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166 lines
6.4 KiB
166 lines
6.4 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import gzip
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import json
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from abc import ABC, abstractmethod
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from typing import Any, List
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import numpy as np
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from miplearn.types import TrainingSample
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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
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problem.
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In the knapsack problem, for example, this class could hold the number of items,
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their weights and costs, as well as the size of the knapsack. Objects
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implementing this class are able to convert themselves into a concrete
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optimization model, which can be optimized by a solver, or into arrays of
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features, which can be provided as inputs to machine learning models.
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"""
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def __init__(self):
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self.training_data: List[TrainingSample] = []
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@abstractmethod
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def to_model(self) -> Any:
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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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def get_instance_features(self):
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"""
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Returns a 1-dimensional Numpy array of (numerical) features describing the
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entire instance.
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The array is used by LearningSolver to determine how similar two instances
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are. It may also be used to predict, in combination with variable-specific
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features, the values of binary decision variables in the problem.
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There is not necessarily a one-to-one correspondence between models and
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instance features: the features may encode only part of the data necessary to
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generate the complete model. Features may also be statistics computed from
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the original data. For example, in the knapsack problem, an implementation
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may decide to provide as instance features only the average weights, average
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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
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the problem. If two instances map into arrays of different lengths,
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they cannot be solved by the same LearningSolver object.
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By default, returns [0].
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"""
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return np.zeros(1)
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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
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decision variable.
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The argument `var` is a pyomo.core.Var object, which represents a collection
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of decision variables. The argument `index` specifies which variable in the
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collection is the relevant one.
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In combination with instance features, variable features are used by
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LearningSolver to predict, among other things, the optimal value of each
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decision variable before the optimization takes place. In the knapsack
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problem, for example, an implementation could provide as variable features
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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
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length for all variables within the same category, for all relevant instances
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of the problem.
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By default, returns [0].
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"""
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return np.zeros(1)
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def get_variable_category(self, var, index):
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"""
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Returns the category (a string, an integer or any hashable type) for each
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decision variable.
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If two variables have the same category, LearningSolver will use the same
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internal ML model to predict the values of both variables. If the returned
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category is None, ML models will ignore the variable.
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By default, returns "default".
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"""
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return "default"
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def get_constraint_features(self, cid):
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return np.zeros(1)
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def get_constraint_category(self, cid):
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return cid
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def has_static_lazy_constraints(self):
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return False
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def has_dynamic_lazy_constraints(self):
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return False
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def is_constraint_lazy(self, cid):
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return False
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def find_violated_lazy_constraints(self, model):
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"""
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Returns lazy constraint violations found for the current solution.
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After solving a model, LearningSolver will ask the instance to identify which
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lazy constraints are violated by the current solution. For each identified
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violation, LearningSolver will then call the build_lazy_constraint, add the
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generated Pyomo constraint to the model, then resolve the problem. The
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process repeats until no further lazy constraint violations are found.
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Each "violation" is simply a string, a tuple or any other hashable type which
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allows the instance to identify unambiguously which lazy constraint should be
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generated. In the Traveling Salesman Problem, for example, a subtour
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violation could be a frozen set containing the cities in the subtour.
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For a concrete example, see TravelingSalesmanInstance.
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"""
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return []
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def build_lazy_constraint(self, model, violation):
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"""
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Returns a Pyomo constraint which fixes a given violation.
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This method is typically called immediately after
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find_violated_lazy_constraints. The violation object provided to this method
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is exactly the same object returned earlier by
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find_violated_lazy_constraints. After some training, LearningSolver may
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decide to proactively build some lazy constraints at the beginning of the
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optimization process, before a solution is even available. In this case,
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build_lazy_constraints will be called without a corresponding call to
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find_violated_lazy_constraints.
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The implementation should not directly add the constraint to the model. The
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constraint will be added by LearningSolver after the method returns.
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For a concrete example, see TravelingSalesmanInstance.
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"""
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pass
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def find_violated_user_cuts(self, model):
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return []
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def build_user_cut(self, model, violation):
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pass
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def load(self, filename):
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with gzip.GzipFile(filename, "r") as f:
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data = json.loads(f.read().decode("utf-8"))
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self.__dict__ = data
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def dump(self, filename):
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data = json.dumps(self.__dict__, indent=2).encode("utf-8")
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with gzip.GzipFile(filename, "w") as f:
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f.write(data)
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