def test_max_acq(_, _2):
train_data, test_data = get_classification_data_loaders()
clean_dir(TEST_TEMP_DIR)
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=TEST_TEMP_DIR, metric=Accuracy,
loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
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) != 0.0
clean_dir(TEST_TEMP_DIR)
def test_max_acq(_, _2):
train_data, test_data = get_classification_data_loaders()
clean_dir(TEST_TEMP_DIR)
Constant.N_NEIGHBOURS = 2
Constant.SEARCH_MAX_ITER = 0
Constant.T_MIN = 0.8
Constant.BETA = 1
generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
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) != 0.0
clean_dir(TEST_TEMP_DIR)
def same_graph(des1, des2):
return edit_distance(des1, des2, 1) == 0
def contain(descriptors, target_descriptor):
for descriptor in descriptors:
if edit_distance(descriptor, target_descriptor, 1) < 1e-5:
return True
return False
def same_graph(des1, des2):
return edit_distance(des1, des2, 1) == 0
@patch('torch.multiprocessing.get_context', side_effect=MockProcess)
@patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
def test_max_acq(_, _2):
train_data, test_data = get_classification_data_loaders()
clean_dir(TEST_TEMP_DIR)
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=TEST_TEMP_DIR, metric=Accuracy,
loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
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) != 0.0
clean_dir(TEST_TEMP_DIR)
@patch('torch.multiprocessing.get_context', side_effect=MockProcess)
@patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_out_of_memory_train)
def test_out_of_memory(_, _2):
train_data, test_data = get_classification_data_loaders()
clean_dir(TEST_TEMP_DIR)
Constant.N_NEIGHBOURS = 2
Constant.SEARCH_MAX_ITER = 0
Constant.T_MIN = 0.8
Constant.BETA = 1
generator = BayesianSearcher(3, (28, 28, 3), verbose=True, path=TEST_TEMP_DIR, metric=Accuracy,
loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
