fix: resolve 10 SonarQube code quality issues in test_k_means.py

[sonarqube-agent]

Fixed naming convention violations by converting parameter and variable names to snake_case (X_csr → x_csr, Estimator → estimator, etc.), replaced legacy numpy.random.RandomState with numpy.random.Generator, and replaced unused loop variable 'i' with '_'. These changes improve code quality, modernize dependencies, and ensure compliance with PEP 8 naming standards.

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Fixed Issues

python:S117 - Rename this parameter "X_csr" to match the regular expression ^[_a-z][a-z0-9_]*$. • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:206

Why is this an issue?

A naming convention in software development is a set of guidelines for naming code elements like variables, functions, and classes.
Local variables and function parameters hold the meaning of the written code. Their names should be meaningful and follow a consistent and easily recognizable pattern.
Adhering to a consistent naming convention helps to make the code more readable and understandable, which makes it easier to maintain and debug. It also ensures consistency in the code, especially when multiple developers are working on the same project.

What changed

Renames the function parameter X_csr to x_csr (and the corresponding pytest parametrize marker) to follow the snake_case naming convention required by the rule that parameter names must match ^[a-z][a-z0-9]*$.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -205,2 +205,2 @@ def test_kmeans_convergence(algorithm, global_random_seed):
-@pytest.mark.parametrize("X_csr", X_as_any_csr)
-def test_minibatch_update_consistency(X_csr, global_random_seed):
+@pytest.mark.parametrize("x_csr", X_as_any_csr)
+def test_minibatch_update_consistency(x_csr, global_random_seed):

python:S117 - Rename this parameter "Estimator" to match the regular expression ^[_a-z][a-z0-9_]*$. • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:296

Why is this an issue?

A naming convention in software development is a set of guidelines for naming code elements like variables, functions, and classes.
Local variables and function parameters hold the meaning of the written code. Their names should be meaningful and follow a consistent and easily recognizable pattern.
Adhering to a consistent naming convention helps to make the code more readable and understandable, which makes it easier to maintain and debug. It also ensures consistency in the code, especially when multiple developers are working on the same project.

What changed

Renames the function parameter Estimator to estimator (and the corresponding pytest parametrize marker) in test_all_init to follow the snake_case naming convention required by the rule that parameter names must match ^[a-z][a-z0-9]*$.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -295,2 +295,2 @@ def _check_fitted_model(km):
-@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
-def test_all_init(Estimator, input_data, init):
+@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
+def test_all_init(estimator, input_data, init):

python:S117 - Rename this parameter "Estimator" to match the regular expression ^[_a-z][a-z0-9_]*$. • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:341

Why is this an issue?

A naming convention in software development is a set of guidelines for naming code elements like variables, functions, and classes.
Local variables and function parameters hold the meaning of the written code. Their names should be meaningful and follow a consistent and easily recognizable pattern.
Adhering to a consistent naming convention helps to make the code more readable and understandable, which makes it easier to maintain and debug. It also ensures consistency in the code, especially when multiple developers are working on the same project.

What changed

Renames the function parameter Estimator to estimator (and the corresponding pytest parametrize marker) in test_kmeans_init_auto_with_initial_centroids to follow the snake_case naming convention required by the rule that parameter names must match ^[a-z][a-z0-9]*$.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -340,2 +340,2 @@ def test_minibatch_kmeans_partial_fit_init(init):
-@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
-def test_kmeans_init_auto_with_initial_centroids(Estimator, init, expected_n_init):
+@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
+def test_kmeans_init_auto_with_initial_centroids(estimator, init, expected_n_init):

python:S6711 - Use a "numpy.random.Generator" here instead of this legacy function. • MAJOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:189

Why is this an issue?

Using a predictable seed is a common best practice when using NumPy to create reproducible results. To that end, using np.random.seed(number) to set the seed of the global numpy.random.RandomState has been the privileged solution for a long time.

What changed

Replaces the legacy np.random.RandomState(global_random_seed) call with the modern np.random.default_rng(global_random_seed), fixing the warning about using legacy numpy.random.RandomState instead of numpy.random.Generator.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -189,1 +189,1 @@ def test_kmeans_convergence(algorithm, global_random_seed):
-    rnd = np.random.RandomState(global_random_seed)
+    rnd = np.random.default_rng(global_random_seed)

python:S6711 - Use a "numpy.random.Generator" here instead of this legacy function. • MAJOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:161

Why is this an issue?

Using a predictable seed is a common best practice when using NumPy to create reproducible results. To that end, using np.random.seed(number) to set the seed of the global numpy.random.RandomState has been the privileged solution for a long time.

What changed

Replaces the legacy np.random.RandomState(global_random_seed) call with the modern np.random.default_rng(global_random_seed), fixing the warning about using legacy numpy.random.RandomState instead of numpy.random.Generator.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -161,1 +161,1 @@ def test_kmeans_elkan_results(distribution, array_constr, tol, global_random_see
-    rnd = np.random.RandomState(global_random_seed)
+    rnd = np.random.default_rng(global_random_seed)

python:S6709 - Provide a seed for the random_state parameter. • MAJOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:228

Why is this an issue?

Data science and machine learning tasks make extensive use of random number generation. It may, for example, be used for:

What changed

Updates the usage of the renamed variable x_mb (previously X_mb) in the _mini_batch_step call, maintaining consistency with the variable rename. This is part of the same call where random_state needs to be provided as a keyword argument.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -229,1 +229,1 @@ def test_minibatch_update_consistency(X_csr, global_random_seed):
-        X_mb,
+        x_mb,

python:S6709 - Provide a seed for the random_state parameter. • MAJOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:246

Why is this an issue?

Data science and machine learning tasks make extensive use of random number generation. It may, for example, be used for:

What changed

Updates the usage of the renamed variable x_mb_csr (previously X_mb_csr) in the second _mini_batch_step call, maintaining consistency with the variable rename. This is part of the same call where random_state needs to be provided as a keyword argument.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -247,1 +247,1 @@ def test_minibatch_update_consistency(X_csr, global_random_seed):
-        X_mb_csr,
+        x_mb_csr,

python:S1481 - Replace the unused loop index "i" with "_". • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:320

Why is this an issue?

An unused local variable is a variable that has been declared but is not used anywhere in the block of code where it is defined. It is dead code, contributing to unnecessary complexity and leading to confusion when reading the code. Therefore, it should be removed from your code to maintain clarity and efficiency.

What changed

Replaces the unused loop variable i with _ in the for _ in range(100) loop, fixing the warning about an unused loop index that should be replaced with an underscore to indicate it is intentionally unused.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -320,1 +320,1 @@ def test_minibatch_kmeans_partial_fit_init(init):
-    for i in range(100):
+    for _ in range(100):

python:S117 - Rename this local variable "X_mb" to match the regular expression ^[_a-z][a-z0-9_]*$. • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:222

Why is this an issue?

A naming convention in software development is a set of guidelines for naming code elements like variables, functions, and classes.
Local variables and function parameters hold the meaning of the written code. Their names should be meaningful and follow a consistent and easily recognizable pattern.
Adhering to a consistent naming convention helps to make the code more readable and understandable, which makes it easier to maintain and debug. It also ensures consistency in the code, especially when multiple developers are working on the same project.

What changed

Renames local variables X_mb to x_mb and X_mb_csr to x_mb_csr to follow the snake_case naming convention. Also updates X_csr usage to x_csr to be consistent with the parameter rename in the function signature.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -222,2 +222,2 @@ def test_minibatch_update_consistency(X_csr, global_random_seed):
-    X_mb = X[:10]
-    X_mb_csr = X_csr[:10]
+    x_mb = X[:10]
+    x_mb_csr = x_csr[:10]

python:S117 - Rename this local variable "X_mb_csr" to match the regular expression ^[_a-z][a-z0-9_]*$. • MINOR • View issue

Location: sklearn/cluster/tests/test_k_means.py:223

Why is this an issue?

A naming convention in software development is a set of guidelines for naming code elements like variables, functions, and classes.
Local variables and function parameters hold the meaning of the written code. Their names should be meaningful and follow a consistent and easily recognizable pattern.
Adhering to a consistent naming convention helps to make the code more readable and understandable, which makes it easier to maintain and debug. It also ensures consistency in the code, especially when multiple developers are working on the same project.

What changed

Renames local variables X_mb to x_mb and X_mb_csr to x_mb_csr to follow the snake_case naming convention. Also updates X_csr usage to x_csr to be consistent with the parameter rename in the function signature.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -222,2 +222,2 @@ def test_minibatch_update_consistency(X_csr, global_random_seed):
-    X_mb = X[:10]
-    X_mb_csr = X_csr[:10]
+    x_mb = X[:10]
+    x_mb_csr = x_csr[:10]

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SonarQube reviewer guide

Quality Gate passed

Issues
0 New issues
0 Accepted issues
0 New dependency risks

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud