CutoffSplitter#

class CutoffSplitter(cutoffs: Union[list, numpy.ndarray, pandas.core.indexes.base.Index], fh: Union[int, list, numpy.ndarray, pandas.core.indexes.base.Index, sktime.forecasting.base._fh.ForecastingHorizon] = 1, window_length: Union[int, float, pandas._libs.tslibs.timedeltas.Timedelta, datetime.timedelta, numpy.timedelta64, pandas._libs.tslibs.offsets.DateOffset] = 10)[source]#

Cutoff window splitter.

Split time series at given cutoff points into a fixed-length training and test set.

Here the user is expected to provide a set of cutoffs (train set endpoints), which using the notation provided in BaseSplitter, can be written as \((k_1,\ldots,k_n)\) for integer based indexing, or \((t(k_1),\ldots,t(k_n))\) for datetime based indexing.

For a cutoff \(k_i\) and a window_length \(w\) the training window is \((k_i-w+1,k_i-w+2,k_i-w+3,\ldots,k_i)\). Training window’s last point is equal to the cutoff.

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 test window will consist of the indices \((k_n+h_1,\ldots, k_n+h_H)\).

The number of splits returned by .get_n_splits is then trivially equal to \(n\).

The sorted array of cutoffs returned by .get_cutoffs is then equal to :math:(t(k_1),ldots,t(k_n))` with \(k_i<k_{i+1}\).

Parameters
cutoffslist or np.ndarray or pd.Index

Cutoff points, positive and integer- or datetime-index like. Type should match the type of fh input.

fhint, timedelta, list or np.ndarray of ints or timedeltas

Type should match the type of cutoffs input.

window_lengthint or timedelta or pd.DateOffset

Examples

>>> import numpy as np
>>> from sktime.forecasting.model_selection import CutoffSplitter
>>> ts = np.arange(10)
>>> splitter = CutoffSplitter(fh=[2, 4], cutoffs=np.array([3, 5]), window_length=3)
>>> list(splitter.split(ts)) 
[(array([1, 2, 3]), array([5, 7])), (array([3, 4, 5]), array([7, 9]))]

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 splitter.

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.

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

Return the number of splits.

For this splitter the number is trivially equal to the number of cutoffs given during instance initialization.

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

Time series to split

Returns
n_splitsint

The number of splits.

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

Return the cutoff points in .iloc[] context.

This method trivially returns the cutoffs given during instance initialization, in case these cutoffs are integer .iloc[] friendly indices. The only change is that the set of cutoffs is sorted from smallest to largest. When the given cutoffs are datetime-like, then this method returns corresponding integer indices.

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

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the splitter.

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

clone()[source]#

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)[source]#

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')[source]#

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')[source]#

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)[source]#

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()[source]#

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_fh() sktime.forecasting.base._fh.ForecastingHorizon[source]#

Return the forecasting horizon.

Returns
fhForecastingHorizon

The forecasting horizon

classmethod get_param_defaults()[source]#

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()[source]#

Get parameter names for the object.

Returns
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]#

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)[source]#

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()[source]#

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.

is_composite()[source]#

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()[source]#

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)[source]#

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)[source]#

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]][source]#

Get iloc references to train/test slits of y.

Parameters
ypd.Index or time series in sktime compatible time series format,

time series can be in any Series, Panel, or Hierarchical mtype format

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]][source]#

Get loc references to train/test splits of y.

Parameters
ypd.Index or time series in sktime compatible time series format,

time series can be in any Series, Panel, or Hierarchical mtype format

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]]][source]#

Split y into training and test windows.

Parameters
ytime series in sktime compatible time series format,

time series can be in any Series, Panel, or Hierarchical mtype format

e.g., pd.Series, pd.DataFrame, np.ndarray Time series to split, or index of time series to split

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