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.base.BasePipeline]] = 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: etna.auto.auto.AutoBase

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. By default, mean aggregation is used.

  • backtest_params (Optional[dict]) – Custom parameters for backtest instead of default backtest parameters.

  • experiment_folder (Optional[str]) – Name for saving experiment results, it determines the name for optuna study. By default, isn’t set.

  • pool (Union[etna.auto.pool.generator.Pool, List[etna.pipeline.base.BasePipeline]]) – Pool of pipelines to choose from. By default, default pool from Pool is used.

  • runner (Optional[etna.auto.runner.base.AbstractRunner]) – Runner to use for distributed training. By default, LocalRunner is used.

  • storage (Optional[optuna.storages._base.BaseStorage]) – Optuna storage to use. By default, sqlite storage is used.

  • metrics (Optional[List[etna.metrics.base.Metric]]) – List of metrics to compute. By default, Sign, SMAPE, MAE, MSE, MedAE metrics are used.

Inherited-members

Methods

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

Start automatic pipeline selection.

objective(ts, target_metric, ...[, ...])

Optuna objective wrapper for the pool stage.

summary()

Get Auto trials summary.

top_k([k])

Get top k pipelines with the best metric value.
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.

There are two stages:

  • Pool stage: trying every pipeline in a pool

  • Tuning stage: tuning tune_size best pipelines from a previous stage by using :py:class`~etna.auto.auto.Tune`.

Tuning stage starts only if limits on n_trials and timeout aren’t exceeded. Tuning goes from the best pipeline to the worst, and trial limits (n_trials, timeout) are divided evenly between each pipeline. If there are no limits on number of trials only the first pipeline will be tuned until user stops the process.

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 – Parameter tune_size (default: 0) determines how many pipelines to fit during tuning stage. Other parameters are passed into optuna optuna.study.Study.optimize().

Return type

etna.pipeline.base.BasePipeline

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 for the pool stage.

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]) – 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.

Returns

function that runs specified trial and returns its evaluated score

Return type

objective

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

Get Auto trials summary.

There are columns:

  • hash: hash of the pipeline;

  • pipeline: pipeline object;

  • metrics: columns with metrics’ values;

  • state: state of the trial;

  • study: name of the study in which trial was made.

Returns

dataframe with detailed info on each performed trial

Return type

study_dataframe

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

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]