Width#

class Width(quantiles: Tuple[float, float] = (0.025, 0.975), mode: str = MetricAggregationMode.per_segment, **kwargs)[source]#

Bases: Metric, _QuantileMetricMixin

Mean width of prediction intervals.

\[Width(y\_true, y\_pred) = \frac{\sum_{i=0}^{n-1}\mid y\_pred_i^{upper\_quantile} - y\_pred_i^{lower\_quantile} \mid}{n}\]

Notes

Works just if quantiles presented in y_pred

Init metric.

Parameters:

mode ('macro' or 'per-segment') – metrics aggregation mode
kwargs – metric’s computation arguments
quantiles (Tuple[float, float]) –

Methods

`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`__call__`(y_true, y_pred)	Compute metric's value with y_true and y_pred.

Attributes

This class stores its __init__ parameters as attributes.

`greater_is_better`	Whether higher metric value is better.
`name`	Name of the metric for representation.

__call__(y_true: TSDataset, y_pred: TSDataset) → float | Dict[str, float][source]#

Compute metric’s value with y_true and y_pred.

Notes

Note that if y_true and y_pred are not sorted Metric will sort it anyway

Parameters:

y_true (TSDataset) – dataset with true time series values
y_pred (TSDataset) – dataset with predicted time series values

Return type:

metric’s value aggregated over segments or not (depends on mode)

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

property greater_is_better: bool[source]#: Whether higher metric value is better.

property name: str[source]#: Name of the metric for representation.