create_explainability ¶

def create_explainability(
    self,
    explainability_name: "capo_forecast.types.name.Name",
    resource_arn: "capo_forecast.types.arn.Arn",
    explainability_config: "capo_forecast.types.explainability_config.ExplainabilityConfig",
    *,
    config_overrides: Optional[forecastClientConfig] = None,
    data_source: Optional["capo_forecast.types.data_source.DataSource"] = None,
    schema: Optional["capo_forecast.types.schema.Schema"] = None,
    enable_visualization: Optional["capo_forecast.types.boolean.Boolean"] = None,
    start_date_time: Optional[
        "capo_forecast.types.local_date_time.LocalDateTime"
    ] = None,
    end_date_time: Optional[
        "capo_forecast.types.local_date_time.LocalDateTime"
    ] = None,
    tags: Optional["capo_forecast.types.tags.Tags"] = None,
) -> "capo_forecast.types.create_explainability_response.CreateExplainabilityResponse":
    """<note> <p>Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (<a>CreateAutoPredictor</a>)</p> </note> <p>Creates an Amazon Forecast Explainability.</p> <p>Explainability helps you better understand how the attributes in your datasets impact forecast. Amazon Forecast uses a metric called Impact scores to quantify the relative impact of each attribute and determine whether they increase or decrease forecast values.</p> <p>To enable Forecast Explainability, your predictor must include at least one of the following: related time series, item metadata, or additional datasets like Holidays and the Weather Index.</p> <p>CreateExplainability accepts either a Predictor ARN or Forecast ARN. To receive aggregated Impact scores for all time series and time points in your datasets, provide a Predictor ARN. To receive Impact scores for specific time series and time points, provide a Forecast ARN.</p> <p> <b>CreateExplainability with a Predictor ARN</b> </p> <note> <p>You can only have one Explainability resource per predictor. If you already enabled <code>ExplainPredictor</code> in <a>CreateAutoPredictor</a>, that predictor already has an Explainability resource.</p> </note> <p>The following parameters are required when providing a Predictor ARN:</p> <ul> <li> <p> <code>ExplainabilityName</code> - A unique name for the Explainability.</p> </li> <li> <p> <code>ResourceArn</code> - The Arn of the predictor.</p> </li> <li> <p> <code>TimePointGranularity</code> - Must be set to “ALL”.</p> </li> <li> <p> <code>TimeSeriesGranularity</code> - Must be set to “ALL”.</p> </li> </ul> <p>Do not specify a value for the following parameters:</p> <ul> <li> <p> <code>DataSource</code> - Only valid when TimeSeriesGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>Schema</code> - Only valid when TimeSeriesGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>StartDateTime</code> - Only valid when TimePointGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>EndDateTime</code> - Only valid when TimePointGranularity is “SPECIFIC”.</p> </li> </ul> <p> <b>CreateExplainability with a Forecast ARN</b> </p> <note> <p>You can specify a maximum of 50 time series and 500 time points.</p> </note> <p>The following parameters are required when providing a Predictor ARN:</p> <ul> <li> <p> <code>ExplainabilityName</code> - A unique name for the Explainability.</p> </li> <li> <p> <code>ResourceArn</code> - The Arn of the forecast.</p> </li> <li> <p> <code>TimePointGranularity</code> - Either “ALL” or “SPECIFIC”.</p> </li> <li> <p> <code>TimeSeriesGranularity</code> - Either “ALL” or “SPECIFIC”.</p> </li> </ul> <p>If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:</p> <ul> <li> <p> <code>DataSource</code> - The S3 location of the CSV file specifying your time series.</p> </li> <li> <p> <code>Schema</code> - The Schema defines the attributes and attribute types listed in the Data Source.</p> </li> </ul> <p>If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:</p> <ul> <li> <p> <code>StartDateTime</code> - The first timestamp in the range of time points.</p> </li> <li> <p> <code>EndDateTime</code> - The last timestamp in the range of time points.</p> </li> </ul>

    Args:
        explainability_name: <p>A unique name for the Explainability.</p>
        resource_arn: <p>The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.</p>
        explainability_config: <p>The configuration settings that define the granularity of time series and time points for the Explainability.</p>
        enable_visualization: <p>Create an Explainability visualization that is viewable within the Amazon Web Services console.</p>
        start_date_time: <p>If <code>TimePointGranularity</code> is set to <code>SPECIFIC</code>, define the first point for the Explainability.</p> <p>Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)</p>
        end_date_time: <p>If <code>TimePointGranularity</code> is set to <code>SPECIFIC</code>, define the last time point for the Explainability.</p> <p>Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)</p>
        tags: <p>Optional metadata to help you categorize and organize your resources. 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>

    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.
    """

    def _handler(
        req: "OperationRequest[capo_forecast.types.create_explainability_request.CreateExplainabilityRequest]",
    ) -> OperationResponse[
        "capo_forecast.types.create_explainability_response.CreateExplainabilityResponse"
    ]:
        import capo_forecast._operations.amazon_forecast.create_explainability

        output, http_response = (
            capo_forecast._operations.amazon_forecast.create_explainability.create_explainability(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

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

        return capo_forecast.types.create_explainability_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateExplainability", redact_input=_redact_input),
    )
    input_: capo_forecast.types.create_explainability_request.CreateExplainabilityRequest = {
        "explainability_name": explainability_name,
        "resource_arn": resource_arn,
        "explainability_config": explainability_config,
    }
    if data_source is not None:
        input_["data_source"] = data_source
    if schema is not None:
        input_["schema"] = schema
    if enable_visualization is not None:
        input_["enable_visualization"] = enable_visualization
    if start_date_time is not None:
        input_["start_date_time"] = start_date_time
    if end_date_time is not None:
        input_["end_date_time"] = end_date_time
    if tags is not None:
        input_["tags"] = tags

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