MAINT: silence invalid warning for np.logaddexp(2) with NaN input (#32733)

doc/release/upcoming_changes/32733.improvement.rst

@@ -0,0 +1,4 @@
+``logaddexp`` and ``logaddexp2`` no longer warn for NaN inputs
+--------------------------------------------------------------
+`numpy.logaddexp` and `numpy.logaddexp2` now propagate NaN inputs without
+emitting an invalid floating-point warning.

numpy/_core/src/npymath/npy_math_internal.h.src

@@ -471,11 +471,12 @@ NPY_INPLACE @type@ npy_logaddexp@c@(@type@ x, @type@ y)
         return x + LOGE2;
     }
     else {
+        const @type@ ZERO = 0.0;
         const @type@ tmp = x - y;
-        if (tmp > 0) {
+        if (isgreater(tmp, ZERO)) {
             return x + npy_log1p@c@(npy_exp@c@(-tmp));
         }
-        else if (tmp <= 0) {
+        else if (islessequal(tmp, ZERO)) {
             return y + npy_log1p@c@(npy_exp@c@(tmp));
         }
         else {
@@ -492,11 +493,12 @@ NPY_INPLACE @type@ npy_logaddexp2@c@(@type@ x, @type@ y)
         return x + 1;
     }
     else {
+        const @type@ ZERO = 0.0;
         const @type@ tmp = x - y;
-        if (tmp > 0) {
+        if (isgreater(tmp, ZERO)) {
             return x + npy_log2_1p@c@(npy_exp2@c@(-tmp));
         }
-        else if (tmp <= 0) {
+        else if (islessequal(tmp, ZERO)) {
             return y + npy_log2_1p@c@(npy_exp2@c@(tmp));
         }
         else {

numpy/_core/tests/test_umath.py

@@ -1399,7 +1399,7 @@ class TestExp2:
             assert_almost_equal(np.exp2(yf), xf)
 
 
-class TestLogAddExp2(_FilterInvalids):
+class TestLogAddExp2:
     # Need test for intermediate precisions
     def test_logaddexp2_values(self):
         x = [1, 2, 3, 4, 5]
@@ -1433,12 +1433,18 @@ class TestLogAddExp2(_FilterInvalids):
                 logzf = np.array(z, dtype=dt)
                 assert_equal(np.logaddexp2(logxf, logyf), logzf)
 
-    def test_nan(self):
-        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
-        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
-        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
-        assert_(np.isnan(np.logaddexp2(0, np.nan)))
-        assert_(np.isnan(np.logaddexp2(np.nan, np.nan)))
+    @pytest.mark.parametrize("dtype", ['f', 'd', 'g'])
+    @pytest.mark.parametrize("x, y", [
+        (np.nan, np.inf),
+        (np.inf, np.nan),
+        (np.nan, 0),
+        (0, np.nan),
+        (np.nan, np.nan),
+    ])
+    def test_nan(self, dtype, x, y):
+        x, y = np.array([x, y], dtype=dtype)
+        with np.errstate(invalid='raise'):
+            assert np.isnan(np.logaddexp2(x, y))
 
     def test_reduce(self):
         assert_equal(np.logaddexp2.identity, -np.inf)
@@ -2266,7 +2272,7 @@ class TestAVXFloat32Transcendental:
                 assert_array_almost_equal_nulp(np.sin(x_large), sin_true[::jj], nulp=2)
                 assert_array_almost_equal_nulp(np.cos(x_large), cos_true[::jj], nulp=2)
 
-class TestLogAddExp(_FilterInvalids):
+class TestLogAddExp:
     def test_logaddexp_values(self):
         x = [1, 2, 3, 4, 5]
         y = [5, 4, 3, 2, 1]
@@ -2299,12 +2305,18 @@ class TestLogAddExp(_FilterInvalids):
                 logzf = np.array(z, dtype=dt)
                 assert_equal(np.logaddexp(logxf, logyf), logzf)
 
-    def test_nan(self):
-        assert_(np.isnan(np.logaddexp(np.nan, np.inf)))
-        assert_(np.isnan(np.logaddexp(np.inf, np.nan)))
-        assert_(np.isnan(np.logaddexp(np.nan, 0)))
-        assert_(np.isnan(np.logaddexp(0, np.nan)))
-        assert_(np.isnan(np.logaddexp(np.nan, np.nan)))
+    @pytest.mark.parametrize("dtype", ['f', 'd', 'g'])
+    @pytest.mark.parametrize("x, y", [
+        (np.nan, np.inf),
+        (np.inf, np.nan),
+        (np.nan, 0),
+        (0, np.nan),
+        (np.nan, np.nan),
+    ])
+    def test_nan(self, dtype, x, y):
+        x, y = np.array([x, y], dtype=dtype)
+        with np.errstate(invalid='raise'):
+            assert np.isnan(np.logaddexp(x, y))
 
     def test_reduce(self):
         assert_equal(np.logaddexp.identity, -np.inf)

pytest.ini

@@ -12,7 +12,6 @@ filterwarnings =
     ignore:numpy.ufunc size changed
     ignore::UserWarning:cpuinfo,
     ignore: divide by zero encountered in log
-    ignore: invalid value encountered in log
 # Matrix PendingDeprecationWarning.
     ignore:the matrix subclass is not:PendingDeprecationWarning
     ignore:Importing from numpy.matlib is:PendingDeprecationWarning