ENH friendlier message for calling predict before fit on SVMs

Fixes scikit-learn#3601.

Author: larsmans

sklearn/svm/base.py

@@ -381,6 +381,9 @@ def decision_function(self, X):
         return dec_func
 
     def _validate_for_predict(self, X):
+        if not hasattr(self, "support_"):
+            raise ValueError("this %s has not been fitted yet"
+                             % type(self).__name__)
         X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
         if self._sparse and not sp.isspmatrix(X):
             X = sp.csr_matrix(X)

sklearn/svm/tests/test_svm.py

@@ -16,7 +16,7 @@
 from sklearn.utils import check_random_state
 from sklearn.utils import ConvergenceWarning
 from sklearn.utils.testing import assert_greater, assert_in, assert_less
-from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import assert_raises_regexp, assert_warns
 
 
 # toy sample
@@ -664,6 +664,18 @@ def test_timeout():
     assert_warns(ConvergenceWarning, a.fit, X, Y)
 
 
+def test_unfitted():
+    X = "foo!"      # input validation not required when SVM not fitted
+
+    clf = svm.SVC()
+    assert_raises_regexp(Exception, r".*\bSVC\b.*\bnot\b.*\bfitted\b",
+                         clf.predict, X)
+
+    clf = svm.NuSVR()
+    assert_raises_regexp(Exception, r".*\bNuSVR\b.*\bnot\b.*\bfitted\b",
+                         clf.predict, X)
+
+
 def test_consistent_proba():
     a = svm.SVC(probability=True, max_iter=1, random_state=0)
     proba_1 = a.fit(X, Y).predict_proba(X)