YtoX

YtoX

class YtoX(subset_index=False)[source]

Create exogenous features which are a copy of the endogenous data.

Replaces exogenous features (X) by endogeneous data (y).

To add instead of replace, use FeatureUnion.

Common use cases include:

• creating exogenous variables from transformed endogenous variables

• creating exogenous data from index, if no exogenous data is available

• manual construction of reduction strategies, in combination with YfromX

Parameters:

subset_indexboolean, optional, default=False

if True, subsets the output of transform to X.index, i.e., outputs y.loc[X.index]

Attributes:

is_fitted

Whether fit has been called.

Examples

Use case: creating exogenous data from index, if no exogenous data is available.

>>> from sktime.datasets import load_airline
>>> from sktime.transformations.compose import YtoX
>>> from sktime.transformations.fourier import FourierFeatures
>>> from sktime.forecasting.arima import ARIMA
>>> from sktime.forecasting.compose import ForecastingPipeline
>>>
>>> # data with no exogenous features
>>> y = load_airline()
>>>
>>> # create a pipeline with Fourier features and ARIMA
>>> pipe = ForecastingPipeline(
...     [
...             YtoX(),
...             FourierFeatures(sp_list=[24, 24 * 7], fourier_terms_list=[10, 5]),
...             ARIMA(order=(1, 1, 1))
...     ]
... )
>>>
>>> # fit and forecast, using Fourier features as exogenous data
>>> pred = pipe.fit_predict(y, fh=[1, 2, 3, 4, 5])

Use case: using lagged endogenous variables as exogenous data.

>>> from sktime.datasets import load_airline
>>> from sktime.transformations.compose import YtoX
>>> from sktime.transformations.lag import Lag
>>> from sktime.transformations.impute import Imputer
>>> from sktime.forecasting.sarimax import SARIMAX
>>>
>>> # data with no exogenous features
>>> y = load_airline()
>>>
>>> # create the pipeline
>>> lagged_y_trafo = YtoX() * Lag(1, index_out="original") * Imputer()
>>>
>>> # we need to specify index_out="original" as otherwise ARIMA gets 1 and 2 ahead
>>> # use lagged_y_trafo to generate X
>>> forecaster = lagged_y_trafo ** SARIMAX()
>>>
>>> # fit and forecast next value, with lagged y as exogenous data
>>> forecaster.fit(y, fh=[1])
>>> y_pred = forecaster.predict()

Use case: using summarized endogenous variables as exogenous data.

>>> from sktime.datasets import load_airline
>>> from sktime.transformations.summarize import WindowSummarizer
>>> from sktime.transformations.compose import YtoX
>>> from sktime.forecasting.compose import make_reduction
>>> from sktime.forecasting.compose import ForecastingPipeline
>>> from sklearn.ensemble import GradientBoostingRegressor
>>>
>>> # data with no exogenous features
>>> y = load_airline()
>>>
>>> # keyword arguments for WindowSummarizer
>>> kwargs = {
...     "lag_feature": {
...         "lag": [1],
...         "mean": [[1, 3], [3, 6]],
...         "std": [[1, 4]],
...     },
...     "truncate": 'bfill',
... }
>>>
>>> # create forecaster from sklearn regressor using make_reduction
>>> forecaster = make_reduction(
...     GradientBoostingRegressor(),
...     strategy="recursive",
...     pooling="global",
...     window_length=12,
... )
>>>
>>> # create the pipeline
>>> pipe = ForecastingPipeline(
...     steps=[
...         ("ytox", YtoX()),
...         ("summarizer", WindowSummarizer(**kwargs)),
...         ("forecaster", forecaster),
...     ]
... )
>>>
>>> # fit and forecast, with summarized y as exogenous data
>>> preds = pipe.fit_predict(y=y, fh=range(1, 20))

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])	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.