Comment out test non compat with python2.5

Fabian Pedregosa · Fabian Pedregosa · commit 0f7e30895f99 · 2011-09-21T15:58:26.000+02:00
diff --git a/sklearn/linear_model/tests/test_omp.py b/sklearn/linear_model/tests/test_omp.py
@@ -59,18 +59,18 @@ def test_with_without_gram_tol():
         orthogonal_mp(X, y, tol=1., precompute_gram=True))
 
 
-def test_unreachable_accuracy():
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter('always')
-        assert_array_almost_equal(
-            orthogonal_mp(X, y, tol=0),
-            orthogonal_mp(X, y, n_nonzero_coefs=n_features))
+# def test_unreachable_accuracy():
+#     with warnings.catch_warnings(record=True) as w:
+#         warnings.simplefilter('always')
+#         assert_array_almost_equal(
+#             orthogonal_mp(X, y, tol=0),
+#             orthogonal_mp(X, y, n_nonzero_coefs=n_features))
 
-        assert_array_almost_equal(
-            orthogonal_mp(X, y, tol=0, precompute_gram=True),
-            orthogonal_mp(X, y, precompute_gram=True,
-                          n_nonzero_coefs=n_features))
-        assert len(w) > 0  # warnings should be raised
+#         assert_array_almost_equal(
+#             orthogonal_mp(X, y, tol=0, precompute_gram=True),
+#             orthogonal_mp(X, y, precompute_gram=True,
+#                           n_nonzero_coefs=n_features))
+#         assert len(w) > 0  # warnings should be raised
 
 
 def test_bad_input():
@@ -118,13 +118,13 @@ def test_estimator_shapes():
     assert count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs
 
 
-def test_identical_regressors():
-    newX = X.copy()
-    newX[:, 1] = newX[:, 0]
-    gamma = np.zeros(n_features)
-    gamma[0] = gamma[1] = 1.
-    newy = np.dot(newX, gamma)
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter('always')
-        orthogonal_mp(newX, newy, 2)
-        assert len(w) == 1
+# def test_identical_regressors():
+#     newX = X.copy()
+#     newX[:, 1] = newX[:, 0]
+#     gamma = np.zeros(n_features)
+#     gamma[0] = gamma[1] = 1.
+#     newy = np.dot(newX, gamma)
+#     with warnings.catch_warnings(record=True) as w:
+#         warnings.simplefilter('always')
+#         orthogonal_mp(newX, newy, 2)
+#         assert len(w) == 1
