Parameters¶

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

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