remove xgboost because of issue #271

automl · May 9, 2017 · a4c17dd · a4c17dd
1 parent 36c8f44
commit a4c17dd
Show file tree

Hide file tree

Showing 9 changed files with 87 additions and 90 deletions.
diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py
@@ -10,7 +10,6 @@
 lockfile>=0.10
 ConfigSpace>=0.3.3,<0.4
 pyrfr>=0.4.0,<0.5
-xgboost==0.6a2
 '''
 
 dependencies.verify_packages(__MANDATORY_PACKAGES__)
diff --git a/autosklearn/pipeline/components/classification/xgradient_boosting.py b/autosklearn/pipeline/components/classification/xgradient_boosting.py
@@ -11,7 +11,7 @@
 from autosklearn.pipeline.constants import *
 
 
-class XGradientBoostingClassifier(AutoSklearnClassificationAlgorithm):
+class XGradientBoostingClassifier():#AutoSklearnClassificationAlgorithm):
     def __init__(self, learning_rate, n_estimators, subsample,
                  max_depth, colsample_bylevel, colsample_bytree, gamma,
                  min_child_weight, max_delta_step, reg_alpha, reg_lambda,

diff --git a/autosklearn/pipeline/components/regression/xgradient_boosting.py b/autosklearn/pipeline/components/regression/xgradient_boosting.py
@@ -11,7 +11,7 @@
 from autosklearn.pipeline.constants import *
 
 
-class XGradientBoostingRegressor(AutoSklearnRegressionAlgorithm):
+class XGradientBoostingRegressor():#AutoSklearnRegressionAlgorithm):
     def __init__(self, learning_rate, n_estimators, subsample,
                  max_depth, colsample_bylevel, colsample_bytree, gamma,
                  min_child_weight, max_delta_step, reg_alpha, reg_lambda,

diff --git a/requirements.txt b/requirements.txt
@@ -15,7 +15,6 @@ psutil
 pyyaml
 liac-arff
 pandas
-xgboost==0.6a2
 
 ConfigSpace>=0.3.3,<0.4
 pynisher>=0.4

diff --git a/setup.py b/setup.py
@@ -25,7 +25,6 @@
     "pyyaml",
     "liac-arff",
     "pandas",
-    "xgboost==0.6a2",
     "ConfigSpace>=0.3.3,<0.4",
     "pynisher>=0.4",
     "pyrfr>=0.4,<0.5",

diff --git a/test/test_pipeline/components/classification/test_xgradient_boosting.py b/test/test_pipeline/components/classification/test_xgradient_boosting.py
@@ -1,42 +1,42 @@
-import unittest
-
-from autosklearn.pipeline.components.classification.xgradient_boosting import \
-    XGradientBoostingClassifier
-from autosklearn.pipeline.util import _test_classifier, \
-    _test_classifier_iterative_fit
-
-import sklearn.metrics
-import sklearn.ensemble
-
-
-class XGradientBoostingComponentTest(unittest.TestCase):
-    def test_default_configuration(self):
-        for i in range(2):
-            predictions, targets = \
-                _test_classifier(XGradientBoostingClassifier)
-            self.assertAlmostEqual(0.92,
-                sklearn.metrics.accuracy_score(predictions, targets))
-
-    def test_default_configuration_sparse(self):
-        for i in range(2):
-            predictions, targets = _test_classifier(XGradientBoostingClassifier,
-                                                    sparse=True)
-            self.assertAlmostEqual(0.88,
-                                   sklearn.metrics.accuracy_score(predictions,
-                                                                  targets))
-
-    def test_default_configuration_binary(self):
-        for i in range(2):
-            predictions, targets = _test_classifier(
-                XGradientBoostingClassifier, make_binary=True)
-            self.assertAlmostEqual(1.0,
-                                   sklearn.metrics.accuracy_score(predictions,
-                                                                  targets))
-
-    def test_default_configuration_binary_sparse(self):
-        for i in range(2):
-            predictions, targets = _test_classifier(
-                XGradientBoostingClassifier, make_binary=True, sparse=True)
-            self.assertAlmostEqual(0.95999999999999996,
-                                   sklearn.metrics.accuracy_score(predictions,
-                                                                  targets))
+# import unittest
+#
+# from autosklearn.pipeline.components.classification.xgradient_boosting import \
+#     XGradientBoostingClassifier
+# from autosklearn.pipeline.util import _test_classifier, \
+#     _test_classifier_iterative_fit
+#
+# import sklearn.metrics
+# import sklearn.ensemble
+#
+#
+# class XGradientBoostingComponentTest(unittest.TestCase):
+#     def test_default_configuration(self):
+#         for i in range(2):
+#             predictions, targets = \
+#                 _test_classifier(XGradientBoostingClassifier)
+#             self.assertAlmostEqual(0.92,
+#                 sklearn.metrics.accuracy_score(predictions, targets))
+#
+#     def test_default_configuration_sparse(self):
+#         for i in range(2):
+#             predictions, targets = _test_classifier(XGradientBoostingClassifier,
+#                                                     sparse=True)
+#             self.assertAlmostEqual(0.88,
+#                                    sklearn.metrics.accuracy_score(predictions,
+#                                                                   targets))
+#
+#     def test_default_configuration_binary(self):
+#         for i in range(2):
+#             predictions, targets = _test_classifier(
+#                 XGradientBoostingClassifier, make_binary=True)
+#             self.assertAlmostEqual(1.0,
+#                                    sklearn.metrics.accuracy_score(predictions,
+#                                                                   targets))
+#
+#     def test_default_configuration_binary_sparse(self):
+#         for i in range(2):
+#             predictions, targets = _test_classifier(
+#                 XGradientBoostingClassifier, make_binary=True, sparse=True)
+#             self.assertAlmostEqual(0.95999999999999996,
+#                                    sklearn.metrics.accuracy_score(predictions,
+#                                                                   targets))
diff --git a/test/test_pipeline/components/regression/test_xgradient_boosting.py b/test/test_pipeline/components/regression/test_xgradient_boosting.py
@@ -1,40 +1,40 @@
-import unittest
-
-from autosklearn.pipeline.components.regression.xgradient_boosting import \
-    XGradientBoostingRegressor
-from autosklearn.pipeline.util import _test_regressor, \
-    _test_regressor_iterative_fit
-
-
-import sklearn.metrics
-import sklearn.ensemble
-
-
-class XGradientBoostingComponentTest(unittest.TestCase):
-    def test_default_configuration(self):
-        for i in range(2):
-            predictions, targets = _test_regressor(XGradientBoostingRegressor)
-            self.assertAlmostEqual(0.34009199992306871,
-                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(XGradientBoostingRegressor,
-                                                   sparse=True)
-            self.assertAlmostEqual(0.20743694821393754,
-                sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
-
-    #def test_default_configuration_iterative_fit(self):
-    #    for i in range(10):
-    #        predictions, targets = \
-    #            _test_regressor_iterative_fit(XGradientBoostingRegressor)
-    #        self.assertAlmostEqual(0.40965687834764064,
-    #            sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
-
-    #def test_default_configuration_iterative_fit_sparse(self):
-    #    for i in range(10):
-    #        predictions, targets = \
-    #            _test_regressor_iterative_fit(XGradientBoostingRegressor,
-    #                                          sparse=True)
-    #        self.assertAlmostEqual(0.40965687834764064,
-    #            sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
+# import unittest
+#
+# from autosklearn.pipeline.components.regression.xgradient_boosting import \
+#     XGradientBoostingRegressor
+# from autosklearn.pipeline.util import _test_regressor, \
+#     _test_regressor_iterative_fit
+#
+#
+# import sklearn.metrics
+# import sklearn.ensemble
+#
+#
+# class XGradientBoostingComponentTest(unittest.TestCase):
+#     def test_default_configuration(self):
+#         for i in range(2):
+#             predictions, targets = _test_regressor(XGradientBoostingRegressor)
+#             self.assertAlmostEqual(0.34009199992306871,
+#                 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(XGradientBoostingRegressor,
+#                                                    sparse=True)
+#             self.assertAlmostEqual(0.20743694821393754,
+#                 sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
+#
+#     #def test_default_configuration_iterative_fit(self):
+#     #    for i in range(10):
+#     #        predictions, targets = \
+#     #            _test_regressor_iterative_fit(XGradientBoostingRegressor)
+#     #        self.assertAlmostEqual(0.40965687834764064,
+#     #            sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
+#
+#     #def test_default_configuration_iterative_fit_sparse(self):
+#     #    for i in range(10):
+#     #        predictions, targets = \
+#     #            _test_regressor_iterative_fit(XGradientBoostingRegressor,
+#     #                                          sparse=True)
+#     #        self.assertAlmostEqual(0.40965687834764064,
+#     #            sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py
@@ -343,12 +343,12 @@ def test_get_hyperparameter_search_space(self):
         self.assertEqual(len(cs.get_hyperparameter(
             'rescaling:__choice__').choices), 4)
         self.assertEqual(len(cs.get_hyperparameter(
-            'classifier:__choice__').choices), 16)
+            'classifier:__choice__').choices), 15)
         self.assertEqual(len(cs.get_hyperparameter(
             'preprocessor:__choice__').choices), 13)
 
         hyperparameters = cs.get_hyperparameters()
-        self.assertEqual(154, len(hyperparameters))
+        self.assertEqual(141, len(hyperparameters))
 
         #for hp in sorted([str(h) for h in hyperparameters]):
         #    print hp

diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py
@@ -221,7 +221,7 @@ def test_get_hyperparameter_search_space(self):
         self.assertIsInstance(cs, ConfigurationSpace)
         conditions = cs.get_conditions()
         hyperparameters = cs.get_hyperparameters()
-        self.assertEqual(143, len(hyperparameters))
+        self.assertEqual(130, len(hyperparameters))
         self.assertEqual(len(hyperparameters) - 5, len(conditions))
 
     def test_get_hyperparameter_search_space_include_exclude_models(self):