SlidingWindowSplitter#

class SlidingWindowSplitter(fh: int | list | ndarray | Index | ForecastingHorizon = 1, window_length: int | float | Timedelta | timedelta | timedelta64 | DateOffset = 10, step_length: int | Timedelta | timedelta | timedelta64 | DateOffset = 1, initial_window: int | float | Timedelta | timedelta | timedelta64 | DateOffset | None = None, start_with_window: bool = True)[source]#

Sliding window splitter.

Split time series repeatedly into a fixed-length training and 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 window_length = 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

Forecasting horizon

window_lengthint or timedelta or pd.DateOffset

Window length

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

Step length between windows

initial_windowint or timedelta or pd.DateOffset, optional (default=None)

Window length of first window

start_with_windowbool, optional (default=True)
  • If True, starts with full window.

  • If False, starts with empty window.

Examples

>>> import numpy as np
>>> from aeon.forecasting.model_selection import SlidingWindowSplitter
>>> ts = np.arange(10)
>>> splitter = SlidingWindowSplitter(fh=[2, 4], window_length=3, step_length=2)
>>> list(splitter.split(ts)) 
[(array([0, 1, 2]), array([4, 6])), (array([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_metadata_routing()

Get metadata routing of this object.

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.

get_tags()

Get tags from estimator class.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

is_composite()

Check if the object is composite.

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])

Save serialized self to bytes-like object or to (.zip) file.

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 splits 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()[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:
estimatorobject

Estimator 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.

See also

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True
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_cutoffs(y: Series | DataFrame | ndarray | Index | None = None) ndarray[source]#

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() ForecastingHorizon[source]#

Return the forecasting horizon.

Returns:
fhForecastingHorizon

The forecasting horizon

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_n_splits(y: Series | DataFrame | ndarray | Index | None = None) int[source]#

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()[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.

Uses dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, 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()

See also

get_tags

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True
get_tags()[source]#

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:
dict

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.

See also

get_tag

Get a single tag from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()
classmethod get_test_params(parameter_set='default')[source]#

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()[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.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters:
serialobject

Result of ZipFile(path).open(“object).

Returns:
deserialized self resulting in output at path, of cls.save(path)
classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:
serialobject

First element of output of cls.save(None).

Returns:
deserialized self resulting in output serial, of cls.save(None).
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

save(path=None)[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:
pathNone or file location (str or Path).

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:
if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.
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 setting tag values in tag_dict as dynamic tags in self.

split(y: Series | DataFrame | ndarray | Index) Iterator[Tuple[ndarray, ndarray]][source]#

Get iloc references to train/test splits of y.

Parameters:
ypd.Index or time series in aeon 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: Series | DataFrame | ndarray | Index) Iterator[Tuple[Index, Index]][source]#

Get loc references to train/test splits of y.

Parameters:
ypd.Index or time series in aeon 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: Series | DataFrame | ndarray | Index) Iterator[Tuple[Series, Series] | Tuple[Series, Series, DataFrame, DataFrame]][source]#

Split y into training and test windows.

Parameters:
ytime series in aeon 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 aeon type as y

training series in the split

testtime series of same aeon type as y

test series in the split