def test_score_with_sample_count_and_weights(self):
name = "score"
turns = 10
noise = 0
repetitions = 5
num_states = 2
opponents = [s() for s in axl.demo_strategies]
size = 10
objective = dojo.prepare_objective(name=name,
turns=turns,
noise=noise,
repetitions=repetitions)
population = dojo.Population(
params_class=dojo.FSMParams,
params_kwargs={"num_states": num_states},
size=size,
objective=objective,
output_filename=self.temporary_file.name,
opponents=opponents,
sample_count=2, # Randomly sample 2 opponents at each step
weights=[5, 1, 1, 1, 1],
bottleneck=2,
mutation_probability=.01,
processes=1)
generations = 4
axl.seed(0)
population.run(generations, print_output=False)
self.assertEqual(population.generation, 4)
# Manually read from tempo file to find best strategy
best_score, best_params = 0, None
with open(self.temporary_file.name, "r") as f:
reader = csv.reader(f)
for row in reader:
_, mean_score, sd_score, max_score, arg_max = row
if float(max_score) > best_score:
best_score = float(max_score)
best_params = arg_max
# Test the load params function
for num in range(1, 4 + 1):
best = dojo.load_params(params_class=dojo.FSMParams,
filename=self.temporary_file.name,
num=num)
self.assertEqual(len(best), num)
for parameters in best:
self.assertIsInstance(parameters, dojo.FSMParams)
self.assertEqual(best[0].__repr__(), best_params)
def test_score_with_sample_count_and_weights(self):
name = "score"
turns = 10
noise = 0
repetitions = 5
num_states = 2
opponents = [s() for s in axl.demo_strategies]
size = 10
objective = dojo.prepare_objective(name=name,
turns=turns,
noise=noise,
repetitions=repetitions)
population = dojo.Population(params_class=dojo.FSMParams,
params_kwargs={"num_states": num_states},
size=size,
objective=objective,
output_filename=self.temporary_file.name,
opponents=opponents,
sample_count=2, # Randomly sample 2 opponents at each step
weights=[5, 1, 1, 1, 1],
bottleneck=2,
mutation_probability = .01,
processes=1)
generations = 4
axl.seed(0)
population.run(generations, print_output=False)
self.assertEqual(population.generation, 4)
# Manually read from tempo file to find best strategy
best_score, best_params = 0, None
with open(self.temporary_file.name, "r") as f:
reader = csv.reader(f)
for row in reader:
_, mean_score, sd_score, max_score, arg_max = row
if float(max_score) > best_score:
best_score = float(max_score)
best_params = arg_max
# Test the load params function
for num in range(1, 4 + 1):
best = dojo.load_params(params_class=dojo.FSMParams,
filename=self.temporary_file.name,
num=num)
self.assertEqual(len(best), num)
for parameters in best:
self.assertIsInstance(parameters, dojo.FSMParams)
self.assertEqual(best[0].__repr__(), best_params)
def test_score(self):
name = "score"
turns = 10
noise = 0
repetitions = 5
num_states = 2
opponents = [s() for s in axl.demo_strategies]
size = 10
objective = dojo.prepare_objective(name=name,
turns=turns,
noise=noise,
repetitions=repetitions)
population = dojo.Population(player_class=player_class,
params_kwargs={"num_states": num_states},
size=size,
objective=objective,
output_filename=self.temporary_file.name,
opponents=opponents,
bottleneck=2,
mutation_probability = .01,
processes=1)
generations = 4
axl.seed(0)
population.run(generations, print_output=False)
self.assertEqual(population.generation, 4)
# Manually read from tempo file to find best strategy
best_score, best_params = 0, None
with open(self.temporary_file.name, "r") as f:
reader = csv.reader(f)
for row in reader:
_, mean_score, sd_score, max_score, arg_max = row
if float(max_score) > best_score:
best_score = float(max_score)
best_params = arg_max
# Test the load params function
for num in range(1, 4 + 1):
best = dojo.load_params(player_class=player_class,
filename=self.temporary_file.name,
num=num)
self.assertEqual(len(best), num)
# Test that can use these loaded params in a new algorithm instance
population = dojo.Population(player_class=player_class,
params_kwargs={"num_states": num_states},
size=size,
objective=objective,
output_filename=self.temporary_file.name,
opponents=opponents,
population=best,
bottleneck=2,
mutation_probability = .01,
processes=1)
generations = 4
axl.seed(0)
population.run(generations, print_output=False)
self.assertEqual(population.generation, 4)
