def train_aetcnn(x_train: List, x_test: List, y_train: np.array,
y_test: np.array) -> Dict:
sc = CustomMinMaxScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
model = AETCN()
n_experiments = 100
embeddings_dim = x_train[0].shape[1]
params = {
'epochs':
np.random.choice(np.arange(1, 10), size=n_experiments).tolist(),
'learning_rate':
np.random.choice(10**np.linspace(-4, -0.5),
size=n_experiments).tolist(),
'batch_size':
np.random.choice([2**i for i in range(3, 8)],
size=n_experiments).tolist(),
'input_shape': [embeddings_dim] * n_experiments,
'layers':
generate_layer_settings(embeddings_dim, n_experiments),
'kernel_size':
np.random.choice([2 * i + 1 for i in range(1, 6)],
size=n_experiments).tolist(),
'window':
np.random.randint(10, 100, size=n_experiments).tolist(),
'dropout':
np.random.uniform(0, 0.5, size=n_experiments).tolist()
}
evaluated_hyperparams = random_search(
(x_train[y_train == 0], x_test, None, y_test), model, params)
return evaluated_hyperparams
def train_sa_cnn1d(x_train: List, x_test: List, y_train: np.array,
y_test: np.array) -> Dict:
sc = CustomMinMaxScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
model = SACNN1D()
n_experiments = 100
embeddings_dim = x_train[0].shape[1]
encoder_kernel_sizes = np.random.choice([2 * i + 1 for i in range(1, 4)],
size=n_experiments).tolist()
layers = generate_layer_settings(embeddings_dim, n_experiments)
params = {
'epochs':
np.random.choice(np.arange(1, 10), size=n_experiments).tolist(),
'learning_rate':
np.random.choice(10**np.linspace(-4, -0.5),
size=n_experiments).tolist(),
'batch_size':
np.random.choice([2**i for i in range(3, 8)],
size=n_experiments).tolist(),
'input_shape': [embeddings_dim] * n_experiments,
'layers':
layers,
'encoder_kernel_size':
encoder_kernel_sizes,
'decoder_kernel_size':
np.random.choice([2 * i + 1 for i in range(2, 7)],
size=n_experiments).tolist(),
'encoder_heads':
get_encoder_heads(layers),
'decoder_heads':
get_decoder_heads(layers),
'window':
get_1d_window_size(encoder_kernel_sizes, layers, get_encoder_size),
'dropout':
np.random.uniform(0, 0.3, size=n_experiments).tolist()
}
evaluated_hyperparams = random_search(
(x_train[y_train == 0], x_test, None, y_test), model, params)
return evaluated_hyperparams