Struct aws_sdk_forecast::client::fluent_builders::CreateExplainability

pub struct CreateExplainability { /* private fields */ }

Fluent builder constructing a request to CreateExplainability.

Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (CreateAutoPredictor)

Creates an Amazon Forecast Explainability.

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.

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.

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.

CreateExplainability with a Predictor ARN

You can only have one Explainability resource per predictor. If you already enabled ExplainPredictor in CreateAutoPredictor, that predictor already has an Explainability resource.

The following parameters are required when providing a Predictor ARN:

• ExplainabilityName - A unique name for the Explainability.

• ResourceArn - The Arn of the predictor.

• TimePointGranularity - Must be set to “ALL”.

• TimeSeriesGranularity - Must be set to “ALL”.

Do not specify a value for the following parameters:

• DataSource - Only valid when TimeSeriesGranularity is “SPECIFIC”.

• Schema - Only valid when TimeSeriesGranularity is “SPECIFIC”.

• StartDateTime - Only valid when TimePointGranularity is “SPECIFIC”.

• EndDateTime - Only valid when TimePointGranularity is “SPECIFIC”.

CreateExplainability with a Forecast ARN

You can specify a maximum of 50 time series and 500 time points.

The following parameters are required when providing a Predictor ARN:

• ExplainabilityName - A unique name for the Explainability.

• ResourceArn - The Arn of the forecast.

• TimePointGranularity - Either “ALL” or “SPECIFIC”.

• TimeSeriesGranularity - Either “ALL” or “SPECIFIC”.

If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:

• DataSource - The S3 location of the CSV file specifying your time series.

• Schema - The Schema defines the attributes and attribute types listed in the Data Source.

If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:

• StartDateTime - The first timestamp in the range of time points.

• EndDateTime - The last timestamp in the range of time points.

Implementations

impl CreateExplainability

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreateExplainability, AwsResponseRetryClassifier>, SdkError<CreateExplainabilityError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

pub async fn send(
    self
) -> Result<CreateExplainabilityOutput, SdkError<CreateExplainabilityError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn explainability_name(self, input: impl Into<String>) -> Self

A unique name for the Explainability.

pub fn set_explainability_name(self, input: Option<String>) -> Self

A unique name for the Explainability.

pub fn resource_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.

pub fn set_resource_arn(self, input: Option<String>) -> Self

The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.

pub fn explainability_config(self, input: ExplainabilityConfig) -> Self

The configuration settings that define the granularity of time series and time points for the Explainability.

pub fn set_explainability_config(
    self,
    input: Option<ExplainabilityConfig>
) -> Self

The configuration settings that define the granularity of time series and time points for the Explainability.

pub fn data_source(self, input: DataSource) -> Self

The source of your data, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an AWS Key Management Service (KMS) key.

pub fn set_data_source(self, input: Option<DataSource>) -> Self

The source of your data, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an AWS Key Management Service (KMS) key.

pub fn schema(self, input: Schema) -> Self

Defines the fields of a dataset.

pub fn set_schema(self, input: Option<Schema>) -> Self

Defines the fields of a dataset.

pub fn enable_visualization(self, input: bool) -> Self

Create an Explainability visualization that is viewable within the AWS console.

pub fn set_enable_visualization(self, input: Option<bool>) -> Self

Create an Explainability visualization that is viewable within the AWS console.

pub fn start_date_time(self, input: impl Into<String>) -> Self

If TimePointGranularity is set to SPECIFIC, define the first point for the Explainability.

Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

pub fn set_start_date_time(self, input: Option<String>) -> Self

If TimePointGranularity is set to SPECIFIC, define the first point for the Explainability.

Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

pub fn end_date_time(self, input: impl Into<String>) -> Self

If TimePointGranularity is set to SPECIFIC, define the last time point for the Explainability.

Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

pub fn set_end_date_time(self, input: Option<String>) -> Self

If TimePointGranularity is set to SPECIFIC, define the last time point for the Explainability.

Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

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.

The following restrictions apply to tags:

• For each resource, each tag key must be unique and each tag key must have one value.

• Maximum number of tags per resource: 50.

• Maximum key length: 128 Unicode characters in UTF-8.

• Maximum value length: 256 Unicode characters in UTF-8.

• 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.

• Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws 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 aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

The following restrictions apply to tags:

• For each resource, each tag key must be unique and each tag key must have one value.

• Maximum number of tags per resource: 50.

• Maximum key length: 128 Unicode characters in UTF-8.

• Maximum value length: 256 Unicode characters in UTF-8.

• 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.

• Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws 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 aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.

Trait Implementations

impl Clone for CreateExplainability

fn clone(&self) -> CreateExplainability

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateExplainability

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.