RelativeLoss

class RelativeLoss(multioutput='uniform_average', multilevel='uniform_average', relative_loss_function=<function mean_absolute_error>)

Calculate relative loss of forecast versus benchmark forecast.

Applies a forecasting performance metric to a set of forecasts and benchmark forecasts and reports ratio of the metric from the forecasts to the the metric from the benchmark forecasts. Relative loss output is non-negative floating point. The best value is 0.0.

If the score of the benchmark predictions for a given loss function is zero then a large value is returned.

This function allows the calculation of scale-free relative loss metrics. Unlike mean absolute scaled error (MASE) the function calculates the scale-free metric relative to a defined loss function on a benchmark method instead of the in-sample training data. Like MASE, metrics created using this function can be used to compare forecast methods on a single series and also to compare forecast accuracy between series.

This is useful when a scale-free comparison is beneficial but the training data used to generate some (or all) predictions is unknown such as when comparing the loss of 3rd party forecasts or surveys of professional forecasters.

Only metrics that do not require y_train are curretnly supported.

Parameters:

relative_loss_functionfunction

Function to use in calculation relative loss.

multioutput{‘raw_values’, ‘uniform_average’} or array-like of shape (n_outputs,), default=’uniform_average’

Defines how to aggregate metric for multivariate (multioutput) data. If array-like, values used as weights to average the errors. If ‘raw_values’, returns a full set of errors in case of multioutput input. If ‘uniform_average’, errors of all outputs are averaged with uniform weight.

References

Hyndman, R. J and Koehler, A. B. (2006). “Another look at measures of forecast accuracy”, International Journal of Forecasting, Volume 22, Issue 4.

Examples

>>> import numpy as np
>>> from aeon.performance_metrics.forecasting import RelativeLoss
>>> from aeon.performance_metrics.forecasting import mean_squared_error
>>> y_true = np.array([3, -0.5, 2, 7, 2])
>>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
>>> y_pred_benchmark = y_pred*1.1
>>> relative_mae = RelativeLoss()
>>> relative_mae(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.8148148148148147
>>> relative_mse = RelativeLoss(relative_loss_function=mean_squared_error)
>>> relative_mse(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.5178095088655261
>>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
>>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
>>> y_pred_benchmark = y_pred*1.1
>>> relative_mae = RelativeLoss()
>>> relative_mae(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.8490566037735847
>>> relative_mae = RelativeLoss(multioutput='raw_values')
>>> relative_mae(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
array([0.625     , 1.03448276])
>>> relative_mae = RelativeLoss(multioutput=[0.3, 0.7])
>>> relative_mae(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.927272727272727

Methods

__call__(y_true, y_pred, **kwargs)	Calculate metric value using underlying metric function.
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.
evaluate(y_true, y_pred, **kwargs)	Evaluate the desired metric on given inputs.
evaluate_by_index(y_true, y_pred, **kwargs)	Return the metric evaluated at each time point.
func(y_pred[, relative_loss_function, ...])	Relative loss of forecast versus benchmark forecast for a given metric.
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_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.

func(y_pred, relative_loss_function=<function mean_absolute_error>, horizon_weight=None, multioutput='uniform_average', **kwargs)

Relative loss of forecast versus benchmark forecast for a given metric.

Applies a forecasting performance metric to a set of forecasts and benchmark forecasts and reports ratio of the metric from the forecasts to the the metric from the benchmark forecasts. Relative loss output is non-negative floating point. The best value is 0.0.

If the score of the benchmark predictions for a given loss function is zero then a large value is returned.

This function allows the calculation of scale-free relative loss metrics. Unlike mean absolute scaled error (MASE) the function calculates the scale-free metric relative to a defined loss function on a benchmark method instead of the in-sample training data. Like MASE, metrics created using this function can be used to compare forecast methods on a single series and also to compare forecast accuracy between series.

This is useful when a scale-free comparison is beneficial but the training data used to generate some (or all) predictions is unknown such as when comparing the loss of 3rd party forecasts or surveys of professional forecasters.

Only metrics that do not require y_train are curretnly supported.

Parameters:

y_truepd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon

Ground truth (correct) target values.

y_predpd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon

Forecasted values.

y_pred_benchmarkpd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon, default=None

Forecasted values from benchmark method.

relative_loss_functionfunction, default=mean_absolute_error

Function to use in calculation relative loss. The function must comply with API interface of aeon forecasting performance metrics. Metrics requiring y_train or y_pred_benchmark are not supported.

horizon_weightarray-like of shape (fh,), default=None

Forecast horizon weights.

multioutput{‘raw_values’, ‘uniform_average’} or array-like of shape (n_outputs,), default=’uniform_average’

Defines how to aggregate metric for multivariate (multioutput) data. If array-like, values used as weights to average the errors. If ‘raw_values’, returns a full set of errors in case of multioutput input. If ‘uniform_average’, errors of all outputs are averaged with uniform weight.

Returns:

relative_lossfloat

Loss for a method relative to loss for a benchmark method for a given loss metric. If multioutput is ‘raw_values’, then relative loss is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average relative loss of all output errors is returned.

References

Hyndman, R. J and Koehler, A. B. (2006). “Another look at measures of forecast accuracy”, International Journal of Forecasting, Volume 22, Issue 4.

Examples

>>> import numpy as np
>>> from aeon.performance_metrics.forecasting import relative_loss
>>> from aeon.performance_metrics.forecasting import mean_squared_error
>>> y_true = np.array([3, -0.5, 2, 7, 2])
>>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
>>> y_pred_benchmark = y_pred*1.1
>>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.8148148148148147
>>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark,     relative_loss_function=mean_squared_error)
0.5178095088655261
>>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
>>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
>>> y_pred_benchmark = y_pred*1.1
>>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark)
0.8490566037735847
>>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark,     multioutput='raw_values')
array([0.625     , 1.03448276])
>>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark,     multioutput=[0.3, 0.7])
0.927272727272727

__call__(y_true, y_pred, **kwargs)

Calculate metric value using underlying metric function.

Parameters:

y_truetime series in aeon compatible data container format

Ground truth (correct) target values y can be in one of the following formats: Series type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame

Hierarchical type: pd.DataFrame with 3 or more level row MultiIndex

y_pred :time series in aeon compatible data container format

Forecasted values to evaluate must be of same format as y_true, same indices and columns if indexed

Returns:

lossfloat, np.ndarray, or pd.DataFrame

Calculated metric, averaged or by variable. float if self.multioutput=”uniform_average” or array-like

and self.multilevel=”uniform_average” or “uniform_average_time” value is metric averaged over variables and levels (see class docstring)

np.ndarray of shape (y_true.columns,) if self.multioutput=”raw_values”

and self.multilevel=”uniform_average” or “uniform_average_time” i-th entry is metric calculated for i-th variable

pd.DataFrame if self.multilevel=raw.values

of shape (n_levels, ) if self.multioutput = “uniform_average” or array of shape (n_levels, y_true.columns) if self.multioutput=”raw_values” metric is applied per level, row averaging (yes/no) as in multioutput

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:

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')

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.

evaluate(y_true, y_pred, **kwargs)

Evaluate the desired metric on given inputs.

Parameters:

y_truetime series in aeon compatible data container format

Ground truth (correct) target values y can be in one of the following formats: Series type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame

Hierarchical type: pd.DataFrame with 3 or more level row MultiIndex

y_pred :time series in aeon compatible data container format

Forecasted values to evaluate must be of same format as y_true, same indices and columns if indexed

Returns:

lossfloat, np.ndarray, or pd.DataFrame

Calculated metric, averaged or by variable. float if self.multioutput=”uniform_average” or array-like

and self.multilevel=”uniform_average” or “uniform_average_time” value is metric averaged over variables and levels (see class docstring)

np.ndarray of shape (y_true.columns,) if self.multioutput=”raw_values”

and self.multilevel=”uniform_average” or “uniform_average_time” i-th entry is metric calculated for i-th variable

pd.DataFrame if self.multilevel=raw.values

of shape (n_levels, ) if self.multioutput = “uniform_average” or array of shape (n_levels, y_true.columns) if self.multioutput=”raw_values” metric is applied per level, row averaging (yes/no) as in multioutput

evaluate_by_index(y_true, y_pred, **kwargs)

Return the metric evaluated at each time point.

Parameters:

y_truetime series in aeon compatible data container format

Ground truth (correct) target values y can be in one of the following formats: Series type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame

Hierarchical type: pd.DataFrame with 3 or more level row MultiIndex

y_pred :time series in aeon compatible data container format

Forecasted values to evaluate must be of same format as y_true, same indices and columns if indexed

Returns:

losspd.Series or pd.DataFrame

Calculated metric, by time point (default=jackknife pseudo-values). pd.Series if self.multioutput=”uniform_average” or array-like

index is equal to index of y_true entry at index i is metric at time i, averaged over variables

pd.DataFrame if self.multioutput=”raw_values”

index and columns equal to those of y_true i,j-th entry is metric at time i, at variable j

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.

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()

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.

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.

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()

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')

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.

classmethod load_from_path(serial)

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)

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()

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)

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)

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.