AutoAbstract

class AutoAbstract[source]

Bases: abc.ABC

Interface for Auto object.

Inherited-members

Methods

fit(ts[, timeout, n_trials, initializer, ...])

Start automatic pipeline selection.

summary()

Get trials summary.

top_k([k])

Get top k pipelines with the best metric value.
abstract fit(ts: etna.datasets.tsdataset.TSDataset, timeout: Optional[int] = None, n_trials: Optional[int] = None, initializer: Optional[etna.auto.auto._Initializer] = None, callback: Optional[etna.auto.auto._Callback] = None, **kwargs) → etna.pipeline.base.BasePipeline[source]

Start automatic pipeline selection.

Parameters

  • ts (etna.datasets.tsdataset.TSDataset) – TSDataset to fit on.

  • timeout (Optional[int]) – Timeout for optuna. N.B. this is timeout for each worker. By default, isn’t set.

  • n_trials (Optional[int]) – Number of trials for optuna. N.B. this is number of trials for each worker. By default, isn’t set.

  • initializer (Optional[etna.auto.auto._Initializer]) – Object that is called before each pipeline backtest, can be used to initialize loggers.

  • callback (Optional[etna.auto.auto._Callback]) – Object that is called after each pipeline backtest, can be used to log extra metrics.

  • **kwargs – Additional parameters for the method.

Return type

etna.pipeline.base.BasePipeline

abstract summary() → pandas.core.frame.DataFrame[source]

Get trials summary.

Returns

dataframe with detailed info on each performed trial

Return type

study_dataframe

abstract top_k(k: int = 5) → List[etna.pipeline.base.BasePipeline][source]

Get top k pipelines with the best metric value.

Only complete and non-duplicate studies are taken into account.

Parameters

k (int) – Number of pipelines to return.

Returns

List of top k pipelines.

Return type

List[etna.pipeline.base.BasePipeline]