FIX: inheritance in DenseBaseSVM

Sequel for d079dde, including a test

sklearn/svm/base.py

@@ -323,18 +323,20 @@ def _dense_predict(self, X):
                              "the number of features at training time" %
                              (n_features, self.shape_fit_[1]))
 
-        params = self.get_params()
-        if 'scale_C' in params:
-            del params['scale_C']
-        if "sparse" in params:
-            del params["sparse"]
+        epsilon = self.epsilon
+        if epsilon == None:
+            epsilon = 0.1
 
         svm_type = LIBSVM_IMPL.index(self.impl)
         return libsvm.predict(
             X, self.support_, self.support_vectors_, self.n_support_,
             self.dual_coef_, self.intercept_,
             self.label_, self.probA_, self.probB_,
-            svm_type=svm_type, **params)
+            svm_type=svm_type,
+            kernel=self.kernel, C=self.C, nu=self.nu,
+            probability=self.probability, degree=self.degree,
+            shrinking=self.shrinking, tol=self.tol, cache_size=self.cache_size,
+            coef0=self.coef0, gamma=self.gamma, epsilon=epsilon)
 
     def _sparse_predict(self, X):
         X = sp.csr_matrix(X, dtype=np.float64)
@@ -393,18 +395,19 @@ def predict_proba(self, X):
     def _dense_predict_proba(self, X):
         X = self._compute_kernel(X)
 
-        params = self.get_params()
-        if 'scale_C' in params:
-            del params['scale_C']
-        if "sparse" in params:
-            del params["sparse"]
+        epsilon = self.epsilon
+        if epsilon == None:
+            epsilon = 0.1
 
         svm_type = LIBSVM_IMPL.index(self.impl)
         pprob = libsvm.predict_proba(
             X, self.support_, self.support_vectors_, self.n_support_,
             self.dual_coef_, self.intercept_, self.label_,
             self.probA_, self.probB_,
-            svm_type=svm_type, **params)
+            svm_type=svm_type, kernel=self.kernel, C=self.C, nu=self.nu,
+            probability=self.probability, degree=self.degree,
+            shrinking=self.shrinking, tol=self.tol, cache_size=self.cache_size,
+            coef0=self.coef0, gamma=self.gamma, epsilon=epsilon)
 
         return pprob
 
@@ -478,18 +481,18 @@ def decision_function(self, X):
 
         X = array2d(X, dtype=np.float64, order="C")
 
-        params = self.get_params()
-        if 'scale_C' in params:
-            del params['scale_C']
-        if "sparse" in params:
-            del params["sparse"]
-
+        epsilon = self.epsilon
+        if epsilon == None:
+            epsilon = 0.1
         dec_func = libsvm.decision_function(
             X, self.support_, self.support_vectors_, self.n_support_,
             self.dual_coef_, self.intercept_, self.label_,
             self.probA_, self.probB_,
             svm_type=LIBSVM_IMPL.index(self.impl),
-            **params)
+            kernel=self.kernel, C=self.C, nu=self.nu,
+            probability=self.probability, degree=self.degree,
+            shrinking=self.shrinking, tol=self.tol, cache_size=self.cache_size,
+            coef0=self.coef0, gamma=self.gamma, epsilon=epsilon)
 
         return dec_func
 

sklearn/svm/tests/test_svm.py

@@ -561,6 +561,17 @@ def test_immutable_coef_property():
         assert_raises(AttributeError, clf.__setattr__, 'coef_', np.arange(3))
         assert_raises(RuntimeError, clf.coef_.__setitem__, (0, 0), 0)
 
+def test_inheritance():
+    # check that SVC classes can do inheritance
+    class ChildSVC(svm.SVC):
+        def __init__(self, foo=0):
+            self.foo = foo
+            svm.SVC.__init__(self)
+
+    clf = ChildSVC()
+    clf.fit(iris.data, iris.target)
+    clf.predict(iris.data[-1])
+    clf.decision_function(iris.data[-1])
 
 if __name__ == '__main__':
     import nose