skbase.base.BaseMetaObject

class skbase.base.BaseMetaObject

Parameter and tag management for objects composed of named objects.

Allows objects to get and set nested parameters when a parameter of the the class has values that follow the named object specification. For example, in a pipeline class with the the “step” parameter accepting named objects, this would allow get_params and set_params to retrieve and update the parameters of the objects in each step.

Note: if inheriting from an abstract descendant of BaseObject, use BaseMetaObjectMixin and not BaseMetaObject.

See also

BaseMetaObjectMixin

Mixin for inheriting from abstract descendants of BaseObject. Same as BaseMetaObject, but does not inherit from BaseObject.

BaseMetaEstimator

Expands on BaseMetaObject by adding functionality for getting fitted parameters from a class’s component estimators.

Methods

clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	Clone tags from another estimator as dynamic override.
create_test_instance([parameter_set])	Construct Estimator instance if possible.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
get_class_tag(tag_name[, tag_value_default])	Get a class tag's value.
get_class_tags()	Get class tags from the class and all its parent classes.
get_config()	Get config flags for self.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from estimator class and dynamic tag overrides.
get_tags()	Get tags from estimator class and dynamic tag overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composite.
reset()	Reset the object to a clean post-init state.
set_config(**config_dict)	Set config flags to given values.
set_params(**kwargs)	Set the object's direct parameters and the parameters of components.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone()

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)

Clone tags from another estimator as dynamic override.

Parameters:

estimatorestimator inheriting from :class:BaseEstimator

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

classmethod get_class_tag(tag_name, tag_value_default=None)

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr

Name of tag value.

tag_value_defaultany

Default/fallback value if tag is not found.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()

Get class tags from the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict

Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()

Get config flags for self.

Returns:

config_dictdict

Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

classmethod get_param_defaults()

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]

Keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names(sort=True)

Get object’s parameter names.

Parameters:

sortbool, default=True

Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]

List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)

Get a dict of parameters values for this object.

This expands on get_params of standard BaseObject by also retrieving components parameters when deep=True a component’s follows the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

Parameters:

deepbool, default=True

Whether to return parameters of components.

• If True, will return a dict of parameter name : value for this object, including parameters of components.

• If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

dict[str, Any]

Dictionary of parameter name and value pairs. Includes direct parameters and indirect parameters whose values implement get_params or follow the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

• If deep=False the name-value pairs for this object’s direct parameters (you can see these via get_param_names) are returned.

• If deep=True then the parameter name-value pairs are returned for direct and component (indirect) parameters.

  • When a BaseObject’s direct parameter value implements get_params the component parameters are returned as [direct_param_name]__[component_param_name] for 1st level components. Arbitrary levels of component recursion are supported (if the component has parameter’s whose values are objects that implement get_params). In this case, return parameters follow [direct_param_name]__[component_param_name]__[param_name] format.

  • When a BaseObject’s direct parameter value is a sequence of (name, BaseObject) tuples or dict[str, BaseObject] the parameters name and value pairs of all component objects are returned. The parameter naming follows scikit-learn convention of treating named component objects like they are direct parameters; therefore, the names are assigned as [component_param_name]__[param_name].

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class and dynamic tag overrides.

Parameters:

tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError if raise_error is True i.e. if tag_name is not in

self.get_tags().keys()

get_tags()

Get tags from estimator class and dynamic tag overrides.

Returns:

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

classmethod get_test_params(parameter_set='default')

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

is_composite()

Check if the object is composite.

A composite object is an object which contains objects as parameter values.

Returns:

bool

Whether self contains a parameter whose value is a BaseObject, list of (str, BaseObject) tuples or dict[str, BaseObject].

reset()

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

• hyper-parameters = arguments of __init__

• object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:

self

Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

set_config(**config_dict)

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs.

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**kwargs)

Set the object’s direct parameters and the parameters of components.

Valid parameter keys can be listed with get_params().

Like BaseObject implementation it allows values of indirect parameters of a component to be set when a parameter’s value is an object that implements set_params. This also also expands the functionality to allow parameter to allow the indirect parameters of components to be set when a parameter’s values follow the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

Returns:

Self

Instance of self.

set_random_state(random_state=None, deep=True, self_policy='copy')

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

• “copy” : estimator.random_state is set to input random_state

• “keep” : estimator.random_state is kept as is

• “new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name: tag value pairs.

Returns:

Self

Reference to self.

Notes

Changes object state by setting tag values in tag_dict as dynamic tags in self.