# 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, List, Set, Hashable 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], "LazyStatic: All": Set[str], "LazyStatic: Enforced": Set[str], "Lower bound": Optional[float], "MIP log": str, "Solution": Optional[Solution], "Upper bound": Optional[float], "slacks": Dict, }, total=False, ) LPSolveStats = TypedDict( "LPSolveStats", { "LP log": str, "LP value": Optional[float], }, ) MIPSolveStats = TypedDict( "MIPSolveStats", { "Lower bound": Optional[float], "MIP log": str, "Nodes": Optional[int], "Sense": str, "Upper bound": Optional[float], "Wallclock time": float, "Warm start value": Optional[float], }, ) LearningSolveStats = TypedDict( "LearningSolveStats", { "Gap": Optional[float], "Instance": Union[str, int], "LP log": str, "LP value": Optional[float], "Lower bound": Optional[float], "MIP log": str, "Mode": str, "Nodes": Optional[int], "Objective: Predicted lower bound": float, "Objective: Predicted upper bound": float, "Primal: Free": int, "Primal: One": int, "Primal: Zero": int, "Sense": str, "Solver": str, "Upper bound": Optional[float], "Wallclock time": float, "Warm start value": Optional[float], "LazyStatic: Removed": int, "LazyStatic: Kept": int, "LazyStatic: Restored": int, "LazyStatic: Iterations": int, }, total=False, ) InstanceFeatures = TypedDict( "InstanceFeatures", { "User features": List[float], "Lazy constraint count": int, }, total=False, ) VariableFeatures = TypedDict( "VariableFeatures", { "Category": Optional[Hashable], "User features": Optional[List[float]], }, total=False, ) ConstraintFeatures = TypedDict( "ConstraintFeatures", { "RHS": float, "LHS": Dict[str, float], "Sense": str, "Category": Optional[Hashable], "User features": Optional[List[float]], "Lazy": bool, }, total=False, ) Features = TypedDict( "Features", { "Instance": InstanceFeatures, "Variables": Dict[str, Dict[VarIndex, VariableFeatures]], "Constraints": Dict[str, ConstraintFeatures], }, total=False, ) IterationCallback = Callable[[], bool] LazyCallback = Callable[[Any, Any], None] SolverParams = Dict[str, Any] BranchPriorities = Solution class Constraint: pass