etna.ensembles.VotingEnsemble#

class VotingEnsemble(pipelines: List[BasePipeline], weights: List[float] | Literal['auto'] | None = None, regressor: DecisionTreeRegressor | ExtraTreeRegressor | RandomForestRegressor | ExtraTreesRegressor | GradientBoostingRegressor | CatBoostRegressor | None = None, n_folds: int = 3, n_jobs: int = 1, joblib_params: Dict[str, Any] | None = None)[source]#

Bases: EnsembleMixin, SaveEnsembleMixin, BasePipeline

VotingEnsemble is a pipeline that forecast future values with weighted averaging of it’s pipelines forecasts.

Examples

>>> from etna.datasets import generate_ar_df
>>> from etna.datasets import TSDataset
>>> from etna.ensembles import VotingEnsemble
>>> from etna.models import NaiveModel
>>> from etna.models import ProphetModel
>>> from etna.pipeline import Pipeline
>>> df = generate_ar_df(periods=30, start_time="2021-06-01", ar_coef=[1.2], n_segments=3)
>>> ts = TSDataset(df, "D")
>>> prophet_pipeline = Pipeline(model=ProphetModel(), transforms=[], horizon=7)
>>> naive_pipeline = Pipeline(model=NaiveModel(lag=10), transforms=[], horizon=7)
>>> ensemble = VotingEnsemble(
...     pipelines=[prophet_pipeline, naive_pipeline],
...     weights=[0.7, 0.3]
... )
>>> _ = ensemble.fit(ts=ts)
>>> forecast = ensemble.forecast()
>>> forecast
segment         segment_0        segment_1       segment_2
feature            target           target          target
timestamp
2021-07-01          -8.84          -186.67          130.99
2021-07-02          -8.96          -198.16          138.81
2021-07-03          -9.57          -212.48          148.48
2021-07-04         -10.48          -229.16          160.13
2021-07-05         -11.20          -248.93          174.39
2021-07-06         -12.47          -281.90          197.82
2021-07-07         -13.51          -307.02          215.73

Init VotingEnsemble.

Parameters:

  • pipelines (List[BasePipeline]) – List of pipelines that should be used in ensemble

  • weights (List[float] | Literal['auto'] | None) –

    List of pipelines’ weights.

    • If None, use uniform weights

    • If List[float], use this weights for the base estimators, weights will be normalized automatically

    • If “auto”, use importances of the base estimators forecasts as weights of base estimators

  • regressor (DecisionTreeRegressor | ExtraTreeRegressor | RandomForestRegressor | ExtraTreesRegressor | GradientBoostingRegressor | CatBoostRegressor | None) – Regression model with fit/predict interface which will be used to evaluate weights of the base estimators. It should have feature_importances_ property (e.g. all tree-based regressors in sklearn)

  • n_folds (int) – Number of folds to use in the backtest. Backtest is used to obtain the forecasts from the base estimators; forecasts will be used to evaluate the estimator’s weights.

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

  • joblib_params (Dict[str, Any] | None) – Additional parameters for joblib.Parallel

Raises:

ValueError: – If the number of the pipelines is less than 2 or pipelines have different horizons.

Methods

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

Run backtest with the pipeline.

fit(ts[, save_ts])

Fit pipelines in ensemble.

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

Make a forecast of the next points of a dataset.

get_historical_forecasts(ts[, n_folds, ...])

Estimate forecast for each fold on the historical dataset.

load(path[, ts])

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.

Attributes

This class stores its __init__ parameters as attributes.

backtest(ts: TSDataset, metrics: List[Metric], n_folds: int | List[FoldMask] = 5, mode: str | None = None, aggregate_metrics: bool = False, n_jobs: int = 1, refit: bool | int = True, stride: int | None = None, joblib_params: Dict[str, Any] | None = None, forecast_params: Dict[str, Any] | None = None) → Tuple[DataFrame, DataFrame, 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 (TSDataset) – Dataset to fit models in backtest

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

  • n_folds (int | List[FoldMask]) – Number of folds or the list of fold masks

  • mode (str | None) – 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 (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 (int | None) – Number of points between folds. Works only if n_folds is integer. By default, is set to horizon.

  • joblib_params (Dict[str, Any] | None) – Additional parameters for joblib.Parallel

  • forecast_params (Dict[str, Any] | None) – Additional parameters for forecast()

Returns:

Metrics dataframe, forecast dataframe and dataframe with information about folds

Return type:

metrics_df, forecast_df, fold_info_df

Raises:

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

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

fit(ts: TSDataset, save_ts: bool = True) → VotingEnsemble[source]#

Fit pipelines in ensemble.

Parameters:

  • ts (TSDataset) – TSDataset to fit ensemble.

  • save_ts (bool) – Will ts be saved in the pipeline during fit.

Returns:

Fitted ensemble

Return type:

self

forecast(ts: TSDataset | None = None, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), n_folds: int = 3, return_components: bool = False) → 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 (TSDataset | None) – Dataset to forecast. If not given, dataset given during 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:

TSDataset

get_historical_forecasts(ts: TSDataset, n_folds: int | List[FoldMask] = 5, mode: str | None = None, n_jobs: int = 1, refit: bool | int = True, stride: int | None = None, joblib_params: Dict[str, Any] | None = None, forecast_params: Dict[str, Any] | None = None) → DataFrame[source]#

Estimate forecast for each fold on the historical dataset.

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

Parameters:

  • ts (TSDataset) – Dataset to fit models in backtest

  • n_folds (int | List[FoldMask]) – Number of folds or the list of fold masks

  • mode (str | None) – Train generation policy: ‘expand’ or ‘constant’. Works only if n_folds is integer. By default, is set to ‘expand’.

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

  • refit (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 (int | None) – Number of points between folds. Works only if n_folds is integer. By default, is set to horizon.

  • joblib_params (Dict[str, Any] | None) – Additional parameters for joblib.Parallel

  • forecast_params (Dict[str, Any] | None) – Additional parameters for forecast()

Returns:

Forecast dataframe

Raises:

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

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

Return type:

DataFrame

classmethod load(path: Path, ts: TSDataset | None = None) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:

  • path (Path) – Path to load object from.

  • ts (TSDataset | None) – TSDataset to set into loaded pipeline.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get hyperparameter grid to tune.

Currently, returns empty dict, but could have a proper implementation in the future.

Returns:

Grid with hyperparameters.

Return type:

Dict[str, BaseDistribution]

predict(ts: TSDataset, start_timestamp: Timestamp | int | str | None = None, end_timestamp: Timestamp | int | str | None = None, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → 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 start_timestamp and end_timestamp of type str are converted into pd.Timestamp.

Parameters:

  • ts (TSDataset) – Dataset to make predictions on.

  • start_timestamp (Timestamp | int | str | None) – 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 (Timestamp | int | str | None) – 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 – Incorrect type of start_timestamp or end_timestamp is used according to ts.freq

  • 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:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:

**params (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()[source]#

Collect all information about etna object in dict.