Add user cut callbacks; begin rewrite of UserCutsComponent

tests/components/test_user_cuts.py

@@ -1,31 +1,77 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from typing import Any, List
 
-import pytest
-from networkx import Graph
+import logging
+from typing import Any, FrozenSet, Hashable
+
+import gurobipy as gp
 import networkx as nx
-from scipy.stats import randint
+import pytest
+from gurobipy import GRB
+from networkx import Graph
 
-from miplearn import Instance
-from miplearn.problems.stab import MaxWeightStableSetGenerator
+from miplearn import Instance, LearningSolver, GurobiSolver
+from miplearn.components.user_cuts import UserCutsComponentNG
+
+logger = logging.getLogger(__name__)
 
 
 class GurobiStableSetProblem(Instance):
     def __init__(self, graph: Graph) -> None:
         super().__init__()
         self.graph = graph
+        self.nodes = list(self.graph.nodes)
 
     def to_model(self) -> Any:
-        pass
+        model = gp.Model()
+        x = [model.addVar(vtype=GRB.BINARY) for _ in range(len(self.nodes))]
+        model.setObjective(gp.quicksum(x), GRB.MAXIMIZE)
+        for e in list(self.graph.edges):
+            model.addConstr(x[e[0]] + x[e[1]] <= 1)
+        return model
+
+    def has_user_cuts(self) -> bool:
+        return True
+
+    def find_violated_user_cuts(self, model):
+        assert isinstance(model, gp.Model)
+        vals = model.cbGetNodeRel(model.getVars())
+        violations = []
+        for clique in nx.find_cliques(self.graph):
+            lhs = sum(vals[i] for i in clique)
+            if lhs > 1:
+                violations += [frozenset(clique)]
+        return violations
+
+    def build_user_cut(self, model: Any, violation: Hashable) -> Any:
+        assert isinstance(violation, FrozenSet)
+        x = model.getVars()
+        cut = gp.quicksum([x[i] for i in violation]) <= 1
+        return cut
 
 
 @pytest.fixture
-def instance() -> Instance:
-    graph = nx.generators.random_graphs.binomial_graph(50, 0.5)
+def stab_instance() -> Instance:
+    graph = nx.generators.random_graphs.binomial_graph(50, 0.50, seed=42)
     return GurobiStableSetProblem(graph)
 
 
-def test_usage(instance: Instance) -> None:
-    pass
+@pytest.fixture
+def solver() -> LearningSolver:
+    return LearningSolver(
+        solver=lambda: GurobiSolver(),
+        components=[
+            UserCutsComponentNG(),
+        ],
+    )
+
+
+def test_usage(
+    stab_instance: Instance,
+    solver: LearningSolver,
+) -> None:
+    solver.solve(stab_instance)
+    sample = stab_instance.training_data[0]
+    assert sample.user_cuts_enforced is not None
+    assert len(sample.user_cuts_enforced) > 0