MiddleOutReconciler

MiddleOutReconciler

class MiddleOutReconciler(middle_level: int, middle_bottom_reconciler: BaseTransformer = None)[source]

Reconciliation using a middle-out approach.

This reconciliation strategy splits the hierarchy at a given level and applies a bottom-up strategy to the top part of the hierarchy and a topdown strategy to the bottom part of the hierarchy.

The parameter middle-level is determined by the level according to the hierarchy tree. For example, consider the following structure with four levels:

``` __total ├── B1 │ ├── C1 │ │ ├── D1 │ │ └── D2 │ └── C2 │ ├── D3 │ └── D4 └── B2

├── C3 │ ├── D5 │ └── D6

```

If middle_level is set to 0, then the hierarchy is split at the root node. If middle_level is set to 1, then the hierarchy is split at the first level below the root node, in this example, the nodes [B1, B2].

It is important to note that the height of this hierarchy tree don’t necessarily coincides with the number of levels in the pd.DataFrame index. For example, the following index has 3 levels, but the tree has 4 levels:

` __total, __total, __total B1, __total, __total B1, C1, __total ... B2, __total, __total B2, C3, __total ... `

Parameters:

middle_levelint

The level at which to split the hierarchy for reconciliation.

middle_bottom_reconcilerBaseTransformer

The transformer to use for each subtree below the middle-level totals.

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.transformations.hierarchical.reconcile import (
...     MiddleOutReconciler)
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.hierarchical.aggregate import Aggregator
>>> y = _make_hierarchical(hierarchy_levels=(2, 2, 4))
>>> pipe = MiddleOutReconciler(middle_level=1) * NaiveForecaster()
>>> pipe = pipe.fit(y)
>>> y_pred = pipe.predict(fh=[1,2,3])

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([parameter_set])	Get test params.
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.