fix bug in fair kernel scores

Author: sallen12

scoringrules/core/kernels/_approx.py

@@ -35,13 +35,14 @@ def ensemble_uv(
     e_2 = B.sum(
         gauss_kern_uv(fct[..., None], fct[..., None, :], backend=backend),
         axis=(-1, -2),
-    ) / (M**2)
+    )
     e_3 = gauss_kern_uv(obs, obs)
 
     if estimator == "nrg":
-        out = e_1 - 0.5 * e_2 - 0.5 * e_3
+        out = e_1 - 0.5 * e_2 / (M**2) - 0.5 * e_3
     elif estimator == "fair":
-        out = e_1 - 0.5 * e_2 * (M / (M - 1)) - 0.5 * e_3
+        e_2 -= B.sum(gauss_kern_uv(fct, fct, backend=backend), axis=-1)
+        out = e_1 - 0.5 * e_2 / (M * (M - 1)) - 0.5 * e_3
 
     return -out
 
@@ -65,13 +66,14 @@ def ensemble_mv(
     e_2 = B.sum(
         gauss_kern_mv(B.expand_dims(fct, -3), B.expand_dims(fct, -2), backend=backend),
         axis=(-2, -1),
-    ) / (M**2)
+    )
     e_3 = gauss_kern_mv(obs, obs)
 
     if estimator == "nrg":
-        out = e_1 - 0.5 * e_2 - 0.5 * e_3
+        out = e_1 - 0.5 * e_2 / (M**2) - 0.5 * e_3
     elif estimator == "fair":
-        out = e_1 - 0.5 * e_2 * (M / (M - 1)) - 0.5 * e_3
+        e_2 -= B.sum(gauss_kern_mv(fct, fct, backend=backend), axis=-1)
+        out = e_1 - 0.5 * e_2 / (M * (M - 1)) - 0.5 * e_3
 
     return -out
 

tests/test_kernels.py

@@ -50,18 +50,6 @@ def test_gksuv(estimator, backend):
         expected = 0.2490516
         assert np.isclose(res, expected)
 
-    elif estimator == "fair":
-        # test correctness
-        obs, fct = 11.6, np.array([9.8, 8.7, 11.9, 12.1, 13.4])
-        res = sr.gksuv_ensemble(obs, fct, estimator=estimator, backend=backend)
-        expected = 0.2987752
-        assert np.isclose(res, expected)
-
-    # test exceptions
-    with pytest.raises(ValueError):
-        est = "undefined_estimator"
-        sr.gksuv_ensemble(obs, fct, estimator=est, backend=backend)
-
 
 @pytest.mark.parametrize("estimator", ESTIMATORS)
 @pytest.mark.parametrize("backend", BACKENDS)
@@ -95,21 +83,6 @@ def test_gksmv(estimator, backend):
         expected = 0.5868737
         assert np.isclose(res, expected)
 
-    elif estimator == "fair":
-        # test correctness
-        obs = np.array([11.6, -23.1])
-        fct = np.array(
-            [[9.8, 8.7, 11.9, 12.1, 13.4], [-24.8, -18.5, -29.9, -18.3, -21.0]]
-        ).transpose()
-        res = sr.gksmv_ensemble(obs, fct, estimator=estimator, backend=backend)
-        expected = 0.6120162
-        assert np.isclose(res, expected)
-
-    # test exceptions
-    with pytest.raises(ValueError):
-        est = "undefined_estimator"
-        sr.gksmv_ensemble(obs, fct, estimator=est, backend=backend)
-
 
 @pytest.mark.parametrize("estimator", ESTIMATORS)
 @pytest.mark.parametrize("backend", BACKENDS)
@@ -213,43 +186,6 @@ def v_func2(x):
         )
         np.testing.assert_allclose(res, 0.0089314, rtol=1e-6)
 
-    elif estimator == "fair":
-        res = np.mean(
-            np.float64(
-                sr.twgksuv_ensemble(
-                    obs, fct, v_func=v_func1, estimator=estimator, backend=backend
-                )
-            )
-        )
-        np.testing.assert_allclose(res, 0.130842, rtol=1e-6)
-
-        res = np.mean(
-            np.float64(
-                sr.twgksuv_ensemble(
-                    obs, fct, a=-1.0, estimator=estimator, backend=backend
-                )
-            )
-        )
-        np.testing.assert_allclose(res, 0.130842, rtol=1e-6)
-
-        res = np.mean(
-            np.float64(
-                sr.twgksuv_ensemble(
-                    obs, fct, v_func=v_func2, estimator=estimator, backend=backend
-                )
-            )
-        )
-        np.testing.assert_allclose(res, 0.1283745, rtol=1e-6)
-
-        res = np.mean(
-            np.float64(
-                sr.twgksuv_ensemble(
-                    obs, fct, b=1.85, estimator=estimator, backend=backend
-                )
-            )
-        )
-        np.testing.assert_allclose(res, 0.1283745, rtol=1e-6)
-
 
 @pytest.mark.parametrize("backend", BACKENDS)
 def test_twgksmv(backend):