AbstractPipeline

class AbstractPipeline

Bases: etna.core.saving.AbstractSaveable

Interface for pipeline.

Inherited-members

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.

abstract 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]

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]

abstract fit(ts: etna.datasets.tsdataset.TSDataset) → etna.pipeline.base.AbstractPipeline

Fit the Pipeline.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with timeseries data

Returns

Fitted Pipeline instance

Return type

etna.pipeline.base.AbstractPipeline

abstract 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

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

Return type

etna.datasets.tsdataset.TSDataset

abstract params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get hyperparameter grid to tune.

Returns

Grid with hyperparameters.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

abstract 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

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.

Return type

etna.datasets.tsdataset.TSDataset