Package software.amazon.awssdk.services.machinelearning.model

Class GetMLModelResponse

java.lang.Object

software.amazon.awssdk.AmazonWebServiceResult<ResponseMetadata>

software.amazon.awssdk.services.machinelearning.model.GetMLModelResponse

All Implemented Interfaces:

ToCopyableBuilder<GetMLModelResponse.Builder,GetMLModelResponse>

@Generated("software.amazon.awssdk:codegen")
public class GetMLModelResponse
extends AmazonWebServiceResult<ResponseMetadata>
implements ToCopyableBuilder<GetMLModelResponse.Builder,GetMLModelResponse>

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static interface	GetMLModelResponse.Builder

Method Summary

Modifier and Type	Method	Description
static GetMLModelResponse.Builder	builder()
Long	computeTime()	The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources.
Date	createdAt()	The time that the MLModel was created.
String	createdByIamUser()	The AWS user account from which the MLModel was created.
RealtimeEndpointInfo	endpointInfo()	The current endpoint of the MLModel
boolean	equals(Object obj)
Date	finishedAt()	The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED.
int	hashCode()
String	inputDataLocationS3()	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
Date	lastUpdatedAt()	The time of the most recent edit to the MLModel.
String	logUri()	A link to the file that contains logs of the CreateMLModel operation.
String	message()	A description of the most recent details about accessing the MLModel.
String	mlModelId()	The MLModel ID, which is same as the MLModelId in the request.
String	mlModelType()	Identifies the MLModel category.
String	name()	A user-supplied name or description of the MLModel.
String	recipe()	The recipe to use when training the MLModel.
String	schema()	The schema used by all of the data files referenced by the DataSource.
Float	scoreThreshold()	The scoring threshold is used in binary classification MLModel models.
Date	scoreThresholdLastUpdatedAt()	The time of the most recent edit to the ScoreThreshold.
static Class<? extends GetMLModelResponse.Builder>	serializableBuilderClass()
Long	sizeInBytes()
Date	startedAt()	The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS.
String	status()	The current status of the MLModel.
GetMLModelResponse.Builder	toBuilder()	Take this object and create a builder that contains all of the current property values of this object.
String	toString()
String	trainingDataSourceId()	The ID of the training DataSource.
Map<String,String>	trainingParameters()	A list of the training parameters in the MLModel.

Methods inherited from class software.amazon.awssdk.AmazonWebServiceResult

setSdkHttpMetadata, setSdkResponseMetadata

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Method Detail

mlModelId

public String mlModelId()

The MLModel ID, which is same as the MLModelId in the request.

Returns:

The MLModel ID, which is same as the MLModelId in the request.

trainingDataSourceId

public String trainingDataSourceId()

The ID of the training DataSource.

Returns:

The ID of the training DataSource.

createdByIamUser

public String createdByIamUser()

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.

Returns:

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.

createdAt

public Date createdAt()

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

Returns:

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

lastUpdatedAt

public Date lastUpdatedAt()

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

Returns:

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

name

public String name()

A user-supplied name or description of the MLModel.

Returns:

A user-supplied name or description of the MLModel.

status

public String status()

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.

Returns:

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.

See Also:

EntityStatus

sizeInBytes

public Long sizeInBytes()

Returns:

endpointInfo

public RealtimeEndpointInfo endpointInfo()

The current endpoint of the MLModel

Returns:

The current endpoint of the MLModel

trainingParameters

public Map<String,String> trainingParameters()

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 10000. 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.

Returns:

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 10000. 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.

inputDataLocationS3

public String inputDataLocationS3()

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

Returns:

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

mlModelType

public String mlModelType()

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?"

Returns:

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?"

See Also:

MLModelType

scoreThreshold

public Float scoreThreshold()

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.

Returns:

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.

scoreThresholdLastUpdatedAt

public Date scoreThresholdLastUpdatedAt()

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

Returns:

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

logUri

public String logUri()

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

Returns:

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

message

public String message()

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

Returns:

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

computeTime

public Long computeTime()

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.

Returns:

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.

finishedAt

public Date finishedAt()

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.

Returns:

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.

startedAt

public Date startedAt()

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

Returns:

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

recipe

public String recipe()

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.

This parameter is provided as part of the verbose format.

Returns:

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.

This parameter is provided as part of the verbose format.

schema

public String schema()

The schema used by all of the data files referenced by the DataSource.

This parameter is provided as part of the verbose format.

Returns:

The schema used by all of the data files referenced by the DataSource.

This parameter is provided as part of the verbose format.

toBuilder

public GetMLModelResponse.Builder toBuilder()

Description copied from interface: ToCopyableBuilder

Take this object and create a builder that contains all of the current property values of this object.

Specified by:

toBuilder in interface ToCopyableBuilder<GetMLModelResponse.Builder,GetMLModelResponse>

Returns:

a builder for type T

builder

public static GetMLModelResponse.Builder builder()

serializableBuilderClass

public static Class<? extends GetMLModelResponse.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

toString

public String toString()

Overrides:

toString in class Object