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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():
X, _ = make_classification(n_samples=30, n_features=10, random_state=42)
umap = UMAP(
n_neighbors=10,
min_dist=0.01,
n_epochs=200,
random_state=42,
n_components=3,
).fit(X)
# get_feature_names_out should not care about passed features.
features_names_in = [f"feature{i}" for i in range(10)]
feature_names_out = umap.get_feature_names_out(input_features=features_names_in)
expected = ["umap0", "umap1", "umap2"]
np.testing.assert_array_equal(feature_names_out, expected)
def test_get_feature_names_out_default():
X, _ = make_classification(n_samples=30, n_features=10, random_state=42)
umap = UMAP(
n_neighbors=10,
min_dist=0.01,
n_epochs=200,
random_state=42,
n_components=3,
).fit(X)
# 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 = ["umap0", "umap1", "umap2"]
np.testing.assert_array_equal(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.
X, _ = make_classification(n_samples=30, n_features=10, random_state=42)
umap = UMAP(
n_neighbors=10,
min_dist=0.01,
n_epochs=200,
random_state=42,
n_components=9,
).fit(X)
result_umap = umap.get_feature_names_out()
expected_umap_result = [f"umap{i}" for i in range(9)]
assert len(result_umap) == 9
np.testing.assert_array_equal(result_umap, expected_umap_result)
def test_get_feature_names_out_featureunion():
X, _ = make_classification(n_samples=30, n_features=10, random_state=42)
pipeline = Pipeline(
[
(
"umap_pipeline",
FeatureUnion(
[
("umap1", UMAP(n_components=2)),
("umap2", UMAP(n_components=3)),
]
),
)
]
)
pipeline.fit(X)
feature_names = pipeline.get_feature_names_out()
expected_feature_names = np.array(
[
"umap1__umap0",
"umap1__umap1",
"umap2__umap0",
"umap2__umap1",
"umap2__umap2",
]
)
np.testing.assert_array_equal(feature_names, expected_feature_names)
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