Update NumFOCUS contribution link in README#1245
Build #20260301.1 had test failures
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azure-pipelines / lmcinnes.umap
Build log #L625
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azure-pipelines / lmcinnes.umap
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azure-pipelines / lmcinnes.umap
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1 test(s) failed, 208 test(s) collected.
Check failure on line 1 in umap/tests/test_umap_on_iris.py::test_precomputed_transform_on_iris
azure-pipelines / lmcinnes.umap
umap/tests/test_umap_on_iris.py::test_precomputed_transform_on_iris
iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
iris_selection = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True,... True, True, False, True, False, True, False, True, True,
True, True, True, True, False, False])
def test_precomputed_transform_on_iris(iris, iris_selection):
data = iris.data[iris_selection]
distance_matrix = squareform(pdist(data))
fitter = UMAP(
n_neighbors=10,
min_dist=0.01,
random_state=42,
n_epochs=100,
metric="precomputed",
).fit(distance_matrix)
new_data = iris.data[~iris_selection]
new_distance_matrix = cdist(new_data, data)
embedding = fitter.transform(new_distance_matrix)
trust = trustworthiness(new_data, embedding, n_neighbors=10)
> assert (
trust >= 0.85
), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust)
E AssertionError: Insufficiently trustworthy transform foriris dataset: 0.8489000628535512
E assert np.float64(0.8489000628535512) >= 0.85
umap/tests/test_umap_on_iris.py:169: AssertionErrorRaw output
/home/vsts/work/1/s/umap/tests/test_umap_on_iris.py:169: AssertionError: Insufficiently trustworthy transform foriris dataset: 0.8489000628535512