets = _test_regressor_iterative_fit(SGD) - self.assertAlmostEqual(0.066576586105546731, - sklearn.metrics.r2_score(y_true=targets, - y_pred=predictions)) \ No newline at end of file + sk_mod = sklearn.linear_model.SGDRegressor + + module = SGD \ No newline at end of file diff --git a/test/test_pipeline/components/regression/test_support_vector_regression.py b/test/test_pipeline/components/regression/test_support_vector_regression.py index 102097f1e1..696fa0e795 100644 --- a/test/test_pipeline/components/regression/test_support_vector_regression.py +++ b/test/test_pipeline/components/regression/test_support_vector_regression.py @@ -1,23 +1,22 @@ -import unittest +import sklearn.linear_model from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR -from autosklearn.pipeline.util import _test_regressor +from .test_base import BaseRegressionComponentTest -import sklearn.metrics +class SupportVectorComponentTest(BaseRegressionComponentTest): + __test__ = True + res = dict() + res["default_boston"] = -0.030006883949312613 + res["default_boston_iterative"] = None + res["default_boston_sparse"] = -0.062749211736050192 + res["default_boston_iterative_sparse"] = None + res["default_diabetes"] = 0.12849591861430087 + res["default_diabetes_iterative"] = None + res["default_diabetes_sparse"] = 0.0098877566961463881 + res["default_diabetes_iterative_sparse"] = None -class SupportVectorComponentTest(unittest.TestCase): + sk_mod = sklearn.svm.SVR - def test_default_configuration(self): - for i in range(2): - predictions, targets = _test_regressor(LibSVM_SVR) - self.assertAlmostEqual(0.12849591861430087, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = _test_regressor(LibSVM_SVR, - sparse=True) - self.assertAlmostEqual(0.0098877566961463881, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) + module = LibSVM_SVR \ No newline at end of file