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import numpy as np
from sklearn.datasets import make_classification
from sklearn.pipeline import Pipeline, FeatureUnion
from ..umap_ import UMAP
def test_get_feature_names_out_passthrough():
umap = UMAP()
# get_feature_names_out should return same names if feature are passed in directly.
example_passthrough = ['feature1', 'feature2']
passthrough_result = umap.get_feature_names_out(feature_names_out=example_passthrough)
assert example_passthrough == passthrough_result
def test_get_feature_names_out_default():
umap = UMAP()
# get_feature_names_out should generate feature names in a certain format if no names are passed.
default_result = umap.get_feature_names_out()
expected_default_result = ["umap_component_1", "umap_component_2"]
assert default_result == expected_default_result
def test_get_feature_names_out_multicomponent():
# The output length should be equal to the number of components UMAP generates.
umap10 = UMAP(n_components=10)
result_umap10 = umap10.get_feature_names_out()
expected_umap10_result = [f"umap_component_{i+1}" for i in range(10)]
assert len(result_umap10) == 10
assert result_umap10 == expected_umap10_result
def test_get_feature_names_out_featureunion():
X, _ = make_classification(n_samples=10)
pipeline = Pipeline(
[
(
"umap_pipeline",
FeatureUnion(
[
("umap1", UMAP()),
("umap2", UMAP(n_components=3)),
]
),
)
]
)
pipeline.fit(X)
feature_names = pipeline.get_feature_names_out()
expected_feature_names = np.array(["umap1__umap_component_1", "umap1__umap_component_2", "umap2__umap_component_1",
"umap2__umap_component_2", "umap2__umap_component_3"])
np.testing.assert_array_equal(feature_names, expected_feature_names)
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