This example illustrates one usage of GetEvaluation.
Sample Request
POST / HTTP/1.1
Host: machinelearning.<region>.<domain>
x-amz-Date: <Date>
Authorization: AWS4-HMAC-SHA256 Credential=<Credential>,
SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn-requestid,Signature=<Signature>
User-Agent: <UserAgentString>
Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes>
Connection: Keep-Alive
X-Amz-Target: AmazonML_20141212.GetEvaluation {"EvaluationId": "ev-2014-09-12-15-14-04-924"}
Sample Response
HTTP/1.1 200 OK
x-amzn-RequestId: <RequestId>
Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes>
See Also
Date: <Date>
{ "CreatedAt":1410560805.669,
"CreatedByIamUser":"arn:aws:iam::<awsAccountId>:user/user", "EvaluationDataSourceId":"EXAMPLE-ev-ds-2014-09-12-15-14-04-411", "EvaluationId":"ev-2014-09-12-15-14-04-924",
"InputDataLocationS3": "s3://eml-test-EXAMPLE/example.csv", "LastUpdatedAt":1410560805.669,
"LogUri": "https://s3bucket/locationToLogs/logname.tar.gz", "Name":"EXAMPLE",
"PerformanceMetrics":{"Properties":{}},
"MLModelId":"EXAMPLE-pr-2014-09-12-15-14-04-924", "Status":"COMPLETED",
"ComputeTime":"185200", "FinishedAt":1410560805.669, "StartedAt":1410560805.669 }
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following:
• AWS Command Line Interface
• AWS SDK for .NET
• AWS SDK for C++
• AWS SDK for Go
• AWS SDK for Java V2
• AWS SDK for JavaScript
• AWS SDK for PHP V3
• AWS SDK for Python
• AWS SDK for Ruby V3
GetMLModel
GetMLModel
Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
Request Syntax
{
"MLModelId": "string", "Verbose": boolean }
Request Parameters
For information about the parameters that are common to all actions, see Common Parameters (p. 159).
The request accepts the following data in JSON format.
MLModelId (p. 98)
The ID assigned to the MLModel at creation.
Type: String
Length Constraints: Minimum length of 1. Maximum length of 64.
Pattern: [a-zA-Z0-9_.-]+
Required: Yes Verbose (p. 98)
Specifies whether the GetMLModel operation should return Recipe.
If true, Recipe is returned.
If false, Recipe is not returned.
Type: Boolean Required: No
Response Syntax
{ "ComputeTime": number, "CreatedAt": number,
"CreatedByIamUser": "string", "EndpointInfo": {
"CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string",
"PeakRequestsPerSecond": number
Response Elements
},
"FinishedAt": number,
"InputDataLocationS3": "string", "LastUpdatedAt": number,
"LogUri": "string", "Message": "string", "MLModelId": "string", "MLModelType": "string", "Name": "string", "Recipe": "string", "Schema": "string", "ScoreThreshold": number,
"ScoreThresholdLastUpdatedAt": number, "SizeInBytes": number,
"StartedAt": number, "Status": "string",
"TrainingDataSourceId": "string", "TrainingParameters": {
"string" : "string"
}}
Response Elements
If the action is successful, the service sends back an HTTP 200 response.
The following data is returned in JSON format by the service.
ComputeTime (p. 98)
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.
Type: Long CreatedAt (p. 98)
The time that the MLModel was created. The time is expressed in epoch time.
Type: Timestamp CreatedByIamUser (p. 98)
The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
Type: String
Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) EndpointInfo (p. 98)
The current endpoint of the MLModel Type: RealtimeEndpointInfo (p. 146) object FinishedAt (p. 98)
The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED.
FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.
Type: Timestamp
Response Elements
InputDataLocationS3 (p. 98)
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
Type: String
Length Constraints: Maximum length of 2048.
Pattern: s3://([^/]+)(/.*)?
LastUpdatedAt (p. 98)
The time of the most recent edit to the MLModel. The time is expressed in epoch time.
Type: Timestamp LogUri (p. 98)
A link to the file that contains logs of the CreateMLModel operation.
Type: String Message (p. 98)
A description of the most recent details about accessing the MLModel.
Type: String
Length Constraints: Maximum length of 10240.
MLModelId (p. 98)
The MLModel ID, which is same as the MLModelId in the request.
Type: String
Length Constraints: Minimum length of 1. Maximum length of 64.
Pattern: [a-zA-Z0-9_.-]+
MLModelType (p. 98)
Identifies the MLModel category. The following are the available types:
• REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"
• BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
• MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
Type: String
Valid Values: REGRESSION | BINARY | MULTICLASS Name (p. 98)
A user-supplied name or description of the MLModel.
Type: String
Length Constraints: Maximum length of 1024.
Recipe (p. 98)
The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
Response Elements
Note: This parameter is provided as part of the verbose format.
Type: String
Length Constraints: Maximum length of 131071.
Schema (p. 98)
The schema used by all of the data files referenced by the DataSource.
Note: This parameter is provided as part of the verbose format.
Type: String
Length Constraints: Maximum length of 131071.
ScoreThreshold (p. 98)
The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.
Type: Float
ScoreThresholdLastUpdatedAt (p. 98)
The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
Type: Timestamp SizeInBytes (p. 98)
Long integer type that is a 64-bit signed number.
Type: Long StartedAt (p. 98)
The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.
Type: Timestamp Status (p. 98)
The current status of the MLModel. This element can have one of the following values:
• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
• INPROGRESS - The request is processing.
• FAILED - The request did not run to completion. The ML model isn't usable.
• COMPLETED - The request completed successfully.
• DELETED - The MLModel is marked as deleted. It isn't usable.
Type: String
Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED TrainingDataSourceId (p. 98)
The ID of the training DataSource.
Errors
Type: String
Length Constraints: Minimum length of 1. Maximum length of 64.
Pattern: [a-zA-Z0-9_.-]+
TrainingParameters (p. 98)
A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.
The following is the current set of training parameters:
• sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 100. The default value is 10.
• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.
• sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1
normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
• sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values.
If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2
normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
Type: String to string map
Errors
For information about the errors that are common to all actions, see Common Errors (p. 161).
InternalServerException
An error on the server occurred when trying to process a request.
HTTP Status Code: 500 InvalidInputException
An error on the client occurred. Typically, the cause is an invalid input value.
HTTP Status Code: 400 ResourceNotFoundException
A specified resource cannot be located.
HTTP Status Code: 400
Examples