ExpandingWindowSplitter

class ExpandingWindowSplitter(fh: Union[int, list, numpy.ndarray, pandas.core.indexes.base.Index, sktime.forecasting.base._fh.ForecastingHorizon] = 1, initial_window: Union[int, float, pandas._libs.tslibs.timedeltas.Timedelta, datetime.timedelta, numpy.timedelta64, pandas._libs.tslibs.offsets.DateOffset] = 10, step_length: Union[int, pandas._libs.tslibs.timedeltas.Timedelta, datetime.timedelta, numpy.timedelta64, pandas._libs.tslibs.offsets.DateOffset] = 1, start_with_window: bool = True)

Expanding window splitter.

Split time series repeatedly into an growing training set and a fixed-size test set.

Test window is defined by forecasting horizons relative to the end of the training window. It will contain as many indices as there are forecasting horizons provided to the fh argument. For a forecasating horizon \((h_1,\ldots,h_H)\), the training window will consist of the indices \((k_n+h_1,\ldots,k_n+h_H)\).

For example for initial_window = 5, step_length = 1 and fh = [1, 2, 3] here is a representation of the folds:

|-----------------------|
| * * * * * x x x - - - |
| * * * * * * x x x - - |
| * * * * * * * x x x - |
| * * * * * * * * x x x |

* = training fold.

x = test fold.

Parameters

fhint, list or np.array, optional (default=1)

Forecasting horizon

initial_windowint or timedelta or pd.DateOffset, optional (default=10)

Window length

step_lengthint or timedelta or pd.DateOffset, optional (default=1)

Step length between windows

start_with_windowbool, optional (default=True)

• If True, starts with full window.

• If False, starts with empty window.

Examples

>>> import numpy as np
>>> from sktime.forecasting.model_selection import ExpandingWindowSplitter
>>> ts = np.arange(10)
>>> splitter = ExpandingWindowSplitter(fh=[2, 4], initial_window=5, step_length=2)
>>> list(splitter.split(ts)) 
'[(array([0, 1, 2, 3, 4]), array([6, 8]))]'

Methods

clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
create_test_instance([parameter_set])	Construct Estimator instance if possible.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
get_class_tag(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_cutoffs([y])	Return the cutoff points in .iloc[] context.
get_fh()	Return the forecasting horizon.
get_n_splits([y])	Return the number of splits.
get_param_defaults()	Get parameter defaults for the object.
get_param_names()	Get parameter names for the object.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, tag_value_default, …])	Get tag value from estimator class and dynamic tag overrides.
get_tags()	Get tags from estimator class and dynamic tag overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composite.
reset()	Reset the object to a clean post-init state.
set_params(**params)	Set the parameters of this object.
set_tags(**tag_dict)	Set dynamic tags to given values.
split(y)	Get iloc references to train/test slits of y.
split_loc(y)	Get loc references to train/test splits of y.
split_series(y)	Split y into training and test windows.

clone()

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)

clone/mirror tags from another estimator as dynamic override.

Parameters

estimatorestimator inheriting from :class:BaseEstimator

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns

Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')

Construct Estimator instance if possible.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')

Create list of all test instances and a list of names for them.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

classmethod get_class_tag(tag_name, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters

tag_namestr

Name of tag value.

tag_value_defaultany type

Default/fallback value if tag is not found.

Returns

tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_cutoffs(y: Optional[Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.indexes.base.Index]] = None) → numpy.ndarray

Return the cutoff points in .iloc[] context.

Parameters

ypd.Series or pd.Index, optional (default=None)

Time series to split

Returns

cutoffs1D np.ndarray of int

iloc location indices, in reference to y, of cutoff indices

get_fh() → sktime.forecasting.base._fh.ForecastingHorizon

Return the forecasting horizon.

Returns

fhForecastingHorizon

The forecasting horizon

get_n_splits(y: Optional[Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.indexes.base.Index]] = None) → int

Return the number of splits.

Parameters

ypd.Series or pd.Index, optional (default=None)

Time series to split

Returns

n_splitsint

The number of splits.

classmethod get_param_defaults()

Get parameter defaults for the object.

Returns

default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()

Get parameter names for the object.

Returns

param_names: list of str, alphabetically sorted list of parameter names of cls

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class and dynamic tag overrides.

Parameters

tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises

ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(

).keys()

get_tags()

Get tags from estimator class and dynamic tag overrides.

Returns

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

classmethod get_test_params(parameter_set='default')

Return testing parameter settings for the estimator.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

paramsdict or list of dict, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

is_composite()

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns

composite: bool, whether self contains a parameter which is BaseObject

reset()

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

set_params(**params)

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

BaseObject parameters

Returns

selfreference to self (after parameters have been set)

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters

tag_dictdict

Dictionary of tag name : tag value pairs.

Returns

Self

Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.

split(y: Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.indexes.base.Index]) → Iterator[Tuple[numpy.ndarray, numpy.ndarray]]

Get iloc references to train/test slits of y.

Parameters

ypd.Index or time series in sktime compatible time series format (any)

Index of time series to split, or time series to split If time series, considered as index of equivalent pandas type container:

pd.DataFrame, pd.Series, pd-multiindex, or pd_multiindex_hier mtype

Yields

train1D np.ndarray of dtype int

Training window indices, iloc references to training indices in y

test1D np.ndarray of dtype int

Test window indices, iloc references to test indices in y

split_loc(y: Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.indexes.base.Index]) → Iterator[Tuple[pandas.core.indexes.base.Index, pandas.core.indexes.base.Index]]

Get loc references to train/test splits of y.

Parameters

ypd.Index or time series in sktime compatible time series format (any)

Time series to split, or index of time series to split

Yields

trainpd.Index

Training window indices, loc references to training indices in y

testpd.Index

Test window indices, loc references to test indices in y

split_series(y: Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.indexes.base.Index]) → Iterator[Union[Tuple[pandas.core.series.Series, pandas.core.series.Series], Tuple[pandas.core.series.Series, pandas.core.series.Series, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]]]

Split y into training and test windows.

Parameters

ypd.Series, pd.DataFrame, or np.ndarray (1D or 2D), optional (default=None)

Time series to split, must conform with one of the sktime type conventions.

Yields

traintime series of same sktime mtype as y

training series in the split

testtime series of same sktime mtype as y

test series in the split