FIX KMeansSMOTE: uniform weights for degenerate clusters (#1186)

[chaoz23]

Toward #1186 — fixes the crash (issue 1). The exact-count allocation (issue 2) is intentionally left as a follow-up, so this does not auto-close the issue.

What

When every sample within each valid cluster is identical, all cluster sparsities are 0, so cluster_sparsities / cluster_sparsities.sum() divides by zero and yields NaN weights. The subsequent math.ceil(...) then raises ValueError: cannot convert float NaN to integer.

This can happen with duplicated rows or discrete/one-hot features where a whole cluster collapses to a single point.

Change

• Weight such degenerate clusters uniformly when the total sparsity is 0, instead of dividing by zero.
• Move the existing empty-valid_clusters RuntimeError guard ahead of the weight computation (previously it ran an unnecessary division on an empty array first).
• Add a non-regression test reproducing the reported case.

Reproducer (from the issue)

import numpy as np
from imblearn.over_sampling import KMeansSMOTE
from sklearn.cluster import KMeans

X = np.array([[1., 1.]]*4 + [[2., 2.]]*4 + [[3., 3.]]*4)
y = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
KMeansSMOTE(
    kmeans_estimator=KMeans(n_clusters=2, random_state=42),
    random_state=42, cluster_balance_threshold=0.1,
).fit_resample(X, y)   # was: ValueError; now: resamples cleanly

Scope

Limited to issue 1 (the crash). Issue 2 (making the total number of generated samples exactly match the requested count) is a separate behavioral change — happy to follow up on it in its own PR.