Adapt front-end

kmeans/kmeans.py

+import numpy as np
+from threadpoolctl import threadpool_limits
+
+from kmeans._kmeans import lloyd_iter_chunked_dense, _inertia_dense
+
+
 def _kmeans_single_lloyd(X, sample_weight, centers_init, max_iter=300,
                          verbose=False, x_squared_norms=None, tol=1e-4,
                          n_threads=1):
@@ -49,12 +55,11 @@ def _kmeans_single_lloyd(X, sample_weight, centers_init, max_iter=300,
     weight_in_clusters = np.zeros(n_clusters, dtype=X.dtype)
     center_shift = np.zeros(n_clusters, dtype=X.dtype)
 
-    if sp.issparse(X):
-        lloyd_iter = lloyd_iter_chunked_sparse
-        _inertia = _inertia_sparse
-    else:
-        lloyd_iter = lloyd_iter_chunked_dense
-        _inertia = _inertia_dense
+    # NOTE: We only rely on dense array for those analysis
+    if sample_weight is None:
+        sample_weight = np.ones((X.shape[0],), dtype=X.dtype)
+    lloyd_iter = lloyd_iter_chunked_dense
+    _inertia = _inertia_dense
 
     strict_convergence = False