FittedParamExtractor

class FittedParamExtractor(forecaster, param_names, n_jobs=None)[source]

Fitted parameter extractor.

Extract parameters of a fitted forecaster as features for a subsequent tabular learning task. This class first fits a forecaster to the given time series and then returns the fitted parameters. The fitted parameters can be used as features for a tabular estimator (e.g. classification).

Parameters:

forecasterestimator object: sktime estimator to extract features from
param_namesstr: Name of parameters to extract from the forecaster.
n_jobsint, optional (default=None): Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> import pandas as pd
>>> from sktime.forecasting.trend import TrendForecaster
>>> from sktime.transformations.summarize import FittedParamExtractor
>>> X = pd.DataFrame({
...     "series": [
...         pd.Series([1.0, 2.0, 3.0, 4.0]),
...         pd.Series([10.0, 8.0, 6.0, 4.0]),
...     ]
... })
>>> t = FittedParamExtractor(
...     forecaster=TrendForecaster(), param_names="regressor__intercept"
... )
>>> t.fit_transform(X)
   regressor__intercept
0                   1.0
1                  10.0

Multiple fitted parameters can be extracted at once, one column each:

>>> t = FittedParamExtractor(
...     forecaster=TrendForecaster(),
...     param_names=["regressor__intercept", "regressor__coef"],
... )
>>> t.fit_transform(X)
   regressor__intercept  regressor__coef
0                   1.0              1.0
1                  10.0             -2.0

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.