Added basic unit test for callable kernel

Georgi Peev · Georgi Peev · commit 8e5bf21b7b76 · 2018-06-15T18:10:02.000-04:00
diff --git a/sklearn/svm/base.py b/sklearn/svm/base.py
@@ -217,7 +217,7 @@ def fit(self, X, y, sample_weight=None):
         if hasattr(X, 'shape'):
             self.shape_fit_ = X.shape
         else:
-            self.shape_fit_ = (_num_samples(X), )
+            self.shape_fit_ = (_num_samples(X),)
 
         # In binary case, we need to flip the sign of coef, intercept and
         # decision function. Use self._intercept_ and self._dual_coef_ internally.
diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
@@ -1012,3 +1012,31 @@ def test_gamma_scale():
     # gamma is not explicitly set.
     X, y = [[1, 2], [3, 2 * np.sqrt(6) / 3 + 2]], [0, 1]
     assert_no_warnings(clf.fit, X, y)
+
+
+def test_callable_kernel():
+    data = ["foo", "foof", "b", "a", "qwert", "1234567890", "abcde", "bar", "", "q"]
+    targets = [1, 1, 2, 2, 1, 3, 1, 1, 2, 2]
+    targets = np.array(targets)
+    
+    def string_kernel(X, X2):
+        assert isinstance(X[0], str)
+        len = _num_samples(X)
+        len2 = _num_samples(X2)
+        ret = np.zeros((len, len2))
+        smaller = np.min(ret.shape)
+        ret[np.arange(smaller), np.arange(smaller)] = 1
+        return ret
+
+    svc = svm.SVC(kernel=string_kernel)
+    svc.fit(data, targets)
+    svc.score(data, targets)
+    svc.score(np.array(data), targets)
+
+    svc.fit(np.array(data), targets)
+    svc.score(data, targets)
+    svc.score(np.array(data), targets)
+
+def test_string_kernel():
+    # meaningful string kernel test
+    assert True
