deprecate SklLearner.support_weights

biolab · PrimozGodec · Sep 29, 2023 · Sep 21, 2023 · Sep 22, 2023 · Sep 25, 2023
commit e9b2a16654a706aea4823325e03629cd2f2fdf2b
diff --git a/Orange/base.py b/Orange/base.py
@@ -19,6 +19,7 @@
 from Orange.util import Reprable, OrangeDeprecationWarning, wrap_callback, \
     dummy_callback
 
+
 __all__ = ["Learner", "Model", "SklLearner", "SklModel",
            "ReprableWithPreprocessors"]
 
@@ -596,6 +597,9 @@ def fit(self, X, Y, W=None):
     def supports_weights(self):
         """Indicates whether this learner supports weighted instances.
         """
+        warnings.warn('SklLearner.supports_weights property is deprecated. All '
+                      'subclasses should redefine the supports_weights attribute.',
+                      OrangeDeprecationWarning)
         varnames = self.__wraps__.fit.__code__.co_varnames
         # scikit-learn often uses decorators on fit()
         if hasattr(self.__wraps__.fit, "__wrapped__"):

diff --git a/Orange/classification/gb.py b/Orange/classification/gb.py
@@ -23,6 +23,7 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
 class GBClassifier(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_ensemble.GradientBoostingClassifier
     __returns__ = SklModel
+    supports_weights = True
 
     def __init__(self,
                  loss="log_loss",

diff --git a/Orange/classification/knn.py b/Orange/classification/knn.py
@@ -7,3 +7,4 @@
 
 class KNNLearner(KNNBase, SklLearner):
     __wraps__ = skl_neighbors.KNeighborsClassifier
+    supports_weights = False
diff --git a/Orange/classification/logistic_regression.py b/Orange/classification/logistic_regression.py
@@ -33,6 +33,7 @@ class LogisticRegressionLearner(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_linear_model.LogisticRegression
     __returns__ = LogisticRegressionClassifier
     preprocessors = SklLearner.preprocessors
+    supports_weights = True
 
     def __init__(self, penalty="l2", dual=False, tol=0.0001, C=1.0,
                  fit_intercept=True, intercept_scaling=1, class_weight=None,

diff --git a/Orange/classification/neural_network.py b/Orange/classification/neural_network.py
@@ -25,6 +25,7 @@ class MLPClassifierWCallback(skl_nn.MLPClassifier, NIterCallbackMixin):
 
 class NNClassificationLearner(NNBase, SklLearner):
     __wraps__ = MLPClassifierWCallback
+    supports_weights = False
 
     def _initialize_wrapped(self):
         clf = SklLearner._initialize_wrapped(self)

diff --git a/Orange/classification/outlier_detection.py b/Orange/classification/outlier_detection.py
@@ -89,6 +89,7 @@ class OneClassSVMLearner(_OutlierLearner):
     name = "One class SVM"
     __wraps__ = OneClassSVM
     preprocessors = SklLearner.preprocessors + [AdaptiveNormalize()]
+    supports_weights = True
 
     def __init__(self, kernel='rbf', degree=3, gamma="auto", coef0=0.0,
                  tol=0.001, nu=0.5, shrinking=True, cache_size=200,
@@ -100,6 +101,7 @@ def __init__(self, kernel='rbf', degree=3, gamma="auto", coef0=0.0,
 class LocalOutlierFactorLearner(_OutlierLearner):
     __wraps__ = LocalOutlierFactor
     name = "Local Outlier Factor"
+    supports_weights = False
 
     def __init__(self, n_neighbors=20, algorithm="auto", leaf_size=30,
                  metric="minkowski", p=2, metric_params=None,
@@ -112,6 +114,7 @@ def __init__(self, n_neighbors=20, algorithm="auto", leaf_size=30,
 class IsolationForestLearner(_OutlierLearner):
     __wraps__ = IsolationForest
     name = "Isolation Forest"
+    supports_weights = True
 
     def __init__(self, n_estimators=100, max_samples='auto',
                  contamination='auto', max_features=1.0, bootstrap=False,
@@ -156,6 +159,7 @@ class EllipticEnvelopeLearner(_OutlierLearner):
     __wraps__ = EllipticEnvelope
     __returns__ = EllipticEnvelopeClassifier
     name = "Covariance Estimator"
+    supports_weights = False
 
    def __init__(self, store_precision=True, assume_centered=False,
                  support_fraction=None, contamination=0.1,

diff --git a/Orange/classification/random_forest.py b/Orange/classification/random_forest.py
@@ -38,6 +38,7 @@ def wrap(tree, i):
 class RandomForestLearner(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_ensemble.RandomForestClassifier
     __returns__ = RandomForestClassifier
+    supports_weights = True
 
     def __init__(self,
                  n_estimators=10,

diff --git a/Orange/classification/sgd.py b/Orange/classification/sgd.py
@@ -12,6 +12,7 @@ class SGDClassificationLearner(SklLearner):
     __wraps__ = SGDClassifier
     __returns__ = LinearModel
     preprocessors = SklLearner.preprocessors + [Normalize()]
+    supports_weights = True
 
     def __init__(self, loss='hinge', penalty='l2', alpha=0.0001,
                  l1_ratio=0.15, fit_intercept=True, max_iter=5,

diff --git a/Orange/classification/tree.py b/Orange/classification/tree.py
@@ -233,6 +233,7 @@ class SklTreeLearner(SklLearner):
     __wraps__ = skl_tree.DecisionTreeClassifier
     __returns__ = SklTreeClassifier
     name = 'tree'
+    supports_weights = True
 
     def __init__(self, criterion="gini", splitter="best", max_depth=None,
                  min_samples_split=2, min_samples_leaf=1,

diff --git a/Orange/classification/xgb.py b/Orange/classification/xgb.py
@@ -25,6 +25,7 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
 class XGBClassifier(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBClassifier
     __returns__ = SklModel
+    supports_weights = True
 
     def __init__(self,
                  max_depth=None,
@@ -88,6 +89,7 @@ def __init__(self,
 class XGBRFClassifier(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBRFClassifier
     __returns__ = SklModel
+    supports_weights = True
 
     def __init__(self,
                  max_depth=None,

diff --git a/Orange/ensembles/ada_boost.py b/Orange/ensembles/ada_boost.py
@@ -18,6 +18,7 @@ class SklAdaBoostClassifier(SklModelClassification):
 class SklAdaBoostClassificationLearner(SklLearnerClassification):
     __wraps__ = skl_ensemble.AdaBoostClassifier
     __returns__ = SklAdaBoostClassifier
+    supports_weights = True
 
     def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.,
                  algorithm='SAMME.R', random_state=None, preprocessors=None):
@@ -40,6 +41,7 @@ class SklAdaBoostRegressor(SklModelRegression):
 class SklAdaBoostRegressionLearner(SklLearnerRegression):
     __wraps__ = skl_ensemble.AdaBoostRegressor
     __returns__ = SklAdaBoostRegressor
+    supports_weights = True
 
     def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.,
                  loss='linear', random_state=None, preprocessors=None):

diff --git a/Orange/regression/gb.py b/Orange/regression/gb.py
@@ -23,6 +23,7 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
 class GBRegressor(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_ensemble.GradientBoostingRegressor
     __returns__ = SklModel
+    supports_weights = True
 
     def __init__(self,
                  loss="squared_error",

diff --git a/Orange/regression/knn.py b/Orange/regression/knn.py
@@ -7,3 +7,4 @@
 
 class KNNRegressionLearner(KNNBase, SklLearner):
     __wraps__ = skl_neighbors.KNeighborsRegressor
+    supports_weights = False
diff --git a/Orange/regression/linear.py b/Orange/regression/linear.py
@@ -27,6 +27,7 @@ def score(self, data):
 
 class LinearRegressionLearner(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_linear_model.LinearRegression
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, preprocessors=None, fit_intercept=True):
@@ -40,6 +41,7 @@ def fit(self, X, Y, W=None):
 
 class RidgeRegressionLearner(LinearRegressionLearner):
     __wraps__ = skl_linear_model.Ridge
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, alpha=1.0, fit_intercept=True, copy_X=True,
@@ -50,6 +52,7 @@ def __init__(self, alpha=1.0, fit_intercept=True, copy_X=True,
 
 class LassoRegressionLearner(LinearRegressionLearner):
     __wraps__ = skl_linear_model.Lasso
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, alpha=1.0, fit_intercept=True, precompute=False,
@@ -61,6 +64,7 @@ def __init__(self, alpha=1.0, fit_intercept=True, precompute=False,
 
 class ElasticNetLearner(LinearRegressionLearner):
     __wraps__ = skl_linear_model.ElasticNet
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
@@ -72,6 +76,7 @@ def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
 
 class ElasticNetCVLearner(LinearRegressionLearner):
     __wraps__ = skl_linear_model.ElasticNetCV
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None,
@@ -85,6 +90,7 @@ def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None,
 class SGDRegressionLearner(LinearRegressionLearner):
     __wraps__ = skl_linear_model.SGDRegressor
     preprocessors = SklLearner.preprocessors + [Normalize()]
+    supports_weights = True
 
     # Arguments are needed for signatures, pylint: disable=unused-argument
     def __init__(self, loss='squared_error', penalty='l2', alpha=0.0001,

diff --git a/Orange/regression/neural_network.py b/Orange/regression/neural_network.py
@@ -12,6 +12,7 @@ class MLPRegressorWCallback(skl_nn.MLPRegressor, NIterCallbackMixin):
 
 class NNRegressionLearner(NNBase, SklLearner):
     __wraps__ = MLPRegressorWCallback
+    supports_weights = False
 
     def _initialize_wrapped(self):
         clf = SklLearner._initialize_wrapped(self)

diff --git a/Orange/regression/random_forest.py b/Orange/regression/random_forest.py
@@ -38,6 +38,7 @@ def wrap(tree, i):
 class RandomForestRegressionLearner(SklLearner, _FeatureScorerMixin):
     __wraps__ = skl_ensemble.RandomForestRegressor
     __returns__ = RandomForestRegressor
+    supports_weights = True
 
     def __init__(self,
                  n_estimators=10,

diff --git a/Orange/regression/tree.py b/Orange/regression/tree.py
@@ -184,6 +184,7 @@ class SklTreeRegressionLearner(SklLearner):
     __wraps__ = skl_tree.DecisionTreeRegressor
     __returns__ = SklTreeRegressor
     name = 'regression tree'
+    supports_weights = True
 
     def __init__(self, criterion="squared_error", splitter="best", max_depth=None,
                  min_samples_split=2, min_samples_leaf=1,

diff --git a/Orange/regression/xgb.py b/Orange/regression/xgb.py
@@ -23,6 +23,7 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
 
 class XGBRegressor(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBRegressor
+    supports_weights = True
 
     def __init__(self,
                  max_depth=None,
@@ -75,6 +76,7 @@ def __init__(self,
 
 class XGBRFRegressor(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBRFRegressor
+    supports_weights = True
 
     def __init__(self,
                  max_depth=None,

diff --git a/Orange/tests/test_base.py b/Orange/tests/test_base.py
@@ -7,6 +7,7 @@
 from Orange.data import Domain, Table
 from Orange.preprocess import Discretize, Randomize, Continuize
 from Orange.regression import LinearRegressionLearner
+from Orange.util import OrangeDeprecationWarning
 
 
 class DummyLearner(Learner):
@@ -102,7 +103,8 @@ def fit(self, X, y, sample_weight=None):
         class DummyLearner(SklLearner):
             __wraps__ = DummySklLearner
 
-        self.assertTrue(DummyLearner().supports_weights)
+        with self.assertWarns(OrangeDeprecationWarning):
+            self.assertTrue(DummyLearner().supports_weights)
 
         class DummySklLearner:
             def fit(self, X, y):
@@ -111,7 +113,8 @@ def fit(self, X, y):
         class DummyLearner(SklLearner):
             __wraps__ = DummySklLearner
 
-        self.assertFalse(DummyLearner().supports_weights)
+        with self.assertWarns(OrangeDeprecationWarning):
+            self.assertFalse(DummyLearner().supports_weights)
 
     def test_linreg(self):
         self.assertTrue(
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,3 +7,4 @@

		class KNNLearner(KNNBase, SklLearner):
		__wraps__ = skl_neighbors.KNeighborsClassifier
		supports_weights = False
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,3 +7,4 @@

		class KNNRegressionLearner(KNNBase, SklLearner):
		__wraps__ = skl_neighbors.KNeighborsRegressor
		supports_weights = False