refactor in prep for enemies
This commit is contained in:
Joshua Burman
parent
49178380a4
commit
11979193de
@ -11,8 +11,9 @@ 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]) -> 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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@ -47,7 +48,7 @@ def build_constraints(solver_run: SolverRun, problem: LpProblem,
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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:
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if selected_bundles != None and 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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@ -55,6 +56,52 @@ def build_constraints(solver_run: SolverRun, problem: LpProblem,
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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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@ -62,7 +109,7 @@ def build_constraints(solver_run: SolverRun, problem: LpProblem,
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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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@ -43,7 +43,7 @@ class ServiceListener(Consumer):
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logging.error(f'action of type {action} does not exist.')
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def main():
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logging.info('Starting IRT Service: That Was Rasch (v1.5.0)...')
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logging.info('Starting IRT Service: The Enemies Within (v1.7.0)...')
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# ToDo: Figure out a much better way of doing this.
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# LocalStack wants 'endpoint_url', while prod doesnt :(
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@ -29,3 +29,10 @@ class Form(BaseModel):
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tcc_results=irt_helper.generate_tcc_results(items, solver_run),
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status=status,
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solver_variables=solver_variables)
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def has_item(self, item: Item) -> bool:
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for i in self.items:
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if item == i:
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return True
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return False
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3
app/models/irt_problem.py
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3
app/models/irt_problem.py
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@ -0,0 +1,3 @@
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from models.problem import Problem
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class IrtProblem(Problem):
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79
app/models/problem.py
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79
app/models/problem.py
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@ -0,0 +1,79 @@
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from pydantic import BaseModel
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from typing import Any, List
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from pulp import LpProblem, LpVariable, lpSum
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import logging, math
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from helpers import solver_helper
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from models.solver_run import SolverRun
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from models.solution import Solution
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from models.item import Item
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from models.bundle import Bundle
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class Problem(BaseModel):
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items: List[Item]
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bundles: List[Bundle]
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problem: Any
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solver_items_var: Any = None
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solver_bundles_var: Any = None
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def __init__(self, **data) -> None:
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super().__init__(**data)
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# setup common Solver variables
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self.solver_items_var = LpVariable.dicts("Item",
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[item.id for item in self.items],
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lowBound=0,
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upBound=1,
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cat='Binary')
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self.solver_bundles_var = LpVariable.dicts("Bundle",
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[bundle.id for bundle in self.bundles],
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lowBound=0,
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upBound=1,
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cat='Binary')
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# objective function
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self.problem += lpSum([
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bundle.count * self.solver_bundles_var[bundle.id]
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for bundle in self.bundles
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] + [
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self.solver_items_var[item.id]
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for item in self.items
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])
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def solve(self) -> LpProblem:
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self.problem.solve()
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return self.problem
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def generate(self, solution: Solution, solver_run: SolverRun):
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# Form Constraints
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self.problem += lpSum(
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[
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bundle.count * self.solver_bundles_var[bundle.id]
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for bundle in self.bundles
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] + [
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1 * self.solver_items_var[item.id]
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for item in self.items
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]
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) == solver_run.total_form_items, f'Total bundle form items for form'
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# each time a form is generated, we want to ensure
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# that it is unique to all other forms generated before it
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self.problem += lpSum(
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[
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solution.items_exist_in_forms(bundle.items) * self.solver_bundles_var[bundle.id]
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for bundle in self.bundles
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] + [
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solution.items_exist_in_forms([item]) * self.solver_items_var[item.id]
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for item in self.items
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]
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) <= solver_run.total_form_items - 1, f'Ensuring uniqueness for form'
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def generate_constraints(self, solver_run: SolverRun, current_drift: int):
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self.problem = solver_helper.build_constraints(
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solver_run, self.problem, self.solver_items_var, self.solver_bundles_var, self.items, self.bundles, current_drift)
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@ -2,8 +2,21 @@ from pydantic import BaseModel
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from typing import List
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from models.form import Form
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from models.item import Item
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class Solution(BaseModel):
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response_id: int
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forms: List[Form]
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def items_exist_in_forms(self, items: [Item]) -> bool:
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items_found = 0
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for item in items:
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for form in self.forms:
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if form.has_item(item):
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items_found += 1
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return items_found
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@ -8,6 +8,7 @@ from lib.errors.item_generation_error import ItemGenerationError
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from models.solver_run import SolverRun
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from models.solution import Solution
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from models.problem import Problem
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from models.form import Form
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from models.target import Target
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@ -71,24 +72,6 @@ class FormGenerationService(Base):
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form_number = form_count + 1
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current_drift = 0 # FF Tokyo Drift
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# adding an element of randomness to the items and bundles used
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# may need to change impl based on limit of items available
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selected_items = self.solver_run.select_items_by_percent(30)
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selected_bundles = self.solver_run.select_bundles_by_percent(
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30)
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# setup common Solver variables
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items = LpVariable.dicts("Item",
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[item.id for item in selected_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("Bundle",
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[bundle.id for bundle in selected_bundles],
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lowBound=0,
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upBound=1,
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cat='Binary')
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logging.info(f'Generating Solution for Form {form_number} using the {self.solver_run.irt_model.model} IRT model')
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while current_drift <= Target.max_drift():
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@ -97,99 +80,11 @@ class FormGenerationService(Base):
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drift_percent)
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# create problem
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problem = LpProblem('ata-form-generate', LpMinimize)
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# objective function
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problem += lpSum([
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bundle.count * bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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items[item.id]
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for item in selected_items
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])
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# Form Constraints
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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 selected_bundles
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] + [
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1 * items[item.id]
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for item in selected_items
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]
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) == self.solver_run.total_form_items, f'Total bundle form items for form {form_number}'
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# Dynamic constraints.. currently we only support Metadata and Bundles(Cases/Passages)
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problem = solver_helper.build_constraints(
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self.solver_run, problem, items, bundles, selected_items, selected_bundles)
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# form uniqueness constraints
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# for form in solution.forms:
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# form_item_options = [
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# bundles[bundle.id]
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# for bundle in selected_bundles
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# ] + [
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# items[item.id]
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# for item in selected_items
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# ]
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# problem += len(
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# set(form.solver_variables)
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# & set(form_item_options)) / float(
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# len(
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# set(form.solver_variables)
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# | set(form_item_options))) * 100 >= 10
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logging.info('Creating TIF and TCC Elastic constraints')
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# Behold our very own Elastic constraints!
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for tif_target in self.solver_run.objective_function.tif_targets:
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problem += lpSum([
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bundle.tif(self.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(self.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(self.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(self.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 self.solver_run.objective_function.tcc_targets:
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problem += lpSum([
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bundle.trf(self.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(self.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(self.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(self.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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logging.info(
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f'Solving for Form {form_number} with a drift of {current_drift}%'
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)
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problem.solve()
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problem_handler = Problem(items = self.solver_run.items, bundles = self.solver_run.bundles, problem = LpProblem('ata-form-generate', LpMinimize))
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problem_handler.generate(solution, self.solver_run)
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problem_handler.generate_constraints(self.solver_run, current_drift)
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problem = problem_handler.solve()
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if LpStatus[problem.status] == 'Infeasible':
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logging.info(
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