BasePipeline

class BasePipeline(horizon: int)[source]

Bases: etna.pipeline.base.AbstractPipeline, etna.core.mixins.BaseMixin

Base class for pipeline.

Inherited-members

Parameters

horizon (int) –

Methods

backtest(ts, metrics[, n_folds, mode, ...])

Run backtest with the pipeline.

fit(ts)

Fit the Pipeline.

forecast([ts, prediction_interval, ...])

Make a forecast of the next points of a dataset.

load(path)

Load an object.

params_to_tune()

Get hyperparameter grid to tune.

predict(ts[, start_timestamp, ...])

Make in-sample predictions on dataset in a given range.

save(path)

Save the object.

set_params(**params)

Return new object instance with modified parameters.

to_dict()

Collect all information about etna object in dict.

backtest(ts: etna.datasets.tsdataset.TSDataset, metrics: List[etna.metrics.base.Metric], n_folds: Union[int, List[etna.pipeline.base.FoldMask]] = 5, mode: Optional[str] = None, aggregate_metrics: bool = False, n_jobs: int = 1, refit: Union[bool, int] = True, stride: Optional[int] = None, joblib_params: Optional[Dict[str, Any]] = None, forecast_params: Optional[Dict[str, Any]] = None) Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame][source]

Run backtest with the pipeline.

If refit != True and some component of the pipeline doesn’t support forecasting with gap, this component will raise an exception.

Parameters
  • ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit models in backtest

  • metrics (List[etna.metrics.base.Metric]) – List of metrics to compute for each fold

  • n_folds (Union[int, List[etna.pipeline.base.FoldMask]]) – Number of folds or the list of fold masks

  • mode (Optional[str]) – Train generation policy: ‘expand’ or ‘constant’. Works only if n_folds is integer. By default, is set to ‘expand’.

  • aggregate_metrics (bool) – If True aggregate metrics above folds, return raw metrics otherwise

  • n_jobs (int) – Number of jobs to run in parallel

  • refit (Union[bool, int]) –

    Determines how often pipeline should be retrained during iteration over folds.

    • If True: pipeline is retrained on each fold.

    • If False: pipeline is trained only on the first fold.

    • If value: int: pipeline is trained every value folds starting from the first.

  • stride (Optional[int]) – Number of points between folds. Works only if n_folds is integer. By default, is set to horizon.

  • joblib_params (Optional[Dict[str, Any]]) – Additional parameters for joblib.Parallel

  • forecast_params (Optional[Dict[str, Any]]) – Additional parameters for forecast()

Returns

metrics_df, forecast_df, fold_info_df – Metrics dataframe, forecast dataframe and dataframe with information about folds

Return type

Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]

Raises
  • ValueError: – If mode is set when n_folds are List[FoldMask].

  • ValueError: – If stride is set when n_folds are List[FoldMask].

forecast(ts: Optional[etna.datasets.tsdataset.TSDataset] = None, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), n_folds: int = 3, return_components: bool = False) etna.datasets.tsdataset.TSDataset[source]

Make a forecast of the next points of a dataset.

The result of forecasting starts from the last point of ts, not including it.

Parameters
  • ts (Optional[etna.datasets.tsdataset.TSDataset]) – Dataset to forecast. If not given, dataset given during :py:meth:fit is used.

  • prediction_interval (bool) – If True returns prediction interval for forecast

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% taken to form a 95% prediction interval

  • n_folds (int) – Number of folds to use in the backtest for prediction interval estimation

  • return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions

Raises

NotImplementedError: – Adding target components is not currently implemented

Return type

etna.datasets.tsdataset.TSDataset

predict(ts: etna.datasets.tsdataset.TSDataset, start_timestamp: Optional[pandas._libs.tslibs.timestamps.Timestamp] = None, end_timestamp: Optional[pandas._libs.tslibs.timestamps.Timestamp] = None, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) etna.datasets.tsdataset.TSDataset[source]

Make in-sample predictions on dataset in a given range.

Currently, in situation when segments start with different timestamps we only guarantee to work with start_timestamp >= beginning of all segments.

Parameters
  • ts (etna.datasets.tsdataset.TSDataset) – Dataset to make predictions on.

  • start_timestamp (Optional[pandas._libs.tslibs.timestamps.Timestamp]) – First timestamp of prediction range to return, should be >= than first timestamp in ts; expected that beginning of each segment <= start_timestamp; if isn’t set the first timestamp where each segment began is taken.

  • end_timestamp (Optional[pandas._libs.tslibs.timestamps.Timestamp]) – Last timestamp of prediction range to return; if isn’t set the last timestamp of ts is taken. Expected that value is less or equal to the last timestamp in ts.

  • prediction_interval (bool) – If True returns prediction interval for forecast.

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% taken to form a 95% prediction interval.

  • return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions in [start_timestamp, end_timestamp] range.

Raises
  • ValueError: – Value of end_timestamp is less than start_timestamp.

  • ValueError: – Value of start_timestamp goes before point where each segment started.

  • ValueError: – Value of end_timestamp goes after the last timestamp.

  • NotImplementedError: – Adding target components is not currently implemented

Return type

etna.datasets.tsdataset.TSDataset