pywatts.modules.metrics package

Submodules

pywatts.modules.metrics.mean_absolute_error module

pywatts.modules.metrics.rolling_mae module

class pywatts.modules.metrics.rolling_mae.RollingMAE(name: str = None, window_size=24, window_size_unit='d')

Bases: pywatts.modules.metrics.rolling_metric_base.RollingMetricBase

Module to calculate the Rolling Mean Absolute Error (MAE) :param window_size: Determine the window size for the rolling mae. Default 24 :type window_size: int :param window_size_unit: Determine the unit of the window size. Default Day (d)” :type window_size_unit: str

pywatts.modules.metrics.rolling_metric_base module

class pywatts.modules.metrics.rolling_metric_base.RollingMetricBase(name: str = None, window_size=24, window_size_unit='d')

Bases: pywatts_pipeline.core.transformer.base.BaseTransformer, abc.ABC

Module to calculate a Rolling Metric :param window_size: Determine the window size for the rolling metric. Default 24 :type window_size: int :param window_size_unit: Determine the unit of the window size. Default Day (d)” :type window_size_unit: str

transform(y: xarray.core.dataarray.DataArray, **kwargs) → xarray.core.dataarray.DataArray

Calculates the MAE based on the predefined target and predictions variables.

Parameters:x (Optional[xr.DataArray]) – the input dataset Returns:The calculated MAE Return type:xr.DataArray

pywatts.modules.metrics.rolling_rmse module

class pywatts.modules.metrics.rolling_rmse.RollingRMSE(name: str = None, window_size=24, window_size_unit='d')

Bases: pywatts.modules.metrics.rolling_metric_base.RollingMetricBase

Module to calculate the Rolling Root Mean Squared Error (RMSE) :param window_size: Determine the window size of the rolling rmse. Default 24 :type window_size: int :param window_size_unit: Determine the unit of the window size. Default Day (d)” :type window_size_unit: str

get_min_data()

Returns how much data are at least needed by that transformer

pywatts.modules.metrics.root_mean_squared_error module

Module contents