SummaryTransformer

class SummaryTransformer(summary_function=('mean', 'std', 'min', 'max'), quantiles=(0.1, 0.25, 0.5, 0.75, 0.9), flatten_transform_index=True)[source]

Calculate summary value of a time series.

For univariate time series a combination of summary functions and quantiles of the input series are calculated. If the input is a multivariate time series then the summary functions and quantiles are calculated separately for each column.

Parameters:

summary_functionstr, list, tuple, or None, default=(“mean”, “std”, “min”, “max”): If not None, a string, or list or tuple of strings indicating the pandas summary functions that are used to summarize each column of the dataset. Must be one of (“mean”, “min”, “max”, “median”, “sum”, “skew”, “kurt”, “var”, “std”, “mad”, “sem”, “nunique”, “count”). If None, no summaries are calculated, and quantiles must be non-None.
quantilesstr, list, tuple or None, default=(0.1, 0.25, 0.5, 0.75, 0.9): Optional list of series quantiles to calculate. If None, no quantiles are calculated, and summary_function must be non-None.
flatten_transform_indexbool, optional (default=True): if True, columns of return DataFrame are flat, by “variablename__feature” if False, columns are MultiIndex (variablename__feature) has no effect if return mtype is one without column names

Attributes:

is_fitted: Whether fit has been called.

Notes

This provides a wrapper around pandas DataFrame and Series agg and quantile methods.

Examples

>>> from sktime.transformations.summarize import SummaryTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = SummaryTransformer()
>>> y_mean = transformer.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.