CondAEModel.predict_y

CondAEModel.predict_y(x: ndarray | torch.Tensor, return_untransformed: bool = False, accelerator: str = 'cpu', enable_progress_bar: bool = False, lightning_logger_level: int = 30, disable_user_warnings: bool = True, **kwargs) → ndarray | torch.Tensor | Tuple[ndarray, torch.Tensor][source]

Predict the output data given the input data.

Parameters:

  • x (Union[np.ndarray, torch.Tensor]) – The input data.

  • return_untransformed (bool, optional, default=False) – If True, the predictions are also returned in the original space, by applying the inverse transformation.

  • accelerator (str, optional, default=”cpu”) – Which accelerator should be used (e.g. cpu, gpu, mps, etc.).

  • enable_progress_bar (bool, optional, default=False) – If True, enable the progress bar.

  • lightning_logger_level (int, optional, default=logging.WARNING) – The logging level for PyTorch Lightning.

  • disable_user_warnings (bool, optional, default=True) – If True, disable user warnings.

  • **kwargs – Additional keyword arguments that can be passed to the predict method.

Returns:

Union[np.ndarray, torch.Tensor, Tuple[np.ndarray, torch.Tensor]] – The predicted output data. If return_untransformed is True, a tuple of the predicted output data in the transformed and original space is returned.