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# -- In the sum of squares library -------------------------------------------------------
from abc import ABC, abstractmethod
from enum import Enum, auto
from typing import Callable, Self
from dataclasses import dataclass, field
from itertools import cycle
from functools import wraps
try:
from typing import override
except ImportError:
from typing_extensions import override
class Solver(Enum):
""" Enum to select a solver """
CVXOPT = auto()
@dataclass(frozen=True)
class OptResult(ABC):
""" Generic result from optimization problem """
success: bool # last problem was solved successfully
class Problem(ABC):
""" Optimization Problem. """
@abstractmethod
def solve(self, solver: Solver) -> OptResult:
""" Solve the optimization problem """
class SOSProblem(Problem):
@override
def solve(self, solver: Solver) -> OptResult:
raise NotImplementedError
Stage = Callable[[OptResult], Problem]
SolvableStage = Callable[[OptResult, Solver], OptResult]
@dataclass
class HaltingPredicate:
""" This wrapper is sadly necessary because otherwise we can't use
`isinstance(stage, HaltingPredicate)` in the class below. """
check: Callable[[OptResult], bool]
def __str__(self):
return self.check.__name__
def __call__(self, res: OptResult):
return self.check(res)
@dataclass
class MultiStageProblem(Problem):
""" Pipeline for multi-state optimization problems.
The pipeline is made of stages and halting predicates. If there are no halting
predicates, the pipeline runs the stages by passing the result of each stage to the
next. If there are halting predicates, the pipeline is repeated until one of the
halting predicates tells it to stop.
"""
initial: OptResult
solver: Solver = Solver.CVXOPT
stages: list[Stage | HaltingPredicate] = field(default_factory=list)
iterations: int = 0
# Magic methods
def __str__(self):
i, lines = 0, ["Multi-Stage Problem:"]
for stage in self.stages:
if isinstance(stage, HaltingPredicate):
lines.append(f" halt? {stage}")
else:
lines.append(f" {i:02d} stage {stage.__name__}")
i += 1
return "\n".join(lines)
# Problem behaviour
@override
def solve(self) -> OptResult:
""" Solve the multistage problem """
if HaltingPredicate in map(type, self.stages):
return self._solve_repeating()
return self._solve_once()
def _solve_once(self) -> OptResult:
self.iterations, result = 0, self.initial
for stage in self.stages:
result = stage(result, self.solver)
self.iterations += 1
return result
def _solve_repeating(self) -> OptResult:
self.iterations, result = 0, self.initial
for stage in cycle(self.stages):
if isinstance(stage, HaltingPredicate):
if stage(result):
break
else:
result = stage(result, self.solver)
return result
# Wrappers
@staticmethod
def stage(fn: Stage) -> SolvableStage:
""" Make a stage / step for the pipeline. """
@wraps(fn)
def wrapper(res: OptResult, solver: Solver) -> OptResult:
return fn(res).solve(solver)
return wrapper
@staticmethod
def halt(fn: Callable[[OptResult], bool]) -> HaltingPredicate:
return HaltingPredicate(fn)
# Pipeline construction (plumbing)
def and_then(self, fn: SolvableStage) -> Self:
""" Add a stage to the pipeline that runs only if the previous stage
completed with success. """
@wraps(fn)
def wrapper(res: OptResult, solver: Solver) -> OptResult:
if not res.success:
return res # do nothing
return fn(res, solver)
self.stages.append(fn)
return self
def or_else(self, fn: SolvableStage) -> Self:
""" Add a stage to the pipeline, that runs only if the previous stage
failed. """
@wraps(fn)
def wrapper(res: OptResult, solver: Solver) -> OptResult:
if res.success:
return res # do nothing
return fn(res, solver)
self.stages.append(wrapper)
return self
def stop_if(self, predicate: HaltingPredicate) -> Self:
""" Add a predicate to stop the pipeline. """
self.stages.append(predicate)
return self
# -- In the user script -----------------------------------------------------------------
# from sumofsquares import MultiStageProblem
# Define stages
@MultiStageProblem.stage
def solve_controller(result: OptResult) -> Problem:
return SOSProblem()
@MultiStageProblem.stage
def solve_cbf_clf(result: OptResult) -> Problem:
return SOSProblem()
@MultiStageProblem.halt
def good_op_region(result: OptResult) -> bool:
return abs(result.roi) < 1e-3
# Initialize multi-stage problem and define order
power_converter_prob = (
MultiStageProblem(initial=OptResult) # Pass initialization in constructor
.and_then(solve_controller)
.stop_if(good_op_region)
.and_then(solve_cbf_clf)
)
print(power_converter_prob)
# res: OptResult = power_converter_prob.solve()
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