get_event_prediction ¶

def get_event_prediction(
    self,
    detector_id: "capo_frauddetector.types.string.string",
    event_id: "capo_frauddetector.types.string.string",
    event_type_name: "capo_frauddetector.types.string.string",
    entities: "capo_frauddetector.types.list_of_entities.listOfEntities",
    event_timestamp: "capo_frauddetector.types.utc_timestamp_iso8601.utcTimestampISO8601",
    event_variables: "capo_frauddetector.types.event_variable_map.EventVariableMap",
    *,
    config_overrides: Optional[FraudDetectorClientConfig] = None,
    detector_version_id: Optional[
        "capo_frauddetector.types.whole_number_version_string.wholeNumberVersionString"
    ] = None,
    external_model_endpoint_data_blobs: Optional[
        "capo_frauddetector.types.external_model_endpoint_data_blob_map.ExternalModelEndpointDataBlobMap"
    ] = None,
) -> (
    "capo_frauddetector.types.get_event_prediction_result.GetEventPredictionResult"
):
    """<p>Evaluates an event against a detector version. If a version ID is not provided, the detector’s (<code>ACTIVE</code>) version is used.</p>

    Args:
        detector_id: <p>The detector ID.</p>
        detector_version_id: <p>The detector version ID.</p>
        event_id: <p>The unique ID used to identify the event.</p>
        event_type_name: <p>The event type associated with the detector specified for the prediction.</p>
        entities: <p>The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."</p>
        event_timestamp: <p>Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.</p>
        event_variables: <p>Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.</p> <important> <p>You must provide at least one eventVariable</p> </important> <p>To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:</p> <p> <b>For Amazon Fraud Detector trained models:</b> </p> <p>If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.</p> <p> <b>For imported SageMaker models:</b> </p> <p>If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable. </p>
        external_model_endpoint_data_blobs: <p>The Amazon SageMaker model endpoint input data blobs.</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.conflict_exception.ConflictException: <p>An exception indicating there was a conflict during a delete operation.</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.resource_unavailable_exception.ResourceUnavailableException: <p>An exception indicating that the attached customer-owned (external) model threw an exception when Amazon Fraud Detector invoked the model.</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_request.GetEventPredictionRequest]",
    ) -> OperationResponse[
        "capo_frauddetector.types.get_event_prediction_result.GetEventPredictionResult"
    ]:
        import capo_frauddetector._operations.aws_hawks_nest_service_facade.get_event_prediction

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

    def _redact_input(
        input_: "capo_frauddetector.types.get_event_prediction_request.GetEventPredictionRequest",
    ) -> dict[str, Any]:
        import capo_frauddetector.types.get_event_prediction_request

        return (
            capo_frauddetector.types.get_event_prediction_request.redact_sensitive(
                input_
            )
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("GetEventPrediction", redact_input=_redact_input),
    )
    input_: capo_frauddetector.types.get_event_prediction_request.GetEventPredictionRequest = {
        "detector_id": detector_id,
        "event_id": event_id,
        "event_type_name": event_type_name,
        "entities": entities,
        "event_timestamp": event_timestamp,
        "event_variables": event_variables,
    }
    if detector_version_id is not None:
        input_["detector_version_id"] = detector_version_id
    if external_model_endpoint_data_blobs is not None:
        input_["external_model_endpoint_data_blobs"] = (
            external_model_endpoint_data_blobs
        )

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