Apply simple NumPy 2 fixes and silence most warnings

Author: seberg

hdbscan/_hdbscan_linkage.pyx

@@ -33,7 +33,7 @@ cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core(
     result = np.zeros((distance_matrix.shape[0] - 1, 3))
     node_labels = np.arange(distance_matrix.shape[0], dtype=np.intp)
     current_node = 0
-    current_distances = np.infty * np.ones(distance_matrix.shape[0])
+    current_distances = np.inf * np.ones(distance_matrix.shape[0])
     current_labels = node_labels
     for i in range(1, node_labels.shape[0]):
         label_filter = current_labels != current_node
@@ -100,7 +100,7 @@ cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core_vector(
     result_arr = np.zeros((dim - 1, 3))
     in_tree_arr = np.zeros(dim, dtype=np.int8)
     current_node = 0
-    current_distances_arr = np.infty * np.ones(dim)
+    current_distances_arr = np.inf * np.ones(dim)
     current_sources_arr = np.ones(dim)
 
     result = (<np.double_t[:dim - 1, :3:1]> (<np.double_t *> result_arr.data))

hdbscan/_hdbscan_reachability.pyx

@@ -79,7 +79,7 @@ cpdef sparse_mutual_reachability(object lil_matrix, np.intp_t min_points=5,
         if min_points - 1 < len(sorted_row_data):
             core_distance[i] = sorted_row_data[min_points - 1]
         else:
-            core_distance[i] = np.infty
+            core_distance[i] = np.inf
 
     if alpha != 1.0:
         lil_matrix = lil_matrix / alpha

hdbscan/_hdbscan_tree.pyx

@@ -251,7 +251,7 @@ cdef list bfs_from_cluster_tree(np.ndarray tree, np.intp_t bfs_root):
 
     while to_process.shape[0] > 0:
         result.extend(to_process.tolist())
-        to_process = tree['child'][np.in1d(tree['parent'], to_process)]
+        to_process = tree['child'][np.isin(tree['parent'], to_process)]
 
     return result
 

hdbscan/plots.py

@@ -28,7 +28,7 @@ def _bfs_from_cluster_tree(tree, bfs_root):
 
     while to_process:
         result.extend(to_process)
-        to_process = tree['child'][np.in1d(tree['parent'], to_process)].tolist()
+        to_process = tree['child'][np.isin(tree['parent'], to_process)].tolist()
 
     return result
 

hdbscan/prediction.py

@@ -81,7 +81,7 @@ def _clusters_below(self, cluster):
         while to_process:
             result.extend(to_process)
             to_process = \
-                self.cluster_tree['child'][np.in1d(self.cluster_tree['parent'],
+                self.cluster_tree['child'][np.isin(self.cluster_tree['parent'],
                                                    to_process)]
             to_process = to_process.tolist()
 

hdbscan/validity.py

@@ -180,7 +180,7 @@ def internal_minimum_spanning_tree(mr_distances):
     # A little "fancy" we select from the flattened array reshape back
     # (Fortran format to get indexing right) and take the product to do an and
     # then convert back to boolean type.
-    edge_selection = np.prod(np.in1d(min_span_tree.T[:2], vertices).reshape(
+    edge_selection = np.prod(np.isin(min_span_tree.T[:2], vertices).reshape(
         (min_span_tree.shape[0], 2), order='F'), axis=1).astype(bool)
 
     # Density sparseness is not well defined if there are no