intervals_metrics

Functions

dummy()

Classes

Coverage([quantiles, mode])	Coverage metric for prediction intervals - precenteage of samples in the interval [lower quantile, upper quantile].
Width([quantiles, mode])	Mean width of prediction intervals.
_QuantileMetricMixin()

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

Coverage metric for prediction intervals - precenteage of samples in the interval [lower quantile, upper quantile].

\[Coverage(y\_true, y\_pred) = \frac{\sum_{i=0}^{n-1}{[ y\_true_i \ge y\_pred_i^{lower\_quantile}] * [y\_true_i \le y\_pred_i^{upper\_quantile}] }}{n}\]

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]) –

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

• self (etna.core.mixins.TMixin) –

• params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

property greater_is_better: None

Whether higher metric value is better.

property name: str

Name of the metric for representation.

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

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]) –

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

• self (etna.core.mixins.TMixin) –

• params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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()

Collect all information about etna object in dict.

property greater_is_better: bool

Whether higher metric value is better.

property name: str

Name of the metric for representation.