Allow user to attach arbitrary data to violations

tests/components/test_dynamic_lazy.py

@@ -10,6 +10,7 @@ import pytest
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold
 from miplearn.components import classifier_evaluation_dict
+from miplearn.components.dynamic_common import DynamicConstraintsComponent
 from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
 from miplearn.features.sample import MemorySample
 from miplearn.instance.base import Instance
@@ -24,13 +25,23 @@ def training_instances() -> List[Instance]:
     samples_0 = [
         MemorySample(
             {
-                "mip_constr_lazy_enforced": np.array(["c1", "c2"], dtype="S"),
+                "mip_constr_lazy": DynamicConstraintsComponent.encode(
+                    {
+                        b"c1": 0,
+                        b"c2": 0,
+                    }
+                ),
                 "static_instance_features": np.array([5.0]),
             },
         ),
         MemorySample(
             {
-                "mip_constr_lazy_enforced": np.array(["c2", "c3"], dtype="S"),
+                "mip_constr_lazy": DynamicConstraintsComponent.encode(
+                    {
+                        b"c2": 0,
+                        b"c3": 0,
+                    }
+                ),
                 "static_instance_features": np.array([5.0]),
             },
         ),
@@ -55,7 +66,12 @@ def training_instances() -> List[Instance]:
     samples_1 = [
         MemorySample(
             {
-                "mip_constr_lazy_enforced": np.array(["c3", "c4"], dtype="S"),
+                "mip_constr_lazy": DynamicConstraintsComponent.encode(
+                    {
+                        b"c3": 0,
+                        b"c4": 0,
+                    }
+                ),
                 "static_instance_features": np.array([8.0]),
             },
         )
@@ -83,8 +99,8 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
     comp = DynamicLazyConstraintsComponent()
     comp.pre_fit(
         [
-            np.array(["c1", "c3", "c4"], dtype="S"),
-            np.array(["c1", "c2", "c4"], dtype="S"),
+            {b"c1": 0, b"c3": 0, b"c4": 0},
+            {b"c1": 0, b"c2": 0, b"c4": 0},
         ]
     )
     x_expected = {
@@ -105,7 +121,10 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
 
 def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
     comp = DynamicLazyConstraintsComponent()
-    comp.known_cids.extend([b"c1", b"c2", b"c3", b"c4"])
+    comp.known_violations[b"c1"] = 0
+    comp.known_violations[b"c2"] = 0
+    comp.known_violations[b"c3"] = 0
+    comp.known_violations[b"c4"] = 0
     comp.thresholds[b"type-a"] = MinProbabilityThreshold([0.5, 0.5])
     comp.thresholds[b"type-b"] = MinProbabilityThreshold([0.5, 0.5])
     comp.classifiers[b"type-a"] = Mock(spec=Classifier)

tests/components/test_dynamic_user_cuts.py

@@ -1,9 +1,9 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-
+import json
 import logging
-from typing import Any, FrozenSet, List
+from typing import Any, List, Dict
 
 import gurobipy as gp
 import networkx as nx
@@ -12,12 +12,11 @@ from gurobipy import GRB
 from networkx import Graph
 from overrides import overrides
 
-from miplearn.solvers.learning import InternalSolver
 from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.instance.base import Instance
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.learning import LearningSolver
-from miplearn.types import ConstraintName, ConstraintCategory
+from miplearn.types import ConstraintName
 
 logger = logging.getLogger(__name__)
 
@@ -41,13 +40,14 @@ class GurobiStableSetProblem(Instance):
         return True
 
     @overrides
-    def find_violated_user_cuts(self, model: Any) -> List[ConstraintName]:
+    def find_violated_user_cuts(self, model: Any) -> Dict[ConstraintName, Any]:
         assert isinstance(model, gp.Model)
         vals = model.cbGetNodeRel(model.getVars())
-        violations = []
+        violations = {}
         for clique in nx.find_cliques(self.graph):
             if sum(vals[i] for i in clique) > 1:
-                violations.append(",".join([str(i) for i in clique]).encode())
+                vname = (",".join([str(i) for i in clique])).encode()
+                violations[vname] = list(clique)
         return violations
 
     @overrides
@@ -55,9 +55,8 @@ class GurobiStableSetProblem(Instance):
         self,
         solver: GurobiSolver,
         model: Any,
-        cid: ConstraintName,
+        clique: List[int],
     ) -> Any:
-        clique = [int(i) for i in cid.decode().split(",")]
         x = model.getVars()
         constr = gp.quicksum([x[i] for i in clique]) <= 1
         if solver.cb_where:
@@ -86,9 +85,11 @@ def test_usage(
 ) -> None:
     stats_before = solver.solve(stab_instance)
     sample = stab_instance.get_samples()[0]
-    user_cuts_enforced = sample.get_array("mip_user_cuts_enforced")
-    assert user_cuts_enforced is not None
-    assert len(user_cuts_enforced) > 0
+    user_cuts_encoded = sample.get_scalar("mip_user_cuts")
+    assert user_cuts_encoded is not None
+    user_cuts = json.loads(user_cuts_encoded)
+    assert user_cuts is not None
+    assert len(user_cuts) > 0
     assert stats_before["UserCuts: Added ahead-of-time"] == 0
     assert stats_before["UserCuts: Added in callback"] > 0
 

tests/problems/test_tsp.py

@@ -1,6 +1,7 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+import json
 
 import numpy as np
 from numpy.linalg import norm
@@ -66,9 +67,15 @@ def test_subtour() -> None:
     samples = instance.get_samples()
     assert len(samples) == 1
     sample = samples[0]
-    lazy_enforced = sample.get_array("mip_constr_lazy_enforced")
-    assert lazy_enforced is not None
-    assert len(lazy_enforced) > 0
+
+    lazy_encoded = sample.get_scalar("mip_constr_lazy")
+    assert lazy_encoded is not None
+    lazy = json.loads(lazy_encoded)
+    assert lazy == {
+        "st[0,1,4]": [0, 1, 4],
+        "st[2,3,5]": [2, 3, 5],
+    }
+
     assert_equals(
         sample.get_array("mip_var_values"),
         [