[MRG] API Deprecates probA_ and probB_ in OneClassSVM and svm.SVR

doc/whats_new/v0.23.rst

@@ -171,6 +171,13 @@ Changelog
 - |Efficiency| :class:`preprocessing.OneHotEncoder` is now faster at
   transforming. :pr:`15762` by `Thomas Fan`_.
 
+:mod:`sklearn.svm`
+..................
+
+- |API| :class:`svm.SVR` and :class:`svm.OneClassSVM` attributes, `probA_` and
+  `probB_`, are now deprecated as they were not useful. :pr:`15558` by
+  `Thomas Fan`_.
+
 :mod:`sklearn.tree`
 ...................
 

sklearn/svm/_base.py

@@ -246,8 +246,8 @@ def _dense_fit(self, X, y, sample_weight, solver_type, kernel,
         # we don't pass **self.get_params() to allow subclasses to
         # add other parameters to __init__
         self.support_, self.support_vectors_, self._n_support, \
-            self.dual_coef_, self.intercept_, self.probA_, \
-            self.probB_, self.fit_status_ = libsvm.fit(
+            self.dual_coef_, self.intercept_, self._probA, \
+            self._probB, self.fit_status_ = libsvm.fit(
                 X, y,
                 svm_type=solver_type, sample_weight=sample_weight,
                 class_weight=self.class_weight_, kernel=kernel, C=self.C,
@@ -270,7 +270,7 @@ def _sparse_fit(self, X, y, sample_weight, solver_type, kernel,
 
         self.support_, self.support_vectors_, dual_coef_data, \
             self.intercept_, self._n_support, \
-            self.probA_, self.probB_, self.fit_status_ = \
+            self._probA, self._probB, self.fit_status_ = \
             libsvm_sparse.libsvm_sparse_train(
                 X.shape[1], X.data, X.indices, X.indptr, y, solver_type,
                 kernel_type, self.degree, self._gamma, self.coef0, self.tol,
@@ -334,7 +334,7 @@ def _dense_predict(self, X):
         return libsvm.predict(
             X, self.support_, self.support_vectors_, self._n_support,
             self._dual_coef_, self._intercept_,
-            self.probA_, self.probB_, svm_type=svm_type, kernel=kernel,
+            self._probA, self._probB, svm_type=svm_type, kernel=kernel,
             degree=self.degree, coef0=self.coef0, gamma=self._gamma,
             cache_size=self.cache_size)
 
@@ -359,7 +359,7 @@ def _sparse_predict(self, X):
             C, self.class_weight_,
             self.nu, self.epsilon, self.shrinking,
             self.probability, self._n_support,
-            self.probA_, self.probB_)
+            self._probA, self._probB)
 
     def _compute_kernel(self, X):
         """Return the data transformed by a callable kernel"""
@@ -413,7 +413,7 @@ def _dense_decision_function(self, X):
         return libsvm.decision_function(
             X, self.support_, self.support_vectors_, self._n_support,
             self._dual_coef_, self._intercept_,
-            self.probA_, self.probB_,
+            self._probA, self._probB,
             svm_type=LIBSVM_IMPL.index(self._impl),
             kernel=kernel, degree=self.degree, cache_size=self.cache_size,
             coef0=self.coef0, gamma=self._gamma)
@@ -438,7 +438,7 @@ def _sparse_decision_function(self, X):
             self.C, self.class_weight_,
             self.nu, self.epsilon, self.shrinking,
             self.probability, self._n_support,
-            self.probA_, self.probB_)
+            self._probA, self._probB)
 
     def _validate_for_predict(self, X):
         check_is_fitted(self)
@@ -691,7 +691,7 @@ def _dense_predict_proba(self, X):
         pprob = libsvm.predict_proba(
             X, self.support_, self.support_vectors_, self._n_support,
             self._dual_coef_, self._intercept_,
-            self.probA_, self.probB_,
+            self._probA, self._probB,
             svm_type=svm_type, kernel=kernel, degree=self.degree,
             cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)
 
@@ -717,7 +717,7 @@ def _sparse_predict_proba(self, X):
             self.C, self.class_weight_,
             self.nu, self.epsilon, self.shrinking,
             self.probability, self._n_support,
-            self.probA_, self.probB_)
+            self._probA, self._probB)
 
     def _get_coef(self):
         if self.dual_coef_.shape[0] == 1:
@@ -734,6 +734,14 @@ def _get_coef(self):
 
         return coef
 
+    @property
+    def probA_(self):
+        return self._probA
+
+    @property
+    def probB_(self):
+        return self._probB
+
 
 def _get_liblinear_solver_type(multi_class, penalty, loss, dual):
     """Find the liblinear magic number for the solver.

sklearn/svm/_classes.py

@@ -8,6 +8,7 @@
 from ..utils import check_X_y
 from ..utils.validation import _num_samples
 from ..utils.multiclass import check_classification_targets
+from ..utils.deprecation import deprecated
 
 
 class LinearSVC(BaseEstimator, LinearClassifierMixin,
@@ -968,6 +969,20 @@ def __init__(self, kernel='rbf', degree=3, gamma='scale',
             shrinking=shrinking, probability=False, cache_size=cache_size,
             class_weight=None, max_iter=max_iter, random_state=None)
 
+    @deprecated(
+        "The probA_ attribute is deprecated in version 0.23 and will be "
+        "removed in version 0.25.")
+    @property
+    def probA_(self):
+        return self._probA
+
+    @deprecated(
+        "The probB_ attribute is deprecated in version 0.23 and will be "
+        "removed in version 0.25.")
+    @property
+    def probB_(self):
+        return self._probB
+
 
 class NuSVR(RegressorMixin, BaseLibSVM):
     """Nu Support Vector Regression.
@@ -1287,3 +1302,17 @@ def predict(self, X):
         """
         y = super().predict(X)
         return np.asarray(y, dtype=np.intp)
+
+    @deprecated(
+        "The probA_ attribute is deprecated in version 0.23 and will be "
+        "removed in version 0.25.")
+    @property
+    def probA_(self):
+        return self._probA
+
+    @deprecated(
+        "The probB_ attribute is deprecated in version 0.23 and will be "
+        "removed in version 0.25.")
+    @property
+    def probB_(self):
+        return self._probB

sklearn/svm/tests/test_svm.py

@@ -1233,3 +1233,18 @@ def test_n_support_oneclass_svr():
     assert reg.n_support_ == reg.support_vectors_.shape[0]
     assert reg.n_support_.size == 1
     assert reg.n_support_ == 4
+
+
+# TODO: Remove in 0.25 when probA_ and probB_ are deprecated
+@pytest.mark.parametrize("SVMClass, data", [
+    (svm.OneClassSVM, (X, )),
+    (svm.SVR, (X, Y))
+])
+@pytest.mark.parametrize("deprecated_prob", ["probA_", "probB_"])
+def test_svm_probA_proB_deprecated(SVMClass, data, deprecated_prob):
+    clf = SVMClass().fit(*data)
+
+    msg = ("The {} attribute is deprecated in version 0.23 and will be "
+           "removed in version 0.25.").format(deprecated_prob)
+    with pytest.warns(FutureWarning, match=msg):
+        getattr(clf, deprecated_prob)