Support numpy 2.0 (#57)

* Support numpy 2.0 * Fix return types
audeering · Jun 18, 2024 · aa39c9e · aa39c9e
1 parent 74c3539
commit aa39c9e
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Showing 3 changed files with 29 additions and 29 deletions.
diff --git a/audmetric/core/api.py b/audmetric/core/api.py
@@ -117,7 +117,7 @@ def concordance_cc(
         prediction = prediction[mask]
 
     if len(prediction) < 2:
-        return np.NaN
+        return np.nan
 
     length = prediction.size
     mean_y = np.mean(truth)
@@ -656,7 +656,7 @@ def linkability(
     # Global measure using trapz numerical integration
     d_sys = np.trapz(x=bin_centers, y=d * y1)
 
-    return d_sys
+    return float(d_sys)
 
 
 def mean_absolute_error(
@@ -764,7 +764,7 @@ def pearson_cc(
         prediction = np.array(list(prediction))
 
     if len(prediction) < 2 or prediction.std() == 0:
-        return np.NaN
+        return np.nan
     else:
         return float(np.corrcoef(prediction, truth)[0][1])
 
@@ -997,7 +997,7 @@ def unweighted_average_bias(
     if denominator == 0:
         return np.nan
 
-    return bias / denominator
+    return float(bias / denominator)
 
 
 def unweighted_average_fscore(
@@ -1225,7 +1225,7 @@ def word_error_rate(
         wer += edit_distance(t, p) / n
 
     num_samples = len(truth) if len(truth) > 1 else 1
-    return wer / num_samples
+    return float(wer / num_samples)
 
 
 def _matching_scores(

diff --git a/tests/test_api.py b/tests/test_api.py
@@ -72,7 +72,7 @@ def test_accuracy(truth, prediction, labels, to_string):
         prediction = [str(w) for w in prediction]
 
     if len(prediction) == 0:
-        accuracy = np.NaN
+        accuracy = np.nan
     else:
         if labels:
             mask = np.nonzero(
@@ -426,7 +426,7 @@ def test_mean_squared_error(value_range, num_elements):
 )
 def test_pearson_cc(truth, prediction):
     if len(prediction) < 2 or prediction.std() == 0:
-        pcc = np.NaN
+        pcc = np.nan
     else:
         pcc = np.corrcoef(list(truth), list(prediction))[0][1]
     np.testing.assert_almost_equal(

diff --git a/tests/test_concordance_cc.py b/tests/test_concordance_cc.py
@@ -20,7 +20,7 @@ def expected_ccc(truth, prediction):
         + (prediction.mean() - truth.mean()) ** 2
     )
     if denominator == 0:
-        ccc = np.NaN
+        ccc = np.nan
     else:
         r = np.corrcoef(list(prediction), list(truth))[0][1]
         numerator = 2 * r * prediction.std() * truth.std()
@@ -82,37 +82,37 @@ def test_concordance_cc(truth, prediction, ignore_nan):
             [],
             [],
             True,
-            np.NaN,
+            np.nan,
         ),
         (
             [],
             [],
             False,
-            np.NaN,
+            np.nan,
         ),
         (
             [0],
             [0],
             True,
-            np.NaN,
+            np.nan,
         ),
         (
             [0],
             [0],
             False,
-            np.NaN,
+            np.nan,
         ),
         (
-            [0, np.NaN],
-            [0, np.NaN],
+            [0, np.nan],
+            [0, np.nan],
             True,
-            np.NaN,
+            np.nan,
         ),
         (
-            [0, np.NaN],
-            [0, np.NaN],
+            [0, np.nan],
+            [0, np.nan],
             False,
-            np.NaN,
+            np.nan,
         ),
         (
             [0, 1, 2, 3],
@@ -121,40 +121,40 @@ def test_concordance_cc(truth, prediction, ignore_nan):
             expected_ccc([0, 1, 2, 3], [1, 2, 3, 4]),
         ),
         (
-            [np.NaN, 1, 2, 3],
-            [np.NaN, 2, 3, 4],
+            [np.nan, 1, 2, 3],
+            [np.nan, 2, 3, 4],
             True,
             expected_ccc([1, 2, 3], [2, 3, 4]),
         ),
         (
-            [np.NaN, 1, 2, 3],
-            [1, 2, 3, np.NaN],
+            [np.nan, 1, 2, 3],
+            [1, 2, 3, np.nan],
             True,
             expected_ccc([1, 2], [2, 3]),
         ),
         (
-            [0, np.NaN, 2, 3],
+            [0, np.nan, 2, 3],
             [1, 2, 3, 4],
             True,
             expected_ccc([0, 2, 3], [1, 3, 4]),
         ),
         (
             [0, 1, 2, 3],
-            [1, 2, np.NaN, 4],
+            [1, 2, np.nan, 4],
             True,
             expected_ccc([0, 1, 3], [1, 2, 4]),
         ),
         (
-            [np.NaN, np.NaN, 2, 3],
-            [1, 2, 3, np.NaN],
+            [np.nan, np.nan, 2, 3],
+            [1, 2, 3, np.nan],
             True,
             expected_ccc([2], [3]),
         ),
         (
-            [np.NaN, np.NaN, 2, 3],
-            [1, 2, 3, np.NaN],
+            [np.nan, np.nan, 2, 3],
+            [1, 2, 3, np.nan],
             False,
-            np.NaN,
+            np.nan,
         ),
     ],
 )