get_event_prediction_metadata ¶

def get_event_prediction_metadata(
    self,
    event_id: "capo_frauddetector.types.identifier.identifier",
    event_type_name: "capo_frauddetector.types.identifier.identifier",
    detector_id: "capo_frauddetector.types.identifier.identifier",
    detector_version_id: "capo_frauddetector.types.whole_number_version_string.wholeNumberVersionString",
    prediction_timestamp: "capo_frauddetector.types.time.time",
    *,
    config_overrides: Optional[FraudDetectorClientConfig] = None,
) -> "capo_frauddetector.types.get_event_prediction_metadata_result.GetEventPredictionMetadataResult":
    """<p> Gets details of the past fraud predictions for the specified event ID, event type, detector ID, and detector version ID that was generated in the specified time period. </p>

    Args:
        event_id: <p> The event ID. </p>
        event_type_name: <p> The event type associated with the detector specified for the prediction. </p>
        detector_id: <p> The detector ID. </p>
        detector_version_id: <p> The detector version ID. </p>
        prediction_timestamp: <p> The timestamp that defines when the prediction was generated. The timestamp must be specified using ISO 8601 standard in UTC.</p> <p>We recommend calling <a href="https://docs.aws.amazon.com/frauddetector/latest/api/API_ListEventPredictions.html">ListEventPredictions</a> first, and using the <code>predictionTimestamp</code> value in the response to provide an accurate prediction timestamp value.</p>

    Raises:
        capo_frauddetector.errors.access_denied_exception.AccessDeniedException: <p>An exception indicating Amazon Fraud Detector does not have the needed permissions. This can occur if you submit a request, such as <code>PutExternalModel</code>, that specifies a role that is not in your account.</p>
        capo_frauddetector.errors.internal_server_exception.InternalServerException: <p>An exception indicating an internal server error.</p>
        capo_frauddetector.errors.resource_not_found_exception.ResourceNotFoundException: <p>An exception indicating the specified resource was not found.</p>
        capo_frauddetector.errors.throttling_exception.ThrottlingException: <p>An exception indicating a throttling error.</p>
        capo_frauddetector.errors.validation_exception.ValidationException: <p>An exception indicating a specified value is not allowed.</p>
        capo_frauddetector.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_frauddetector.types.get_event_prediction_metadata_request.GetEventPredictionMetadataRequest]",
    ) -> OperationResponse[
        "capo_frauddetector.types.get_event_prediction_metadata_result.GetEventPredictionMetadataResult"
    ]:
        import capo_frauddetector._operations.aws_hawks_nest_service_facade.get_event_prediction_metadata

        output, http_response = (
            capo_frauddetector._operations.aws_hawks_nest_service_facade.get_event_prediction_metadata.get_event_prediction_metadata(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_output(
        output: "capo_frauddetector.types.get_event_prediction_metadata_result.GetEventPredictionMetadataResult",
    ) -> dict[str, Any]:
        import capo_frauddetector.types.get_event_prediction_metadata_result

        return capo_frauddetector.types.get_event_prediction_metadata_result.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "GetEventPredictionMetadata", redact_output=_redact_output
        ),
    )
    input_: capo_frauddetector.types.get_event_prediction_metadata_request.GetEventPredictionMetadataRequest = {
        "event_id": event_id,
        "event_type_name": event_type_name,
        "detector_id": detector_id,
        "detector_version_id": detector_version_id,
        "prediction_timestamp": prediction_timestamp,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output