tabensemb.model.AbstractModel.inspect_attr#

method

AbstractModel.inspect_attr(model_name: str, attributes: List[str], df=None, derived_data=None, to_numpy=True) → Dict[str, Any][source]#

Get attributes of the model after evaluating the model on training, validation, and testing sets respectively. If df is given, values after evaluating the given set are returned.

Parameters:

model_name: The name of the inspected model.
attributes: The requested attributes. If the model does not have the attribute, None is returned.
df: The tabular dataset.
derived_data:: Unstacked data derived from tabensemb.data.datamodule.DataModule.derive_unstacked(). If None, unstacked data will be re-derived.
to_numpy: If True, call numpy() if the attribute is a torch.Tensor.

Returns:

dict: A dict with keys train, val, and test if df is not given, and each value contains the attributes requested. If df is given, a dict with a single key USER_INPUT and the corresponding value contains the attributes. The prediction is also included with the key prediction.