Testing util improvements and refactoring

python/cugraph/cugraph/testing/__init__.py

@@ -11,7 +11,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH, RAPIDS_DATASET_ROOT_DIR
+from cugraph.testing.utils import (
+    RAPIDS_DATASET_ROOT_DIR_PATH,
+    RAPIDS_DATASET_ROOT_DIR,
+    RAPIDS_RESULTSET_ROOT_DIR_PATH,
+)
+from cugraph.testing.resultset import Resultset, load_resultset, get_resultset
 from cugraph.datasets import (
     cyber,
     dolphins,

python/cugraph/cugraph/testing/generate_resultsets.py

@@ -0,0 +1,277 @@
+# Copyright (c) 2023, 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.
+
+from tempfile import NamedTemporaryFile
+import random
+
+import numpy as np
+import networkx as nx
+
+import cudf
+import cugraph
+from cugraph.datasets import dolphins, netscience, karate_disjoint, karate
+from cugraph.testing import utils, Resultset, SMALL_DATASETS
+
+
+_results_dir = utils.RAPIDS_RESULTSET_ROOT_DIR_PATH
+_resultsets = {}
+
+
+def add_resultset(result_data_dictionary, **kwargs):
+    rs = Resultset(result_data_dictionary)
+    hashable_dict_repr = tuple((k, kwargs[k]) for k in sorted(kwargs.keys()))
+    _resultsets[hashable_dict_repr] = rs
+
+
+if __name__ == "__main__":
+    # =============================================================================
+    # Parameters
+    # =============================================================================
+    SEEDS = [42]
+
+    DIRECTED_GRAPH_OPTIONS = [True, False]
+
+    DEPTH_LIMITS = [None, 1, 5, 18]
+
+    DATASETS = [dolphins, netscience, karate_disjoint]
+
+    # =============================================================================
+    # tests/traversal/test_bfs.py
+    # =============================================================================
+    test_bfs_results = {}
+
+    for ds in DATASETS + [karate]:
+        for seed in SEEDS:
+            for depth_limit in DEPTH_LIMITS:
+                for dirctd in DIRECTED_GRAPH_OPTIONS:
+                    # this is used for get_cu_graph_golden_results_and_params
+                    Gnx = utils.generate_nx_graph_from_file(
+                        ds.get_path(), directed=dirctd
+                    )
+                    random.seed(seed)
+                    start_vertex = random.sample(list(Gnx.nodes()), 1)[0]
+                    golden_values = nx.single_source_shortest_path_length(
+                        Gnx, start_vertex, cutoff=depth_limit
+                    )
+                    vertices = cudf.Series(golden_values.keys())
+                    distances = cudf.Series(golden_values.values())
+                    add_resultset(
+                        {"vertex": vertices, "distance": distances},
+                        graph_dataset=ds.metadata["name"],
+                        graph_directed=str(dirctd),
+                        algo="single_source_shortest_path_length",
+                        start_vertex=str(start_vertex),
+                        cutoff=str(depth_limit),
+                    )
+
+    # these are pandas dataframes
+    for dirctd in DIRECTED_GRAPH_OPTIONS:
+        Gnx = utils.generate_nx_graph_from_file(karate.get_path(), directed=dirctd)
+        golden_result = cugraph.bfs_edges(Gnx, source=7)
+        cugraph_df = cudf.from_pandas(golden_result)
+        add_resultset(
+            cugraph_df,
+            graph_dataset="karate",
+            graph_directed=str(dirctd),
+            algo="bfs_edges",
+            source="7",
+        )
+
+    # =============================================================================
+    # tests/traversal/test_sssp.py
+    # =============================================================================
+    test_sssp_results = {}
+
+    SOURCES = [1]
+
+    for ds in SMALL_DATASETS:
+        for source in SOURCES:
+            Gnx = utils.generate_nx_graph_from_file(ds.get_path(), directed=True)
+            golden_paths = nx.single_source_dijkstra_path_length(Gnx, source)
+            vertices = cudf.Series(golden_paths.keys())
+            distances = cudf.Series(golden_paths.values())
+            add_resultset(
+                {"vertex": vertices, "distance": distances},
+                graph_dataset=ds.metadata["name"],
+                graph_directed="True",
+                algo="single_source_dijkstra_path_length",
+                source=str(source),
+            )
+
+            M = utils.read_csv_for_nx(ds.get_path(), read_weights_in_sp=True)
+            edge_attr = "weight"
+            Gnx = nx.from_pandas_edgelist(
+                M,
+                source="0",
+                target="1",
+                edge_attr=edge_attr,
+                create_using=nx.DiGraph(),
+            )
+
+            M["weight"] = M["weight"].astype(np.int32)
+            Gnx = nx.from_pandas_edgelist(
+                M,
+                source="0",
+                target="1",
+                edge_attr="weight",
+                create_using=nx.DiGraph(),
+            )
+            golden_paths_datatypeconv = nx.single_source_dijkstra_path_length(
+                Gnx, source
+            )
+            vertices_datatypeconv = cudf.Series(golden_paths_datatypeconv.keys())
+            distances_datatypeconv = cudf.Series(golden_paths_datatypeconv.values())
+            add_resultset(
+                {"vertex": vertices_datatypeconv, "distance": distances_datatypeconv},
+                graph_dataset=ds.metadata["name"],
+                graph_directed="True",
+                algo="single_source_dijkstra_path_length",
+                test="data_type_conversion",
+                source=str(source),
+            )
+
+    for dirctd in DIRECTED_GRAPH_OPTIONS:
+        for source in SOURCES:
+            Gnx = utils.generate_nx_graph_from_file(
+                karate.get_path(), directed=dirctd, edgevals=True
+            )
+            add_resultset(
+                cugraph.sssp(Gnx, source),
+                graph_dataset="karate",
+                graph_directed=str(dirctd),
+                algo="sssp_nonnative",
+                source=str(source),
+            )
+
+    Gnx = nx.Graph()
+    Gnx.add_edge(0, 1, other=10)
+    Gnx.add_edge(1, 2, other=20)
+    df = cugraph.sssp(Gnx, 0, edge_attr="other")
+    add_resultset(df, algo="sssp_nonnative", test="network_edge_attr")
+
+    # =============================================================================
+    # tests/traversal/test_paths.py
+    # =============================================================================
+    CONNECTED_GRAPH = """1,5,3
+    1,4,1
+    1,2,1
+    1,6,2
+    1,7,2
+    4,5,1
+    2,3,1
+    7,6,2
+    """
+
+    DISCONNECTED_GRAPH = CONNECTED_GRAPH + "8,9,4"
+
+    paths = [("1", "1"), ("1", "5"), ("1", "3"), ("1", "6")]
+    invalid_paths = {
+        "connected": [("-1", "1"), ("0", "42")],
+        "disconnected": [("1", "10"), ("1", "8")],
+    }
+
+    with NamedTemporaryFile(mode="w+", suffix=".csv") as graph_tf:
+        graph_tf.writelines(DISCONNECTED_GRAPH)
+        graph_tf.seek(0)
+        Gnx_DIS = nx.read_weighted_edgelist(graph_tf.name, delimiter=",")
+
+    res1 = nx.shortest_path_length(Gnx_DIS, source="1", weight="weight")
+    vertices = cudf.Series(res1.keys())
+    distances = cudf.Series(res1.values())
+    add_resultset(
+        {"vertex": vertices, "distance": distances},
+        algo="shortest_path_length",
+        graph_dataset="DISCONNECTED",
+        graph_directed="True",
+        source="1",
+        weight="weight",
+    )
+
+    # NOTE: Currently, only traversal result files are generated
+    random.seed(24)
+    traversal_mappings = cudf.DataFrame(
+        columns=[
+            "#UUID",
+            "arg0",
+            "arg0val",
+            "arg1",
+            "arg1val",
+            "arg2",
+            "arg2val",
+            "arg3",
+            "arg3val",
+            "arg4",
+            "arg4val",
+            "arg5",
+            "arg5val",
+            "arg6",
+            "arg6val",
+            "arg7",
+            "arg7val",
+            "arg8",
+            "arg8val",
+            "arg9",
+            "arg9val",
+        ]
+    )
+    # Generating ALL results files
+    if not utils.RAPIDS_RESULTSET_ROOT_DIR_PATH.exists():
+        utils.RAPIDS_RESULTSET_ROOT_DIR_PATH.mkdir(parents=True, exist_ok=True)
+
+    for temp in _resultsets:
+        res = _resultsets[temp].get_cudf_dataframe()
+        temp_filename = str(random.getrandbits(50))
+        temp_dict = dict(temp)
+        argnames, argvals = [t for t in temp_dict.keys()], [
+            t for t in temp_dict.values()
+        ]
+        single_mapping = np.empty(21, dtype=object)
+        dict_length = len(argnames)
+
+        single_mapping[0] = temp_filename
+        for i in np.arange(dict_length):
+            single_mapping[2 * i + 1] = argnames[i]
+            single_mapping[2 * i + 2] = argvals[i]
+        temp_mapping = cudf.DataFrame(
+            [single_mapping],
+            columns=[
+                "#UUID",
+                "arg0",
+                "arg0val",
+                "arg1",
+                "arg1val",
+                "arg2",
+                "arg2val",
+                "arg3",
+                "arg3val",
+                "arg4",
+                "arg4val",
+                "arg5",
+                "arg5val",
+                "arg6",
+                "arg6val",
+                "arg7",
+                "arg7val",
+                "arg8",
+                "arg8val",
+                "arg9",
+                "arg9val",
+            ],
+        )
+        traversal_mappings = cudf.concat(
+            [traversal_mappings, temp_mapping], axis=0, ignore_index=True
+        )
+        res.to_csv(_results_dir / (temp_filename + ".csv"), index=False)
+    traversal_mappings.to_csv(
+        _results_dir / "traversal_mappings.csv", index=False, sep=" "
+    )