def test_load_pth():
""""Testing the model load functionality"""
model_name = "Test_CPU"
device = 'cpu'
path = "Results/Pths/Univariate/"
model = model_load(model_name=model_name, device=device, path=path)
assert isinstance(model, LSTM)
def test_evaluate():
data_X = rand(500, 132, 1)
data_y = rand(500, 1)
device = 'cpu'
model = model_load("Test_CPU", device, path="Results/Pths/Univariate/")
model.device = device
model.to(device)
learning = DeepLearning(model=model,
data_X=data_X,
data_y=data_y,
optimiser=Adam(model.parameters()))
learning.train_val_test()
learning.create_data_loaders()
assert learning.evaluate(learning.model, learning.test_loader) < 1e6
def test_mtl():
"""Check that an output is obtained for MTL model"""
data_X = rand(100, 132, 7)
data_y = rand(100, 5)
device = 'cpu'
model = model_load("Test_MTL_CPU", device, path="Results/Pths/MTL/")
model.device = device
model.to(device)
learning = DeepLearning(model=model,
data_X=data_X,
data_y=data_y,
optimiser=Adam(model.parameters()),
n_epochs=1,
debug=False)
learning.train_val_test()
learning.create_data_loaders()
learning.training_wrapper()
assert learning.best_val_score < np.inf
def test_validate():
"""Check that an output is obtained for single task mode;"""
data_X = rand(10, 132, 1)
data_y = rand(10, 1)
device = 'cpu'
model = model_load("Test_CPU", device, path="Results/Pths/Univariate/")
model.device = device
model.to(device)
learning = DeepLearning(model=model,
data_X=data_X,
data_y=data_y,
optimiser=Adam(model.parameters()),
n_epochs=1,
disp_freq=1e6,
fig_disp_freq=1e6)
learning.train_val_test()
learning.training_wrapper()
assert learning.best_val_score < np.inf
def test_train_val_test():
""""Checking that the deeplearning class splits
the data correctly"""
model_name = "Test_CPU"
device = 'cpu'
path = "Results/Pths/Univariate/"
model = model_load(model_name=model_name, device=device, path=path)
data_X = rand(100, 20, 5)
data_y = rand(100, 5)
learning = DeepLearning(model=model,
data_X=data_X,
data_y=data_y,
optimiser=Adam(model.parameters()))
learning.train_val_test()
assert list(learning.X_train.shape) == [60, 20, 5]
assert list(learning.X_val.size()) == [20, 20, 5]
assert list(learning.X_test.shape) == [20, 20, 5]