nip.parameters.dataset.DatasetParameters

class nip.parameters.dataset.DatasetParameters(binarification_method: Literal['select_two', 'merge', 'random'] = 'merge', selected_classes: tuple[int, int] | None = None, binarification_seed: int | None = None, make_balanced: bool = True, max_train_size: int | None = None)

Additional parameters for the dataset.

Parameters:

• binarification_method (BinarificationMethodType) – The method to use to turn the multi-class classification task into a binary classification task.

• selected_classes (tuple[int, int], optional) – When selecting two classes from the original dataset, the indices of the classes to select. If not provided, the default for the dataset is used.

• binarification_seed (int, optional) – The seed used when doing a randomised binarification. If not provided, the default for the dataset is used.

• make_balanced (bool) – Whether to make sure the dataset is balanced.

• max_train_size (int, optional) – The size to reduce the training set to. If not provided, the dataset is not reduced, and the full training set is used.

Methods Summary

__eq__(other)	Return self==value.
__init__([binarification_method, ...])
__post_init__()
__repr__()	Return repr(self).
_get_param_class_from_dict(param_dict)	Try to get the parameter class from a dictionary of serialised parameters.
construct_test_params()	Construct a set of basic parameters for testing.
from_dict(params_dict[, ignore_extra_keys])	Create a parameters object from a dictionary.
get(address)	Get a value from the parameters object using a dot-separated address.
to_dict()	Convert the parameters object to a dictionary.

Attributes

binarification_method
binarification_seed
make_balanced
max_train_size
selected_classes

Methods

__eq__(other)

Return self==value.

__init__(binarification_method: Literal['select_two', 'merge', 'random'] = 'merge', selected_classes: tuple[int, int] | None = None, binarification_seed: int | None = None, make_balanced: bool = True, max_train_size: int | None = None) → None

__post_init__()

__repr__()

Return repr(self).

classmethod _get_param_class_from_dict(param_dict: dict) → type[ParameterValue] | None

Try to get the parameter class from a dictionary of serialised parameters.

Parameters:

param_dict (dict) – A dictionary of parameters, which may have come from a to_dict method. This dictionary may contain a _type key, which is used to determine the class of the parameter.

Returns:

param_class (type[ParameterValue] | None) – The class of the parameter, if it can be determined.

Raises:

ValueError – If the class specified in the dictionary is not a valid parameter class.

classmethod construct_test_params() → BaseHyperParameters

Construct a set of basic parameters for testing.

classmethod from_dict(params_dict: dict, ignore_extra_keys: bool = False) → BaseHyperParameters

Create a parameters object from a dictionary.

Parameters:

• params_dict (dict) – A dictionary of the parameters.

• ignore_extra_keys (bool, default=False) – If True, ignore keys in the dictionary that do not correspond to fields in the parameters object.

Returns:

hyper_params (BaseParameters) – The parameters object.

get(address: str) → Any

Get a value from the parameters object using a dot-separated address.

Parameters:

address (str) – The path to the value in the parameters object, separated by dots.

Returns:

value (Any) – The value at the address.

Raises:

KeyError – If the address does not exist.

to_dict() → dict

Convert the parameters object to a dictionary.

Turns enums into strings, and sub-parameters into dictionaries. Includes the is_random parameter if it exists.

Returns:

params_dict (dict) – A dictionary of the parameters.