fix: resolve 5 SonarQube naming convention and code quality issues

[sonarqube-agent]

This PR was automatically created by the Remediation Agent's Scheduled backlog remediation feature.

Fixes naming convention violations in test parameter and variable names to match Python standards (lowercase with underscores), and optimizes a generator expression to allow short-circuit evaluation in any(). These changes improve code quality and adherence to PEP 8 conventions without altering functionality.

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Fixed Issues

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:933

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' in the test_scaled_weights function signature and its pytest parametrize decorator. This fixes the naming convention violation where the parameter name started with an uppercase letter, which doesn't match the expected pattern ^[a-z][a-z0-9]*$ for function parameters.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -951,2 +951,2 @@ def test_unit_weights_vs_no_weights(Estimator, input_data, global_random_seed):
-@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
-def test_scaled_weights(Estimator, input_data, global_random_seed):
+@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
+def test_scaled_weights(estimator, input_data, 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:952

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' in the test_result_equal_in_diff_n_threads function signature and its pytest parametrize decorator. This fixes the naming convention violation where the parameter name started with an uppercase letter, which doesn't match the expected pattern ^[a-z][a-z0-9]*$ for function parameters.

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -987,2 +987,2 @@ def test_kmeans_empty_cluster_relocated(array_constr):
-@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
-def test_result_equal_in_diff_n_threads(Estimator, global_random_seed):
+@pytest.mark.parametrize("estimator", [KMeans, MiniBatchKMeans])
+def test_result_equal_in_diff_n_threads(estimator, 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:988

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 local variable 'X_sparse' to 'x_sparse' in the test_inertia function. This is part of fixing the naming convention violations for both 'X_dense' and 'X_sparse' local variables, which don't match the expected pattern ^[a-z][a-z0-9]*$. The 'X_sparse' variable is used to compute 'X_dense', so both need to be renamed together. While the static analysis only flagged 'X_dense' explicitly, 'X_sparse' must also be renamed since 'X_dense' is derived from it via .toarray().

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -1078,1 +1078,1 @@ def test_inertia(dtype, global_random_seed):
-    X_sparse = _sparse_random_array(
+    x_sparse = _sparse_random_array(

python:S7492 - Unpack this comprehension expression • MINOR • View issue

Location: sklearn/datasets/tests/test_common.py:104

Why is this an issue?

Using a list comprehension inside any() or all() forces the entire list to be created in memory before the check begins. This prevents the short-circuiting behavior that these functions are designed to leverage, where any() stops at the first True and all() stops at the first False.

What changed

Replaces a list comprehension [name.startswith(t) for t in dataset_type] inside any() with a generator expression name.startswith(t) for t in dataset_type. This allows any() to short-circuit (stop iterating as soon as it finds a True value) instead of forcing the entire list to be built in memory first, which is the code smell flagged by the rule about using list comprehensions as parameters to any() or all().

--- a/sklearn/datasets/tests/test_common.py
+++ b/sklearn/datasets/tests/test_common.py
@@ -104,1 +104,1 @@ def _generate_func_supporting_param(param, dataset_type=("load", "fetch")):
-        is_dataset_type = any([name.startswith(t) for t in dataset_type])
+        is_dataset_type = any(name.startswith(t) for t in dataset_type)

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

Location: sklearn/cluster/tests/test_k_means.py:1081

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 local variable 'X_sparse' to 'x_sparse' in the test_inertia function. This is part of fixing the naming convention violations for both 'X_dense' and 'X_sparse' local variables, which don't match the expected pattern ^[a-z][a-z0-9]*$. The 'X_sparse' variable is used to compute 'X_dense', so both need to be renamed together. While the static analysis only flagged 'X_dense' explicitly, 'X_sparse' must also be renamed since 'X_dense' is derived from it via .toarray().

--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -1078,1 +1078,1 @@ def test_inertia(dtype, global_random_seed):
-    X_sparse = _sparse_random_array(
+    x_sparse = _sparse_random_array(

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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

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