create_predictor_backtest_export_job async ¶

async def create_predictor_backtest_export_job(
    self,
    predictor_backtest_export_job_name: "capo_forecast.types.name.Name",
    predictor_arn: "capo_forecast.types.arn.Arn",
    destination: "capo_forecast.types.data_destination.DataDestination",
    *,
    config_overrides: Optional[AsyncforecastClientConfig] = None,
    tags: Optional["capo_forecast.types.tags.Tags"] = None,
    format: Optional["capo_forecast.types.format.Format"] = None,
) -> "capo_forecast.types.create_predictor_backtest_export_job_response.CreatePredictorBacktestExportJobResponse":
    """<p>Exports backtest forecasts and accuracy metrics generated by the <a>CreateAutoPredictor</a> or <a>CreatePredictor</a> operations. Two folders containing CSV or Parquet files are exported to your specified S3 bucket.</p> <p> The export file names will match the following conventions:</p> <p> <code><ExportJobName>_<ExportTimestamp>_<PartNumber>.csv</code> </p> <p>The <ExportTimestamp> component is in Java SimpleDate format (yyyy-MM-ddTHH-mm-ssZ).</p> <p>You must specify a <a>DataDestination</a> object that includes an Amazon S3 bucket and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see <a>aws-forecast-iam-roles</a>.</p> <note> <p>The <code>Status</code> of the export job must be <code>ACTIVE</code> before you can access the export in your Amazon S3 bucket. To get the status, use the <a>DescribePredictorBacktestExportJob</a> operation.</p> </note>

    Args:
        predictor_backtest_export_job_name: <p>The name for the backtest export job.</p>
        predictor_arn: <p>The Amazon Resource Name (ARN) of the predictor that you want to export.</p>
        tags: <p>Optional metadata to help you categorize and organize your backtests. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.</p> <p>The following restrictions apply to tags:</p> <ul> <li> <p>For each resource, each tag key must be unique and each tag key must have one value.</p> </li> <li> <p>Maximum number of tags per resource: 50.</p> </li> <li> <p>Maximum key length: 128 Unicode characters in UTF-8.</p> </li> <li> <p>Maximum value length: 256 Unicode characters in UTF-8.</p> </li> <li> <p>Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply. </p> </li> <li> <p>Key prefixes cannot include any upper or lowercase combination of <code>aws:</code> or <code>AWS:</code>. Values can have this prefix. If a tag value has <code>aws</code> as its prefix but the key does not, Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of <code>aws</code> do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.</p> </li> </ul>
        format: <p>The format of the exported data, CSV or PARQUET. The default value is CSV.</p>

    Raises:
        capo_forecast.errors.invalid_input_exception.InvalidInputException: <p>We can't process the request because it includes an invalid value or a value that exceeds the valid range.</p>
        capo_forecast.errors.limit_exceeded_exception.LimitExceededException: <p>The limit on the number of resources per account has been exceeded.</p>
        capo_forecast.errors.resource_already_exists_exception.ResourceAlreadyExistsException: <p>There is already a resource with this name. Try again with a different name.</p>
        capo_forecast.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_forecast.errors.resource_not_found_exception.ResourceNotFoundException: <p>We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.</p>
        capo_forecast.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_forecast.types.create_predictor_backtest_export_job_request.CreatePredictorBacktestExportJobRequest]",
    ) -> AsyncOperationResponse[
        "capo_forecast.types.create_predictor_backtest_export_job_response.CreatePredictorBacktestExportJobResponse"
    ]:
        import capo_forecast._operations.amazon_forecast.create_predictor_backtest_export_job

        (
            output,
            http_response,
        ) = await capo_forecast._operations.amazon_forecast.create_predictor_backtest_export_job.async_create_predictor_backtest_export_job(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_forecast.types.create_predictor_backtest_export_job_request.CreatePredictorBacktestExportJobRequest",
    ) -> dict[str, Any]:
        import capo_forecast.types.create_predictor_backtest_export_job_request

        return capo_forecast.types.create_predictor_backtest_export_job_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "CreatePredictorBacktestExportJob", redact_input=_redact_input
        ),
    )
    input_: capo_forecast.types.create_predictor_backtest_export_job_request.CreatePredictorBacktestExportJobRequest = {
        "predictor_backtest_export_job_name": predictor_backtest_export_job_name,
        "predictor_arn": predictor_arn,
        "destination": destination,
    }
    if tags is not None:
        input_["tags"] = tags
    if format is not None:
        input_["format"] = format

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output