Forecasters

Module that handles all forecaster objects for training PyTorch models.

class forecasters.Forecaster(model, loss, optimizer, n_epochs=1, device='cpu', checkpoint_path='./', verbose=True)

Handles training of a PyTorch model and can be used to generate samples from approximate posterior predictive distribution.

Arguments:

• model (torch.nn.Module): Instance of Deep4cast models.
• loss (torch.distributions): Instance of PyTorch distribution.
• optimizer (torch.optim): Instance of PyTorch optimizer.
• n_epochs (int): Number of training epochs.
• device (str): Device used for training (cpu or cuda).
• checkpoint_path (str): File system path for writing model checkpoints.
• verbose (bool): Verbosity of forecaster.

embed(dataloader, n_samples=100) → <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e2719e630>

Generate embedding vectors.

Arguments:

• dataloader (torch.utils.data.DataLoader): Data to make embedding vectors.
• n_samples (int): Number of forecast samples.

fit(dataloader_train, dataloader_val=None, eval_model=False)

Fits a model to a given a dataset.

Arguments:

• dataloader_train (torch.utils.data.DataLoader): Training data.
• dataloader_val (torch.utils.data.DataLoader): Validation data.
• eval_model (bool): Flag to switch on model evaluation after every epoch.

predict(dataloader, n_samples=100) → <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e2719e9e8>

Generates predictions.

Arguments:

• dataloader (torch.utils.data.DataLoader): Data to make forecasts.
• n_samples (int): Number of forecast samples.