def train_nn(
dataset: str, batch_size: int, depth: int, epochs: int
) -> Tuple[CNN, Tuple[Union[np.ndarray, np.ndarray], Union[
np.ndarray, np.ndarray]], Tuple[Union[np.ndarray, np.ndarray], Union[
np.ndarray, np.ndarray]]]:
experiment = Experiment(project_name="cphap", auto_output_logging=False)
experiment.add_tag(dataset)
experiment.add_tag("NN-depth-{}".format(depth))
(x_train, y_train), (x_test, y_test) = fetch_dataset(dataset)
scaler = TimeSeriesScalerMeanVariance()
x_train: np.ndarray = scaler.fit_transform(x_train)
x_test: np.ndarray = scaler.transform(x_test)
x_train = x_train.transpose((0, 2, 1)).astype(np.float32)
x_test = x_test.transpose((0, 2, 1)).astype(np.float32)
n_features = x_train.shape[1]
n_targets = len(np.unique(y_train))
train_ds = get_dataset(x_train, y_train)
test_ds = get_dataset(x_test, y_test)
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)
model = CNN(n_features, 32, n_targets, depth=depth)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
runner = ClassificationRunner(model, optimizer, criterion, experiment)
runner.add_loader("train", train_loader)
runner.add_loader("test", test_loader)
runner.train_config(epochs=epochs)
runner.run()
runner.quite()
return runner.model.eval(), (x_train, x_test), (y_train, y_test)
def tsleanr_scaler(data):
result_list = data.values.tolist()
TSS = TimeSeriesScalerMeanVariance().fit(result_list)
scaled = TSS.transform(result_list)
return scaled