Baseline¶
- class pytorch_forecasting.models.baseline.Baseline(output_size: int = 7, loss: pytorch_forecasting.metrics.MultiHorizonMetric = QuantileLoss())[source]¶
Bases:
pytorch_forecasting.models.base_model.BaseModelBaseline model that uses last known target value to make prediction.
BaseModel for timeseries forecasting from which to inherit from
- Parameters
log_interval (Union[int, float], optional) – Batches after which predictions are logged. If < 1.0, will log multiple entries per batch. Defaults to -1.
log_val_interval (Union[int, float], optional) – batches after which predictions for validation are logged. Defaults to None/log_interval.
learning_rate (float, optional) – Learning rate. Defaults to 1e-3.
log_gradient_flow (bool) – If to log gradient flow, this takes time and should be only done to diagnose training failures. Defaults to False.
loss (Metric, optional) – metric to optimize, can also be list of metrics. Defaults to SMAPE().
logging_metrics (nn.ModuleList[MultiHorizonMetric]) – list of metrics that are logged during training. Defaults to [].
reduce_on_plateau_patience (int) – patience after which learning rate is reduced by a factor of 10. Defaults to 1000
reduce_on_plateau_min_lr (float) – minimum learning rate for reduce on plateua learning rate scheduler. Defaults to 1e-5
weight_decay (float) – weight decay. Defaults to 0.0.
optimizer_params (Dict[str, Any]) – additional parameters for the optimizer. Defaults to {}.
monotone_constaints (Dict[str, int]) – dictionary of monotonicity constraints for continuous decoder variables mapping position (e.g.
"0"for first position) to constraint (-1for negative and+1for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.output_transformer (Callable) – transformer that takes network output and transforms it to prediction space. Defaults to None which is equivalent to
lambda out: out["prediction"].optimizer (str) – Optimizer, “ranger”, “sgd”, “adam”, “adamw” or class name of optimizer in
torch.optim. Defaults to “ranger”.
Methods
forward(x)Network forward pass.