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Sep 15, 2021
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TST enable tests K-means for MacOS #21052

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60a305f
TST enable tests K-means for MacOS
glemaitre Sep 15, 2021
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[cd build] build everything
glemaitre Sep 15, 2021
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address jeremie commetns
glemaitre Sep 15, 2021
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glemaitre Sep 15, 2021
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[cd build] trigger CD
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glemaitre Sep 15, 2021
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Update test_k_means.py
glemaitre Sep 15, 2021
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glemaitre Sep 15, 2021
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60 changes: 3 additions & 57 deletions sklearn/cluster/tests/test_k_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -339,19 +339,6 @@ def test_fortran_aligned_data(Estimator):
)
def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol):
# check that fit.predict gives same result as fit_predict
# There's a very small chance of failure with elkan on unstructured dataset
# because predict method uses fast euclidean distances computation which
# may cause small numerical instabilities.
# NB: This test is largely redundant with respect to test_predict and
# test_predict_equal_labels. This test has the added effect of
# testing idempotence of the fittng procesdure which appears to
# be where it fails on some MacOS setups.
if sys.platform == "darwin":
pytest.xfail(
"Known failures on MacOS, See "
"https://github.com/scikit-learn/scikit-learn/issues/12644"
)

rng = np.random.RandomState(seed)

X = make_blobs(n_samples=1000, n_features=10, centers=10, random_state=rng)[
Expand All @@ -365,12 +352,7 @@ def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol):

labels_1 = kmeans.fit(X).predict(X)
labels_2 = kmeans.fit_predict(X)

# Due to randomness in the order in which chunks of data are processed when
# using more than one thread, the absolute values of the labels can be
# different between the 2 strategies but they should correspond to the same
# clustering.
assert v_measure_score(labels_1, labels_2) == pytest.approx(1, abs=1e-15)
assert_array_equal(labels_1, labels_2)


def test_minibatch_kmeans_verbose():
Expand Down Expand Up @@ -633,21 +615,10 @@ def test_score_max_iter(Estimator):
def test_predict(Estimator, algorithm, init, dtype, array_constr):
# Check the predict method and the equivalence between fit.predict and
# fit_predict.

# There's a very small chance of failure with elkan on unstructured dataset
# because predict method uses fast euclidean distances computation which
# may cause small numerical instabilities.
if sys.platform == "darwin":
pytest.xfail(
"Known failures on MacOS, See "
"https://github.com/scikit-learn/scikit-learn/issues/12644"
)

X, _ = make_blobs(n_samples=500, n_features=10, centers=10, random_state=0)
X = array_constr(X)

# With n_init = 1
km = Estimator(n_clusters=10, init=init, n_init=1, random_state=0)
km = Estimator(n_clusters=10, init=init, n_init=10, random_state=0)
if algorithm is not None:
km.set_params(algorithm=algorithm)
km.fit(X)
Expand All @@ -665,31 +636,6 @@ def test_predict(Estimator, algorithm, init, dtype, array_constr):
pred = km.predict(km.cluster_centers_)
assert_array_equal(pred, np.arange(10))

# With n_init > 1
# Due to randomness in the order in which chunks of data are processed when
# using more than one thread, there might be different rounding errors for
# the computation of the inertia between 2 runs. This might result in a
# different ranking of 2 inits, hence a different labeling, even if they
# give the same clustering. We only check the labels up to a permutation.

km = Estimator(n_clusters=10, init=init, n_init=10, random_state=0)
if algorithm is not None:
km.set_params(algorithm=algorithm)
km.fit(X)
labels = km.labels_

# re-predict labels for training set using predict
pred = km.predict(X)
assert_allclose(v_measure_score(pred, labels), 1)

# re-predict labels for training set using fit_predict
pred = km.fit_predict(X)
assert_allclose(v_measure_score(pred, labels), 1)

# predict centroid labels
pred = km.predict(km.cluster_centers_)
assert_allclose(v_measure_score(pred, np.arange(10)), 1)


@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
def test_dense_sparse(Estimator):
Expand Down Expand Up @@ -745,7 +691,7 @@ def test_integer_input(Estimator, array_constr, dtype, init):
assert km.cluster_centers_.dtype == np.float64

expected_labels = [0, 1, 1, 0, 0, 1]
assert_allclose(v_measure_score(km.labels_, expected_labels), 1)
assert_array_equal(km.labels_, expected_labels)

# Same with partial_fit (#14314)
if Estimator is MiniBatchKMeans:
Expand Down