simulate_perfect: Do not overwrite original file

miplearn/solvers/learning.py

@@ -11,6 +11,7 @@ import gzip
 from copy import deepcopy
 from typing import Optional, List
 from p_tqdm import p_map
+from tempfile import NamedTemporaryFile
 
 from . import RedirectOutput
 from .. import (
@@ -211,13 +212,16 @@ class LearningSolver:
             details.
         """
         if self.simulate_perfect:
-            self._solve(
-                instance=instance,
-                model=model,
-                output=output,
-                tee=tee,
-            )
-            self.fit([instance])
+            if not isinstance(instance, str):
+                raise Exception("Not implemented")
+            with tempfile.NamedTemporaryFile(suffix=os.path.basename(instance)) as tmp:
+                self._solve(
+                    instance=instance,
+                    model=model,
+                    output=tmp.name,
+                    tee=tee,
+                )
+                self.fit([tmp.name])
         return self._solve(
             instance=instance,
             model=model,

miplearn/solvers/tests/test_learning_solver.py

@@ -7,8 +7,11 @@ import pickle
 import tempfile
 import os
 
-from miplearn import DynamicLazyConstraintsComponent
-from miplearn import LearningSolver
+from miplearn import (
+    LearningSolver,
+    GurobiSolver,
+    DynamicLazyConstraintsComponent,
+)
 
 from . import _get_instance, _get_internal_solvers
 
@@ -109,3 +112,18 @@ def test_solve_fit_from_disk():
             os.remove(filename)
         for filename in output:
             os.remove(filename)
+
+
+def test_simulate_perfect():
+    internal_solver = GurobiSolver()
+    instance = _get_instance(internal_solver)
+    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
+        pickle.dump(instance, tmp)
+        tmp.flush()
+        solver = LearningSolver(
+            solver=internal_solver,
+            simulate_perfect=True,
+        )
+
+        stats = solver.solve(tmp.name)
+        assert stats["Lower bound"] == stats["Predicted LB"]