Move test to small nn data not iris for speed reasons in coverage
@@ -613,27 +613,28 @@ def test_umap_sparse_trustworthiness():
)
-def test_umap_trustworthiness_on_iris():
- data = iris.data
- embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(data)
- trust = trustworthiness(iris.data, embedding, 10)
+def test_umap_trustworthiness_fast_approx():
+ data = nn_data[:50]
+ embedding = UMAP(n_neighbors=10,
+ min_dist=0.01,
+ random_state=42,
+ force_approximation_algorithm=True).fit_transform(data)
+ trust = trustworthiness(nn_data[:50], embedding, 10)
assert_greater_equal(
trust,
- 0.97,
- "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust),
+ 0.90,
+ "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust),
)
-def test_umap_trustworthiness_on_iris_fast_approx():
+
+def test_umap_trustworthiness_on_iris():
data = iris.data
- embedding = UMAP(n_neighbors=10,
- min_dist=0.01,
- random_state=42,
- force_approximation_algorithm=True).fit_transform(data)
+ embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(data)
trust = trustworthiness(iris.data, embedding, 10)
assert_greater_equal(
trust,
- 0.90,
+ 0.97,
"Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust),
)
