explabox.ingestibles
Ingestibles are your model and data, which can be turned into digestibles that explore/examine/explain/expose your data and/or model.
- class explabox.ingestibles.Ingestible(data=None, model=None, splits={'test': 'test', 'train': 'train', 'validation': 'validation'})
Bases:
dict- Parameters:
data (Environment | None) –
model (AbstractClassifier | None) –
- check_requirements(elements=['data', 'model'])
Check if the required elements are in the ingestibles.
- Parameters:
elements (List[str], optional) – Elements to check. Defaults to [‘data’, ‘model’].
- Raises:
ValueError – The required element is not in the ingestibles.
- Returns:
True if all requirements are included.
- Return type:
bool
- property data
- get_named_split(name, validate=False)
Get split by name.
- Parameters:
name (KT) – Name of split.
validate (bool, optional) – Return None if no split is found or throw an error. Defaults to False.
- Raises:
ValueError – Unknown split
- Returns:
Provider of split if it exists, else None.
- Return type:
Optional[InstanceProvider]
- property labels
Labelprovider.
- property labelset
Label names.
- property model
Predictive model.
- property splits
Names of splits.
- property test
Test data split.
- property train
Train data split.
- property validation
Validation data split.
Subpackages: