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117 lines
4.4 KiB
117 lines
4.4 KiB
# MIPLearn, an extensible framework for Learning-Enhanced 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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from scipy.stats import uniform, randint, bernoulli
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from scipy.stats.distributions import rv_frozen
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class MaxWeightStableSetChallengeA:
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def __init__(self):
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self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
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n=randint(low=200, high=201),
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p=uniform(loc=0.05, scale=0.0),
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fix_graph=True)
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def get_training_instances(self):
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return self.generator.generate(300)
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def get_test_instances(self):
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return self.generator.generate(50)
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class MaxWeightStableSetGenerator:
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"""Random instance generator for the Maximum-Weight Stable Set Problem.
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The generator has two modes of operation. When `fix_graph=True` is provided, one random
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Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
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from user-provided probability distributions `n` and `p`. To generate each instance, the
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generator independently samples each $w_v$ from the user-provided probability distribution `w`.
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When `fix_graph=False`, a new random graph is generated for each instance; the remaining
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parameters are sampled in the same way.
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"""
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def __init__(self,
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w=uniform(loc=10.0, scale=1.0),
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n=randint(low=250, high=251),
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p=uniform(loc=0.05, scale=0.0),
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fix_graph=True):
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"""Initialize the problem generator.
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Parameters
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----------
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w: rv_continuous
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Probability distribution for vertex weights.
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n: rv_discrete
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Probability distribution for parameter $n$ in Erdős-Rényi model.
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p: rv_continuous
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Probability distribution for parameter $p$ in Erdős-Rényi model.
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"""
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assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
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assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
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assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
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self.w = w
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self.n = n
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self.p = p
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self.fix_graph = fix_graph
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self.graph = None
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if fix_graph:
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self.graph = self._generate_graph()
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def generate(self, n_samples):
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def _sample():
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if self.graph is not None:
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graph = self.graph
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else:
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graph = self._generate_graph()
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weights = self.w.rvs(graph.number_of_nodes())
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return MaxWeightStableSetInstance(graph, weights)
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return [_sample() for _ in range(n_samples)]
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def _generate_graph(self):
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return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
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class MaxWeightStableSetInstance(Instance):
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"""An instance of the Maximum-Weight Stable Set Problem.
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Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
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set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
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a subset of vertices, no two of which are adjacent.
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This is one of Karp's 21 NP-complete problems.
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"""
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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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self.model = None
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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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self.model = model = pe.ConcreteModel()
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model.x = pe.Var(nodes, domain=pe.Binary)
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model.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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model.edge_eqs = pe.ConstraintList()
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for edge in edges:
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model.edge_eqs.add(model.x[edge[0]] + model.x[edge[1]] <= 1)
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return model
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def get_instance_features(self):
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return np.array(self.weights)
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def get_variable_features(self, var, index):
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return np.ones(0)
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def get_variable_category(self, var, index):
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return index
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