TransformIf

TransformIf

class TransformIf(if_estimator, param=None, condition='bool', condition_value=None, then_trafo=None, else_trafo=None)[source]

Conditional execution of a transformer given a condition from a fittable object.

Compositor to construct conditionally executed transformers, e.g.,

• compute first differences if a stationarity test is positive

• deseasonalize if a seasonality test is positive

This compositor allows to specify a condition, and an if/else transformer. The default “else” transformer is “no transformation”.

The specific algorithm implemented is as follows:

In fit, for inputs X, y: 1. fits if_estimator to X, y 2. checks the condition for if_estimator fitted parameter param:

whether param satisfies condition with condition_value

3. If yes, fits then_est to X, y, and behaves as then_est from then on

If no, fits else_est to X, y, and behaves as else_est from then on

In other methods, behaves as then_est or else_est, as above.

Note: then_trafo and else_trafo must have the same input/output signature, e.g., Series-to-Series, or Series-to-Primitives.

Parameters:

if_estimatorsktime estimator, must have fit

sktime estimator to fit and apply to series. this is a “blueprint” estimator, state does not change when fit is called

paramstr, optional, default = first boolean parameter of fitted if_estimator

conditionstr, optional, default = “bool”

condition that defines whether self behaves like then_est or else_est this estimator behaves like then_est iff: “bool” = if param is True “>”, “>=”, “==”, “<”, “<=”, “!=” = if param condition condition_value

condition_valuerequired for some conditions, see above; otherwise optional

then_trafosktime transformer, optional, default=``if_estimator``

transformer that this behaves as if condition is satisfied this is a “blueprint” transformer, state does not change when fit is called

else_trafosktime transformer, optional default=``Id`` (identity/no transform)

transformer that this behaves as if condition is not satisfied this is a “blueprint” transformer, state does not change when fit is called

Attributes:

transformer_transformer,

this clone is fitted when fit is called if condition is satisfied, a clone of then_est if condition is not satisfied, a clone of else_est

condition_bool,

True if condition was true, False if it was false

if_estimator_estimator

this clone of if_estimator is fitted when fit is called

Examples

>>> from sktime.param_est.seasonality import SeasonalityACF
>>> from sktime.transformations.compose import TransformIf
>>> from sktime.transformations.detrend import Deseasonalizer
>>> from sktime.datasets import load_airline
>>>
>>> y = load_airline()
>>>
>>> seasonal = SeasonalityACF(candidate_sp=12)
>>> deseason = Deseasonalizer(sp=12)
>>> cond_deseason = TransformIf(seasonal, "sp", "!=", 1, deseason)
>>> y_hat = cond_deseason.fit_transform(y)

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.