ty: un-ignore parameter-already-assigned (1 file, 1 spot)

plt.axes(projection="3d") is typed to return the base 2D Axes, so
ax.scatter's 3rd positional arg (meant as zs) was resolving against 2D
Axes.scatter's `s` parameter and colliding with the explicit s=100
kwarg. Cast ax to Axes3D so ty resolves the correct (xs, ys, zs, ...)
overload instead.

Part of #15187

Author: HasanGenis

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")
@@ -157,7 +159,10 @@ def plot_heterogeneity(heterogeneity, k) -> None:
 
 
 def plot_kmeans(data, centroids, cluster_assignment) -> None:
-    ax = plt.axes(projection="3d")
+    # plt.axes() is typed to return the base 2D Axes, but projection="3d" makes
+    # it an Axes3D at runtime; cast so ty resolves Axes3D.scatter's (xs, ys, zs)
+    # signature instead of colliding its 3rd positional arg with the `s` kwarg.
+    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"

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"