added constant drift incrementing for tcc, tif values
This commit is contained in:
Joshua Burman
parent
46d3538690
commit
f18e048081
@ -49,27 +49,6 @@ def build_constraints(solver_run: SolverRun, problem: LpProblem,
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bundles[bundle.id] for bundle in solver_run.bundles
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]) == randint(int(constraint.minimum),
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int(constraint.maximum))
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# total_bundle_items = 0
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# selected_bundles = get_random_bundles(solver_run.total_form_items, solver_run.bundles, int(constraint.minimum), int(constraint.maximum))
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# for bundle in selected_bundles:
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# con = dict(zip([item.id for item in solver_run.items],
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# [(getattr(item, bundle.type, False) == bundle.id)
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# for item in solver_run.items]))
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# problem += lpSum([con[item.id]
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# * items[item.id]
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# for item in solver_run.items]) == bundle.count, f'Bundle constraint for {bundle.type} ({bundle.id})'
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# total_bundle_items += bundle.count
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# # make sure all other items added to the form
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# # are not a part of any bundle
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# # currently only supports single bundle constraints, will need refactoring for multiple bundle constraints
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# con = dict(zip([item.id for item in solver_run.items],
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# [(getattr(item, attribute.id, None) == None)
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# for item in solver_run.items]))
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# problem += lpSum([con[item.id]
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# * items[item.id]
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# for item in solver_run.items]) == solver_run.total_form_items - total_bundle_items, f'Remaining items are not of a bundle type'
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logging.info('Constraints Created...')
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return problem
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@ -24,7 +24,7 @@ class ServiceListener(SqsListener):
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def main():
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logging.info('Starting Solver Service (v1.1.2)...')
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logging.info('Starting Solver Service (v1.1.3)...')
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listener = ServiceListener(
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os.environ['SQS_QUEUE'],
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region_name=os.environ['AWS_REGION'],
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@ -12,3 +12,32 @@ class ObjectiveFunction(BaseModel):
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tcc_targets: List[Target]
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objective: AnyStr = "minimize"
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weight: Dict = {'tif': 1, 'tcc': 1}
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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[Target] = []) -> 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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print(self.tif_targets)
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print(self.tcc_targets)
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return amount
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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[Target]:
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return self.tif_targets + self.tcc_targets
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@ -1,5 +1,5 @@
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from pydantic import BaseModel
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from typing import List, Optional
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from typing import List, Literal, Optional
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import logging
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@ -20,6 +20,7 @@ class SolverRun(BaseModel):
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total_form_items: int
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total_forms: int = 1
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theta_cut_score: float = 0.00
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drift_style: Literal['constant', 'variable'] = 'constant'
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advanced_options: Optional[AdvancedOptions]
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engine: str
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@ -6,3 +6,4 @@ class Target(BaseModel):
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theta: float
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value: float
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result: Optional[float]
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drift: float = 0.0
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@ -122,99 +122,132 @@ class LoftService(Base):
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# iterate for number of forms that require creation
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# currently creates distinc forms with no item overlap
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while f < self.solver_run.total_forms:
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# setup vars
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items = LpVariable.dicts(
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"Item", [item.id for item in self.solver_run.items],
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lowBound=1,
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upBound=1,
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cat='Binary')
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bundles = LpVariable.dicts(
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"Bundle", [bundle.id for bundle in self.solver_run.bundles],
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lowBound=1,
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upBound=1,
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cat='Binary')
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# currently constant drift is supported
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# plan to support variable drift
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problem = None
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# create problem
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problem = LpProblem("ata-form-generate", LpMinimize)
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problem_objective_functions = []
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# allow target drift to increment to keep trying
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# once limit has been reached loop will stop
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# at this point the latest solve attempt will be used
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# though likely to be infeasible
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while self.solver_run.objective_function.minimum_drift() <= 2.0:
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# setup vars
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items = LpVariable.dicts(
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"Item", [item.id for item in self.solver_run.items],
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lowBound=0,
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upBound=1,
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cat='Binary')
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bundles = LpVariable.dicts(
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"Bundle",
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[bundle.id for bundle in self.solver_run.bundles],
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lowBound=0,
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upBound=1,
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cat='Binary')
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# dummy objective function, because it just makes things easier™
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# problem += lpSum(
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# [items[item.id] for item in self.solver_run.items])
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# create problem
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problem = LpProblem("ata-form-generate", LpMinimize)
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problem_objective_functions = []
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# constraints
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# problem += lpSum([items[item.id]
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# for item in self.solver_run.items]) == self.solver_run.total_form_items, 'Total form items'
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problem += lpSum(
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[
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bundle.count * bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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1 * items[item.id]
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for item in self.solver_run.unbundled_items()
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]
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) == self.solver_run.total_form_items, 'Total bundle form items for form'
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# dummy objective function, because it just makes things easier™
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# problem += lpSum(
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# [items[item.id] for item in self.solver_run.items])
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# dynamic constraints
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problem = solver_helper.build_constraints(self.solver_run, problem,
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items, bundles)
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# constraints
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# problem += lpSum([items[item.id]
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# for item in self.solver_run.items]) == self.solver_run.total_form_items, 'Total form items'
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problem += lpSum(
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[
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bundle.count * bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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1 * items[item.id]
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for item in self.solver_run.unbundled_items()
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]
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) == self.solver_run.total_form_items, 'Total bundle form items for form'
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# multi-objective constraints
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logging.info('Creating TIF and TCC constraints')
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for target in self.solver_run.objective_function.tif_targets:
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# dynamic constraints
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problem = solver_helper.build_constraints(
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self.solver_run, problem, items, bundles)
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tif = lpSum([
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bundle.tif(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id] for bundle in self.solver_run.bundles
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] + [
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item.iif(self.solver_run, target.theta) * items[item.id]
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for item in self.solver_run.items
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])
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problem_objective_functions.append(tif)
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# problem += lpSum([
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# bundle.tif(self.solver_run.irt_model, target.theta) *
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# bundles[bundle.id] for bundle in self.solver_run.bundles
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# ] + [
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# item.iif(self.solver_run, target.theta) * items[item.id]
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# for item in self.solver_run.items
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# ]) >= target.value - 5, f'max tif theta ({target.theta}) target value {target.value}'
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# problem += lpSum([
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# bundle.tif(self.solver_run.irt_model, target.theta) *
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# bundles[bundle.id] for bundle in self.solver_run.bundles
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# ] + [
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# item.iif(self.solver_run, target.theta) * items[item.id]
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# for item in self.solver_run.items
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# ]) <= target.value + 5, f'min tif theta ({target.theta}) target value {target.value}'
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# multi-objective constraints
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logging.info('Creating TIF and TCC constraints')
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for target in self.solver_run.objective_function.tif_targets:
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for target in self.solver_run.objective_function.tcc_targets:
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tcc = lpSum([
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bundle.trf(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id] for bundle in self.solver_run.bundles
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] + [
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item.irf(self.solver_run, target.theta) * items[item.id]
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for item in self.solver_run.items
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])
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problem_objective_functions.append(tcc)
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# problem += lpSum([
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# bundle.trf(self.solver_run.irt_model, target.theta) *
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# bundles[bundle.id] for bundle in self.solver_run.bundles
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# ] + [
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# item.irf(self.solver_run, target.theta) * items[item.id]
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# for item in self.solver_run.items
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# ]) >= target.value - 15, f'max tcc theta ({target.theta}) target value {target.value}'
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# problem += lpSum([
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# bundle.trf(self.solver_run.irt_model, target.theta) *
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# bundles[bundle.id] for bundle in self.solver_run.bundles
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# ] + [
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# item.irf(self.solver_run, target.theta) * items[item.id]
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# for item in self.solver_run.items
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# ]) <= target.value + 15, f'min tcc theta ({target.theta}) target value {target.value}'
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tif = lpSum([
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bundle.tif(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.iif(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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])
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problem_objective_functions.append(tif)
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problem += lpSum([
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bundle.tif(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.iif(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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]) >= target.value - target.value * target.drift, f'max tif theta ({target.theta}) target value {target.value}'
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problem += lpSum([
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bundle.tif(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.iif(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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]) <= target.value + target.value * target.drift, f'min tif theta ({target.theta}) target value {target.value}'
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# solve problem
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logging.info('Solving...')
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# problem.solve()
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problem.sequentialSolve(problem_objective_functions)
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logging.info('Solved...generating form and adding to solution')
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for target in self.solver_run.objective_function.tcc_targets:
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tcc = lpSum([
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bundle.trf(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.irf(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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])
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problem_objective_functions.append(tcc)
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problem += lpSum([
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bundle.trf(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.irf(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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]) >= target.value - target.value * target.drift, f'max tcc theta ({target.theta}) target value {target.value}'
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problem += lpSum([
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bundle.trf(self.solver_run.irt_model, target.theta) *
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bundles[bundle.id]
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for bundle in self.solver_run.bundles
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] + [
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item.irf(self.solver_run, target.theta) *
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items[item.id] for item in self.solver_run.items
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]) <= target.value + target.value * target.drift, f'min tcc theta ({target.theta}) target value {target.value}'
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# solve problem
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logging.info('Solving...')
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# problem.solve()
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problem.sequentialSolve(problem_objective_functions)
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# optimal solution found!
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if LpStatus[problem.status] == 'Optimal':
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logging.info(
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f'Problem solved...generating Form and adding to Solution with {self.solver_run.drift_style} drift \n\
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tif target drift: \n\
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{["theta @" + str(target.theta) + " - " + str(target.drift) for target in self.solver_run.objective_function.tif_targets]} \n\
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tcc target drift: \n\
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{["theta @" + str(target.theta) + " - " + str(target.drift) for target in self.solver_run.objective_function.tcc_targets]}'
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)
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break
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else:
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# increment drift to attempt to find optimal solution
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increment = self.solver_run.objective_function.increment_targets_drift(
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2.0, True)
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logging.info(
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f'Non Optimal Solution...widening Target ranges to {increment} using {self.solver_run.drift_style} drift'
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)
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# add return items and create as a form
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form_items = service_helper.solution_items(problem.variables(),
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@ -224,7 +257,7 @@ class LoftService(Base):
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solution.forms.append(
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Form.create(form_items, self.solver_run,
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LpStatus[problem.status]))
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logging.info('Form generated and added to solution...')
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logging.info('Form generated and added to Solution...')
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# successfull form, increment
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f += 1
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