correct test eigenpro and huber

Author: TimotheeMathieu

sklearn_extra/kernel_methods/tests/test_eigenpro.py

@@ -31,7 +31,7 @@ def gen_classification(params):
 @pytest.mark.parametrize(
     "params, err_msg",
     [
-        ({"kernel": "not_a_kernel"}, "Unknown kernel 'not_a_kernel'"),
+        ({"kernel": "not_a_kernel"}, "The 'metric' parameter of pairwise_kernels must be a str among {'cosine', 'poly', 'laplacian', 'polynomial', 'chi2', 'linear', 'sigmoid', 'additive_chi2', 'precomputed', 'rbf'} or a callable. Got 'not_a_kernel' instead."),
         ({"n_epoch": 0}, "n_epoch should be positive, was 0"),
         ({"n_epoch": -1}, "n_epoch should be positive, was -1"),
         ({"n_components": -1}, "n_components should be non-negative, was -1"),

sklearn_extra/robust/tests/test_mean_estimators.py

@@ -27,7 +27,6 @@ def test_mom():
 
 def test_huber():
     X = np.hstack([np.zeros(90), np.ones(10)])
-    with pytest.warns(None) as record:
-        mu = huber(X, c=0.5)
+    mu = huber(X, c=0.5)
     assert len(record) == 0
     assert np.abs(mu) < 0.1