refactor create constraints for targets
This commit is contained in:
Joshua Burman
@ -37,6 +37,5 @@ def get_object_tags(key: str, bucket: str) -> list:
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tags = s3.get_object_tagging(Bucket=bucket, Key=key)['TagSet']
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return tags
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def file_stream_upload(buffer: io.BytesIO, name: str, bucket: str, action: str = None):
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return s3.upload_fileobj(buffer, bucket, name, ExtraArgs={'Tagging': f'action={action}'})
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@ -1,133 +0,0 @@
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from pulp import lpSum, LpProblem
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from random import randint, sample
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import logging
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from helpers.common_helper import *
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from models.bundle import Bundle
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from models.solver_run import SolverRun
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from models.item import Item
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from lib.errors.item_generation_error import ItemGenerationError
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# should probably be factored out into a bundle class method or a method in the solver run
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def build_constraints(solver_run: SolverRun, problem: LpProblem,
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items: list[Item], bundles: list[Bundle], selected_items: list[Item], selected_bundles: list[Bundle], current_drift: int) -> LpProblem:
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logging.info('Creating Constraints...')
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try:
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total_form_items = solver_run.total_form_items
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constraints = solver_run.constraints
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for constraint in constraints:
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attribute = constraint.reference_attribute
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min = constraint.minimum
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max = constraint.maximum
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if attribute.type == 'metadata':
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logging.info('Metadata Constraint Generating...')
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problem += lpSum(
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[
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len(bundle.items_with_attribute(attribute)) * bundles[bundle.id] for bundle in selected_bundles
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] +
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[
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item.attribute_exists(attribute).real * items[item.id] for item in selected_items
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]
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) >= round(total_form_items * (min / 100)), f'{attribute.id} - {attribute.value} - min'
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problem += lpSum(
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[
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len(bundle.items_with_attribute(attribute)) * bundles[bundle.id] for bundle in selected_bundles
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] +
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[
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item.attribute_exists(attribute).real * items[item.id] for item in selected_items
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]
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) <= round(total_form_items * (max / 100)), f'{attribute.id} - {attribute.value} - max'
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elif attribute.type == 'bundle':
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logging.info('Bundles Constraint Generating...')
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# TODO: account for many different bundle types, since the id condition in L33 could yield duplicates
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if selected_bundles != None and len(selected_bundles) > 0:
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# make sure the total bundles used in generated form is limited between min-max set
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problem += lpSum([
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bundles[bundle.id] for bundle in selected_bundles
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]) == randint(int(constraint.minimum),
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int(constraint.maximum))
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logging.info('Constraints Created...')
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# Behold our very own Elastic constraints!
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for tif_target in solver_run.objective_function.tif_targets:
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problem += lpSum([
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bundle.tif(solver_run.irt_model, tif_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.iif(solver_run, tif_target.theta) *
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items[item.id]
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for item in selected_items
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]) >= tif_target.minimum(
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), f'Min TIF theta({tif_target.theta}) at target {tif_target.value} drift at {current_drift}%'
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problem += lpSum([
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bundle.tif(solver_run.irt_model, tif_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.iif(solver_run, tif_target.theta) *
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items[item.id]
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for item in selected_items
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]) <= tif_target.maximum(
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), f'Max TIF theta({tif_target.theta}) at target {tif_target.value} drift at {current_drift}%'
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for tcc_target in solver_run.objective_function.tcc_targets:
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problem += lpSum([
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bundle.trf(solver_run.irt_model, tcc_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.irf(solver_run, tcc_target.theta) *
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items[item.id]
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for item in selected_items
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]) >= tcc_target.minimum(
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), f'Min TCC theta({tcc_target.theta}) at target {tcc_target.value} drift at {current_drift}%'
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problem += lpSum([
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bundle.trf(solver_run.irt_model, tcc_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.irf(solver_run, tcc_target.theta) *
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items[item.id]
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for item in selected_items
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]) <= tcc_target.maximum(
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), f'Max TCC theta({tcc_target.theta}) at target {tcc_target.value} drift at {current_drift}%'
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return problem
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except ValueError as error:
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logging.error(error)
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raise ItemGenerationError(
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"Bundle min and/or max larger than bundle amount provided",
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error.args[0])
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# should probably be factored out into a bundle class method or a method in the solver run
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def get_random_bundles(total_form_items: int,
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bundles: list[Bundle],
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min: int,
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max: int,
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found_bundles=False) -> list[Bundle]:
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selected_bundles = None
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total_bundle_items = 0
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total_bundles = randint(min, max)
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logging.info(f'Selecting Bundles (total of {total_bundles})...')
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while found_bundles == False:
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selected_bundles = sample(bundles, total_bundles)
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total_bundle_items = sum(bundle.count for bundle in selected_bundles)
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if total_bundle_items <= total_form_items:
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found_bundles = True
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if found_bundles == True:
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return selected_bundles
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else:
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return get_random_bundles(total_form_items, total_bundles - 1, bundles)
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