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test_solver_manager.py
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test_solver_manager.py
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import pytest
import optapy
import optapy.types
import optapy.score
import optapy.config
import optapy.constraint
def test_solve():
from threading import Lock
import time
from org.optaplanner.core.api.solver import SolverStatus
lock = Lock()
def get_lock(entity):
lock.acquire()
lock.release()
return False
@optapy.problem_fact
class Value:
def __init__(self, value):
self.value = value
@optapy.planning_id
def get_id(self):
return self.value
def __str__(self):
return f'Value({self.value})'
def __repr__(self):
return str(self)
@optapy.planning_entity
class Entity:
def __init__(self, code, value=None):
self.code = code
self.value = value
@optapy.planning_variable(Value, value_range_provider_refs=['value_range'])
def get_value(self):
return self.value
def set_value(self, value):
self.value = value
@optapy.planning_id
def get_id(self):
return self.code
def __str__(self):
return f'Entity(code={self.code}, value={self.value})'
def __repr__(self):
return str(self)
@optapy.constraint_provider
def my_constraints(constraint_factory: optapy.constraint.ConstraintFactory):
return [
constraint_factory.for_each(Entity)
.filter(get_lock)
.reward('Wait for lock', optapy.score.SimpleScore.ONE),
constraint_factory.for_each(Entity)
.reward('Maximize Value', optapy.score.SimpleScore.ONE, lambda entity: entity.value.value),
constraint_factory.for_each_unique_pair(Entity,
optapy.constraint.Joiners.equal(lambda entity: entity.value.value))
.penalize('Same Value', optapy.score.SimpleScore.of(12)),
]
@optapy.planning_solution
class Solution:
def __init__(self, entity_list, value_range, score=None):
self.entity_list = entity_list
self.value_range = value_range
self.score = score
@optapy.planning_entity_collection_property(Entity)
def get_entity_list(self):
return self.entity_list
@optapy.deep_planning_clone
@optapy.problem_fact_collection_property(Value)
@optapy.value_range_provider(range_id='value_range')
def get_value_range(self):
return self.value_range
def set_value_range(self, value_range):
self.value_range = value_range
@optapy.planning_score(optapy.score.SimpleScore)
def get_score(self) -> optapy.score.SimpleScore:
return self.score
def set_score(self, score):
self.score = score
def __str__(self):
return f'Solution(entity_list={self.entity_list[0]}, value_list={self.value_range[0]}, score={self.score})'
@optapy.problem_change
class UseOnlyEntityAndValueProblemChange:
def __init__(self, entity, value):
self.entity = entity
self.value = value
def doChange(self, solution: Solution, problem_change_director: optapy.types.ProblemChangeDirector):
problem_facts_to_remove = solution.value_range.copy()
entities_to_remove = solution.entity_list.copy()
for problem_fact in problem_facts_to_remove:
problem_change_director.removeProblemFact(problem_fact,
lambda value: solution.value_range.remove(problem_fact))
for removed_entity in entities_to_remove:
problem_change_director.removeEntity(removed_entity,
lambda entity: solution.entity_list.remove(removed_entity))
problem_change_director.addEntity(self.entity, lambda entity: solution.entity_list.append(entity))
problem_change_director.addProblemFact(self.value, lambda value: solution.value_range.append(value))
solver_config = optapy.config.solver.SolverConfig()
termination_config = optapy.config.solver.termination.TerminationConfig()
termination_config.setBestScoreLimit('6')
solver_config.withSolutionClass(Solution) \
.withEntityClasses(Entity) \
.withConstraintProviderClass(my_constraints) \
.withTerminationConfig(termination_config)
problem: Solution = Solution([Entity('A'), Entity('B'), Entity('C')], [Value(1), Value(2), Value(3)],
optapy.score.SimpleScore.ONE)
def assert_solver_run(solver_manager, solver_job):
assert solver_manager.getSolverStatus(1) != SolverStatus.NOT_SOLVING
lock.release()
solution = solver_job.getFinalBestSolution()
assert solution.get_score().getScore() == 6
value_list = [entity.value.value for entity in solution.entity_list]
assert 1 in value_list
assert 2 in value_list
assert 3 in value_list
assert solver_manager.getSolverStatus(1) == SolverStatus.NOT_SOLVING
time.sleep(0.1) # Sleep so cleanup is guaranteed to be executed
solver_run_dicts = solver_manager._optapy_debug_get_solver_runs_dicts()
assert len(solver_run_dicts['solver_run_id_to_refs']) == 0
def assert_problem_change_solver_run(solver_manager, solver_job):
assert solver_manager.getSolverStatus(1) != SolverStatus.NOT_SOLVING
solver_manager.addProblemChange(1, UseOnlyEntityAndValueProblemChange(Entity('D'), Value(6)))
lock.release()
solution = solver_job.getFinalBestSolution()
assert solution.get_score().getScore() == 6
assert len(solution.entity_list) == 1
assert len(solution.value_range) == 1
assert solution.entity_list[0].code == 'D'
assert solution.entity_list[0].value.value == 6
assert solution.value_range[0].value == 6
assert solver_manager.getSolverStatus(1) == SolverStatus.NOT_SOLVING
time.sleep(0.1) # Sleep so cleanup is guaranteed to be executed
solver_run_dicts = solver_manager._optapy_debug_get_solver_runs_dicts()
assert len(solver_run_dicts['solver_run_id_to_refs']) == 0
with optapy.solver_manager_create(solver_config) as solver_manager:
lock.acquire()
solver_job = solver_manager.solve(1, problem)
assert_solver_run(solver_manager, solver_job)
lock.acquire()
solver_job = solver_manager.solve(1, problem)
assert_problem_change_solver_run(solver_manager, solver_job)
def get_problem(problem_id):
assert problem_id == 1
return problem
lock.acquire()
solver_job = solver_manager.solve(1, get_problem)
assert_solver_run(solver_manager, solver_job)
lock.acquire()
solver_job = solver_manager.solve(1, get_problem)
assert_problem_change_solver_run(solver_manager, solver_job)
solution_list = []
def on_best_solution_changed(solution):
solution_list.append(solution)
lock.acquire()
solver_job = solver_manager.solve(1, get_problem, on_best_solution_changed)
assert_solver_run(solver_manager, solver_job)
assert len(solution_list) == 1
solution_list = []
lock.acquire()
solver_job = solver_manager.solveAndListen(1, get_problem, on_best_solution_changed)
assert_problem_change_solver_run(solver_manager, solver_job)
assert len(solution_list) == 1
solution_list = []
lock.acquire()
solver_job = solver_manager.solveAndListen(1, get_problem, on_best_solution_changed, on_best_solution_changed)
assert_solver_run(solver_manager, solver_job)
assert len(solution_list) == 2
solution_list = []
lock.acquire()
solver_job = solver_manager.solveAndListen(1, get_problem, on_best_solution_changed, on_best_solution_changed)
assert_problem_change_solver_run(solver_manager, solver_job)
assert len(solution_list) == 2
time.sleep(1) # ensure the thread factory close
@pytest.mark.filterwarnings("ignore:.*Exception in thread.*:pytest.PytestUnhandledThreadExceptionWarning")
def test_error():
@optapy.problem_fact
class Value:
def __init__(self, value):
self.value = value
@optapy.planning_id
def get_id(self):
return self.value
def __str__(self):
return f'Value({self.value})'
def __repr__(self):
return str(self)
@optapy.planning_entity
class Entity:
def __init__(self, code, value=None):
self.code = code
self.value = value
@optapy.planning_variable(Value, value_range_provider_refs=['value_range'])
def get_value(self):
return self.value
def set_value(self, value):
self.value = value
@optapy.planning_id
def get_id(self):
return self.code
def __str__(self):
return f'Entity(code={self.code}, value={self.value})'
def __repr__(self):
return str(self)
@optapy.constraint_provider
def my_constraints(constraint_factory: optapy.constraint.ConstraintFactory):
return [
constraint_factory.for_each(Entity)
.filter(lambda e: e.missing_attribute == 1)
.reward('Maximize Value', optapy.score.SimpleScore.ONE, lambda entity: entity.value.value)
]
@optapy.planning_solution
class Solution:
def __init__(self, entity_list, value_range, score=None):
self.entity_list = entity_list
self.value_range = value_range
self.score = score
@optapy.planning_entity_collection_property(Entity)
def get_entity_list(self):
return self.entity_list
@optapy.deep_planning_clone
@optapy.problem_fact_collection_property(Value)
@optapy.value_range_provider(range_id='value_range')
def get_value_range(self):
return self.value_range
def set_value_range(self, value_range):
self.value_range = value_range
@optapy.planning_score(optapy.score.SimpleScore)
def get_score(self) -> optapy.score.SimpleScore:
return self.score
def set_score(self, score):
self.score = score
def __str__(self):
return f'Solution(entity_list={self.entity_list[0]}, value_list={self.value_range[0]}, score={self.score})'
solver_config = optapy.config.solver.SolverConfig()
termination_config = optapy.config.solver.termination.TerminationConfig()
termination_config.setBestScoreLimit('6')
solver_config.withSolutionClass(Solution) \
.withEntityClasses(Entity) \
.withConstraintProviderClass(my_constraints) \
.withTerminationConfig(termination_config)
problem: Solution = Solution([Entity('A'), Entity('B'), Entity('C')], [Value(1), Value(2), Value(3)],
optapy.score.SimpleScore.ONE)
with optapy.solver_manager_create(solver_config) as solver_manager:
import time
the_problem_id = None
the_exception = None
def my_exception_handler(problem_id, exception):
nonlocal the_problem_id
nonlocal the_exception
the_problem_id = problem_id
the_exception = exception
solver_manager.solve(1, problem, exception_handler=my_exception_handler)
time.sleep(0.1) # Sleep so solve is executed
assert the_problem_id == 1
assert the_exception is not None
the_problem_id = None
the_exception = None
solver_manager.solveAndListen(1, problem, best_solution_consumer=lambda solution: None,
exception_handler=my_exception_handler)
time.sleep(0.1) # Sleep so solve is executed
assert the_problem_id == 1
assert the_exception is not None