pywatts.modules.postprocessing package¶
Submodules¶
pywatts.modules.postprocessing.ensemble module¶
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class
pywatts.modules.postprocessing.ensemble.Ensemble(weights: Union[str, list] = None, k_best: Union[str, int] = None, loss_metric: pywatts.modules.postprocessing.ensemble.Ensemble.LossMetric = <LossMetric.RMSE: 1>, name: str = 'Ensemble')¶ Bases:
pywatts.core.base.BaseEstimatorAggregation step to ensemble the given time series, ether by simple or weighted averaging. By default simple averaging is applied.
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class
LossMetric¶ Bases:
enum.IntEnumEnum which contains the different loss metrics of the ensemble module.
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MAE= 2¶
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RMSE= 1¶
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fit(**kwargs)¶ Fit the model, e.g. optimize parameters such that model(x) = y
Parameters: kwargs – key word arguments as input. If the key word starts with target, then it is a target variable. Returns:
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get_params() → Dict[str, object]¶ Get parameters for the Ensemble object. :return: Parameters as dict object. :rtype: Dict[str, object]
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set_params(weights: Union[str, list] = None, loss_metric: pywatts.modules.postprocessing.ensemble.Ensemble.LossMetric = None, k_best: Union[str, int] = None)¶ Set or change Ensemble object parameters. :param weights: List of individual weights of the given forecasts for weighted averaging. Passing “auto” estimates the weights depending on the given loss values. :type weights: list, optional :param loss_metric: Specifies the loss metric for automated optimal weight estimation. :type loss_metric: LossMetric, optional :param k_best: Drop poor forecasts in the automated weight estimation. Passing “auto” drops poor forecasts based on the given loss values by applying the 1.5*IQR rule. :type k_best: str or int, optional
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transform(**kwargs) → xarray.core.dataarray.DataArray¶ Ensemble the given time series by simple or weighted averaging. :return: Xarray dataset aggregated by simple or weighted averaging. :rtype: xr.DataArray
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class