[ aws . machinelearning ]
Returns an Evaluation
that includes metadata as well as the current status of the Evaluation
.
See also: AWS API Documentation
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]
--evaluation-id
(string)
The ID of the
Evaluation
to retrieve. The evaluation of eachMLModel
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.
--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.
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 anMLModel
.
INPROGRESS
- The evaluation is underway.
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable.
COMPLETED
- The evaluation process completed successfully.
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
PerformanceMetrics -> (structure)
Measurements of how well the
MLModel
performed using observations referenced by theDataSource
. One of the following metric is returned based on the type of theMLModel
:
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 theEvaluation
is in theCOMPLETED
state.
FinishedAt -> (timestamp)
The epoch time when Amazon Machine Learning marked the
Evaluation
asCOMPLETED
orFAILED
.FinishedAt
is only available when theEvaluation
is in theCOMPLETED
orFAILED
state.
StartedAt -> (timestamp)
The epoch time when Amazon Machine Learning marked the
Evaluation
asINPROGRESS
.StartedAt
isn’t available if theEvaluation
is in thePENDING
state.