Auto¶

class Auto(target_metric: etna.metrics.base.Metric, horizon: int, metric_aggregation: Literal['median', 'mean', 'std', 'percentile_5', 'percentile_25', 'percentile_75', 'percentile_95'] = 'mean', backtest_params: Optional[dict] = None, experiment_folder: Optional[str] = None, pool: Union[etna.auto.pool.generator.Pool, List[etna.pipeline.pipeline.Pipeline]] = Pool.default, runner: Optional[etna.auto.runner.base.AbstractRunner] = None, storage: Optional[optuna.storages._base.BaseStorage] = None, metrics: Optional[List[etna.metrics.base.Metric]] = None)[source]¶

Bases: object

Automatic pipeline selection via defined or custom pipeline pool.

Initialize Auto class.

Parameters

target_metric (etna.metrics.base.Metric) – metric to optimize
horizon (int) – horizon to forecast for
metric_aggregation (Literal['median', 'mean', 'std', 'percentile_5', 'percentile_25', 'percentile_75', 'percentile_95']) – aggregation method for per-segment metrics
backtest_params (Optional[dict]) – custom parameters for backtest instead of default backtest parameters
experiment_folder (Optional[str]) – folder to store experiment results and name for optuna study
pool (Union[etna.auto.pool.generator.Pool, List[etna.pipeline.pipeline.Pipeline]]) – pool of pipelines to choose from
runner (Optional[etna.auto.runner.base.AbstractRunner]) – runner to use for distributed training
storage (Optional[optuna.storages._base.BaseStorage]) – optuna storage to use
metrics (Optional[List[etna.metrics.base.Metric]]) – list of metrics to compute

Inherited-members

Methods

`fit`(ts[, timeout, n_trials, initializer, ...])	Start automatic pipeline selection.
`objective`(ts, target_metric, ...[, ...])	Optuna objective wrapper.
`summary`()	Get Auto trials summary.
`top_k`([k])	Get top k pipelines.

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, **optuna_kwargs) → etna.pipeline.pipeline.Pipeline[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
n_trials (Optional[int]) – number of trials for optuna. N.B. this is number of trials for each worker
initializer (Optional[etna.auto.auto._Initializer]) – is called before each pipeline backtest, can be used to initialize loggers
callback (Optional[etna.auto.auto._Callback]) – is called after each pipeline backtest, can be used to log extra metrics
optuna_kwargs – additional kwargs for optuna optuna.study.Study.optimize()

Return type

etna.pipeline.pipeline.Pipeline

static objective(ts: etna.datasets.tsdataset.TSDataset, target_metric: etna.metrics.base.Metric, metric_aggregation: Literal['median', 'mean', 'std', 'percentile_5', 'percentile_25', 'percentile_75', 'percentile_95'], metrics: List[etna.metrics.base.Metric], backtest_params: dict, initializer: Optional[etna.auto.auto._Initializer] = None, callback: Optional[etna.auto.auto._Callback] = None) → Callable[[optuna.trial._trial.Trial], float][source]¶

Optuna objective wrapper.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – tsdataset to fit on
target_metric (etna.metrics.base.Metric) – metric to optimize
metric_aggregation (Literal['median', 'mean', 'std', 'percentile_5', 'percentile_25', 'percentile_75', 'percentile_95']) – aggregation method for per-segment metrics
metrics (List[etna.metrics.base.Metric]) – list of metrics to compute
backtest_params (dict) – custom parameters for backtest instead of default backtest parameters
initializer (Optional[etna.auto.auto._Initializer]) – is called before each pipeline backtest, can be used to initialize loggers
callback (Optional[etna.auto.auto._Callback]) – is called after each pipeline backtest, can be used to log extra metrics

Return type

Callable[[optuna.trial._trial.Trial], float]

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

Get Auto trials summary.

Get top k pipelines.