def test_shac_simple_custom_basepath():
total_budget = 50
batch_size = 5
objective = 'max'
params = get_hyperparameter_list()
h = hp.HyperParameterList(params)
shac = engine.KerasSHAC(h,
total_budget=total_budget,
max_gpu_evaluators=1,
num_batches=batch_size,
objective=objective,
save_dir='custom')
assert shac.total_classifiers == min(max(batch_size - 1, 1), 18)
assert shac._per_classifier_budget == 10
assert shac.num_workers == 10
assert len(shac.classifiers) == 0
assert len(shac.dataset) == 0
# do sequential work for debugging
shac.num_parallel_generators = 1
shac.num_parallel_evaluators = 1
# training
shac.fit(evaluation_simple_keras_tf)
assert len(shac.classifiers) <= shac.total_classifiers
assert os.path.exists('custom/datasets/dataset.csv')
assert os.path.exists('custom/classifiers/classifiers.pkl')
# Serialization
shac.save_data()
# Restore with different batchsize
shac2 = engine.KerasSHAC(None,
total_budget=total_budget,
max_gpu_evaluators=0,
num_batches=10,
objective=objective,
save_dir='custom')
shac2.restore_data()
# test no file found, yet no error
shutil.rmtree('custom/')
shac2.dataset = None
shac2.classifiers = None
shac2.restore_data()
def test_shac_simple_early_stop():
total_budget = 100
batch_size = 20
objective = 'max'
params = get_hyperparameter_list()
h = hp.HyperParameterList(params)
shac = engine.KerasSHAC(h,
total_budget=total_budget,
max_gpu_evaluators=0,
num_batches=batch_size,
objective=objective)
assert shac.total_classifiers == min(max(batch_size - 1, 1), 18)
assert shac._per_classifier_budget == 5
assert shac.num_workers == 5
assert len(shac.classifiers) == 0
assert len(shac.dataset) == 0
# do sequential work for debugging
shac.num_parallel_generators = 1
shac.num_parallel_evaluators = 1
# training (with failure)
shac.fit(evaluation_simple_keras_tf, early_stop=True, skip_cv_checks=True)
assert len(shac.classifiers) == 0
def test_shac_simple():
total_budget = 100
batch_size = 5
objective = 'max'
params = get_hyperparameter_list()
h = hp.HyperParameterList(params)
shac = engine.KerasSHAC(h, total_budget=total_budget, max_gpu_evaluators=1,
num_batches=batch_size, objective=objective)
assert shac.total_classifiers == min(max(batch_size - 1, 1), 18)
assert shac._per_classifier_budget == 20
assert shac.num_workers == 20
assert len(shac.classifiers) == 0
assert len(shac.dataset) == 0
# do sequential work for debugging
shac.num_parallel_generators = 1
shac.num_parallel_evaluators = 1
print("Evaluating before training")
np.random.seed(0)
random_samples = shac.predict(num_batches=16, num_workers_per_batch=1) # random sample predictions
random_eval = [evaluation_simple(0, sample) for sample in random_samples]
random_mean = np.mean(random_eval)
print()
# training
shac.fit(evaluation_simple_keras_tf)
assert len(shac.classifiers) <= shac.total_classifiers
assert os.path.exists('shac/datasets/dataset.csv')
assert os.path.exists('shac/classifiers/classifiers.pkl')
print()
print("Evaluating after training")
np.random.seed(0)
predictions = shac.predict(num_batches=16, num_workers_per_batch=1)
pred_evals = [evaluation_simple(0, pred) for pred in predictions]
pred_mean = np.mean(pred_evals)
print()
print("Random mean : ", random_mean)
print("Predicted mean : ", pred_mean)
assert random_mean < pred_mean
# Serialization
shac.save_data()
# Restore with different batchsize
shac2 = engine.KerasSHAC(None, total_budget=total_budget, max_gpu_evaluators=0,
num_batches=10, objective=objective)
shac2.restore_data()
np.random.seed(0)
predictions = shac.predict(num_batches=10, num_workers_per_batch=1)
pred_evals = [evaluation_simple(0, pred) for pred in predictions]
pred_mean = np.mean(pred_evals)
print()
print("Random mean : ", random_mean)
print("Predicted mean : ", pred_mean)
assert random_mean < pred_mean
# test no file found, yet no error
shutil.rmtree('shac/')
shac2.dataset = None
shac2.classifiers = None
shac2.restore_data()