Multifeature Output

[jabowery]

Request: Allow multi-feature output. e.g. allow the final layer to output a list or dictionary with one feature per element.

Here is a (probably incorrect) example of a way to output a list in the case of a rolling regressor, but at least it works for a dynamical system, which is simply to predict features based on a time window of prior features.

It is probably better to use a dictionary template (ie: list of keys) corresponding to the names of the various features output.

class LstmModule(nn.Module):
    def __init__(self, n_features, hidden_size=1):
        super().__init__()
        self.n_features=n_features
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size=n_features, hidden_size=hidden_size, num_layers=1, bidirectional=False)
        self.fc = nn.Linear(in_features=hidden_size,out_features=n_features)

    def forward(self, X, **kwargs):
        output, (hn, cn) = self.lstm(X)  # lstm with input, hidden, and internal state
        return self.fc(output[-1, 0])

metric = metrics.RMSE()
model_pipeline = preprocessing.StandardScaler()
model_pipeline |= RollingRegressor(
    module=LstmModule,
    loss_fn=torch.nn.functional.mse_loss, #'mse',
    optimizer_fn='sgd',
    window_size=window_size,
    lr=lr, #1e-2,
    hidden_size=32,  # parameters of MyModule can be overwritten
    append_predict=True,
    device='cuda',
)