Explain Model Output
- flood_forecast.explain_model_output.handle_dl_output(dl, dl_class: str, datetime_start: datetime, device: str) Tuple[Tensor, int] [source]
- Parameters:
dl (Union[CSVTestLoader, TemporalTestLoader]) – The test data-loader. Should be passed directly
dl_class (str) – A string that is the name of DL passef from the params file
datetime_start (datetime) – The start datetime for the forecast
device (str) – Typical device should be either cpu or cuda
- Returns:
Returns a tuple containing either a..
- Return type:
Tuple[torch.Tensor, int]
- flood_forecast.explain_model_output.deep_explain_model_summary_plot(model, csv_test_loader: CSVTestLoader, datetime_start: datetime | None = None) None [source]
Generate feature summary plot for trained deep learning models Args:
model (object): trained model csv_test_loader (CSVTestLoader): test data loader datetime_start (datetime, optional): start date of the test prediction,
Defaults to None, i.e. using model inference parameters.
- flood_forecast.explain_model_output.deep_explain_model_heatmap(model, csv_test_loader: CSVTestLoader, datetime_start: datetime | None = None) None [source]
Generate feature heatmap for prediction at a start time Args:
model ([type]): trained model csv_test_loader ([CSVTestLoader]): test data loader datetime_start (Optional[datetime], optional): start date of the test prediction,
Defaults to None, i.e. using model inference parameters.
- Returns:
None