tw.anomaly_detection.rca

tw.anomaly_detection.rca(configuration=dict,dataset=pandas.DataFrame,model=dict)

Run root-cause analysis (RCA) on anomaly-detection results. Submits an RCA job, polls until it completes, and returns the RCA table together with job metadata.

Parameters

configuration : dict

RCA configuration payload as expected by the API.

dataset : pandas.DataFrame

Time-series data associated with the anomalies to analyse.

model: dict

A trained anomaly-detection model (as returned by method: “build_model”).

Returns

dict

A dictionary with keys:

"id" – job identifier (str).

"rca_table" – root-cause analysis table (dict).

"status"`` – final job status response (dict).

Example Configuration

CODE

anomaly_detection_rca_configuration = {
    "model_indexes": [
        1
    ]
}

Example Usage

CODE

anomaly_detection_rca_response = tw.anomaly_detection.rca(
    configuration = anomaly_detection_rca_configuration,
    dataset = dataset,
    model = anomaly_detection_build_model_model
)

Example Output

CODE

anomaly_detection_rca_id = anomaly_detection_rca_response['id']
anomaly_detection_rca_results = anomaly_detection_rca_response['rca_table']
anomaly_detection_rca_status = anomaly_detection_rca_response['status']