scikit-learn · glemaitre · Jul 27, 2022 · Jul 26, 2022 · Jul 26, 2022 · Jul 26, 2022
diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py
@@ -1,6 +1,7 @@
 """
 Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
 """
+import re
 import warnings
 import numpy as np
 from numpy.testing import assert_allclose
@@ -1233,9 +1234,14 @@ def test_gradient_boosting_with_init_pipeline():
     # sure we make the distinction between ValueError raised by a pipeline that
     # was passed sample_weight, and a ValueError raised by a regular estimator
     # whose input checking failed.
-    with pytest.raises(ValueError, match="nu <= 0 or nu > 1"):
+    invalid_nu = 1.5
+    err_msg = (
+        "The 'nu' parameter of NuSVR must be a float in the"
+        f" range (0.0, 1.0]. Got {invalid_nu} instead."
+    )
+    with pytest.raises(ValueError, match=re.escape(err_msg)):
         # Note that NuSVR properly supports sample_weight
-        init = NuSVR(gamma="auto", nu=1.5)
+        init = NuSVR(gamma="auto", nu=invalid_nu)
         gb = GradientBoostingRegressor(init=init)
         gb.fit(X, y, sample_weight=np.ones(X.shape[0]))
 

diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py
@@ -1,6 +1,6 @@
 import warnings
-import numbers
 from abc import ABCMeta, abstractmethod
+from numbers import Integral, Real
 
 import numpy as np
 import scipy.sparse as sp
@@ -22,6 +22,7 @@
 from ..utils.validation import _num_samples
 from ..utils.validation import _check_sample_weight, check_consistent_length
 from ..utils.multiclass import check_classification_targets
+from ..utils._param_validation import Interval, StrOptions
 from ..exceptions import ConvergenceWarning
 from ..exceptions import NotFittedError
 
@@ -69,6 +70,30 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
     Parameter documentation is in the derived `SVC` class.
     """
 
+    _parameter_constraints = {
+        "kernel": [
+            StrOptions({"linear", "poly", "rbf", "sigmoid", "precomputed"}),
+            callable,
+        ],
+        "degree": [Interval(Integral, 0, None, closed="left")],
+        "gamma": [
+            StrOptions({"scale", "auto"}),
+            Interval(Real, 0.0, None, closed="neither"),
+        ],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "tol": [Interval(Real, 0.0, None, closed="neither")],
+        "C": [Interval(Real, 0.0, None, closed="neither")],
+        "nu": [Interval(Real, 0.0, 1.0, closed="right")],
+        "epsilon": [Interval(Real, 0.0, None, closed="left")],
+        "shrinking": ["boolean"],
+        "probability": ["boolean"],
+        "cache_size": [Interval(Real, 0, None, closed="neither")],
+        "class_weight": [StrOptions({"balanced"}), dict, None],
+        "verbose": ["verbose"],
+        "max_iter": [Interval(Integral, -1, None, closed="left")],
+        "random_state": ["random_state"],
+    }
+
     # The order of these must match the integer values in LibSVM.
     # XXX These are actually the same in the dense case. Need to factor
     # this out.
@@ -152,6 +177,7 @@ def fit(self, X, y, sample_weight=None):
         If X is a dense array, then the other methods will not support sparse
         matrices as input.
         """
+        self._validate_params()
 
         rnd = check_random_state(self.random_state)
 
@@ -160,13 +186,6 @@ def fit(self, X, y, sample_weight=None):
             raise TypeError("Sparse precomputed kernels are not supported.")
         self._sparse = sparse and not callable(self.kernel)
 
-        if hasattr(self, "decision_function_shape"):
-            if self.decision_function_shape not in ("ovr", "ovo"):
-                raise ValueError(
-                    "decision_function_shape must be either 'ovr' or 'ovo', "
-                    f"got {self.decision_function_shape}."
-                )
-
         if callable(self.kernel):
             check_consistent_length(X, y)
         else:
@@ -222,26 +241,8 @@ def fit(self, X, y, sample_weight=None):
                 self._gamma = 1.0 / (X.shape[1] * X_var) if X_var != 0 else 1.0
             elif self.gamma == "auto":
                 self._gamma = 1.0 / X.shape[1]
-            else:
-                raise ValueError(
-                    "When 'gamma' is a string, it should be either 'scale' or "
-                    f"'auto'. Got '{self.gamma!r}' instead."
-                )
-        elif isinstance(self.gamma, numbers.Real):
-            if self.gamma <= 0:
-                msg = (
-                    f"gamma value must be > 0; {self.gamma!r} is invalid. Use"
-                    " a positive number or use 'auto' to set gamma to a"
-                    " value of 1 / n_features."
-                )
-                raise ValueError(msg)
+        elif isinstance(self.gamma, Real):
             self._gamma = self.gamma
-        else:
-            msg = (
-                "The gamma value should be set to 'scale', 'auto' or a"
-                f" positive float value. {self.gamma!r} is not a valid option"
-            )
-            raise ValueError(msg)
 
         fit = self._sparse_fit if self._sparse else self._dense_fit
         if self.verbose:
@@ -691,6 +692,14 @@ def n_support_(self):
 class BaseSVC(ClassifierMixin, BaseLibSVM, metaclass=ABCMeta):
     """ABC for LibSVM-based classifiers."""
 
+    _parameter_constraints = {
+        **BaseLibSVM._parameter_constraints,  # type: ignore
+        "decision_function_shape": [StrOptions({"ovr", "ovo"})],
+        "break_ties": ["boolean"],
+    }
+    for unused_param in ["epsilon", "nu"]:
+        _parameter_constraints.pop(unused_param)
+
     @abstractmethod
     def __init__(
         self,

diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -587,14 +587,15 @@ class SVC(BaseSVC):
 
     degree : int, default=3
         Degree of the polynomial kernel function ('poly').
-        Ignored by all other kernels.
+        Must be non-negative. Ignored by all other kernels.
 
     gamma : {'scale', 'auto'} or float, default='scale'
         Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features.
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.
@@ -850,14 +851,15 @@ class NuSVC(BaseSVC):
 
     degree : int, default=3
         Degree of the polynomial kernel function ('poly').
-        Ignored by all other kernels.
+        Must be non-negative. Ignored by all other kernels.
 
     gamma : {'scale', 'auto'} or float, default='scale'
         Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features.
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.
@@ -1037,6 +1039,12 @@ class NuSVC(BaseSVC):
 
     _impl = "nu_svc"
 
+    _parameter_constraints = {
+        **BaseSVC._parameter_constraints,  # type: ignore
+        "nu": [Interval(Real, 0.0, 1.0, closed="right")],
+    }
+    _parameter_constraints.pop("C")
+
     def __init__(
         self,
         *,
@@ -1114,14 +1122,15 @@ class SVR(RegressorMixin, BaseLibSVM):
 
     degree : int, default=3
         Degree of the polynomial kernel function ('poly').
-        Ignored by all other kernels.
+        Must be non-negative. Ignored by all other kernels.
 
     gamma : {'scale', 'auto'} or float, default='scale'
         Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features.
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.
@@ -1142,7 +1151,7 @@ class SVR(RegressorMixin, BaseLibSVM):
          Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
          within which no penalty is associated in the training loss function
          with points predicted within a distance epsilon from the actual
-         value.
+         value. Must be non-negative.
 
     shrinking : bool, default=True
         Whether to use the shrinking heuristic.
@@ -1247,6 +1256,10 @@ class SVR(RegressorMixin, BaseLibSVM):
 
     _impl = "epsilon_svr"
 
+    _parameter_constraints = {**BaseLibSVM._parameter_constraints}  # type: ignore
+    for unused_param in ["class_weight", "nu", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
     def __init__(
         self,
         *,
@@ -1329,14 +1342,15 @@ class NuSVR(RegressorMixin, BaseLibSVM):
 
     degree : int, default=3
         Degree of the polynomial kernel function ('poly').
-        Ignored by all other kernels.
+        Must be non-negative. Ignored by all other kernels.
 
     gamma : {'scale', 'auto'} or float, default='scale'
         Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features.
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.
@@ -1451,6 +1465,10 @@ class NuSVR(RegressorMixin, BaseLibSVM):
 
     _impl = "nu_svr"
 
+    _parameter_constraints = {**BaseLibSVM._parameter_constraints}  # type: ignore
+    for unused_param in ["class_weight", "epsilon", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
     def __init__(
         self,
         *,
@@ -1523,14 +1541,15 @@ class OneClassSVM(OutlierMixin, BaseLibSVM):
 
     degree : int, default=3
         Degree of the polynomial kernel function ('poly').
-        Ignored by all other kernels.
+        Must be non-negative. Ignored by all other kernels.
 
     gamma : {'scale', 'auto'} or float, default='scale'
         Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features.
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.
@@ -1645,6 +1664,10 @@ class OneClassSVM(OutlierMixin, BaseLibSVM):
 
     _impl = "one_class"
 
+    _parameter_constraints = {**BaseLibSVM._parameter_constraints}  # type: ignore
+    for unused_param in ["C", "class_weight", "epsilon", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
     def __init__(
         self,
         *,

diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
@@ -455,9 +455,6 @@ def test_decision_function_shape(SVM):
     dec = clf.decision_function(X_train)
     assert dec.shape == (len(X_train), 10)
 
-    with pytest.raises(ValueError, match="must be either 'ovr' or 'ovo'"):
-        SVM(decision_function_shape="bad").fit(X_train, y_train)
-
 
 def test_svr_predict():
     # Test SVR's decision_function
@@ -685,19 +682,10 @@ def test_auto_weight():
 
 
 def test_bad_input():
-    # Test that it gives proper exception on deficient input
-    # impossible value of C
-    with pytest.raises(ValueError):
-        svm.SVC(C=-1).fit(X, Y)
-
-    # impossible value of nu
-    clf = svm.NuSVC(nu=0.0)
-    with pytest.raises(ValueError):
-        clf.fit(X, Y)
-
+    # Test dimensions for labels
     Y2 = Y[:-1]  # wrong dimensions for labels
     with pytest.raises(ValueError):
-        clf.fit(X, Y2)
+        svm.SVC().fit(X, Y2)
 
     # Test with arrays that are non-contiguous.
     for clf in (svm.SVC(), svm.LinearSVC(random_state=0)):
@@ -745,60 +733,6 @@ def test_svc_nonfinite_params():
         clf.fit(X, y)
 
 
-@pytest.mark.parametrize(
-    "Estimator, data",
-    [
-        (svm.SVC, datasets.load_iris(return_X_y=True)),
-        (svm.NuSVC, datasets.load_iris(return_X_y=True)),
-        (svm.SVR, datasets.load_diabetes(return_X_y=True)),
-        (svm.NuSVR, datasets.load_diabetes(return_X_y=True)),
-        (svm.OneClassSVM, datasets.load_iris(return_X_y=True)),
-    ],
-)
-@pytest.mark.parametrize(
-    "gamma, err_msg",
-    [
-        (
-            "auto_deprecated",
-            "When 'gamma' is a string, it should be either 'scale' or 'auto'",
-        ),
-        (
-            -1,
-            "gamma value must be > 0; -1 is invalid. Use"
-            " a positive number or use 'auto' to set gamma to a"
-            " value of 1 / n_features.",
-        ),
-        (
-            0.0,
-            "gamma value must be > 0; 0.0 is invalid. Use"
-            " a positive number or use 'auto' to set gamma to a"
-            " value of 1 / n_features.",
-        ),
-        (
-            np.array([1.0, 4.0]),
-            "The gamma value should be set to 'scale',"
-            f" 'auto' or a positive float value. {np.array([1.0, 4.0])!r}"
-            " is not a valid option",
-        ),
-        (
-            [],
-            "The gamma value should be set to 'scale', 'auto' or a positive"
-            f" float value. {[]} is not a valid option",
-        ),
-        (
-            {},
-            "The gamma value should be set to 'scale', 'auto' or a positive"
-            " float value. {} is not a valid option",
-        ),
-    ],
-)
-def test_svm_gamma_error(Estimator, data, gamma, err_msg):
-    X, y = data
-    est = Estimator(gamma=gamma)
-    with pytest.raises(ValueError, match=(re.escape(err_msg))):
-        est.fit(X, y)
-
-
 def test_unicode_kernel():
     # Test that a unicode kernel name does not cause a TypeError
     clf = svm.SVC(kernel="linear", probability=True)

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -475,12 +475,9 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
     "MultiTaskLasso",
     "NearestCentroid",
     "NeighborhoodComponentsAnalysis",
-    "NuSVC",
-    "NuSVR",
     "Nystroem",
     "OAS",
     "OPTICS",
-    "OneClassSVM",
     "OneVsOneClassifier",
     "OneVsRestClassifier",
     "PatchExtractor",
@@ -494,8 +491,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
     "RegressorChain",
     "RidgeCV",
     "RidgeClassifierCV",
-    "SVC",
-    "SVR",
     "SelectFdr",
     "SelectFpr",
     "SelectFromModel",