def test_simulation_add_algorithm_valid(self):
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='/notNeeded')
simulation.add_algorithm('GreyWolfOptimizer')
simulation.add_algorithm('GeneticAlgorithm')
added_algorithms = simulation.algorithms()
self.assertEqual('GreyWolfOptimizer', added_algorithms[0])
self.assertEqual('GeneticAlgorithm', added_algorithms[1])
def test_simulation_add_algorithm_none(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='/notNeeded')
simulation.add_algorithm(None)
self.fail('None value should not be accepted or ignored')
except InvalidAlgorithmName:
pass
def test_simulation_should_not_start_with_no_save_options(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='../results')
simulation.add_algorithm('GreyWolfOptimizer')
simulation.run(sort_by_best=SortAttribute.fitness)
self.fail("Simulation started with no algorithms")
except InvalidSimulationInitialState:
pass
def test_simulation_add_save_opt_valid_config_no_args(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='/notNeeded')
simulation.add_save_option(
OutputOptionConfig(class_name='ConsoleOutputOption',
included_kwargs=[]))
except InvalidSaveOptionName:
self.fail('Valid save option kwargs was not accepted.')
def test_simulation_add_save_opt_none_name_string(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='/notNeeded')
simulation.add_save_option(
OutputOptionConfig(class_name=None,
included_kwargs=['dir_path', 'dataset']))
self.fail('None value save option should raise error')
except InvalidSaveOptionName:
pass
def test_simulation_add_save_opt_invalid_kwargs(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='/notNeeded')
simulation.add_save_option(
OutputOptionConfig(class_name='GraphOutputOption',
included_kwargs=[]))
self.fail('Invalid save option kwargs should raise error')
except InvalidSaveOptionName:
pass
def test_simulation_should_not_start_with_no_algorithms(self):
try:
dataset = Dataset('name', 30, 5, 5, 5, [])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='../results')
simulation.add_save_option(
OutputOptionConfig(class_name='ConsoleOutputOption',
included_kwargs=[]))
simulation.run(sort_by_best=SortAttribute.fitness)
self.fail("Simulation started with no algorithms")
except InvalidSimulationInitialState:
pass
def make(self, title: str, pack_c: int, stat_n: int,
cargo_dim: int) -> Dataset:
"""Generates new dataset. Throws ValueError if any.
Args:
title: Data set name, without file extension.
pack_c: Total number of packages.
stat_n: Total number of stations.
cargo_dim: Cargo stowage space width and height.
Returns: Generated dataset.
"""
self._validate(title, pack_c, stat_n, cargo_dim)
package_collection = []
in_index_start = 1
dist_in = [0 for _ in range(stat_n)]
dist_out = [0 for _ in range(stat_n)]
for i in range(1, pack_c + 1, 1):
if in_index_start == stat_n:
in_index_start = 1
station_in = in_index_start
out_next_station = random.randint(0, 100) > 40
station_out = station_in + 1 if out_next_station else random.randint(
station_in + 1, stat_n)
dist_in[station_in - 1] += 1
dist_out[station_out - 1] += 1
package_collection.append(
Package(id_num=i,
station_in=station_in,
station_out=station_out,
weight=random.randint(1, 99)))
in_index_start += 1
# Performs final check, so indexes won't go out of bounds for columns in cargo space.
self._final_check(package_collection, stat_n, cargo_dim * cargo_dim)
return Dataset(title=title,
total_packages=pack_c,
total_stations=stat_n,
width=cargo_dim,
height=cargo_dim,
packages=package_collection)
def test_simulation_should_not_start_with_invalid_dataset_invalid_package_count(
self):
try:
dataset = Dataset(
'name', 30, 5, 5, 5,
[Package(id_num=1, station_in=1, station_out=3, weight=30)])
simulation = Simulation(dataset=dataset,
n_fes=30,
np=5,
save_to_dir='../results')
simulation.add_algorithm('GreyWolfOptimizer')
simulation.add_save_option(
OutputOptionConfig(class_name='ConsoleOutputOption',
included_kwargs=[]))
simulation.run(sort_by_best=SortAttribute.fitness)
self.fail("Simulation started with no algorithms")
except InvalidSimulationInitialState:
pass