Rename the CAGRA prune function to optimize

cpp/include/raft/neighbors/cagra.cuh

@@ -57,10 +57,11 @@ namespace raft::neighbors::experimental::cagra {
  *   auto knn_graph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 128);
  *   // create knn graph
  *   cagra::build_knn_graph(res, dataset, knn_graph.view(), 2, build_params, search_params);
- *   auto pruned_gaph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 64);
- *   cagra::prune(res, dataset, knn_graph.view(), pruned_graph.view());
- *   // Construct an index from dataset and pruned knn_graph
- *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset, pruned_graph.view());
+ *   auto optimized_gaph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 64);
+ *   cagra::optimize(res, dataset, knn_graph.view(), optimized_graph.view());
+ *   // Construct an index from dataset and optimized knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset,
+ * optimized_graph.view());
  * @endcode
  *
  * @tparam T data element type
@@ -96,10 +97,10 @@ void build_knn_graph(raft::resources const& res,
 
 /**
  * @brief Sort a KNN graph index.
- * Preprocessing step for `cagra::prune`: If a KNN graph is not built using
+ * Preprocessing step for `cagra::optimize`: If a KNN graph is not built using
  * `cagra::build_knn_graph`, then it is necessary to call this function before calling
- * `cagra::prune`. If the graph is built by `cagra::build_knn_graph`, it is already sorted and you
- * do not need to call this function.
+ * `cagra::optimize`. If the graph is built by `cagra::build_knn_graph`, it is already sorted and
+ * you do not need to call this function.
  *
  * Usage example:
  * @code{.cpp}
@@ -110,10 +111,11 @@ void build_knn_graph(raft::resources const& res,
  *   // build(knn_graph, dataset, ...);
  *   // sort graph index
  *   sort_knn_graph(res, dataset.view(), knn_graph.view());
- *   // prune graph
- *   cagra::prune(res, dataset, knn_graph.view(), pruned_graph.view());
- *   // Construct an index from dataset and pruned knn_graph
- *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset, pruned_graph.view());
+ *   // optimize graph
+ *   cagra::optimize(res, dataset, knn_graph.view(), optimized_graph.view());
+ *   // Construct an index from dataset and optimized knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset,
+ * optimized_graph.view());
  * @endcode
  *
  * @tparam DataT type of the data in the source dataset
@@ -162,14 +164,14 @@ void sort_knn_graph(raft::resources const& res,
  * @param[in] res raft resources
  * @param[in] knn_graph a matrix view (host or device) of the input knn graph [n_rows,
  * knn_graph_degree]
- * @param[out] new_graph a host matrix view of the pruned knn graph [n_rows, graph_degree]
+ * @param[out] new_graph a host matrix view of the optimized knn graph [n_rows, graph_degree]
  */
 template <typename IdxT = uint32_t,
           typename g_accessor =
             host_device_accessor<std::experimental::default_accessor<IdxT>, memory_type::host>>
-void prune(raft::resources const& res,
-           mdspan<IdxT, matrix_extent<IdxT>, row_major, g_accessor> knn_graph,
-           raft::host_matrix_view<IdxT, IdxT, row_major> new_graph)
+void optimize(raft::resources const& res,
+              mdspan<IdxT, matrix_extent<IdxT>, row_major, g_accessor> knn_graph,
+              raft::host_matrix_view<IdxT, IdxT, row_major> new_graph)
 {
   using internal_IdxT = typename std::make_unsigned<IdxT>::type;
 
@@ -186,21 +188,21 @@ void prune(raft::resources const& res,
       knn_graph.extent(0),
       knn_graph.extent(1));
 
-  detail::graph::prune(res, knn_graph_internal, new_graph_internal);
+  detail::graph::optimize(res, knn_graph_internal, new_graph_internal);
 }
 
 /**
  * @brief Build the index from the dataset for efficient search.
  *
- * The build consist of two steps: build an intermediate knn-graph, and prune it to
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
  * create the final graph. The index_params struct controls the node degree of these
  * graphs.
  *
- * It is required that dataset and the pruned graph fit the GPU memory.
+ * It is required that dataset and the optimized graph fit the GPU memory.
  *
  * To customize the parameters for knn-graph building and pruning, and to reuse the
  * intermediate results, you could build the index in two steps using
- * [cagra::build_knn_graph](#cagra::build_knn_graph) and [cagra::prune](#cagra::prune).
+ * [cagra::build_knn_graph](#cagra::build_knn_graph) and [cagra::optimize](#cagra::optimize).
  *
  * The following distance metrics are supported:
  * - L2
@@ -260,9 +262,9 @@ index<T, IdxT> build(raft::resources const& res,
 
   auto cagra_graph = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), graph_degree);
 
-  prune<IdxT>(res, knn_graph.view(), cagra_graph.view());
+  optimize<IdxT>(res, knn_graph.view(), cagra_graph.view());
 
-  // Construct an index from dataset and pruned knn graph.
+  // Construct an index from dataset and optimized knn graph.
   return index<T, IdxT>(res, params.metric, dataset, cagra_graph.view());
 }
 

cpp/include/raft/neighbors/detail/cagra/graph_core.cuh

@@ -317,9 +317,9 @@ void sort_knn_graph(raft::resources const& res,
 template <typename IdxT = uint32_t,
           typename g_accessor =
             host_device_accessor<std::experimental::default_accessor<IdxT>, memory_type::host>>
-void prune(raft::resources const& res,
-           mdspan<IdxT, matrix_extent<IdxT>, row_major, g_accessor> knn_graph,
-           raft::host_matrix_view<IdxT, IdxT, row_major> new_graph)
+void optimize(raft::resources const& res,
+              mdspan<IdxT, matrix_extent<IdxT>, row_major, g_accessor> knn_graph,
+              raft::host_matrix_view<IdxT, IdxT, row_major> new_graph)
 {
   RAFT_LOG_DEBUG(
     "# Pruning kNN graph (size=%lu, degree=%lu)\n", knn_graph.extent(0), knn_graph.extent(1));