CombineTransformers

class CombineTransformers(transformers, op)[source]

Combination operation applied to the outputs of multiple transformers.

Applies op to the value result of multiple transformers, obtaining op(output1, output2, ...), where outputi is the output of transformer i coerced to a numpy.ndarray.

If op is a numpy ufunc, the operation is applied elementwise.

This transformer applies a user-supplied combination operation (such as addition, subtraction, multiplication, division, or any custom function) to the outputs of two or more transformers. The operation is performed across all outputs, and the result is returned as a single DataFrame or Series.

All transformers must produce outputs with matching indexes and columns. If the indexes or columns do not match, a ValueError is raised. The operation must accept as many arguments as there are transformers, and should return a DataFrame or Series.

Parameters:

transformerslist of (str, transformer) tuples: List of transformers to apply to the input data. Each tuple contains a name and a transformer instance. All transformers must inherit from BaseTransformer.
opnumpy ufuncs or callable of same signature: Function to apply to the outputs of the transformers. Should accept N arrays/Series/DataFrames and return a DataFrame or Series. Examples include numpy ufuncs (e.g., np.add, np.divide) or custom functions.

Attributes:

is_fitted: Whether fit has been called.

Notes

All transformers must output DataFrames/Series with matching indexes and columns.
The operation must accept as many arguments as there are transformers.
Broadcasting is not supported; indexes and columns must match exactly.
This transformer is useful for combining features via arithmetic or custom logic.

References

Inspired by scikit-learn’s FunctionTransformer and FeatureUnion.

Examples

>>> import numpy as np
>>> from sktime.utils._testing.series import _make_series
>>> from sktime.transformations.exponent import ExponentTransformer
>>> from sktime.transformations.compose import CombineTransformers
>>> transformers = [
...     ("t1", ExponentTransformer(power=2)),
...     ("t2", ExponentTransformer(power=1)),
... ]
>>> op = CombineTransformers(transformers, op=np.divide)
>>> X = _make_series(n_timepoints=10, n_columns=2, random_state=42)
>>> Xt = op.fit_transform(X)
>>> # Xt contains the elementwise ratio of squared to original values

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 parameters of estimator.
`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])	Generate a set of test parameters for the transformer.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`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`(**kwargs)	Set the parameters of estimator.
`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.