Move collected data to instance.training_data

miplearn/components/objective.py

@@ -78,7 +78,15 @@ class ObjectiveValueComponent(Component):
 
     def evaluate(self, instances):
         y_pred = self.predict(instances)
-        y_true = np.array([[inst.lower_bound, inst.upper_bound] for inst in instances])
+        y_true = np.array(
+            [
+                [
+                    inst.training_data[0]["Lower bound"],
+                    inst.training_data[0]["Upper bound"],
+                ]
+                for inst in instances
+            ]
+        )
         y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
         y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
         ev = {

miplearn/components/primal.py

@@ -68,7 +68,8 @@ class PrimalSolutionComponent(Component):
             for label in [0, 1]:
                 y_train = solutions[category][:, label].astype(int)
 
-                # If all samples are either positive or negative, make constant predictions
+                # If all samples are either positive or negative, make constant
+                # predictions
                 y_avg = np.average(y_train)
                 if y_avg < 0.001 or y_avg >= 0.999:
                     self.classifiers[category, label] = round(y_avg)
@@ -130,7 +131,7 @@ class PrimalSolutionComponent(Component):
             desc="Evaluate (primal)",
         ):
             instance = instances[instance_idx]
-            solution_actual = instance.solution
+            solution_actual = instance.training_data[0]["Solution"]
             solution_pred = self.predict(instance)
 
             vars_all, vars_one, vars_zero = set(), set(), set()

miplearn/components/steps/tests/test_convert_tight.py

@@ -24,8 +24,8 @@ def test_convert_tight_usage():
     )
 
     # Solve original problem
-    solver.solve(instance)
-    original_upper_bound = instance.upper_bound
+    stats = solver.solve(instance)
+    original_upper_bound = stats["Upper bound"]
 
     # Should collect training data
     assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
@@ -35,12 +35,12 @@ def test_convert_tight_usage():
     stats = solver.solve(instance)
 
     # Objective value should be the same
-    assert instance.upper_bound == original_upper_bound
+    assert stats["Upper bound"] == original_upper_bound
     assert stats["ConvertTight: Inf iterations"] == 0
     assert stats["ConvertTight: Subopt iterations"] == 0
 
 
-class TestInstance(Instance):
+class SampleInstance(Instance):
     def to_model(self):
         import gurobipy as grb
 
@@ -70,9 +70,9 @@ def test_convert_tight_infeasibility():
         components=[comp],
         solve_lp_first=False,
     )
-    instance = TestInstance()
+    instance = SampleInstance()
     stats = solver.solve(instance)
-    assert instance.lower_bound == 5.0
+    assert stats["Upper bound"] == 5.0
     assert stats["ConvertTight: Inf iterations"] == 1
     assert stats["ConvertTight: Subopt iterations"] == 0
 
@@ -93,9 +93,9 @@ def test_convert_tight_suboptimality():
         components=[comp],
         solve_lp_first=False,
     )
-    instance = TestInstance()
+    instance = SampleInstance()
     stats = solver.solve(instance)
-    assert instance.lower_bound == 5.0
+    assert stats["Upper bound"] == 5.0
     assert stats["ConvertTight: Inf iterations"] == 0
     assert stats["ConvertTight: Subopt iterations"] == 1
 
@@ -116,8 +116,8 @@ def test_convert_tight_optimal():
         components=[comp],
         solve_lp_first=False,
     )
-    instance = TestInstance()
+    instance = SampleInstance()
     stats = solver.solve(instance)
-    assert instance.lower_bound == 5.0
+    assert stats["Upper bound"] == 5.0
     assert stats["ConvertTight: Inf iterations"] == 0
     assert stats["ConvertTight: Subopt iterations"] == 0

miplearn/components/tests/test_objective.py

@@ -15,8 +15,8 @@ def test_usage():
     instances, models = get_test_pyomo_instances()
     comp = ObjectiveValueComponent()
     comp.fit(instances)
-    assert instances[0].lower_bound == 1183.0
-    assert instances[0].upper_bound == 1183.0
+    assert instances[0].training_data[0]["Lower bound"] == 1183.0
+    assert instances[0].training_data[0]["Upper bound"] == 1183.0
     assert np.round(comp.predict(instances), 2).tolist() == [
         [1183.0, 1183.0],
         [1070.0, 1070.0],

miplearn/components/tests/test_primal.py

@@ -50,7 +50,7 @@ def test_evaluate():
     comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
     comp.fit(instances[:1])
     assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
-    assert instances[0].solution == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
+    assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
     ev = comp.evaluate(instances[:1])
     assert ev == {
         "Fix one": {