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Support numpy 2.0 #57

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Jun 18, 2024
Merged

Support numpy 2.0 #57

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853b474
Support numpy 2.0
hagenw Jun 18, 2024
5fdc8e2
Fix return types
hagenw Jun 18, 2024
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10 changes: 5 additions & 5 deletions audmetric/core/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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(
Expand Down Expand Up @@ -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])

Expand Down Expand Up @@ -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(
Expand Down Expand Up @@ -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(
Expand Down
4 changes: 2 additions & 2 deletions tests/test_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down Expand Up @@ -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(
Expand Down
44 changes: 22 additions & 22 deletions tests/test_concordance_cc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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()
Expand Down Expand Up @@ -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],
Expand All @@ -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,
),
],
)
Expand Down
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