Reuse datasets path env to load resultsets near datasets, testing imp…

…rovements

python/cugraph/cugraph/testing/__init__.py

@@ -11,8 +11,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from cugraph.testing.resultset import get_resultset
+# from cugraph.testing.resultset import get_resultset, get_resultset2
 from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH, RAPIDS_DATASET_ROOT_DIR
+from cugraph.testing.resultset import load_resultset, get_resultset
 from cugraph.datasets import (
     cyber,
     dolphins,

python/cugraph/cugraph/testing/generate_resultsets.py

@@ -0,0 +1,325 @@
+# 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.experimental.datasets import (
+    dolphins,
+    netscience,
+    karate_disjoint,
+    karate,
+    polbooks,
+)
+from cugraph.testing import utils
+
+
+_results_dir = utils.RAPIDS_DATASET_ROOT_DIR_PATH / "tests" / "resultsets"
+_resultsets = {}
+
+
+def add_resultset(result_data_dictionary, **kwargs):
+    rs = utils.Resultset(result_data_dictionary)
+    hashable_dict_repr = tuple((k, kwargs[k]) for k in sorted(kwargs.keys()))
+    _resultsets[hashable_dict_repr] = rs
+
+
+# =============================================================================
+# Parameters
+# =============================================================================
+# This will be refactored once the datasets variables are fixed/changed
+SEEDS = [42]
+
+DIRECTED_GRAPH_OPTIONS = [True, False]
+
+DEPTH_LIMITS = [None, 1, 5, 18]
+
+DATASETS = [dolphins, netscience, karate_disjoint]
+
+DATASETS_SMALL = [karate, dolphins, polbooks]
+
+# =============================================================================
+# 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 does the work of get_cu_graph_nx_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]
+                nx_values = nx.single_source_shortest_path_length(
+                    Gnx, start_vertex, cutoff=depth_limit
+                )
+                """test_bfs_results[
+                    "{},{},{},{},{}".format(seed, depth_limit, ds, dirctd, start_vertex)
+                ] = nx_values"""
+                vertices = cudf.Series(nx_values.keys())
+                distances = cudf.Series(nx_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),
+                )
+    # test_bfs_results["{},{},starts".format(seed, ds)] = start_vertex
+
+# these are pandas dataframes
+for dirctd in DIRECTED_GRAPH_OPTIONS:
+    Gnx = utils.generate_nx_graph_from_file(karate.get_path(), directed=dirctd)
+    result = cugraph.bfs_edges(Gnx, source=7)
+    cugraph_df = cudf.from_pandas(result)
+    # test_bfs_results["{},{},{}".format(ds, dirctd, "nonnative-nx")] = cugraph_df
+    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 DATASETS_SMALL:
+    for source in SOURCES:
+        Gnx = utils.generate_nx_graph_from_file(ds.get_path(), directed=True)
+        nx_paths = nx.single_source_dijkstra_path_length(Gnx, source)
+        # test_sssp_results["{},{},ssdpl".format(ds, source)] = nx_paths
+        vertices = cudf.Series(nx_paths.keys())
+        distances = cudf.Series(nx_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(),
+        )
+        nx_paths_datatypeconv = nx.single_source_dijkstra_path_length(Gnx, source)
+        """test_sssp_results[
+            "nx_paths,data_type_conversion,{}".format(ds)
+        ] = nx_paths_datatypeconv"""
+        vertices_datatypeconv = cudf.Series(nx_paths_datatypeconv.keys())
+        distances_datatypeconv = cudf.Series(nx_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
+        )
+        """if dirctd:
+            test_sssp_results[
+                "nonnative_input,nx.DiGraph,{}".format(source)
+            ] = cugraph.sssp(Gnx, source)
+        else:
+            test_sssp_results[
+                "nonnative_input,nx.Graph,{}".format(source)
+            ] = cugraph.sssp(Gnx, source)"""
+        add_resultset(
+            cugraph.sssp(Gnx, source),
+            graph_dataset="karate",
+            graph_directed=str(dirctd),
+            algo="sssp_nonnative",
+            source=str(source),
+        )
+
+G = nx.Graph()
+G.add_edge(0, 1, other=10)
+G.add_edge(1, 2, other=20)
+df = cugraph.sssp(G, 0, edge_attr="other")
+# test_sssp_results["network_edge_attr"] = df
+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",
+)
+
+
+# Generating ALL results files
+"""random.seed(24)
+for temp in _resultsets:
+    res = _resultsets[temp].get_cudf_dataframe()
+    # Currently, only traversal results files are generated
+    temp_filename = "traversal-" + str(random.getrandbits(55)) + ".csv"
+    temp_mapping = cudf.DataFrame(
+        [[str(temp), temp_filename]], columns=["hashable_dict_repr", "filename"]
+    )
+    traversal_mappings = cudf.concat(
+        [traversal_mappings, temp_mapping], axis=0, ignore_index=True
+    )
+    # print(temp_filename)
+    # print("traversal_" + temp_filename)
+    res.to_csv(results_dir / temp_filename, index=False)
+traversal_mappings.to_csv(results_dir / "traversal_mappings.csv", index=False)"""
+
+
+def generate_results():
+    # FIXME: Currently, only traversal results 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
+    for temp in _resultsets:
+        res = _resultsets[temp].get_cudf_dataframe()
+        # temp_filename = "traversal-" + str(random.getrandbits(55)) + ".csv"
+        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
+        # single_mapping[1] = argvals[0]
+        # for i in np.arange(1, dict_length):
+        for i in np.arange(dict_length):
+            # single_mapping[2 * i] = argnames[i]
+            # single_mapping[2 * i + 1] = argvals[i]
+            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=" "
+    )
+
+
+# generate_results()