Deprecate connectivity parameter to HDBSCAN (#6936)

Found this while doing the recent `HDBSCAN` refactor. This parameter doesn't do anything and hasn't for a long time. It's undocumented and unused, we should just rip it out.

Authors:
  - Jim Crist-Harif (https://github.com/jcrist)

Approvers:
  - Simon Adorf (https://github.com/csadorf)

URL: #6936

Author: jcrist

python/cuml/cuml/cluster/hdbscan/hdbscan.pyx

@@ -14,6 +14,8 @@
 #
 
 # distutils: language = c++
+import warnings
+
 import cupy as cp
 import numpy as np
 from pylibraft.common.handle import Handle
@@ -750,7 +752,7 @@ class HDBSCAN(Base, InteropMixin, ClusterMixin, CMajorInputTagMixin):
                  gen_min_span_tree=False,
                  handle=None,
                  verbose=False,
-                 connectivity='knn',
+                 connectivity='deprecated',
                  output_type=None,
                  prediction_data=False):
 
@@ -761,9 +763,11 @@ class HDBSCAN(Base, InteropMixin, ClusterMixin, CMajorInputTagMixin):
         if min_samples is None:
             min_samples = min_cluster_size
 
-        if connectivity not in ["knn", "pairwise"]:
-            raise ValueError("'connectivity' can only be one of "
-                             "{'knn', 'pairwise'}")
+        if connectivity != "deprecated":
+            warnings.warn(
+                "The `connectivity` parameter is deprecated and will be removed in 25.10",
+                FutureWarning,
+            )
 
         if 2 < min_samples and min_samples > 1023:
             raise ValueError("'min_samples' must be a positive number "
@@ -888,12 +892,6 @@ class HDBSCAN(Base, InteropMixin, ClusterMixin, CMajorInputTagMixin):
         params.cluster_selection_epsilon = self.cluster_selection_epsilon
         params.allow_single_cluster = self.allow_single_cluster
 
-        if self.connectivity not in {"knn", "pairwise"}:
-            raise ValueError(
-                "`connectivity` must be one of {'knn', 'pairwise'}, "
-                f"got {self.connectivity!r}"
-            )
-
         if self.cluster_selection_method == 'eom':
             params.cluster_selection_method = lib.CLUSTER_SELECTION_METHOD.EOM
         elif self.cluster_selection_method == 'leaf':

python/cuml/cuml/tests/test_hdbscan.py

@@ -154,12 +154,10 @@ def assert_membership_vectors(cu_vecs, sk_vecs):
 @pytest.mark.parametrize("cluster_selection_epsilon", [0.0])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_hdbscan_blobs(
     nrows,
     ncols,
     nclusters,
-    connectivity,
     cluster_selection_epsilon,
     cluster_selection_method,
     allow_single_cluster,
@@ -225,10 +223,8 @@ def test_hdbscan_blobs(
 )
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_hdbscan_sklearn_datasets(
     supervised_learning_dataset,
-    connectivity,
     cluster_selection_epsilon,
     cluster_selection_method,
     min_samples_cluster_size_bounds,
@@ -289,10 +285,8 @@ def test_hdbscan_sklearn_datasets(
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_hdbscan_sklearn_extract_clusters(
     supervised_learning_dataset,
-    connectivity,
     cluster_selection_epsilon,
     cluster_selection_method,
     min_samples,
@@ -333,11 +327,9 @@ def test_hdbscan_sklearn_extract_clusters(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_hdbscan_cluster_patterns(
     dataset,
     nrows,
-    connectivity,
     cluster_selection_epsilon,
     cluster_selection_method,
     min_cluster_size,
@@ -396,11 +388,9 @@ def test_hdbscan_cluster_patterns(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_hdbscan_cluster_patterns_extract_clusters(
     dataset,
     nrows,
-    connectivity,
     cluster_selection_epsilon,
     cluster_selection_method,
     min_cluster_size,
@@ -589,7 +579,6 @@ def test_all_points_membership_vectors_blobs(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 @pytest.mark.parametrize("batch_size", [128, 1000])
 def test_all_points_membership_vectors_moons(
     nrows,
@@ -599,7 +588,6 @@ def test_all_points_membership_vectors_moons(
     min_cluster_size,
     allow_single_cluster,
     max_cluster_size,
-    connectivity,
     batch_size,
 ):
 
@@ -646,7 +634,6 @@ def test_all_points_membership_vectors_moons(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 @pytest.mark.parametrize("batch_size", [128, 1000])
 def test_all_points_membership_vectors_circles(
     nrows,
@@ -656,7 +643,6 @@ def test_all_points_membership_vectors_circles(
     min_cluster_size,
     allow_single_cluster,
     max_cluster_size,
-    connectivity,
     batch_size,
 ):
     X, y = datasets.make_circles(
@@ -780,7 +766,6 @@ def test_approximate_predict_blobs(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_approximate_predict_moons(
     nrows,
     n_points_to_predict,
@@ -790,7 +775,6 @@ def test_approximate_predict_moons(
     allow_single_cluster,
     max_cluster_size,
     cluster_selection_method,
-    connectivity,
 ):
 
     X, y = datasets.make_moons(
@@ -845,7 +829,6 @@ def test_approximate_predict_moons(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_approximate_predict_circles(
     nrows,
     n_points_to_predict,
@@ -855,7 +838,6 @@ def test_approximate_predict_circles(
     allow_single_cluster,
     max_cluster_size,
     cluster_selection_method,
-    connectivity,
 ):
     X, y = datasets.make_circles(
         n_samples=nrows + n_points_to_predict,
@@ -911,7 +893,6 @@ def test_approximate_predict_circles(
 @pytest.mark.parametrize("allow_single_cluster", [False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 def test_approximate_predict_digits(
     n_points_to_predict,
     min_samples,
@@ -920,7 +901,6 @@ def test_approximate_predict_digits(
     allow_single_cluster,
     max_cluster_size,
     cluster_selection_method,
-    connectivity,
 ):
     digits = datasets.load_digits()
     X, y = digits.data, digits.target
@@ -1053,7 +1033,6 @@ def test_membership_vector_blobs(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 @pytest.mark.parametrize("batch_size", [16])
 def test_membership_vector_moons(
     nrows,
@@ -1064,7 +1043,6 @@ def test_membership_vector_moons(
     min_cluster_size,
     allow_single_cluster,
     max_cluster_size,
-    connectivity,
     batch_size,
 ):
 
@@ -1117,7 +1095,6 @@ def test_membership_vector_moons(
 @pytest.mark.parametrize("allow_single_cluster", [True, False])
 @pytest.mark.parametrize("max_cluster_size", [0])
 @pytest.mark.parametrize("cluster_selection_method", ["eom", "leaf"])
-@pytest.mark.parametrize("connectivity", ["knn"])
 @pytest.mark.parametrize("batch_size", [16])
 def test_membership_vector_circles(
     nrows,
@@ -1128,7 +1105,6 @@ def test_membership_vector_circles(
     min_cluster_size,
     allow_single_cluster,
     max_cluster_size,
-    connectivity,
     batch_size,
 ):
     X, y = datasets.make_circles(