def test_bayesian_searcher(_, _1):
train_data, test_data = get_processed_data()
clean_dir(default_test_path)
generator = BayesianSearcher(3, (28, 28, 3), verbose=False, path=default_test_path)
Constant.N_NEIGHBOURS = 1
Constant.T_MIN = 0.8
for _ in range(2):
generator.search(train_data, test_data)
clean_dir(default_test_path)
assert len(generator.history) == 2
def test_max_acq(_, _1):
train_data, test_data = get_processed_data()
clean_dir(default_test_path)
Constant.N_NEIGHBOURS = 2
Constant.SEARCH_MAX_ITER = 0
Constant.T_MIN = 0.8
Constant.BETA = 1
generator = BayesianSearcher(3, (28, 28, 3), verbose=False, path=default_test_path)
for _ in range(3):
generator.search(train_data, test_data)
for index1, descriptor1 in enumerate(generator.descriptors):
for descriptor2 in generator.descriptors[index1 + 1:]:
assert edit_distance(descriptor1, descriptor2, 1) != 0
clean_dir(default_test_path)
def test_export_json(_, _1):
train_data, test_data = get_processed_data()
clean_dir(default_test_path)
generator = BayesianSearcher(3, (28, 28, 3), verbose=False, path=default_test_path)
Constant.N_NEIGHBOURS = 1
Constant.T_MIN = 0.8
for _ in range(3):
generator.search(train_data, test_data)
file_path = os.path.join(default_test_path, 'test.json')
generator.export_json(file_path)
import json
data = json.load(open(file_path, 'r'))
assert len(data['networks']) == 3
assert len(data['tree']['children']) == 2
clean_dir(default_test_path)
assert len(generator.history) == 3
def test_model_trainer():
model = DefaultClassifierGenerator(3,
(28, 28, 3)).generate().produce_model()
train_data, test_data = get_processed_data()
ModelTrainer(model, train_data, test_data, Accuracy,
False).train_model(max_iter_num=3)