Aggregator

Aggregator

class Aggregator(flatten_single_levels=True, bypass_inverse_transform=True)[source]

Prepare hierarchical data, including aggregate levels, from bottom level.

This transformer adds aggregate levels via summation to a DataFrame with a multiindex. The aggregate levels are included with the special tag “__total” in the index. The aggregate nodes are discovered from top-to-bottom from the input data multiindex.

Parameters:

flatten_single_levelboolean (default=True)

Remove aggregate nodes, i.e. (“__total”), where there is only a single child to the level

bypass_inverse_transformboolean (default=True)

If True, the inverse_transform method is skipped. If False, the inverse_transform method is implemented and can be used to remove aggregate levels from the data.

Attributes:

is_fitted

Whether fit has been called.

See also

ReconcilerForecaster

Reconciler

References

[1]

https://otexts.com/fpp3/hierarchical.html

Examples

>>> from sktime.transformations.hierarchical.aggregate import Aggregator
>>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
>>> agg = Aggregator()
>>> y = _bottom_hier_datagen(
...     no_bottom_nodes=3,
...     no_levels=1,
...     random_seed=123,
... )
>>> y = agg.fit_transform(y)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
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 instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params()	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.