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fix: enforce snake_case naming convention in test_k_means.py #639

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fix: enforce snake_case naming convention in test_k_means.py #639

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46 changes: 23 additions & 23 deletions sklearn/cluster/tests/test_k_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -686,22 +686,22 @@ def test_dense_sparse(Estimator, X_csr, global_random_seed):
assert_allclose(km_dense.cluster_centers_, km_sparse.cluster_centers_)


@pytest.mark.parametrize("X_csr", X_as_any_csr)
@pytest.mark.parametrize("x_csr", X_as_any_csr)
@pytest.mark.parametrize(
"init", ["random", "k-means++", centers], ids=["random", "k-means++", "ndarray"]
)
@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
def test_predict_dense_sparse(Estimator, init, X_csr):
def test_predict_dense_sparse(Estimator, init, x_csr):
# check that models trained on sparse input also works for dense input at
# predict time and vice versa.
n_init = 10 if isinstance(init, str) else 1
km = Estimator(n_clusters=n_clusters, init=init, n_init=n_init, random_state=0)

km.fit(X_csr)
km.fit(x_csr)
assert_array_equal(km.predict(X), km.labels_)

km.fit(X)
assert_array_equal(km.predict(X_csr), km.labels_)
assert_array_equal(km.predict(x_csr), km.labels_)


@pytest.mark.parametrize("array_constr", data_containers, ids=data_containers_ids)
Expand Down Expand Up @@ -1075,20 +1075,20 @@ def test_euclidean_distance(dtype, squared, global_random_seed):
def test_inertia(dtype, global_random_seed):
# Check that the _inertia_(dense/sparse) helpers produce correct results.
rng = np.random.RandomState(global_random_seed)
X_sparse = _sparse_random_array(
x_sparse = _sparse_random_array(
(100, 10), density=0.5, format="csr", rng=rng, dtype=dtype
)
X_dense = X_sparse.toarray()
x_dense = x_sparse.toarray()
sample_weight = rng.randn(100).astype(dtype, copy=False)
centers = rng.randn(5, 10).astype(dtype, copy=False)
labels = rng.randint(5, size=100, dtype=np.int32)

distances = ((X_dense - centers[labels]) ** 2).sum(axis=1)
distances = ((x_dense - centers[labels]) ** 2).sum(axis=1)
expected = np.sum(distances * sample_weight)

inertia_dense = _inertia_dense(X_dense, sample_weight, centers, labels, n_threads=1)
inertia_dense = _inertia_dense(x_dense, sample_weight, centers, labels, n_threads=1)
inertia_sparse = _inertia_sparse(
X_sparse, sample_weight, centers, labels, n_threads=1
x_sparse, sample_weight, centers, labels, n_threads=1
)

rtol = 1e-4 if dtype == np.float32 else 1e-6
Expand All @@ -1099,43 +1099,43 @@ def test_inertia(dtype, global_random_seed):
# Check the single_label parameter.
label = 1
mask = labels == label
distances = ((X_dense[mask] - centers[label]) ** 2).sum(axis=1)
distances = ((x_dense[mask] - centers[label]) ** 2).sum(axis=1)
expected = np.sum(distances * sample_weight[mask])

inertia_dense = _inertia_dense(
X_dense, sample_weight, centers, labels, n_threads=1, single_label=label
x_dense, sample_weight, centers, labels, n_threads=1, single_label=label
)
inertia_sparse = _inertia_sparse(
X_sparse, sample_weight, centers, labels, n_threads=1, single_label=label
x_sparse, sample_weight, centers, labels, n_threads=1, single_label=label
)

assert_allclose(inertia_dense, inertia_sparse, rtol=rtol)
assert_allclose(inertia_dense, expected, rtol=rtol)
assert_allclose(inertia_sparse, expected, rtol=rtol)


@pytest.mark.parametrize("Klass, default_n_init", [(KMeans, 10), (MiniBatchKMeans, 3)])
def test_n_init_auto(Klass, default_n_init):
est = Klass(n_init="auto", init="k-means++")
@pytest.mark.parametrize("klass, default_n_init", [(KMeans, 10), (MiniBatchKMeans, 3)])
def test_n_init_auto(klass, default_n_init):
est = klass(n_init="auto", init="k-means++")
est.fit(X)
assert est._n_init == 1

est = Klass(n_init="auto", init="random")
est = klass(n_init="auto", init="random")
est.fit(X)
assert est._n_init == 10 if Klass.__name__ == "KMeans" else 3
assert est._n_init == 10 if klass.__name__ == "KMeans" else 3


@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
def test_sample_weight_unchanged(Estimator):
@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
def test_sample_weight_unchanged(estimator):
# Check that sample_weight is not modified in place by KMeans (#17204)
X = np.array([[1], [2], [4]])
sample_weight = np.array([0.5, 0.2, 0.3])
Estimator(n_clusters=2, random_state=0).fit(X, sample_weight=sample_weight)
estimator(n_clusters=2, random_state=0).fit(X, sample_weight=sample_weight)

assert_array_equal(sample_weight, np.array([0.5, 0.2, 0.3]))


@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
@pytest.mark.parametrize(
"param, match",
[
Expand All @@ -1162,11 +1162,11 @@ def test_sample_weight_unchanged(Estimator):
),
],
)
def test_wrong_params(Estimator, param, match):
def test_wrong_params(estimator, param, match):
# Check that error are raised with clear error message when wrong values
# are passed for the parameters
# Set n_init=1 by default to avoid warning with precomputed init
km = Estimator(n_init=1)
km = estimator(n_init=1)
with pytest.raises(ValueError, match=match):
km.set_params(**param).fit(X)

Expand Down