src.dackar.anomalies.AnomalyBase¶

Created on Dec. 19, 2024

@author: wangc, mandd Base Class for Anomaly Detection

Attributes¶

Classes¶

Anomaly detection base class

Module Contents¶

src.dackar.anomalies.AnomalyBase.logger[source]¶

class src.dackar.anomalies.AnomalyBase.AnomalyBase(norm='robust')[source]¶

Bases: sklearn.base.BaseEstimator

Anomaly detection base class

print_tag = 'AnomalyBase'[source]¶

is_fitted = False[source]¶

_features = None[source]¶

_targets = None[source]¶

_norm = 'robust'[source]¶

_meta[source]¶

_xindex = None[source]¶

_yindex = None[source]¶

_xcolumns = None[source]¶

_ycolumns = None[source]¶

reset()[source]¶: reset

get_params()[source]¶

Get parameters for this estimator.

Returns:: params – Parameter names mapped to their values.
Return type:: dict

set_params(**params)[source]¶

Set the parameters of this estimator.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

fit(X, y=None)[source]¶

perform fitting

Parameters:

X (array-like) – (n_samples, n_features)
y (array-like, optional) – (n_samples, n_features). Defaults to None.

evaluate(X)[source]¶

perform evaluation

Parameters:: X (array-like) – (n_samples, n_features)

plot()[source]¶: plot data

abstract get_anomalies()[source]¶: get the anomalies

abstract _fit(X, y=None)[source]¶

perform fitting

Parameters:

X (array-like) – (n_samples, n_features)
y (array-like, optional) – (n_samples, n_features). Defaults to None.

abstract _evaluate(X)[source]¶

perform evaluation

Parameters:: X (array-like) – (n_samples, n_features)

static check_data(data)[source]¶

Check the format of data

Parameters:: data (_type_) – list, numpy.ndarray or pandas.DataFrame