GetMlModelResponse (AWS Java SDK :: Services :: Amazon Machine Learning 2.16.96 API)

java.lang.Object
- software.amazon.awssdk.core.SdkResponse
- - software.amazon.awssdk.awscore.AwsResponse
  - - software.amazon.awssdk.services.machinelearning.model.MachineLearningResponse
    - - software.amazon.awssdk.services.machinelearning.model.GetMlModelResponse

All Implemented Interfaces:: SdkPojo, ToCopyableBuilder<GetMlModelResponse.Builder,GetMlModelResponse>

@Generated(value="software.amazon.awssdk:codegen")
public final class GetMlModelResponse
extends MachineLearningResponse
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 and Description

static interface GetMlModelResponse.Builder

Nested Classes
Modifier and Type	Class and Description
`static interface`	`GetMlModelResponse.Builder`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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.
`Instant`	`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)`
`boolean`	`equalsBySdkFields(Object obj)`
`Instant`	`finishedAt()` The epoch time when Amazon Machine Learning marked the `MLModel` as `COMPLETED` or `FAILED`.
`<T> Optional<T>`	`getValueForField(String fieldName, Class<T> clazz)`
`int`	`hashCode()`
`boolean`	`hasTrainingParameters()` Returns true if the TrainingParameters property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty).
`String`	`inputDataLocationS3()` The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`Instant`	`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.
`MLModelType`	`mlModelType()` Identifies the `MLModel` category.
`String`	`mlModelTypeAsString()` 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.
`Instant`	`scoreThresholdLastUpdatedAt()` The time of the most recent edit to the `ScoreThreshold`.
`List<SdkField<?>>`	`sdkFields()`
`static Class<? extends GetMlModelResponse.Builder>`	`serializableBuilderClass()`
`Long`	`sizeInBytes()` Returns the value of the SizeInBytes property for this object.
`Instant`	`startedAt()` The epoch time when Amazon Machine Learning marked the `MLModel` as `INPROGRESS`.
`EntityStatus`	`status()` The current status of the `MLModel`.
`String`	`statusAsString()` The current status of the `MLModel`.
`GetMlModelResponse.Builder`	`toBuilder()`
`String`	`toString()` Returns a string representation of this object.
`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.services.machinelearning.model.MachineLearningResponse
responseMetadata

Methods inherited from class software.amazon.awssdk.core.SdkResponse
sdkHttpResponse

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy

- Method Detail
  - mlModelId
```
public final 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 final String trainingDataSourceId()
```
    The ID of the training DataSource.
    
    Returns:
    
    The ID of the training DataSource.
  - createdByIamUser
```
public final 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 final Instant 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 final Instant 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 final String name()
```
    A user-supplied name or description of the MLModel.
    
    Returns:
    
    A user-supplied name or description of the MLModel.
  - status
```
public final EntityStatus 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.
    If the service returns an enum value that is not available in the current SDK version, status will return EntityStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from statusAsString().
    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
  - statusAsString
```
public final String statusAsString()
```
    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.
    If the service returns an enum value that is not available in the current SDK version, status will return EntityStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from statusAsString().
    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 final Long sizeInBytes()
```
    Returns the value of the SizeInBytes property for this object.
    
    Returns:
    
    The value of the SizeInBytes property for this object.
  - endpointInfo
```
public final RealtimeEndpointInfo endpointInfo()
```
    The current endpoint of the MLModel
    
    Returns:
    
    The current endpoint of the MLModel
  - hasTrainingParameters
```
public final boolean hasTrainingParameters()
```
    Returns true if the TrainingParameters property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS service.
  - trainingParameters
```
public final 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.
    Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
    
    You can use hasTrainingParameters() to see if a value was sent in this field.
    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 final 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 final MLModelType 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?"
    If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().
    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
  - mlModelTypeAsString
```
public final String mlModelTypeAsString()
```
    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?"
    If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().
    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 final 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 final Instant 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 final 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 final 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 final 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 final Instant 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 final Instant 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 final 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.
    
    Note: 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.
    
    Note: This parameter is provided as part of the verbose format.
  - schema
```
public final String schema()
```
    The schema used by all of the data files referenced by the DataSource.
    
    Note: This parameter is provided as part of the verbose format.
    
    Returns:
    
    The schema used by all of the data files referenced by the DataSource.
    
    Note: This parameter is provided as part of the verbose format.
  - toBuilder
```
public GetMlModelResponse.Builder toBuilder()
```
    Specified by:
    
    toBuilder in interface ToCopyableBuilder<GetMlModelResponse.Builder,GetMlModelResponse>
    
    Overrides:
    
    toBuilder in class AwsResponse
  - builder
```
public static GetMlModelResponse.Builder builder()
```
  - serializableBuilderClass
```
public static Class<? extends GetMlModelResponse.Builder> serializableBuilderClass()
```
  - hashCode
```
public final int hashCode()
```
    Overrides:
    
    hashCode in class AwsResponse
  - equals
```
public final boolean equals(Object obj)
```
    Overrides:
    
    equals in class AwsResponse
  - equalsBySdkFields
```
public final boolean equalsBySdkFields(Object obj)
```
    Specified by:
    
    equalsBySdkFields in interface SdkPojo
  - toString
```
public final String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
  - getValueForField
```
public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)
```
    Overrides:
    
    getValueForField in class SdkResponse
  - sdkFields
```
public final List<SdkField<?>> sdkFields()
```
    Specified by:
    
    sdkFields in interface SdkPojo

Class GetMlModelResponse

Nested Class Summary

Method Summary

Methods inherited from class software.amazon.awssdk.services.machinelearning.model.MachineLearningResponse

Methods inherited from class software.amazon.awssdk.core.SdkResponse

Methods inherited from class java.lang.Object

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

Method Detail

mlModelId

trainingDataSourceId

createdByIamUser

createdAt

lastUpdatedAt

name

status

statusAsString

sizeInBytes

endpointInfo

hasTrainingParameters

trainingParameters

inputDataLocationS3

mlModelType

mlModelTypeAsString

scoreThreshold

scoreThresholdLastUpdatedAt

logUri

message

computeTime

finishedAt

startedAt

recipe

schema

toBuilder

builder

serializableBuilderClass

hashCode

equals

equalsBySdkFields

toString

getValueForField

sdkFields