From 57b24775ff6bb8eef2a98f4c0995cf57e51618bd Mon Sep 17 00:00:00 2001
From: Giancarlo Cicellyn Comneno <126195429+gcomneno@users.noreply.github.com>
Date: Tue, 6 Oct 2026 14:29:02 +0200
Subject: [PATCH] fix: type k-means 3D plotting and enable
 parameter-already-assigned

---
 machine_learning/k_means_clust.py | 10 ++++++++--
 pyproject.toml                    |  1 -
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py
index 19e510b966b6..255fee01d474 100644
--- a/machine_learning/k_means_clust.py
+++ b/machine_learning/k_means_clust.py
@@ -48,10 +48,12 @@
 """
 
 import warnings
+from typing import cast
 
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
 from sklearn.metrics import pairwise_distances
 
 warnings.filterwarnings("ignore")
@@ -156,8 +158,12 @@ def plot_heterogeneity(heterogeneity, k) -> None:
     plt.show()
 
 
-def plot_kmeans(data, centroids, cluster_assignment) -> None:
-    ax = plt.axes(projection="3d")
+def plot_kmeans(
+    data: np.ndarray,
+    centroids: np.ndarray,
+    cluster_assignment: np.ndarray,
+) -> None:
+    ax = cast(Axes3D, plt.axes(projection="3d"))
     ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=cluster_assignment, cmap="viridis")
     ax.scatter(
         centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
diff --git a/pyproject.toml b/pyproject.toml
index 52bd60b9d3aa..46a1d3295837 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -323,7 +323,6 @@ rules.invalid-return-type = "ignore"
 rules.no-matching-overload = "ignore"
 rules.not-iterable = "ignore"
 rules.not-subscriptable = "ignore"
-rules.parameter-already-assigned = "ignore"
 rules.unresolved-attribute = "ignore"
 rules.unresolved-import = "ignore"
 rules.unsupported-operator = "ignore"