PluginParamsTransformer

PluginParamsTransformer

class PluginParamsTransformer(param_est, transformer, params=None, update_params=False)[source]

Plugs parameters from a parameter estimator into a transformer.

In fit, first fits param_est to data passed:

• X of fit is passed as the first arg to param_est.fit

• y of fit is passed as the second arg to param_est.fit, if param_est.fit has a second arg

Then, does transformer.set_params with desired/selected parameters. Parameters of the fitted param_est are passed on to transformer, from/to pairs are as specified by the params parameter of self, see below.

Then, fits transformer to the data passed in fit.

After that, behaves identically to transformer with those parameters set.

Example: param_est seasonality test to determine sp parameter; transformer a transformer with an sp parameter, e.g., Deseasonalizer.

Parameters:

param_estsktime estimator object with a fit method, inheriting from BaseEstimator

e.g., estimator inheriting from BaseParamFitter or transformer this is a “blueprint” estimator, state does not change when fit is called

transformersktime transformer, i.e., estimator inheriting from BaseTransformer

this is a “blueprint” estimator, state does not change when fit is called

paramsNone, str, list of str, dict with str values/keys, optional, default=None

determines which parameters from param_est are plugged into trafo and where None: all parameters of param_est are plugged into transformer only parameters present in both transformer and param_est are plugged in list of str: parameters in the list are plugged into parameters of the same name only parameters present in both transformer and param_est are plugged in str: considered as a one-element list of str with the string as single element dict: parameter with name of value is plugged into parameter with name of key only keys present in param_est and values in transformer are plugged in

Attributes:

param_est_sktime parameter estimator, clone of estimator in param_est

this clone is fitted in the pipeline when fit is called

transformer_sktime transformer, clone of transformer

this clone is fitted in the pipeline when fit is called

param_map_dict

mapping of parameters from param_est_ to transformer_ used in fit, after filtering for parameters present in both

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.plugin import PluginParamsTransformer
>>> from sktime.param_est.seasonality import SeasonalityACF
>>> from sktime.transformations.detrend import Deseasonalizer
>>> from sktime.transformations.difference import Differencer
>>>
>>> X = load_airline()
>>>
>>> # sp_est is a seasonality estimator
>>> # ACF assumes stationarity so we concat with differencing first
>>> sp_est = Differencer() * SeasonalityACF()

>>> # trafo is a forecaster with a "sp" parameter which we want to tune
>>> trafo = Deseasonalizer()
>>> sp_auto = PluginParamsTransformer(sp_est, trafo)
>>>
>>> # fit sp_auto to data, transform, and inspect the tuned sp parameter
>>> sp_auto.fit(X)
PluginParamsTransformer(...)
>>> Xt = sp_auto.transform(X)
>>> sp_auto.transformer_.get_params()["sp"]
12
>>> # shorthand ways to specify sp_auto, via dunder, does the same
>>> sp_auto = sp_est * trafo
>>> # or entire pipeline in one go
>>> sp_auto = Differencer() * SeasonalityACF() * Deseasonalizer()

using dictionary to plug “foo” parameter into “sp”

>>> from sktime.param_est.fixed import FixedParams
>>> sp_plugin = PluginParamsTransformer(
...     FixedParams({"foo": 12}), Deseasonalizer(), params={"sp": "foo"}
... )

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.