Metric

class Metric(metric_fn: Callable[[...], float], mode: str = MetricAggregationMode.per_segment, **kwargs)

Bases: AbstractMetric, BaseMixin

Base class for all the multi-segment metrics.

How it works: Metric computes metric_fn value for each segment in given forecast dataset and aggregates it according to mode.

Init Metric.

Parameters:

• metric_fn (Callable[[...], float]) – functional metric

• mode (str) –

  “macro” or “per-segment”, way to aggregate metric values over segments:

  • if “macro” computes average value

  • if “per-segment” – does not aggregate metrics

• kwargs – functional metric’s params

Raises:

NotImplementedError: – it non existent mode is used

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]

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

Returns:

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

Return type:

float | Dict[str, float]

set_params(**params: dict) → Self

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()

Collect all information about etna object in dict.

abstract property greater_is_better: bool | None

Whether higher metric value is better.

property name: str

Name of the metric for representation.