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68 lines
3.2 KiB
68 lines
3.2 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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from abc import ABC, abstractmethod
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class Instance(ABC):
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"""
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Abstract class holding all the data necessary to generate a concrete model of the problem.
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In the knapsack problem, for example, this class could hold the number of items, their weights
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and costs, as well as the size of the knapsack. Objects implementing this class are able to
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convert themselves into a concrete optimization model, which can be optimized by solver, or
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into arrays of features, which can be provided as inputs to machine learning models.
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"""
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@abstractmethod
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def to_model(self):
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"""
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Returns a concrete Pyomo model corresponding to this instance.
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"""
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pass
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@abstractmethod
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def get_instance_features(self):
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"""
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Returns a 1-dimensional Numpy array of (numerical) features describing the entire instance.
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The array is used by LearningSolver to determine how similar two instances are. It may also
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be used to predict, in combination with variable-specific features, the values of binary
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decision variables in the problem.
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There is not necessarily a one-to-one correspondence between models and instance features:
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the features may encode only part of the data necessary to generate the complete model.
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Features may also be statistics computed from the original data. For example, in the
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knapsack problem, an implementation may decide to provide as instance features only
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the average weights, average prices, number of items and the size of the knapsack.
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The returned array MUST have the same length for all relevant instances of the problem. If
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two instances map into arrays of different lengths, they cannot be solved by the same
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LearningSolver object.
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"""
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pass
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@abstractmethod
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def get_variable_features(self, var, index):
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"""
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Returns a 1-dimensional array of (numerical) features describing a particular decision
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variable.
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The argument `var` is a pyomo.core.Var object, which represents a collection of decision
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variables. The argument `index` specifies which variable in the collection is the relevant
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one.
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In combination with instance features, variable features are used by LearningSolver to
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predict, among other things, the optimal value of each decision variable before the
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optimization takes place. In the knapsack problem, for example, an implementation could
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provide as variable features the weight and the price of a specific item.
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Like instance features, the arrays returned by this method MUST have the same length for
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all variables, and for all relevant instances of the problem.
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If the value of the given variable should not be predicted, this method MUST return None.
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"""
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pass
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def get_variable_category(self, var, index):
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return "default" |