PaddingTransformer

PaddingTransformer

class PaddingTransformer(pad_length=None, fill_value=0)[source]

Padding panel of unequal length time series to equal, fixed length.

Pads the input dataset to either a optional fixed length (longer than the longest series). Or finds the max length series across all series and dimensions and pads to that with zeroes.

Parameters:

pad_lengthint, optional (default=None) length to pad the series too.

if None, will find the longest sequence and use instead.

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> import pandas as pd
>>> from sktime.transformations.padder import PaddingTransformer
>>>
>>> # Create a sample nested DataFrame with unequal length time series
>>> data = {
...     'feature1': [
...         pd.Series([1, 2, 3]), pd.Series([4, 5]), pd.Series([6, 7, 8, 9])
...     ],
...     'feature2': [
...         pd.Series([10, 11]), pd.Series([12, 13, 14]), pd.Series([15])
...     ]
... }
>>> X = pd.DataFrame(data)
>>>
>>> # Initialize the PaddingTransformer
>>> padder = PaddingTransformer()
>>>
>>> # Fit the transformer to the data
>>> padder.fit(X)
PaddingTransformer()
>>>
>>> # Transform the data
>>> Xt = padder.transform(X)
>>>
>>> # Display the transformed data
>>> # print(Xt)

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.