energy_fault_detector.anomaly_scores

Anomaly score classes.

class MahalanobisScore(pca=True, pca_min_var=0.9, mcd_support_fraction=0.9, scale=False)[source]

Bases: AnomalyScore

Calculate mahalanobis scores using sklearn MinCovDet und optionally a PCA to accelerate calculations.

Parameters:

  • pca (bool) – boolean to indicate whether PCA should be done before determining the covariance. Default true.

  • pca_min_var (float) – parameter for PCA, variance to keep. Default 0.9

  • mcd_support_fraction (float) – parameter for Minimum Covariance Determinant estimation. Default 0.9

  • scale (bool) – If True, std of the training/fit reconstruction errors will be used to scale recon errors before applying MinCovDet. Default: False

Configuration example:

train:
  anomaly_score:
    name: mahalanobis
    params:
      pca: True
      pca_min_var: 0.9
      mcd_support_fraction: 0.9
      scale: False
fit(x, y=None)[source]

Fit MinCovDet object to determine Mahalanobis distance.

Parameters:

  • x (Union[DataFrame, ndarray]) – numpy 2d array or pandas DataFrame with differences between prediction and actual sensor values.

  • y (Optional[Series]) – not used, labels indicating whether sample is normal (True) or anomalous (False).

Return type:

MahalanobisScore

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → MahalanobisScore

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in fit.

Returns

selfobject

The updated object.

set_transform_request(*, x: bool | None | str = '$UNCHANGED$') → MahalanobisScore

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in transform.

Returns

selfobject

The updated object.

transform(x)[source]

Calculate Mahalanobis distance from x.

Parameters:

x (Union[DataFrame, ndarray]) – numpy 2d array or pandas Dataframe with differences between prediction and actual sensor values

Return type:

Series

Returns:

Mahalanobis distance for each sample. Output is a pandas Series if input was a pandas DataFrame

class RMSEScore(scale=True, **kwargs)[source]

Bases: AnomalyScore

Calculate the RMSE of given reconstruction errors.

scale

If True, mean and std of the training/fit reconstruction errors will be used to standardize recon errors during transform. Default: True

Configuration example:

train:
  anomaly_score:
    name: rmse
    params:
      scale: false
fit(x, y=None)[source]

Calculate standard deviation and mean on training data

Parameters:

  • x (Union[DataFrame, ndarray]) – numpy 2d array with differences between prediction and actual sensor values

  • y (Optional[Series]) – not used, labels indicating whether sample is normal (True) or anomalous (False).

Return type:

RMSEScore

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → RMSEScore

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in fit.

Returns

selfobject

The updated object.

set_transform_request(*, x: bool | None | str = '$UNCHANGED$') → RMSEScore

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in transform.

Returns

selfobject

The updated object.

transform(x)[source]

Calculate the RMSE based on the deviation matrix.

Parameters:

x (Union[DataFrame, ndarray]) – numpy 2d array or pandas Dataframe with differences between prediction and actual sensor values

Return type:

Series

Returns:

RMSE for each sample.