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92 lines
2.9 KiB
92 lines
2.9 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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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, List, Union, TYPE_CHECKING
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from miplearn.instance import Instance
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from miplearn.types import MIPSolveStats, TrainingSample
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if TYPE_CHECKING:
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from miplearn.solvers.learning import LearningSolver
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class Component(ABC):
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"""
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A Component is an object which adds functionality to a LearningSolver.
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For better code maintainability, LearningSolver simply delegates most of its
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functionality to Components. Each Component is responsible for exactly one ML
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strategy.
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"""
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def before_solve(
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self,
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solver: LearningSolver,
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instance: Instance,
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model: Any,
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) -> None:
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"""
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Method called by LearningSolver before the problem is solved.
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Parameters
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----------
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solver
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The solver calling this method.
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instance
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The instance being solved.
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model
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The concrete optimization model being solved.
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"""
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return
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@abstractmethod
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def after_solve(
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self,
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solver: LearningSolver,
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instance: Instance,
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model: Any,
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stats: MIPSolveStats,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver after the problem is solved to optimality.
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Parameters
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----------
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solver: LearningSolver
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The solver calling this method.
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instance: Instance
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The instance being solved.
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model:
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The concrete optimization model being solved.
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stats: dict
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A dictionary containing statistics about the solution process, such as
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number of nodes explored and running time. Components are free to add
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their own statistics here. For example, PrimalSolutionComponent adds
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statistics regarding the number of predicted variables. All statistics in
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this dictionary are exported to the benchmark CSV file.
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training_data: dict
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A dictionary containing data that may be useful for training machine
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learning models and accelerating the solution process. Components are
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free to add their own training data here. For example,
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PrimalSolutionComponent adds the current primal solution. The data must
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be pickable.
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"""
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pass
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def fit(
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self,
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training_instances: Union[List[str], List[Instance]],
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) -> None:
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return
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def iteration_cb(self, solver, instance, model):
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return False
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def lazy_cb(self, solver, instance, model):
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return
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