Updates nx-cugraph README.md with latest algos

python/nx-cugraph/Makefile

@@ -2,7 +2,7 @@
 SHELL= /bin/bash
 
 .PHONY: all
-all: plugin-info lint
+all: plugin-info lint readme
 
 .PHONY: lint
 lint:
@@ -15,3 +15,7 @@ lint-update:
 .PHONY: plugin-info
 plugin-info:
 	python _nx_cugraph/__init__.py
+
+.PHONY: readme
+readme:
+	python update_readme.py README.md

python/nx-cugraph/README.md

@@ -89,48 +89,106 @@ interface to its CUDA-based graph analytics library) and
 [CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's
 familiar and easy-to-use API.
 
-Below is the list of algorithms (many listed using pylibcugraph names),
-available today in pylibcugraph or implemented using CuPy, that are or will be
+Below is the list of algorithms that are currently supported or planned to be
 supported in nx-cugraph.
 
-| feature/algo | release/target version |
-| ----- | ----- |
-| analyze_clustering_edge_cut | ? |
-| analyze_clustering_modularity | ? |
-| analyze_clustering_ratio_cut | ? |
-| balanced_cut_clustering | ? |
-| betweenness_centrality | 23.10 |
-| bfs | ? |
-| connected_components | 23.12 |
-| core_number | ? |
-| degree_centrality | 23.12 |
-| ecg | ? |
-| edge_betweenness_centrality | 23.10 |
-| ego_graph | ? |
-| eigenvector_centrality | 23.12 |
-| get_two_hop_neighbors | ? |
-| hits | 23.12 |
-| in_degree_centrality | 23.12 |
-| induced_subgraph | ? |
-| jaccard_coefficients | ? |
-| katz_centrality | 23.12 |
-| k_core | ? |
-| k_truss_subgraph | 23.12 |
-| leiden | ? |
-| louvain | 23.10 |
-| node2vec | ? |
-| out_degree_centrality | 23.12 |
-| overlap_coefficients | ? |
-| pagerank | 23.12 |
-| personalized_pagerank | ? |
-| sorensen_coefficients | ? |
-| spectral_modularity_maximization | ? |
-| sssp | 23.12 |
-| strongly_connected_components | ? |
-| triangle_count | ? |
-| uniform_neighbor_sample | ? |
-| uniform_random_walks | ? |
-| weakly_connected_components | ? |
+| feature/algo                         | release/target version   |
+|:-------------------------------------|:-------------------------|
+| ancestors                            | 24.02                    |
+| average_clustering                   | 24.02                    |
+| barbell_graph                        | 23.12                    |
+| betweenness_centrality               | 23.10                    |
+| bfs_edges                            | 24.02                    |
+| bfs_layers                           | 24.02                    |
+| bfs_predecessors                     | 24.02                    |
+| bfs_successors                       | 24.02                    |
+| bfs_tree                             | 24.02                    |
+| bull_graph                           | 23.12                    |
+| caveman_graph                        | 23.12                    |
+| chvatal_graph                        | 23.12                    |
+| circular_ladder_graph                | 23.12                    |
+| clustering                           | 24.02                    |
+| complement                           | 24.02                    |
+| complete_bipartite_graph             | 23.12                    |
+| complete_graph                       | 23.12                    |
+| complete_multipartite_graph          | 23.12                    |
+| connected_components                 | 23.12                    |
+| core_number                          | 24.02                    |
+| cubical_graph                        | 23.12                    |
+| cycle_graph                          | 23.12                    |
+| davis_southern_women_graph           | 23.12                    |
+| degree_centrality                    | 23.12                    |
+| desargues_graph                      | 23.12                    |
+| descendants                          | 24.02                    |
+| descendants_at_distance              | 24.02                    |
+| diamond_graph                        | 23.12                    |
+| dodecahedral_graph                   | 23.12                    |
+| edge_betweenness_centrality          | 23.10                    |
+| eigenvector_centrality               | 23.12                    |
+| empty_graph                          | 23.12                    |
+| florentine_families_graph            | 23.12                    |
+| from_pandas_edgelist                 | 23.12                    |
+| from_scipy_sparse_array              | 23.12                    |
+| frucht_graph                         | 23.12                    |
+| generic_bfs_edges                    | 24.02                    |
+| heawood_graph                        | 23.12                    |
+| hits                                 | 23.12                    |
+| house_graph                          | 23.12                    |
+| house_x_graph                        | 23.12                    |
+| icosahedral_graph                    | 23.12                    |
+| in_degree_centrality                 | 23.12                    |
+| is_arborescence                      | 24.02                    |
+| is_bipartite                         | 24.02                    |
+| is_branching                         | 24.02                    |
+| is_connected                         | 23.12                    |
+| is_forest                            | 24.02                    |
+| is_isolate                           | 23.10                    |
+| is_strongly_connected                | 24.02                    |
+| is_tree                              | 24.02                    |
+| is_weakly_connected                  | 24.02                    |
+| isolates                             | 23.10                    |
+| k_truss                              | 23.12                    |
+| karate_club_graph                    | 23.12                    |
+| katz_centrality                      | 23.12                    |
+| krackhardt_kite_graph                | 23.12                    |
+| ladder_graph                         | 23.12                    |
+| leiden                               | ?                        |
+| les_miserables_graph                 | 23.12                    |
+| lollipop_graph                       | 23.12                    |
+| louvain_communities                  | 23.10                    |
+| moebius_kantor_graph                 | 23.12                    |
+| node_connected_component             | 23.12                    |
+| null_graph                           | 23.12                    |
+| number_connected_components          | 23.12                    |
+| number_of_isolates                   | 23.10                    |
+| number_strongly_connected_components | 24.02                    |
+| number_weakly_connected_components   | 24.02                    |
+| octahedral_graph                     | 23.12                    |
+| out_degree_centrality                | 23.12                    |
+| overall_reciprocity                  | 24.02                    |
+| pagerank                             | 23.12                    |
+| pappus_graph                         | 23.12                    |
+| path_graph                           | 23.12                    |
+| petersen_graph                       | 23.12                    |
+| reciprocity                          | 24.02                    |
+| reverse                              | 24.02                    |
+| sedgewick_maze_graph                 | 23.12                    |
+| single_source_shortest_path_length   | 23.12                    |
+| single_target_shortest_path_length   | 23.12                    |
+| star_graph                           | 23.12                    |
+| strongly_connected_components        | 24.02                    |
+| tadpole_graph                        | 23.12                    |
+| tetrahedral_graph                    | 23.12                    |
+| transitivity                         | 24.02                    |
+| triangles                            | 24.02                    |
+| trivial_graph                        | 23.12                    |
+| truncated_cube_graph                 | 23.12                    |
+| truncated_tetrahedron_graph          | 23.12                    |
+| turan_graph                          | 23.12                    |
+| tutte_graph                          | 23.12                    |
+| uniform_neighbor_sample              | ?                        |
+| weakly_connected_components          | 24.02                    |
+| wheel_graph                          | 23.12                    |
 
 To request nx-cugraph backend support for a NetworkX API that is not listed
 above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph).

python/nx-cugraph/nx_cugraph/scripts/print_table.py

@@ -59,7 +59,7 @@ def main(path_to_info=None, *, file=sys.stdout):
     if path_to_info is None:
         path_to_info = get_path_to_info(version_added_sep=".")
     lines = ["networkx_path,dispatch_name,version_added,plc,is_incomplete,is_different"]
-    lines.extend(",".join(info) for info in path_to_info.values())
+    lines.extend(",".join([str(i) for i in info]) for info in path_to_info.values())
     text = "\n".join(lines)
     print(text, file=file)
     return text

python/nx-cugraph/update_readme.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import io
+import re
+
+import pandas as pd
+
+from nx_cugraph.scripts.print_table import main as nxcg_print_table
+
+table_header_string = "| feature/algo | release/target version |"
+table_header_patt = re.compile(r"\| feature/algo[\s]+\| release/target version[\s]+\|")
+nxcg_algo_col_name = "dispatch_name"
+readme_algo_col_name = "feature/algo"
+nxcg_version_col_name = "version_added"
+readme_version_col_name = "release/target version"
+
+
+def get_current_nxcg_data():
+    """
+    Returns a DataFrame containing all meta-data from the current nx_cugraph package.
+    """
+    buf = io.StringIO()
+    nxcg_print_table(file=buf)
+    buf.seek(0)
+    return pd.read_csv(buf, dtype={nxcg_version_col_name: str})
+
+
+def get_readme_sections(readme_file_name):
+    """
+    Returns the README as three lists of strings: (before_table, table, after_table)
+    """
+    assert readme_file_name.endswith(".md")
+
+    before_table = []
+    table = []
+    after_table = []
+
+    with open(readme_file_name) as fd:
+        lines = iter([ln.rstrip() for ln in fd.readlines()])
+        line = next(lines, None)
+
+        # everything before the markdown table
+        while line is not None and not table_header_patt.fullmatch(line):
+            before_table.append(line)
+            line = next(lines, None)
+
+        if line is not None and table_header_patt.fullmatch(line):
+            # table body
+            while line is not None and line.startswith("|"):
+                table.append(line)
+                line = next(lines, None)
+
+            # everything after the table
+            while line is not None:
+                after_table.append(line)
+                line = next(lines, None)
+
+        else:
+            raise RuntimeError(
+                "Could not find start of table matching "
+                f"'{table_header_string}' in {readme_file_name}"
+            )
+
+    return (before_table, table, after_table)
+
+
+def get_readme_table_data(table_lines):
+    """
+    Returns a DataFrame containing all meta-data extracted from the markdown
+    table text passed in as a list of strings.
+    """
+    csv_buf = io.StringIO()
+    lines = iter(table_lines)
+    line = next(lines, None)
+
+    # process header
+    # Separate markdown line containing " | " delims and remove leading
+    # and trailing empty fields resulting from start/end "|" borders
+    fields = [f.strip() for f in line.split("|") if f]
+    print(*fields, sep=",", file=csv_buf)
+
+    # Assume header underline line and consume it
+    line = next(lines, None)
+    assert line.startswith("|:-") or line.startswith("| -")
+
+    # Read the table body
+    line = next(lines, None)
+    while line is not None and line.startswith("|"):
+        fields = [f.strip() for f in line.split("|") if f]
+        print(*fields, sep=",", file=csv_buf)
+        line = next(lines, None)
+
+    csv_buf.seek(0)
+    return pd.read_csv(csv_buf, dtype={readme_version_col_name: str})
+
+
+def main(readme_file_name="README.md"):
+    nxcg_data = get_current_nxcg_data()
+    (before_table_lines, table_lines, after_table_lines) = get_readme_sections(
+        readme_file_name
+    )
+    readme_data = get_readme_table_data(table_lines)
+
+    # Use only the data needed for the README
+    nxcg_data_for_readme = nxcg_data[
+        [nxcg_algo_col_name, nxcg_version_col_name]
+    ].rename(
+        {
+            nxcg_algo_col_name: readme_algo_col_name,
+            nxcg_version_col_name: readme_version_col_name,
+        },
+        axis=1,
+    )
+
+    # Update the readme data with the latest nxcg data. This will add new algos
+    # to the readme data and replace any old version values in the readme data
+    # with current nxcg version values.
+    merged = readme_data.merge(
+        nxcg_data_for_readme,
+        how="outer",
+        on=readme_algo_col_name,
+    )
+    x = readme_version_col_name + "_x"
+    y = readme_version_col_name + "_y"
+    merged[readme_version_col_name] = merged[y].fillna(merged[x])
+    merged.drop([x, y], axis=1, inplace=True)
+    merged.sort_values(by=readme_algo_col_name, inplace=True)
+
+    # Rewrite the README with the updated table
+    with open(readme_file_name, "w") as fd:
+        print("\n".join(before_table_lines), file=fd)
+        print(merged.to_markdown(index=False), file=fd)
+        print("\n".join(after_table_lines), file=fd)
+
+
+if __name__ == "__main__":
+    import sys
+
+    main(sys.argv[1])