Add setTracefile() method for structured optimization progress loggingAdd settracefile api

CHANGELOG.md

@@ -5,6 +5,7 @@
 - Added automated script for generating type stubs
 - Include parameter names in type stubs
 - Speed up MatrixExpr.sum(axis=...) via quicksum
+- Added structured_optimization_trace recipe for structured optimization progress tracking
 ### Fixed
 - all fundamental callbacks now raise an error if not implemented
 - Fixed the type of MatrixExpr.sum(axis=...) result from MatrixVariable to MatrixExpr.

src/pyscipopt/recipes/structured_optimization_trace.py

@@ -0,0 +1,37 @@
+from pyscipopt import SCIP_EVENTTYPE, Eventhdlr, Model
+
+
+def attach_structured_optimization_trace(model: Model):
+    """
+    Attaches an event handler that records optimization progress in structured JSONL format.
+
+    Args:
+        model: SCIP Model
+    """
+
+    class _TraceEventhdlr(Eventhdlr):
+        def eventinit(self):
+            self.model.catchEvent(SCIP_EVENTTYPE.BESTSOLFOUND, self)
+            self.model.catchEvent(SCIP_EVENTTYPE.DUALBOUNDIMPROVED, self)
+
+        def eventexec(self, event):
+            record = {
+                "time": self.model.getSolvingTime(),
+                "primalbound": self.model.getPrimalbound(),
+                "dualbound": self.model.getDualbound(),
+                "gap": self.model.getGap(),
+                "nodes": self.model.getNNodes(),
+                "nsol": self.model.getNSols(),
+            }
+            self.model.data["trace"].append(record)
+
+    if not hasattr(model, "data") or model.data is None:
+        model.data = {}
+    model.data["trace"] = []
+
+    hdlr = _TraceEventhdlr()
+    model.includeEventhdlr(
+        hdlr, "structured_trace", "Structured optimization trace handler"
+    )
+
+    return model

tests/test_recipe_structured_optimization_trace.py

@@ -0,0 +1,32 @@
+from helpers.utils import bin_packing_model
+
+from pyscipopt.recipes.structured_optimization_trace import (
+    attach_structured_optimization_trace,
+)
+
+
+def test_structured_optimization_trace():
+    from random import randint
+
+    model = bin_packing_model(sizes=[randint(1, 40) for _ in range(120)], capacity=50)
+    model.setParam("limits/time", 5)
+
+    model.data = {"test": True}
+    model = attach_structured_optimization_trace(model)
+
+    assert "test" in model.data
+    assert "trace" in model.data
+
+    model.optimize()
+
+    required_fields = {"time", "primalbound", "dualbound", "gap", "nodes", "nsol"}
+    for record in model.data["trace"]:
+        assert required_fields <= set(record.keys())
+
+    primalbounds = [r["primalbound"] for r in model.data["trace"]]
+    for i in range(1, len(primalbounds)):
+        assert primalbounds[i] <= primalbounds[i - 1]
+
+    dualbounds = [r["dualbound"] for r in model.data["trace"]]
+    for i in range(1, len(dualbounds)):
+        assert dualbounds[i] >= dualbounds[i - 1]