Add Maximum-Weight Stable Set Problem

miplearn/problems/stab.py

@@ -0,0 +1,50 @@
+# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+import numpy as np
+import pyomo.environ as pe
+import networkx as nx
+from miplearn import Instance
+import random
+
+
+class MaxStableSetGenerator:
+    def __init__(self, graph, base_weights, perturbation_scale=1.0):
+        self.graph = graph
+        self.base_weights = base_weights
+        self.perturbation_scale = perturbation_scale
+        
+    def generate(self):
+        perturbation = np.random.rand(self.graph.number_of_nodes()) * self.perturbation_scale
+        weights = self.base_weights + perturbation
+        return MaxStableSetInstance(self.graph, weights)
+
+
+class MaxStableSetInstance(Instance):
+    def __init__(self, graph, weights):
+        self.graph = graph
+        self.weights = weights
+        self.model = None
+        
+    def to_model(self):
+        nodes = list(self.graph.nodes)
+        edges = list(self.graph.edges)
+        self.model = model = pe.ConcreteModel()
+        model.x = pe.Var(nodes, domain=pe.Binary)
+        model.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.weights[v] for v in nodes),
+                                 sense=pe.maximize)
+        model.edge_eqs = pe.ConstraintList()
+        for edge in edges:
+            model.edge_eqs.add(model.x[edge[0]] + model.x[edge[1]] <= 1)
+
+        return model
+    
+    def get_instance_features(self):
+        return np.array(self.weights)
+    
+    def get_variable_features(self, var, index):
+        return np.ones(0)
+    
+    def get_variable_category(self, var, index):
+        return index

miplearn/tests/test_stab.py

@@ -0,0 +1,31 @@
+# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+from miplearn import LearningSolver
+from miplearn.problems.stab import MaxStableSetInstance, MaxStableSetGenerator
+import networkx as nx
+import numpy as np
+
+
+def test_stab():
+    graph = nx.cycle_graph(5)
+    weights = [1.0, 2.0, 3.0, 4.0, 5.0]
+    instance = MaxStableSetInstance(graph, weights)
+    solver = LearningSolver()
+    solver.solve(instance)
+    assert instance.model.OBJ() == 8.0
+    
+    
+def test_stab_generator():
+    graph = nx.cycle_graph(5)
+    base_weights = [1.0, 2.0, 3.0, 4.0, 5.0]
+    generator = MaxStableSetGenerator(graph=graph,
+                                      base_weights=base_weights,
+                                      perturbation_scale=1.0)
+    instances = [generator.generate() for _ in range(100_000)]
+    weights = np.array([instance.weights for instance in instances])
+    weights_avg = np.round(np.average(weights, axis=0), 2)
+    weights_std = np.round(np.std(weights, axis=0), 2)
+    assert list(weights_avg) == [1.50, 2.50, 3.50, 4.50, 5.50]
+    assert list(weights_std) == [0.29] * 5

requirements.txt

@@ -2,3 +2,4 @@ pyomo
 numpy
 pytest
 sklearn
+networkx

setup.py

@@ -7,5 +7,5 @@ setup(
    author='Alinson S. Xavier',
    author_email='axavier@anl.gov',
    packages=['miplearn'],
-   install_requires=['pyomo', 'numpy', 'sklearn'],
+   install_requires=['pyomo', 'numpy', 'sklearn', 'networkx'],
 )