IxToX

IxToX

class IxToX(coerce_to_type='auto', level=None, ix_source='X')[source]

Create features based on time index or hierarchy values.

Returns index of X in transform as transformed features. By default, time features only. Can also be used to select hierarchy levels in case of hierarchical input, via the levels argument.

Return columns of transform applied to pandas based containers have same name as level if levels have name in transform input, otherwise index (time) and level_{N} where N is the level index integer.

To add instead of replace, use FeatureUnion and/or the + dunder.

Under the default setting of coerce_to_type="auto":

• date-like indices incl periods are coerced to float (via int64) this typically results in units of periods since start of 1970 (first = 0)

• object, string, and category indices are coerced to integer (unique category ID) mapping onto integers is per category levels, after pandas category coercion

For more custom options or a direct pandas interface, an alternative is PandasTransformAdaptor with method="reset_index".

Parameters:

coerce_to_typestr, or dict, optional, default=”auto”

how to coerce the index columns to when passed to X default=”auto” coerces: date-like indices to float (via int64) object, string, and category indices to integer values other than “auto” are passed to DataFrame.astype in transform

levelNone (default), int, str, or iterable of pandas index level name elements

if passed, selects the hierarchy levels that will be turned into columns in X; if passed, passed on as level to reset_index internally

• if str or list/tuple of str, selects levels by name. Exceptions from this rule are below.

• if int or list/tuple of int, selects levels by index (0 is first)

• if None, will convert only the time index (last level) into features Note that this is different from the default of reset_index.

• if the str "__all_but_time", selects all levels except the time index (all but last level, level -1).

• of the str "__all", selects all levels

ix_sourcestr, “X” (default) or “y”, optional

which object to take the index from * “X” = X as passed to transform;

if used within ForecastingPipeline, this means X by default

• “y” = y as passed to transform, if passed (not None), otherwise X

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.transformations.compose import IxToX
>>>
>>> X = load_airline()
>>> t = IxToX()
>>> Xt = t.fit_transform(X)

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.