pywatts.summaries package¶
Submodules¶
pywatts.summaries.f1_summary module¶
-
class
pywatts.summaries.f1_summary.
F1Score
(name: str = 'F1Score', average='micro', cuts=[], offset=0, filter_method=None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the F1Score
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the F1 Score. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the F1 Score.
- cuts (List[Tuple[pd.Timestamp, pd.Timestamp]]) – A list of Tuples of pd.Timestamps which specify intervals on which the metric should be calculated.
- average (str) – The average param for the f1 score sklearn implementation. See `SKLearn https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html`_
pywatts.summaries.confusion_matrix module¶
-
class
pywatts.summaries.confusion_matrix.
ConfusionMatrix
(name: str = 'Confusion Matrix')¶ Bases:
pywatts_pipeline.core.summary.base_summary.BaseSummary
Summary to calculate the confusion matrix
Parameters: name (str) – Name of the confusion matrix -
transform
(file_manager: pywatts_pipeline.core.util.filemanager.FileManager, gt: xarray.core.dataarray.DataArray, **kwargs) → pywatts_pipeline.core.summary.summary_object.SummaryObjectTable¶ Calculates the confusion matrix for all predictions :param file_manager: The filemanager :type file_manager: FileManager :param gt: The ground truth data :type gt: xr.DataArray :param kwargs: The predictions :type kwargs: xr.DataArray :return: A summary containing all confusion matrices :rtype: SummaryObjectTable
-
pywatts.summaries.discriminative_score module¶
pywatts.summaries.mae_summary module¶
-
class
pywatts.summaries.mae_summary.
MAE
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the Mean Absolute Error (MAE)
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the MAE. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the MAE.
pywatts.summaries.mape_summary module¶
-
class
pywatts.summaries.mape_summary.
MAPE
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the Mean Absolute Percentage Error (MAPE)
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the MAPE. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the MAPE.
pywatts.summaries.mase_summary module¶
-
class
pywatts.summaries.mase_summary.
MASE
(name: str = 'MASE', filter_method=None, offset: int = 0, cuts: Optional[List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]]] = None, lag: int = 1)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the Mean Absolute Scaled Error (MAPE)
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the MAPE. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the MAPE.
- lag (int) – The lag determines the persistence forecast that is used for scaling the error.
pywatts.summaries.max_summary module¶
-
class
pywatts.summaries.max_summary.
MaxErr
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the maximal absolute error
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the max. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the max.
pywatts.summaries.metric_base module¶
-
class
pywatts.summaries.metric_base.
MetricBase
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts_pipeline.core.summary.base_summary.BaseSummary
,abc.ABC
Base Class for all Metrics
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the Metric. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the Metric.
-
classmethod
load
(load_information: Dict[KT, VT])¶ Uses the data in load_information for restoring the state of the module.
Parameters: load_information (Dict) – The data needed for restoring the state of the module Returns: The restored module Return type: Base
-
save
(fm: pywatts_pipeline.core.util.filemanager.FileManager) → Dict[KT, VT]¶ Saves the modules and the state of the module and returns a dictionary containing the relevant information.
Parameters: fm (FileManager) – the filemanager which can be used by the module for saving information about the module. Returns: A dictionary containing the information needed for restoring the module :rtype:Dict
-
transform
(file_manager: pywatts_pipeline.core.util.filemanager.FileManager, y: xarray.core.dataarray.DataArray, **kwargs) → pywatts_pipeline.core.summary.summary_object.SummaryObjectList¶ Calculates the MAE based on the predefined target and predictions variables. :param file_manager: The filemanager, it can be used to store data that corresponds to the summary as a file. :type: file_manager: FileManager :param y: the input dataset :type y: xr.DataArray :param kwargs: the predictions :type kwargs: xr.DataArray
Returns: The calculated MAE Return type: xr.DataArray
pywatts.summaries.min_summary module¶
-
class
pywatts.summaries.min_summary.
MinErr
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the minimal absolute error
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the min. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the min.
pywatts.summaries.rmse_summary module¶
-
class
pywatts.summaries.rmse_summary.
RMSE
(name: str = None, filter_method: Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]] = None, offset: int = 0, cuts: List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]] = None)¶ Bases:
pywatts.summaries.metric_base.MetricBase
Module to calculate the Root Mean Squared Error (RMSE)
Parameters: - offset (int) – Offset, which determines the number of ignored values in the beginning for calculating the RMSE. Default 0
- filter_method (Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]) – Filter which should performed on the data before calculating the RMSE.
pywatts.summaries.train_synthetic_test_real module¶
pywatts.summaries.tsne_visualisation module¶
-
class
pywatts.summaries.tsne_visualisation.
TSNESummary
(name='TSNE', max_points=10000, all_in_one_plot=False, tsne_params=None)¶ Bases:
pywatts_pipeline.core.summary.base_summary.BaseSummary
Summary that performs a t-distributed stochastic neighbor embedding (t-SNE) to visualise the data. It is possible to specify masks by providing kwargs to the transform method that end with _masked. :param max_point: The maximum number of points per data set that should be plotted :type max_point: int :param all_in_one_plot: Flag indicating if all input data should be visualised in the same plot. If not the column
GT is visualised like all other in separate plots.Parameters: tsne_params (Dict) – Params for the t-SNE visualisation (see sklearn at `sklearn https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html`_). -
transform
(file_manager, gt: xarray.core.dataarray.DataArray, **kwargs)¶ Calculates the TSNE and visualises it. kwargs that end with _masked are masks for the other input. For example, gt_masked is a mask for gt. :param file_manager: The filemanager, it can be used to store data that corresponds to the summary as a file. :type: file_manager: FileManager :param gt: the gt dataset :type gt: xr.DataArray :param kwargs: the predictions :type kwargs: xr.DataArray
Returns: A SummaryObjectList with the paths to all plots. Return type: SummaryObjectList
-