Transformer

SplitterSummarizer

Create summary values of a time series’ splits.

Quickstart

python

from sktime.transformations.summarize import SplitterSummarizer

estimator = SplitterSummarizer(transformer, splitter=None, index='last', fit_on='transform_train', transform_on='transform_train')

Parameters(6)

transformersktime transformer inheriting from BaseTransformer: series-to-primitives transformer used to convert series to primitives.
splittersktime splitter inheriting from BaseSplitter, optional
(default=None): splitter used to divide the series. If None, it takes ExpandingWindowSplitter with start_with_window=False and otherwise default parameters.
indexstr, optional (default=”last”): Determines the indexing approach for the resulting series. If “last”, the latest index of the split is used. If anything else, the row’s number becomes the index.
fit_onstr, optional (default=”transform_train”): What data to fit transformer on, for the i-th row of the resulting series.
“all_train”: transform the i-th train split obtained from splitter.split_series, called on all data seen in fit and update calls, plus all data seen in transform.
“all_test”: transform the i-th test split obtained from splitter.split_series, called on all data seen in fit and update calls, plus all data seen in transform.
“transform_train”: transform the i-th train split obtained from splitter.split_series, called on the data seen in transform.
“transform_test”: transform the i-th test split obtained from splitter.split_series, called on the data seen in transform.
transform_onstr, optional (default=”transform_train”): What data to transform with transformer, for the i-th row of the resulting series. Values and meaning same as for fit_on.

Examples

>>> from sktime.transformations.summarize import SplitterSummarizer
>>> from sktime.transformations.summarize import SummaryTransformer
>>> from sktime.split import ExpandingWindowSplitter
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = SplitterSummarizer (
... transformer = SummaryTransformer (),
... splitter = ExpandingWindowSplitter ())
>>> y_splitsummarized = transformer. fit_transform (y)