fix: respect known_best in CMLON global funnel proportion

src/lonkit/lon.py

@@ -623,7 +623,7 @@ def compute_network_metrics(self, known_best: float | None = None) -> dict[str,
         else:
             sink_strength = 0.0
 
-        gfunnel = self._compute_global_funnel_proportion()
+        gfunnel = self._compute_global_funnel_proportion(igs)
 
         return {
             "n_optima": n_optima,
@@ -636,9 +636,8 @@ def compute_network_metrics(self, known_best: float | None = None) -> dict[str,
             "global_funnel_proportion": gfunnel,
         }
 
-    def _compute_global_funnel_proportion(self) -> float:
-        """Compute proportion of nodes that can reach a global optimum."""
-        igs = self.get_global_sinks()
+    def _compute_global_funnel_proportion(self, igs: list[int]) -> float:
+        """Compute proportion of nodes that can reach any of the supplied global sinks."""
         if not igs:
             return 0.0
 

tests/test_cmlon_metrics.py

@@ -0,0 +1,29 @@
+"""Regression coverage for CMLON reference optima (issue #41)."""
+
+import pandas as pd
+import pytest
+
+from lonkit import LON, LONConfig
+
+
+@pytest.mark.parametrize("method", ["compute_network_metrics", "compute_metrics"])
+@pytest.mark.parametrize("minimize", [True, False])
+@pytest.mark.parametrize(
+    ("known_best", "expected_funnels", "expected_proportion"),
+    [(0.0, 0, 0.0), (1.0, 1, 1.0), (None, 1, 1.0)],
+)
+def test_global_funnel_proportion_uses_known_best(
+    method, minimize, known_best, expected_funnels, expected_proportion
+):
+    sign = 1 if minimize else -1
+    trace = pd.DataFrame(
+        [(1, sign * 2.0, "a", sign * 1.0, "b")],
+        columns=["run", "fit1", "node1", "fit2", "node2"],
+    )
+    cmlon = LON.from_trace_data(trace, LONConfig(minimize=minimize)).to_cmlon()
+    reference = sign * known_best if known_best is not None else None
+
+    metrics = getattr(cmlon, method)(known_best=reference)
+
+    assert metrics["n_global_funnels"] == expected_funnels
+    assert metrics["global_funnel_proportion"] == expected_proportion