Parameters¶

class Parameters(fixed_params={'model': {}, 'training': {}}, variable_params={'model': {}, 'training': {}})[source]¶

Bases: delira.utils.config.LookupConfig

Class Containing all variable and fixed parameters for training and model instantiation

See also

trixi.util.Config

hierarchy¶

Returns the current hierarchy

Returns: current hierarchy
Return type: str

nested_get(key, *args, **kwargs)¶

Returns all occurances of key in self and subdicts

Parameters

key (str) – the key to search for
*args – positional arguments to provide default value
**kwargs – keyword arguments to provide default value

Raises

KeyError – Multiple Values are found for key (unclear which value should be returned) OR No Value was found for key and no default value was given

Returns

value corresponding to key (or default if value was not found)

Return type

Any

permute_hierarchy()[source]¶

switches hierarchy

Returns: the class with a permuted hierarchy
Return type: Parameters
Raises: AttributeError – if no valid hierarchy is found

permute_to_hierarchy(hierarchy: str)[source]¶

Permute hierarchy to match the specified hierarchy

Parameters: hierarchy (str) – target hierarchy
Raises: ValueError – Specified hierarchy is invalid
Returns: parameters with proper hierarchy
Return type: Parameters

permute_training_on_top()[source]¶

permutes hierarchy in a way that the training-model hierarchy is on top

Returns: Parameters with permuted hierarchy
Return type: Parameters

permute_variability_on_top()[source]¶

permutes hierarchy in a way that the training-model hierarchy is on top

Returns: Parameters with permuted hierarchy
Return type: Parameters

save(filepath: str)[source]¶

Saves class to given filepath (YAML + Pickle)

Parameters: filepath (str) – file to save data to

training_on_top¶

Return whether the training hierarchy is on top

Returns: whether training is on top
Return type: bool

variability_on_top¶

Return whether the variability is on top

Returns: whether variability is on top
Return type: bool