# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Released under the modified BSD license. See COPYING.md for more details. from typing import Optional, Dict, Callable, Any, Union, Tuple from mypy_extensions import TypedDict VarIndex = Union[str, int, Tuple[Union[str, int]]] Solution = Dict[str, Dict[VarIndex, Optional[float]]] TrainingSample = TypedDict( "TrainingSample", { "LP log": str, "LP solution": Optional[Solution], "LP value": Optional[float], "Lower bound": Optional[float], "MIP log": str, "Solution": Optional[Solution], "Upper bound": Optional[float], "slacks": Dict, }, total=False, ) LPSolveStats = TypedDict( "LPSolveStats", { "Optimal value": Optional[float], "Log": str, }, ) MIPSolveStats = TypedDict( "MIPSolveStats", { "Lower bound": Optional[float], "Upper bound": Optional[float], "Wallclock time": float, "Nodes": Optional[int], "Sense": str, "Log": str, "Warm start value": Optional[float], "LP value": Optional[float], }, ) LearningSolveStats = TypedDict( "LearningSolveStats", { "Gap": Optional[float], "Instance": Union[str, int], "LP value": Optional[float], "Log": str, "Lower bound": Optional[float], "Mode": str, "Nodes": Optional[int], "Sense": str, "Solver": str, "Upper bound": Optional[float], "Wallclock time": float, "Warm start value": Optional[float], "Primal: free": int, "Primal: zero": int, "Primal: one": int, "Objective: predicted LB": float, "Objective: predicted UB": float, }, total=False, ) IterationCallback = Callable[[], bool] LazyCallback = Callable[[Any, Any], None] SolverParams = Dict[str, Any] BranchPriorities = Solution class Constraint: pass