update_experiment_run ¶

def update_experiment_run(
    self,
    application_identifier: "capo_appconfig.types.identifier.Identifier",
    experiment_definition_identifier: "capo_appconfig.types.identifier.Identifier",
    run: "capo_appconfig.types.positive_integer.PositiveInteger",
    *,
    config_overrides: Optional[AppConfigClientConfig] = None,
    description: Optional["capo_appconfig.types.description.Description"] = None,
    exposure_percentage: Optional[
        "capo_appconfig.types.nullable_percentage.NullablePercentage"
    ] = None,
    treatment_overrides: Optional[
        "capo_appconfig.types.treatment_overrides.TreatmentOverrides"
    ] = None,
    deployment_parameters: Optional[
        "capo_appconfig.types.deployment_parameters.DeploymentParameters"
    ] = None,
) -> "capo_appconfig.types.experiment_run.ExperimentRun":
    """<p>Updates a running experiment. Use this operation to increase audience exposure, modify treatment assignment overrides, or update the description of an active experiment run. Audience exposure can only be increased, not decreased.</p>

    Args:
        application_identifier: <p>The application ID or name.</p>
        experiment_definition_identifier: <p>The experiment definition ID or name.</p>
        run: <p>The run number to update.</p>
        description: <p>An updated description for the experiment run.</p>
        exposure_percentage: <p>The new exposure percentage. This value can only be increased from the current setting.</p>
        treatment_overrides: <p>The updated treatment assignment overrides that assign specific entity IDs to treatments, bypassing random assignment.</p>
        deployment_parameters: <p>The updated deployment parameters for the experiment run.</p>

    Raises:
        capo_appconfig.errors.bad_request_exception.BadRequestException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_appconfig.errors.conflict_exception.ConflictException: <p>The request could not be processed because of conflict in the current state of the resource.</p>
        capo_appconfig.errors.internal_server_exception.InternalServerException: <p>There was an internal failure in the AppConfig service.</p>
        capo_appconfig.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource could not be found.</p>
        capo_appconfig.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To update an experiment run
        The following UpdateExperimentRun example increases the exposure percentage of a running experiment.

        >>> client.update_experiment_run(application_identifier='339ohji', experiment_definition_identifier='bsxyd7k', run=1, exposure_percentage=75.0)
    """

    def _handler(
        req: "OperationRequest[capo_appconfig.types.update_experiment_run_request.UpdateExperimentRunRequest]",
    ) -> OperationResponse["capo_appconfig.types.experiment_run.ExperimentRun"]:
        import capo_appconfig._operations.amazon_app_config.update_experiment_run

        output, http_response = (
            capo_appconfig._operations.amazon_app_config.update_experiment_run.update_experiment_run(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_appconfig.types.update_experiment_run_request.UpdateExperimentRunRequest",
    ) -> dict[str, Any]:
        import capo_appconfig.types.update_experiment_run_request

        return capo_appconfig.types.update_experiment_run_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("UpdateExperimentRun", redact_input=_redact_input),
    )
    input_: capo_appconfig.types.update_experiment_run_request.UpdateExperimentRunRequest = {
        "application_identifier": application_identifier,
        "experiment_definition_identifier": experiment_definition_identifier,
        "run": run,
    }
    if description is not None:
        input_["description"] = description
    if exposure_percentage is not None:
        input_["exposure_percentage"] = exposure_percentage
    if treatment_overrides is not None:
        input_["treatment_overrides"] = treatment_overrides
    if deployment_parameters is not None:
        input_["deployment_parameters"] = deployment_parameters

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