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51 lines
1.7 KiB
51 lines
1.7 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 miplearn
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import numpy as np
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import pyomo.environ as pe
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class KnapsackInstance(miplearn.Instance):
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def __init__(self, weights, prices, capacity):
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self.weights = weights
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self.prices = prices
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self.capacity = capacity
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def to_model(self):
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model = m = pe.ConcreteModel()
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items = range(len(self.weights))
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m.x = pe.Var(items, domain=pe.Binary)
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m.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.prices[v] for v in items),
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sense=pe.maximize)
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m.eq_capacity = pe.Constraint(rule = lambda m :
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sum(m.x[v] * self.weights[v]
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for v in items) <= self.capacity)
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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.capacity,
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np.average(self.weights),
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])
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def get_variable_features(self, var, index):
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return np.array([
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self.weights[index],
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self.prices[index],
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])
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class KnapsackInstance2(KnapsackInstance):
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"""
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Alternative implementation of the Knapsack Problem, which assigns a different category for each
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decision variable, and therefore trains one machine learning model per variable.
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"""
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def get_instance_features(self):
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return np.hstack([self.weights, self.prices])
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def get_variable_features(self, var, index):
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return np.array([
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])
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def get_variable_category(self, var, index):
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return index |