Permute

class Permute(estimator, permutation=None, steps_arg='steps')[source]

Permutation compositor for permuting forecasting pipeline steps.

The compositor can be used to permute the sequence of any meta-forecaster, including ForecastingPipeline, TransformedTargetForecaster.

The steps_arg parameter needs to be pointed to the “steps”-like parameter of the wrapped forecaster and permutation switches the sequence of steps.

Not very useful on its own, but useful in combination with tuning or auto-ML wrappers on top of this.

Parameters:

estimatorsktime forecaster, inheriting from BaseForecaster: must have parameter with name steps_arg estimator whose steps are being permuted
permutationlist of str, or None, optional, default = None: if not None, must be equal length as getattr(estimator, steps_arg) and elements must be equal to names of estimator.steps_arg estimators names are unique names as created by _get_estimator_tuples (if unnamed list), or first string element of tuples, of estimator.steps_arg list is interpreted as range of permutation of names if None, is interpreted as the identity permutation
steps_argstring, optional, default=”steps”: name of the steps parameter. getattr(estimator, steps_arg) must be list of estimators, or list of (str, estimator) pairs

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.
steps_

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.forecasting.compose import ForecastingPipeline, Permute
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.boxcox import BoxCoxTransformer
>>> from sktime.transformations.exponent import ExponentTransformer

Simple example: permute sequence of estimator in forecasting pipeline

>>> y = load_airline()
>>> fh = ForecastingHorizon([1, 2, 3])
>>> pipe = ForecastingPipeline(
...     [
...         ("boxcox", BoxCoxTransformer()),
...         ("exp", ExponentTransformer(3)),
...         ("naive", NaiveForecaster()),
...     ]
... )
>>> # this results in the pipeline with sequence "exp", "boxcox", "naive"
>>> permuted = Permute(pipe, ["exp", "boxcox", "naive"])
>>> permuted = permuted.fit(y, fh=fh)
>>> y_pred = permuted.predict()

The permuter is useful in combination with grid search (toy example):

>>> from sktime.datasets import load_shampoo_sales
>>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
>>> from sktime.split import ExpandingWindowSplitter
>>> fh = [1,2,3]
>>> cv = ExpandingWindowSplitter(fh=fh)
>>> forecaster = NaiveForecaster()
>>> # check which of the two sequences of transformers is better
>>> param_grid = {
...     "permutation" : [["boxcox", "exp", "naive"], ["exp", "boxcox", "naive"]]
... }
>>> gscv = ForecastingGridSearchCV(
...     forecaster=permuted,
...     param_grid=param_grid,
...     cv=cv)

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`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`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_pretrained_params`([deep])	Get pretrained parameters of this 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])	Return testing parameter settings for the estimator.
`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.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`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.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`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.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.