Multivariate Anomaly Detection - Testing and Best Practices

blob_data_sas_url = "<YOUR_BLOB_SAS_URL_FOR_ZIP_FILE>"
start_time = datetime(2021, 1, 1, 0, 0, 0)
end_time = datetime(2021, 1, 2, 12, 0, 0)
detection_req = DetectionRequest(source = blob_data_sas_url, start_time = start_time, end_time = end_time)
response_header = anomaly_detector_client.detect_anomaly(multi_variate_trained_model_id, 
                                                         detection_req,
                                                        cls = lambda *args: [args[i] for i in range(len(args))]
                                                    )[-1]
result_id = response_header['Location'].split("/")[-1]
results = anomaly_detector_client.get_detection_result(result_id)
while results.summary.status != DetectionStatus.READY and results.summary.status != DetectionStatus.FAILED:
    results = anomaly_detector_client.get_detection_result(result_id)
    time.sleep(1)
if results.summary.status == DetectionStatus.FAILED:
    if results.summary.errors:
        for error in results.summary.errors:
            print("The Error code: {}. ".format(error.code))
            print("The Error Message: {}".format(error.message))
anomaly_detect_results = []
for res in results.results:
    anomaly = {}
    if res.value is not None:
        value = res.value
        is_anomaly = value.is_anomaly
        if is_anomaly is True:
            anomaly["timestamp"] = res.timestamp.strftime("%m/%d/%Y, %H:%M:%S")
            anomaly["variable_contributing"] = []
            anomaly["severity"] = value.severity
            for contributing_factor in value.contributors:
                anomaly["variable_contributing"].append(contributing_factor.variable)
            anomaly_detect_results.append(anomaly)            
print("Total number of anomalies identified: ", len(anomaly_detect_results))
print("Anomaly Identified:")
for anomaly in anomaly_detect_results:
    print(anomaly)