ForecastService.Client.create_explainability(**kwargs)¶Note
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
Note
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
Note
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.See also: AWS API Documentation
Request Syntax
response = client.create_explainability(
ExplainabilityName='string',
ResourceArn='string',
ExplainabilityConfig={
'TimeSeriesGranularity': 'ALL'|'SPECIFIC',
'TimePointGranularity': 'ALL'|'SPECIFIC'
},
DataSource={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
Schema={
'Attributes': [
{
'AttributeName': 'string',
'AttributeType': 'string'|'integer'|'float'|'timestamp'|'geolocation'
},
]
},
EnableVisualization=True|False,
StartDateTime='string',
EndDateTime='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A unique name for the Explainability.
[REQUIRED]
The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.
[REQUIRED]
The configuration settings that define the granularity of time series and time points for the Explainability.
To create an Explainability for all time series in your datasets, use ALL . To create an Explainability for specific time series in your datasets, use SPECIFIC .
Specify time series by uploading a CSV or Parquet file to an Amazon S3 bucket and set the location within the DataDestination data type.
To create an Explainability for all time points in your forecast horizon, use ALL . To create an Explainability for specific time points in your forecast horizon, use SPECIFIC .
Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.
The source of your data, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an Key Management Service (KMS) key.
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the key.
Passing a role across Amazon Web Services accounts is not allowed. If you pass a role that isn't in your account, you get an InvalidInputException error.
The Amazon Resource Name (ARN) of an Key Management Service (KMS) key.
Defines the fields of a dataset.
An array of attributes specifying the name and type of each field in a dataset.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
The name of the dataset field.
The data type of the field.
For a related time series dataset, other than date, item_id, and forecast dimensions attributes, all attributes should be of numerical type (integer/float).
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)
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)
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:
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.The optional metadata that you apply to a resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
aws: , AWS: , or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then 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.One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'ExplainabilityArn': 'string'
}
Response Structure
(dict) --
ExplainabilityArn (string) --
The Amazon Resource Name (ARN) of the Explainability.
Exceptions
ForecastService.Client.exceptions.InvalidInputExceptionForecastService.Client.exceptions.ResourceAlreadyExistsExceptionForecastService.Client.exceptions.ResourceNotFoundExceptionForecastService.Client.exceptions.ResourceInUseExceptionForecastService.Client.exceptions.LimitExceededException