ExpandingGreedySplitter

class ExpandingGreedySplitter(test_size: int, folds: int = 5, step_length: int = None)

Splitter that successively cuts test folds off the end of the series.

Takes an integer test_size that defines the number of steps included in the test set of each fold. The train set of each fold will contain all data before the test set. If the data contains multiple instances, test_size is _per instance_.

If no step_length is defined, the test sets (one for each fold) will be adjacent and disjoint, taken from the end of the dataset.

For example, with test_size=7 and folds=5, the test sets in total will cover the last 35 steps of the data with no overlap.

Parameters:

test_sizeint or float

If int: the number of steps included in the test set of each fold.

Formally, steps are consecutive iloc indices.

If float: the proportion of steps included in the test set of each fold,

as a proportion of the total number of consecutive iloc indices. Must be between 0.0 and 1.0. Proportions are rounded to the next higher integer count of samples (ceil). Cave: not the loc proportion between start and end locations, but a proportion of total number of consecutive iloc indices.

foldsint, default = 5

The number of folds.

step_lengthint, optional

The number of steps advanced for each fold. Defaults to test_size.

Examples

>>> import numpy as np
>>> from sktime.split import ExpandingGreedySplitter

>>> ts = np.arange(10)
>>> splitter = ExpandingGreedySplitter(test_size=3, folds=2)
>>> list(splitter.split(ts))  
[
    (array([0, 1, 2, 3]), array([4, 5, 6])),
    (array([0, 1, 2, 3, 4, 5, 6]), array([7, 8, 9]))
]

Methods

clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	Clone 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 a class tag's value.
get_class_tags()	Get class tags from the class and all its parent classes.
get_config()	Get config flags for self.
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 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 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 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 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()

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.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)

Clone 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__}

classmethod get_class_tag(tag_name, tag_value_default=None)

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr

Name of tag value.

tag_value_defaultany

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 the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict

Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()

Get config flags for self.

Returns:

config_dictdict

Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_cutoffs(y: Series | DataFrame | ndarray | Index | None = None) → 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() → ForecastingHorizon

Return the forecasting horizon.

Returns:

fhForecastingHorizon

The forecasting horizon

get_n_splits(y: Series | DataFrame | ndarray | Index | None = None) → int

Return the number of splits.

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

Returns:

n_splitsint

The number of splits.

classmethod get_param_defaults()

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]

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(sort=True)

Get object’s parameter names.

Parameters:

sortbool, default=True

Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]

List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

• If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).

• If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

• always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction

• if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

• if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

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_valueAny

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 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

is_composite()

Check if the object is composed of other BaseObjects.

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 an object has any parameters whose values are BaseObjects.

classmethod load_from_path(serial)

Load object from file location.

Parameters:

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

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

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

reset()

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

• hyper-parameters = arguments of __init__

• object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:

self

Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

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

save(path=None, serialization_format='pickle')

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/.

serialization_format: str, default = “pickle”

Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.

Returns:

if path is None - in-memory serialized self

if path is file location - ZipFile with reference to the file

set_config(**config_dict)

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

• “diagram” = html box diagram representation

• “text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

• “on” = will raise warnings from sktime

• “off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

• “None”: executes loop sequentally, simple list comprehension

• “loky”, “multiprocessing” and “threading”: uses joblib.Parallel

• “joblib”: custom and 3rd party joblib backends, e.g., spark

• “dask”: uses dask, requires dask package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel:

• “None”: no additional parameters, backend_params is ignored

• “loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

• “joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

• “dask”: any valid keys for dask.compute can be passed, e.g., scheduler

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)

Set the parameters of this object.

The method works on simple estimators as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict

BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

• “copy” : estimator.random_state is set to input random_state

• “keep” : estimator.random_state is kept as is

• “new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

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 setting tag values in tag_dict as dynamic tags in self.

split(y: Series | DataFrame | ndarray | Index) → Iterator[tuple[ndarray, ndarray]]

Get iloc 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 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]]

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 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:

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]]

Split y into training and test windows.

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:

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