62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
from __future__ import annotations
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from pydantic import BaseModel
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from typing import Dict, List, AnyStr
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from pulp import lpSum
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from models.targets.tif_target import TifTarget
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from models.targets.tcc_target import TccTarget
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from models.problem import Problem
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class ObjectiveFunction(BaseModel):
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# minimizing tif/tcc target value is only option currently
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# as we add more we can build this out to be more dynamic
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# likely with models representing each objective function type
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tif_targets: List[TifTarget]
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tcc_targets: List[TccTarget]
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target_variance_percentage: int = 10
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objective: AnyStr = "minimize"
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weight: Dict = {'tif': 1, 'tcc': 1}
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def for_problem(self, problem_handler: Problem) -> None:
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problem_handler.problem += lpSum([
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bundle.count * problem_handler.solver_bundles_var[bundle.id]
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for bundle in problem_handler.bundles
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] + [
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problem_handler.solver_items_var[item.id]
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for item in problem_handler.items
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])
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def increment_targets_drift(self,
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limit: float or bool,
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all: bool = False,
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amount: float = 0.1,
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targets: list[TifTarget|TccTarget] = []) -> bool:
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if all:
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for target in self.tif_targets:
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target.drift = round(target.drift + amount, 2)
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for target in self.tcc_targets:
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target.drift = round(target.drift + amount, 2)
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else:
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for target in targets:
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target.drift = round(target.drift + amount, 2)
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return amount
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def update_targets_drift(self, amount: float = 0.0):
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for target in self.tif_targets:
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target.drift = round(amount, 2)
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for target in self.tcc_targets:
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target.drift = round(amount, 2)
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def minimum_drift(self) -> float:
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minimum_drift = 0.0
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for target in self.all_targets():
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if target.drift < minimum_drift:
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minimum_drift = target.drift
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return minimum_drift
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def all_targets(self) -> list[TifTarget|TccTarget]:
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return self.tif_targets + self.tcc_targets
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