Merge pull request #44 from yardstick/feature/QUANT-3248

QUANT-3248: Multi-Objective functions and Linear relaxation (drift) attempt limit

app/main.py

@@ -43,7 +43,7 @@ class ServiceListener(Consumer):
             logging.error(f'action of type {action} does not exist.')
 
 def main():
-    logging.info('Starting IRT Service: The Enemies Within (v1.7.0)...')
+    logging.info('Starting IRT Service: Tokyo Drift 2: Driftocolypse (v1.8.0)...')
 
     # ToDo: Figure out a much better way of doing this.
     # LocalStack wants 'endpoint_url', while prod doesnt :(

app/models/item.py

@@ -1,6 +1,5 @@
-import logging
 from pydantic import BaseModel, validator
-from typing import List, Optional, Tuple
+from typing import List, Optional
 
 from models.attribute import Attribute
 

app/models/objective_function.py

@@ -1,13 +1,17 @@
 from __future__ import annotations
+from typing import TYPE_CHECKING, Any, Literal, Union
 
-from pydantic import BaseModel
-from typing import Dict, List, AnyStr
-from pulp import lpSum
+from pydantic import BaseModel, validator
+from typing import Dict, List
+from pulp import lpSum, LpMinimize, LpMaximize
 
 from models.targets.tif_target import TifTarget
 from models.targets.tcc_target import TccTarget
 from models.problem import Problem
 
+if TYPE_CHECKING:
+    from models.solver_run import SolverRun
+
 class ObjectiveFunction(BaseModel):
     # minimizing tif/tcc target value is only option currently
     # as we add more we can build this out to be more dynamic
@@ -15,17 +19,45 @@ class ObjectiveFunction(BaseModel):
     tif_targets: List[TifTarget]
     tcc_targets: List[TccTarget]
     target_variance_percentage: int = 10
-    objective: AnyStr = "minimize"
+    objective: Literal[1,-1] = 1
+    functions: List[Literal['tcc', 'tif']] = ['tcc', 'tif']
     weight: Dict = {'tif': 1, 'tcc': 1}
 
-    def for_problem(self, problem_handler: Problem) -> None:
-        problem_handler.problem += lpSum([
-            bundle.count * problem_handler.solver_bundles_var[bundle.id]
-            for bundle in problem_handler.bundles
-        ] + [
-            problem_handler.solver_items_var[item.id]
-            for item in problem_handler.items
-        ])
+    @validator("objective", pre=True)
+    def set_objective(cls, v) -> List[int]:
+        if v == 'minimize':
+            return 1
+        elif v == 'maximize':
+            return -1
+        else:
+            return None
+
+    def for_problem(self, problem_handler: Problem, solver_run: SolverRun) -> List[lpSum]:
+        functions = []
+
+        for function in self.functions:
+            if function == 'tcc':
+                functions.append(lpSum([
+                    bundle.trf(solver_run.irt_model, solver_run.theta_cut_score)
+                    * problem_handler.solver_bundles_var[bundle.id]
+                    for bundle in problem_handler.bundles
+                ] + [
+                    item.irf(solver_run.irt_model, solver_run.theta_cut_score) *
+                    problem_handler.solver_items_var[item.id]
+                    for item in problem_handler.items
+                ]))
+            elif function == 'tif':
+                problem_handler.problem += lpSum([
+                    bundle.tif(solver_run.irt_model, solver_run.theta_cut_score)
+                    * problem_handler.solver_bundles_var[bundle.id]
+                    for bundle in problem_handler.bundles
+                ] + [
+                    item.iif(solver_run.irt_model, solver_run.theta_cut_score) *
+                    problem_handler.solver_items_var[item.id]
+                    for item in problem_handler.items
+                ])
+
+        return functions
 
     def increment_targets_drift(self,
                                 limit: float or bool,

app/models/problem.py

@@ -43,7 +43,12 @@ class Problem(BaseModel):
 
     def solve(self, solver_run: SolverRun, enemy_ids: List[int] = []) -> LpProblem:
         logging.info('solving problem...')
-        self.problem.solve()
+
+        # creating problem multi-objective functions
+        objective_functions = solver_run.objective_function.for_problem(self, solver_run)
+        self.problem.sequentialSolve(objective_functions)
+
+        return self.problem
 
         # NOTICE: Legacy enemies implementation
         # leaving this in, just in case the current impl fails to function
@@ -106,9 +111,6 @@ class Problem(BaseModel):
 
     def generate(self, solution: Solution, solver_run: SolverRun) -> None:
         try:
-            # creating problem objective function
-            solver_run.objective_function.for_problem(self)
-
             logging.info('Creating Constraints...')
             # generic constraints
             for constraint in solver_run.constraints:

app/models/solver_run.py

@@ -18,10 +18,6 @@ from models.bundle import Bundle
 from models.objective_function import ObjectiveFunction
 from models.advanced_options import AdvancedOptions
 
-if TYPE_CHECKING:
-    from models.solution import Solution
-    from models.problem import Problem
-
 ConstraintType = TypeVar('ConstraintType', bound=GenericConstraint)
 
 class SolverRun(BaseModel):
@@ -36,6 +32,8 @@ class SolverRun(BaseModel):
     theta_cut_score: float = 0.00
     drift_style: Literal['constant', 'variable'] = 'constant'
     allow_enemies: bool = False
+    max_attempts: int
+    max_drift: int = 10
     advanced_options: Optional[AdvancedOptions]
     engine: str
 

app/services/form_generation_service.py

@@ -1,6 +1,6 @@
 import json, random, io, logging
 
-from pulp import LpProblem, LpMinimize, LpStatus
+from pulp import LpProblem, LpStatus
 
 from lib.application_configs import ApplicationConfigs
 from helpers import aws_helper, tar_helper, csv_helper, service_helper
@@ -71,17 +71,22 @@ class FormGenerationService(Base):
         # iterate for number of forms that require creation
         for form_count in range(self.solver_run.total_forms):
             form_number = form_count + 1
+            drift_increment = self.solver_run.max_drift / (self.solver_run.max_attempts - 1)
             current_drift = 0  # FF Tokyo Drift
+            current_attempt = 0
 
             logging.info(f'Generating Solution for Form {form_number} using the {self.solver_run.irt_model.model} IRT model')
 
-            while current_drift <= Target.max_drift():
+            # respect max attempts
+            # this will likely be more built out when we add increment rate & drif limit
+            while current_attempt <= self.solver_run.max_attempts:
                 drift_percent = current_drift / 100
                 self.solver_run.objective_function.update_targets_drift(
                     drift_percent)
 
                 # create problem
-                problem_handler = Problem(items = self.solver_run.unbundled_items(), bundles = self.solver_run.bundles, problem = LpProblem('ata-form-generate', LpMinimize))
+                problem = LpProblem('ata-form-generate', self.solver_run.objective_function.objective)
+                problem_handler = Problem(items = self.solver_run.unbundled_items(), bundles = self.solver_run.bundles, problem = problem)
                 problem_handler.generate(solution, self.solver_run)
                 problem = problem_handler.solve(self.solver_run)
 
@@ -90,7 +95,7 @@ class FormGenerationService(Base):
                         f'attempt infeasible for drift of {current_drift}%')
 
                     if current_drift >= Target.max_drift(
-                    ):  # this is the last attempt, so lets finalize the solution
+                    ) or current_attempt >= self.solver_run.max_attempts:  # this is the last attempt, so lets finalize the solution
                         if ApplicationConfigs.local_dev_env:
                             service_helper.print_problem_variables(problem)
 
@@ -103,7 +108,8 @@ class FormGenerationService(Base):
 
                         break
 
-                    current_drift += Target.max_drift_increment()
+                    current_drift += drift_increment
+                    current_attempt += 1
                 else:
                     if ApplicationConfigs.local_dev_env:
                         service_helper.print_problem_variables(problem)