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61 lines
2.1 KiB
61 lines
2.1 KiB
# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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import numpy as np
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import pyomo.environ as pe
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import networkx as nx
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from miplearn import Instance
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import random
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class MaxStableSetGenerator:
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def __init__(self, sizes=[50], densities=[0.1]):
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self.sizes = sizes
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self.densities = densities
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def generate(self):
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size = random.choice(self.sizes)
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density = random.choice(self.densities)
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self.graph = nx.generators.random_graphs.binomial_graph(size, density)
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weights = np.ones(self.graph.number_of_nodes())
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return MaxStableSetInstance(self.graph, weights)
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class MaxStableSetInstance(Instance):
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def __init__(self, graph, weights):
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self.graph = graph
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self.weights = weights
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def to_model(self):
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nodes = list(self.graph.nodes)
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edges = list(self.graph.edges)
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model = m = pe.ConcreteModel()
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m.x = pe.Var(nodes, domain=pe.Binary)
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m.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.weights[v] for v in nodes),
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sense=pe.maximize)
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m.edge_eqs = pe.ConstraintList()
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for edge in edges:
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m.edge_eqs.add(m.x[edge[0]] + m.x[edge[1]] <= 1)
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return m
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def get_instance_features(self):
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return np.array([
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self.graph.number_of_nodes(),
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self.graph.number_of_edges(),
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])
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def get_variable_features(self, var, index):
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first_neighbors = list(self.graph.neighbors(index))
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second_neighbors = [list(self.graph.neighbors(u)) for u in first_neighbors]
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degree = len(first_neighbors)
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neighbor_degrees = sorted([len(nn) for nn in second_neighbors])
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neighbor_degrees = neighbor_degrees + [100.] * 10
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return np.array([
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degree,
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neighbor_degrees[0] - degree,
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neighbor_degrees[1] - degree,
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neighbor_degrees[2] - degree,
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])
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