Make SolutionExtractor use instance.solution and instance.lp_solution

miplearn/__init__.py

@@ -9,7 +9,10 @@ from .components.warmstart import (WarmStartComponent,
                                    AdaptivePredictor,
                                   )
 from .components.branching import BranchPriorityComponent
-from .extractors import UserFeaturesExtractor, SolutionExtractor
+from .extractors import (UserFeaturesExtractor,
+                         SolutionExtractor,
+                         CombinedExtractor,
+                        )
 from .benchmark import BenchmarkRunner
 from .instance import Instance
 from .solvers import LearningSolver

miplearn/components/warmstart.py

@@ -128,15 +128,9 @@ class WarmStartComponent(Component):
         
     
     def before_solve(self, solver, instance, model):
-#         # Solve linear relaxation
-#         lr_solver = pe.SolverFactory("gurobi")
-#         lr_solver.options["threads"] = 4
-#         lr_solver.options["relax_integrality"] = 1
-#         lr_solver.solve(model, tee=solver.tee)
-        
         # Build x_test
         x_test = CombinedExtractor([UserFeaturesExtractor(),
-                                    SolutionExtractor(),
+                                    SolutionExtractor(relaxation=True),
                                    ]).extract([instance], [model])
         
         # Update self.x_train

miplearn/extractors.py

@@ -69,8 +69,13 @@ class UserFeaturesExtractor(Extractor):
 
 
 class SolutionExtractor(Extractor):
-    def extract(self, instances, models):
+    def __init__(self, relaxation=False):
+        self.relaxation = relaxation
+        
+    def extract(self, instances, models=None):
         result = {}
+        if models is None:
+            models = [instance.to_model() for instance in instances]
         for (index, instance) in enumerate(instances):
             model = models[index]
             var_split = self.split_variables(instance, model)
@@ -78,7 +83,10 @@ class SolutionExtractor(Extractor):
                 if category not in result.keys():
                     result[category] = []
                 for (var, index) in var_index_pairs:
-                    v = var[index].value
+                    if self.relaxation:
+                        v = instance.lp_solution[str(var)][index]
+                    else:
+                        v = instance.solution[str(var)][index]
                     if v is None:
                         result[category] += [[0, 0]]
                     else:

miplearn/solvers.py

@@ -168,8 +168,10 @@ class LearningSolver:
             solver.set_gap_tolerance(self.gap_tolerance)
         return solver
         
-    def solve(self, instance, tee=False):
-        model = instance.to_model()
+    def solve(self, instance, model=None, tee=False):
+        if model is None:
+            model = instance.to_model()
+            
         self.tee = tee
         self.internal_solver = self._create_internal_solver()
 

miplearn/tests/test_extractors.py

@@ -3,10 +3,11 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn.problems.knapsack import KnapsackInstance
-from miplearn.extractors import (UserFeaturesExtractor,
-                                 SolutionExtractor,
-                                 CombinedExtractor,
-                                )
+from miplearn import (LearningSolver,
+                      UserFeaturesExtractor,
+                      SolutionExtractor,
+                      CombinedExtractor,
+                     )
 import numpy as np
 import pyomo.environ as pe
 
@@ -37,11 +38,11 @@ def test_user_features():
 def test_solution_extractor():
     instances = _get_instances()
     models = [instance.to_model() for instance in instances]
-    for model in models:
-        solver = pe.SolverFactory("cbc")
-        solver.solve(model)
-    extractor = SolutionExtractor()
-    features = extractor.extract(instances, models)
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    
+    features = SolutionExtractor().extract(instances, models)
     assert isinstance(features, dict)
     assert "default" in features.keys()
     assert isinstance(features["default"], np.ndarray)
@@ -59,6 +60,10 @@ def test_solution_extractor():
 def test_combined_extractor():
     instances = _get_instances()
     models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    
     extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
                                               SolutionExtractor()])
     features = extractor.extract(instances, models)