MeanAsymmetricError#
- class MeanAsymmetricError(multioutput='uniform_average', asymmetric_threshold=0, left_error_function='squared', right_error_function='absolute', left_error_penalty=1.0, right_error_penalty=1.0)[source]#
Calculate mean of asymmetric loss function.
Output is non-negative floating point. The best value is 0.0.
Error values that are less than the asymmetric threshold have left_error_function applied. Error values greater than or equal to asymmetric threshold have right_error_function applied.
Many forecasting loss functions (like those discussed in [1]) assume that over- and under- predictions should receive an equal penalty. However, this may not align with the actual cost faced by users’ of the forecasts. Asymmetric loss functions are useful when the cost of under- and over- prediction are not the same.
Setting asymmetric_threshold to zero, left_error_function to ‘squared’ and right_error_function to ‘absolute` results in a greater penalty applied to over-predictions (y_true - y_pred < 0). The opposite is true for left_error_function set to ‘absolute’ and right_error_function set to ‘squared`.
The left_error_penalty and right_error_penalty can be used to add differing multiplicative penalties to over-predictions and under-predictions.
- Parameters
- asymmetric_thresholdfloat, default = 0.0
The value used to threshold the asymmetric loss function. Error values that are less than the asymmetric threshold have left_error_function applied. Error values greater than or equal to asymmetric threshold have right_error_function applied.
- left_error_function{‘squared’, ‘absolute’}, default=’squared’
Loss penalty to apply to error values less than the asymmetric threshold.
- right_error_function{‘squared’, ‘absolute’}, default=’absolute’
Loss penalty to apply to error values greater than or equal to the asymmetric threshold.
- left_error_penaltyint or float, default=1.0
An additional multiplicative penalty to apply to error values less than the asymetric threshold.
- right_error_penaltyint or float, default=1.0
An additional multiplicative penalty to apply to error values greater than the asymmetric threshold.
- 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.
- Attributes
- namestr
The name of the loss metric
- greater_is_betterbool
Stores whether the metric is optimized by minimization or maximization. If False, minimizing the metric is optimal. If True, maximizing the metric is optimal.
- asymmetric_thresholdfloat
Stores threshold to use applying asymmetric loss to errors
- left_error_function: str
Stores loss penalty to apply to error values less than the asymmetric threshold.
- right_error_function: str
Stores loss penalty to apply to error values greater than or equal to the asymmetric threshold.
- multioutputstr
Stores how the metric should aggregate multioutput data.
See also
Notes
Setting left_error_function and right_error_function to “aboslute”, but choosing different values for left_error_penalty and right_error_penalty results in the “lin-lin” error function discussed in [2].
References
- 1
Hyndman, R. J and Koehler, A. B. (2006). “Another look at measures of forecast accuracy”, International Journal of Forecasting, Volume 22, Issue 4.
- 2
Diebold, Francis X. (2007). “Elements of Forecasting (4th ed.)”, Thomson, South-Western: Ohio, US.
Examples
>>> import numpy as np >>> from sktime.performance_metrics.forecasting import MeanAsymmetricError >>> y_true = np.array([3, -0.5, 2, 7, 2]) >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25]) >>> asymmetric_error = MeanAsymmetricError() >>> asymmetric_error(y_true, y_pred) 0.5 >>> asymmetric_error = MeanAsymmetricError(left_error_function='absolute', right_error_function='squared') >>> asymmetric_error(y_true, y_pred) 0.4625 >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]]) >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]]) >>> asymmetric_error = MeanAsymmetricError() >>> asymmetric_error(y_true, y_pred) 0.75 >>> asymmetric_error = MeanAsymmetricError(left_error_function='absolute', right_error_function='squared') >>> asymmetric_error(y_true, y_pred) 0.7083333333333334 >>> asymmetric_error = MeanAsymmetricError(multioutput='raw_values') >>> asymmetric_error(y_true, y_pred) array([0.5, 1. ]) >>> asymmetric_error = MeanAsymmetricError(multioutput=[0.3, 0.7]) >>> asymmetric_error(y_true, y_pred) 0.85
Methods
__call__
(y_true, y_pred, **kwargs)Calculate metric value using underlying metric function.
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 from estimator class and all its parent classes.
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.
Check if the object is composite.
reset
()Reset the object to a clean post-init state.
set_params
(**params)Set the parameters of this estimator.
set_tags
(**tag_dict)Set dynamic tags to given values.
- __call__(y_true, y_pred, **kwargs)[source]#
Calculate metric value using underlying metric function.
- 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_trainpd.Series, pd.DataFrame or np.array of shape (n_timepoints,) or (n_timepoints, n_outputs), default = None
Optional keyword argument to pass training data.
- Returns
- lossfloat
Calculated loss metric.
- 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_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.
- 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
- 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 estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.