updates

.github/workflows/benchmark.yml

@@ -0,0 +1,39 @@
+name: Python Example with pytest
+on:
+  push:
+    branches:
+      - master
+
+permissions:
+  contents: write
+  deployments: write
+
+jobs:
+  benchmark:
+    name: Run pytest-benchmark benchmark example
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.13
+      - name: Run benchmark
+        run: |
+          cd benchmarks
+          pip install --upgrade pip
+          pip install .[benchmark]
+          pytest bench.py --benchmark-json output.json
+
+      - name: Store benchmark result
+        uses: benchmark-action/github-action-benchmark@v1
+        with:
+          name: Python Benchmark with pytest-benchmark
+          tool: 'pytest'
+          output-file-path: benchmarks/output.json
+          # Use personal access token instead of GITHUB_TOKEN due to https://git.luolix.topmunity/t/github-action-not-triggering-gh-pages-upon-push/16096
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          auto-push: true
+          # Show alert with commit comment on detecting possible performance regression
+          alert-threshold: '200%'
+          comment-on-alert: false
+          fail-on-alert: true

benchmarks/bench.py

@@ -0,0 +1,97 @@
+import pandas as pd
+
+import xgi
+import numpy as np
+import random
+
+
+import pytest
+
+def test_construct_from_edgelist(benchmark):
+    def setup():
+        H = xgi.read_hif("email-enron.json")
+        return (H.edges.members(),), {}
+
+    benchmark.pedantic(xgi.Hypergraph, setup=setup, rounds=10)
+
+
+def test_construct_from_edgedict(benchmark):
+    def setup():
+        H = xgi.read_hif("email-enron.json")
+        return (H.edges.members(dtype=dict),), {}
+
+    benchmark.pedantic(xgi.Hypergraph, setup=setup, rounds=10)
+
+
+def test_construct_from_df(benchmark):
+    def setup():
+        H = xgi.read_hif("email-enron.json")
+        return (xgi.to_bipartite_pandas_dataframe(H),), {}
+
+    benchmark.pedantic(xgi.Hypergraph, setup=setup, rounds=10)
+
+def test_node_memberships(benchmark):
+    def setup():
+        H = xgi.read_hif("email-enron.json")
+        return (H,), {}
+
+    def node_memberships(H):
+        [H.nodes.memberships(n) for n in H.nodes]
+
+    benchmark.pedantic(node_memberships, setup=setup, rounds=10)
+
+
+def test_edge_members(benchmark):
+    def setup():
+        H = xgi.read_hif("email-enron.json")
+        return (H,), {}
+
+    def edge_members(H):
+        [H.edges.members(eid) for eid in H.edges]
+
+    benchmark.pedantic(edge_members, setup=setup, rounds=10)
+
+
+# def setup_benchmarks():
+#     random.seed(1)
+#     # but will seed it nevertheless
+#     np.random.seed(1)
+#     self.hypergraph = xgi.load_xgi_data("email-enron")
+#     self.enron_edgelist = xgi.to_hyperedge_list(self.hypergraph)
+#     self.enron_edgedict = xgi.to_hyperedge_dict(self.hypergraph)
+#     self.enron_df = xgi.to_bipartite_pandas_dataframe(self.hypergraph)
+
+
+# class CoreHypergraph(Benchmark):
+#     def setup(self):
+#         self.hypergraph = xgi.load_xgi_data("email-enron")
+#         self.enron_edgelist = xgi.to_hyperedge_list(self.hypergraph)
+#         self.enron_edgedict = xgi.to_hyperedge_dict(self.hypergraph)
+#         self.enron_df = xgi.to_bipartite_pandas_dataframe(self.hypergraph)
+
+#     def time_node_attributes(self):
+#         [self.hypergraph.nodes[n] for n in self.hypergraph.nodes]
+
+#     def time_edge_attributes(self):
+#         [self.hypergraph.edges[e] for e in self.hypergraph.edges]
+
+#     def time_degree(self):
+#         self.hypergraph.degree()
+
+#     def time_nodestats_degree(self):
+#         self.hypergraph.nodes.degree.asnumpy()
+
+#     def time_edge_size(self):
+#         self.hypergraph.edges.size.asnumpy()
+
+#     def time_isolates(self):
+#         self.hypergraph.nodes.isolates()
+
+#     def time_singletons(self):
+#         self.hypergraph.edges.singletons()
+
+#     def time_copy(self):
+#         self.hypergraph.copy()
+
+#     def time_dual(self):
+#         self.hypergraph.dual()