PandasTransformAdaptor

PandasTransformAdaptor

class PandasTransformAdaptor(method, kwargs=None, apply_to='call')[source]

Adapt pandas transformations to sktime interface.

In transform, executes pd.DataFrame method of name method on data, optionally with keywords arguments passed, via kwargs hyper-parameter. The apply_to parameter controls what the data is upon which method is called: “call” = for X seen in transform, “all”/”all_subset” = all data seen so far. See below for details.

For hierarchical series, operation is applied by instance.

Parameters:

methodstr, optional, default = None = identity transform

name of the method of DataFrame that is applied in transform

kwargsdict, optional, default = empty dict (no kwargs passed to method)

arguments passed to DataFrame.method

apply_tostr, one of “call”, “all”, “all_subset”, optional, default = “call”

“call” = method is applied to X seen in transform only “all” = method is applied to all X seen in fit, update, transform

more precisely, the application to self._X is returned

“all_subset” = method is applied to all X like for “all” value,

but before returning, result is sub-set to indices of X in transform

in “all”, “all_subset”, X seen in transform do not update self._X

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.transformations.adapt import PandasTransformAdaptor
>>> from sktime.datasets import load_airline
>>> y = load_airline()

>>> transformer = PandasTransformAdaptor("diff")
>>> y_hat = transformer.fit_transform(y)

>>> transformer = PandasTransformAdaptor("diff", apply_to="all_subset")
>>> y_hat = transformer.fit(y.iloc[:12])
>>> y_hat = transformer.transform(y.iloc[12:])

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.