simulate_perfect: Do not overwrite original file

miplearn/components/lazy_dynamic.py

@@ -61,7 +61,7 @@ class DynamicLazyConstraintsComponent(Component):
 
         self.classifiers = {}
         violation_to_instance_idx = {}
-        for (idx, instance) in enumerate(training_instances):
+        for (idx, instance) in enumerate(InstanceIterator(training_instances)):
             for v in instance.found_violated_lazy_constraints:
                 if isinstance(v, list):
                     v = tuple(v)

miplearn/components/steps/tests/convert_tight_test.py

@@ -69,9 +69,7 @@ class TestInstanceMin(Instance):
 
 
 def test_convert_tight_infeasibility():
-    comp = ConvertTightIneqsIntoEqsStep(
-        check_converted=True,
-    )
+    comp = ConvertTightIneqsIntoEqsStep()
     comp.classifiers = {
         "c1": Mock(spec=Classifier),
         "c2": Mock(spec=Classifier),
@@ -94,9 +92,7 @@ def test_convert_tight_infeasibility():
 
 
 def test_convert_tight_suboptimality():
-    comp = ConvertTightIneqsIntoEqsStep(
-        check_converted=True,
-    )
+    comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
     comp.classifiers = {
         "c1": Mock(spec=Classifier),
         "c2": Mock(spec=Classifier),
@@ -119,9 +115,7 @@ def test_convert_tight_suboptimality():
 
 
 def test_convert_tight_optimal():
-    comp = ConvertTightIneqsIntoEqsStep(
-        check_converted=True,
-    )
+    comp = ConvertTightIneqsIntoEqsStep()
     comp.classifiers = {
         "c1": Mock(spec=Classifier),
         "c2": Mock(spec=Classifier),

miplearn/components/tests/test_relaxation.py

@@ -134,7 +134,8 @@ def test_drop_redundant_with_check_dropped():
     solver, internal, instance, classifiers = _setup()
 
     component = DropRedundantInequalitiesStep(
-        check_dropped=True, violation_tolerance=1e-3
+        check_feasibility=True,
+        violation_tolerance=1e-3,
     )
     component.classifiers = classifiers
 

miplearn/extractors.py

@@ -29,12 +29,15 @@ class InstanceIterator:
         self.current += 1
         if isinstance(result, str):
             logger.debug("Read: %s" % result)
+            try:
                 if result.endswith(".gz"):
                     with gzip.GzipFile(result, "rb") as file:
                         result = pickle.load(file)
                 else:
                     with open(result, "rb") as file:
                         result = pickle.load(file)
+            except pickle.UnpicklingError:
+                raise Exception(f"Invalid instance file: {result}")
         return result
 
 

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:
+            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=output,
+                    output=tmp.name,
                     tee=tee,
                 )
-            self.fit([instance])
+                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"]