SklearnRegressorPipeline

SklearnRegressorPipeline

class SklearnRegressorPipeline(regressor, transformers)[source]

Pipeline of transformers and a regressor.

The SklearnRegressorPipeline chains transformers and an single regressor.

Similar to RegressorPipeline, but uses a tabular sklearn regressor.

The pipeline is constructed with a list of sktime transformers, plus a regressor,

i.e., transformers following the BaseTransformer interface, regressor follows the scikit-learn regressor interface.

The transformer list can be unnamed - a simple list of transformers -

or string named - a list of pairs of string, estimator.

For a list of transformers trafo1, trafo2, …, trafoN and a regressor reg,

the pipeline behaves as follows:

fit(X, y) - changes styte by running trafo1.fit_transform on X,

them trafo2.fit_transform on the output of trafo1.fit_transform, etc sequentially, with trafo[i] receiving the output of trafo[i-1], and then running reg.fit with X the output of trafo[N] converted to numpy, and y identical with the input to self.fit. X is converted to numpyflat mtype if X is of Panel scitype; X is converted to numpy2D mtype if X is of Table scitype.

predict(X) - result is of executing trafo1.transform, trafo2.transform, etc

with trafo[i].transform input = output of trafo[i-1].transform, then running reg.predict on the numpy converted output of trafoN.transform, and returning the output of reg.predict. Output of trasfoN.transform is converted to numpy, as in fit.

get_params, set_params uses sklearn compatible nesting interface

if list is unnamed, names are generated as names of classes if names are non-unique, f”_{str(i)}” is appended to each name string

where i is the total count of occurrence of a non-unique string inside the list of names leading up to it (inclusive)

SklearnRegressorPipeline can also be created by using the magic multiplication

between sktime transformers and sklearn regressors,

and my_trafo1, my_trafo2 inherit from BaseTransformer, then, for instance, my_trafo1 * my_trafo2 * my_reg will result in the same object as obtained from the constructor SklearnRegressorPipeline(regressor=my_reg, transformers=[t1, t2])

magic multiplication can also be used with (str, transformer) pairs,

as long as one element in the chain is a transformer

Parameters:

regressorsklearn regressor, i.e., inheriting from sklearn RegressorMixin

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

transformerslist of sktime transformers, or

list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called

Attributes:

regressor_sklearn regressor, clone of regressor in regressor

this clone is fitted in the pipeline when fit is called

transformers_list of tuples (str, transformer) of sktime transformers

clones of transformers in transformers which are fitted in the pipeline is always in (str, transformer) format, even if transformers is just a list strings not passed in transformers are unique generated strings i-th transformer in transformers_ is clone of i-th in transformers

Examples

>>> from sklearn.neighbors import KNeighborsRegressor
>>> from sktime.datasets import load_unit_test
>>> from sktime.regression.compose import SklearnRegressorPipeline
>>> from sktime.transformations.exponent import ExponentTransformer
>>> from sktime.transformations.summarize import SummaryTransformer
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> t1 = ExponentTransformer()
>>> t2 = SummaryTransformer()
>>> pipeline = SklearnRegressorPipeline(KNeighborsRegressor(), [t1, t2])
>>> pipeline = pipeline.fit(X_train, y_train)
>>> y_pred = pipeline.predict(X_test)

Alternative construction via dunder method:

>>> pipeline = t1 * t2 * KNeighborsRegressor()

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 time series regressor to training data.
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 parameters of estimator in transformers.
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.
is_composite()	Check if the object is composite.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict(X)	Predicts labels for sequences in X.
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.
score(X, y[, multioutput])	Scores predicted labels against ground truth labels on X.
set_config(**config_dict)	Set config flags to given values.
set_params(**kwargs)	Set the parameters of estimator in transformers.
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.