RADAR.federated_data.algorithms

Submodules

RADAR.federated_data.algorithms.flexanomalies module

class RADAR.federated_data.algorithms.flexanomalies.FlexAnomalyDetection(**kwargs)[source]

Bases: BaseAnomalyDetection

decision_function(X, y=None)[source]

Predict raw anomaly score of X using the fitted detector.

Parameters:

X (numpy array of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:

anomaly_scores – The anomaly score of the input samples.

Return type:

numpy array of shape (n_samples,)

evaluate(X_test, y_test, label_test=None)[source]
fit(X, y)[source]

Fit detector.

Parameters:

  • X (numpy array of shape (n_samples, n_features)) – The input samples.

  • y (numpy array of shape)

Returns:

self – Fitted estimator.

Return type:

object

get_default_params(**params)[source]

Get DEFAULT parameters for this estimator, params is used to configure positional parameters in order to obtain default parameters of the object.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

get_params()[source]

Get parameters for this estimator.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

predict(X, y=None)[source]

Predict raw anomaly scores of X using the fitted detector.

The anomaly score of an input sample is computed based on the fitted detector. For consistency, outliers are assigned with higher anomaly scores.

If label_parser is an attribute, then we execute the particular predict function

Parameters:

X (numpy array of shape (n_samples, n_features)) – The input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:

anomaly_scores – The anomaly score of the input samples.

Return type:

numpy array of shape (n_samples,)

classmethod register_algorithm(name, model_class)[source]

Register a new algorithm in the class. :param - name: The name of the new algorithm. :type - name: str :param - model_class: The class implementing the anomaly detection model. :type - model_class: class

The class should have:

  • An __init__ method that accepts model-specific parameters.

  • A fit(X, y) method to train the model.

  • A predict(X) method to make predictions.

  • Optionally, a decision_function(X) for scoring anomalies.

register_federated_functions(name, functions)[source]
Records the federated functions of a new algorithm.

  • federated_functions (dict): A dictionary defining federated learning functions.

Required keys:

  • “build_model”: Function to initialize the server model.

  • “copy”: Function to distribute the model to clients.

  • “train”: Function to train the model on client data.

  • “collect”: Function to gather updates from clients.

  • “aggregate”: Function to combine client updates.

  • “set_weights”: Function to update the global model.

set_params(**params)[source]

Set the parameters of this estimator. :returns: self :rtype: object

Module contents