[ aws . machinelearning ]

get-evaluation

Description

Returns an Evaluation that includes metadata as well as the current status of the Evaluation .

See also: AWS API Documentation

Synopsis

  get-evaluation
--evaluation-id <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--evaluation-id (string)

The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination.

--output (string)

The formatting style for command output.

• json

• text

• table

• yaml

• yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

• on

• off

• auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

• base64

• raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

EvaluationId -> (string)

The evaluation ID which is same as the EvaluationId in the request.

MLModelId -> (string)

The ID of the MLModel that was the focus of the evaluation.

EvaluationDataSourceId -> (string)

The DataSource used for this evaluation.

InputDataLocationS3 -> (string)

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

CreatedByIamUser -> (string)

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

CreatedAt -> (timestamp)

The time that the Evaluation was created. The time is expressed in epoch time.

LastUpdatedAt -> (timestamp)

The time of the most recent edit to the Evaluation . The time is expressed in epoch time.

Name -> (string)

A user-supplied name or description of the Evaluation .

Status -> (string)

The status of the evaluation. This element can have one of the following values:

• PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel .

• INPROGRESS - The evaluation is underway.

• FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.

• COMPLETED - The evaluation process completed successfully.

• DELETED - The Evaluation is marked as deleted. It is not usable.

PerformanceMetrics -> (structure)

Measurements of how well the MLModel performed using observations referenced by the DataSource . One of the following metric is returned based on the type of the MLModel :

• BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

• RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

• MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide .

Properties -> (map)

key -> (string)

value -> (string)

LogUri -> (string)

A link to the file that contains logs of the CreateEvaluation operation.

Message -> (string)

A description of the most recent details about evaluating the MLModel .

ComputeTime -> (long)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation , normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.

FinishedAt -> (timestamp)

The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED . FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.

StartedAt -> (timestamp)

The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS . StartedAt isn’t available if the Evaluation is in the PENDING state.