AWS SDK for C++  1.9.132
AWS SDK for C++
Public Member Functions | List of all members
Aws::MachineLearning::Model::GetMLModelResult Class Reference

#include <GetMLModelResult.h>

Public Member Functions

 GetMLModelResult ()
 
 GetMLModelResult (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
 
GetMLModelResultoperator= (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
 
const Aws::StringGetMLModelId () const
 
void SetMLModelId (const Aws::String &value)
 
void SetMLModelId (Aws::String &&value)
 
void SetMLModelId (const char *value)
 
GetMLModelResultWithMLModelId (const Aws::String &value)
 
GetMLModelResultWithMLModelId (Aws::String &&value)
 
GetMLModelResultWithMLModelId (const char *value)
 
const Aws::StringGetTrainingDataSourceId () const
 
void SetTrainingDataSourceId (const Aws::String &value)
 
void SetTrainingDataSourceId (Aws::String &&value)
 
void SetTrainingDataSourceId (const char *value)
 
GetMLModelResultWithTrainingDataSourceId (const Aws::String &value)
 
GetMLModelResultWithTrainingDataSourceId (Aws::String &&value)
 
GetMLModelResultWithTrainingDataSourceId (const char *value)
 
const Aws::StringGetCreatedByIamUser () const
 
void SetCreatedByIamUser (const Aws::String &value)
 
void SetCreatedByIamUser (Aws::String &&value)
 
void SetCreatedByIamUser (const char *value)
 
GetMLModelResultWithCreatedByIamUser (const Aws::String &value)
 
GetMLModelResultWithCreatedByIamUser (Aws::String &&value)
 
GetMLModelResultWithCreatedByIamUser (const char *value)
 
const Aws::Utils::DateTimeGetCreatedAt () const
 
void SetCreatedAt (const Aws::Utils::DateTime &value)
 
void SetCreatedAt (Aws::Utils::DateTime &&value)
 
GetMLModelResultWithCreatedAt (const Aws::Utils::DateTime &value)
 
GetMLModelResultWithCreatedAt (Aws::Utils::DateTime &&value)
 
const Aws::Utils::DateTimeGetLastUpdatedAt () const
 
void SetLastUpdatedAt (const Aws::Utils::DateTime &value)
 
void SetLastUpdatedAt (Aws::Utils::DateTime &&value)
 
GetMLModelResultWithLastUpdatedAt (const Aws::Utils::DateTime &value)
 
GetMLModelResultWithLastUpdatedAt (Aws::Utils::DateTime &&value)
 
const Aws::StringGetName () const
 
void SetName (const Aws::String &value)
 
void SetName (Aws::String &&value)
 
void SetName (const char *value)
 
GetMLModelResultWithName (const Aws::String &value)
 
GetMLModelResultWithName (Aws::String &&value)
 
GetMLModelResultWithName (const char *value)
 
const EntityStatusGetStatus () const
 
void SetStatus (const EntityStatus &value)
 
void SetStatus (EntityStatus &&value)
 
GetMLModelResultWithStatus (const EntityStatus &value)
 
GetMLModelResultWithStatus (EntityStatus &&value)
 
long long GetSizeInBytes () const
 
void SetSizeInBytes (long long value)
 
GetMLModelResultWithSizeInBytes (long long value)
 
const RealtimeEndpointInfoGetEndpointInfo () const
 
void SetEndpointInfo (const RealtimeEndpointInfo &value)
 
void SetEndpointInfo (RealtimeEndpointInfo &&value)
 
GetMLModelResultWithEndpointInfo (const RealtimeEndpointInfo &value)
 
GetMLModelResultWithEndpointInfo (RealtimeEndpointInfo &&value)
 
const Aws::Map< Aws::String, Aws::String > & GetTrainingParameters () const
 
void SetTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
GetMLModelResultWithTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
GetMLModelResultWithTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
GetMLModelResultAddTrainingParameters (const Aws::String &key, const Aws::String &value)
 
GetMLModelResultAddTrainingParameters (Aws::String &&key, const Aws::String &value)
 
GetMLModelResultAddTrainingParameters (const Aws::String &key, Aws::String &&value)
 
GetMLModelResultAddTrainingParameters (Aws::String &&key, Aws::String &&value)
 
GetMLModelResultAddTrainingParameters (const char *key, Aws::String &&value)
 
GetMLModelResultAddTrainingParameters (Aws::String &&key, const char *value)
 
GetMLModelResultAddTrainingParameters (const char *key, const char *value)
 
const Aws::StringGetInputDataLocationS3 () const
 
void SetInputDataLocationS3 (const Aws::String &value)
 
void SetInputDataLocationS3 (Aws::String &&value)
 
void SetInputDataLocationS3 (const char *value)
 
GetMLModelResultWithInputDataLocationS3 (const Aws::String &value)
 
GetMLModelResultWithInputDataLocationS3 (Aws::String &&value)
 
GetMLModelResultWithInputDataLocationS3 (const char *value)
 
const MLModelTypeGetMLModelType () const
 
void SetMLModelType (const MLModelType &value)
 
void SetMLModelType (MLModelType &&value)
 
GetMLModelResultWithMLModelType (const MLModelType &value)
 
GetMLModelResultWithMLModelType (MLModelType &&value)
 
double GetScoreThreshold () const
 
void SetScoreThreshold (double value)
 
GetMLModelResultWithScoreThreshold (double value)
 
const Aws::Utils::DateTimeGetScoreThresholdLastUpdatedAt () const
 
void SetScoreThresholdLastUpdatedAt (const Aws::Utils::DateTime &value)
 
void SetScoreThresholdLastUpdatedAt (Aws::Utils::DateTime &&value)
 
GetMLModelResultWithScoreThresholdLastUpdatedAt (const Aws::Utils::DateTime &value)
 
GetMLModelResultWithScoreThresholdLastUpdatedAt (Aws::Utils::DateTime &&value)
 
const Aws::StringGetLogUri () const
 
void SetLogUri (const Aws::String &value)
 
void SetLogUri (Aws::String &&value)
 
void SetLogUri (const char *value)
 
GetMLModelResultWithLogUri (const Aws::String &value)
 
GetMLModelResultWithLogUri (Aws::String &&value)
 
GetMLModelResultWithLogUri (const char *value)
 
const Aws::StringGetMessage () const
 
void SetMessage (const Aws::String &value)
 
void SetMessage (Aws::String &&value)
 
void SetMessage (const char *value)
 
GetMLModelResultWithMessage (const Aws::String &value)
 
GetMLModelResultWithMessage (Aws::String &&value)
 
GetMLModelResultWithMessage (const char *value)
 
long long GetComputeTime () const
 
void SetComputeTime (long long value)
 
GetMLModelResultWithComputeTime (long long value)
 
const Aws::Utils::DateTimeGetFinishedAt () const
 
void SetFinishedAt (const Aws::Utils::DateTime &value)
 
void SetFinishedAt (Aws::Utils::DateTime &&value)
 
GetMLModelResultWithFinishedAt (const Aws::Utils::DateTime &value)
 
GetMLModelResultWithFinishedAt (Aws::Utils::DateTime &&value)
 
const Aws::Utils::DateTimeGetStartedAt () const
 
void SetStartedAt (const Aws::Utils::DateTime &value)
 
void SetStartedAt (Aws::Utils::DateTime &&value)
 
GetMLModelResultWithStartedAt (const Aws::Utils::DateTime &value)
 
GetMLModelResultWithStartedAt (Aws::Utils::DateTime &&value)
 
const Aws::StringGetRecipe () const
 
void SetRecipe (const Aws::String &value)
 
void SetRecipe (Aws::String &&value)
 
void SetRecipe (const char *value)
 
GetMLModelResultWithRecipe (const Aws::String &value)
 
GetMLModelResultWithRecipe (Aws::String &&value)
 
GetMLModelResultWithRecipe (const char *value)
 
const Aws::StringGetSchema () const
 
void SetSchema (const Aws::String &value)
 
void SetSchema (Aws::String &&value)
 
void SetSchema (const char *value)
 
GetMLModelResultWithSchema (const Aws::String &value)
 
GetMLModelResultWithSchema (Aws::String &&value)
 
GetMLModelResultWithSchema (const char *value)
 

Detailed Description

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

See Also:

AWS API Reference

Definition at line 38 of file GetMLModelResult.h.

Constructor & Destructor Documentation

◆ GetMLModelResult() [1/2]

Aws::MachineLearning::Model::GetMLModelResult::GetMLModelResult ( )

◆ GetMLModelResult() [2/2]

Aws::MachineLearning::Model::GetMLModelResult::GetMLModelResult ( const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &  result)

Member Function Documentation

◆ AddTrainingParameters() [1/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( Aws::String &&  key,
Aws::String &&  value 
)
inline

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.

Definition at line 674 of file GetMLModelResult.h.

◆ AddTrainingParameters() [2/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( Aws::String &&  key,
const Aws::String value 
)
inline

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.

Definition at line 606 of file GetMLModelResult.h.

◆ AddTrainingParameters() [3/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( Aws::String &&  key,
const char *  value 
)
inline

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.

Definition at line 742 of file GetMLModelResult.h.

◆ AddTrainingParameters() [4/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( const Aws::String key,
Aws::String &&  value 
)
inline

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.

Definition at line 640 of file GetMLModelResult.h.

◆ AddTrainingParameters() [5/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( const Aws::String key,
const Aws::String value 
)
inline

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.

Definition at line 572 of file GetMLModelResult.h.

◆ AddTrainingParameters() [6/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( const char *  key,
Aws::String &&  value 
)
inline

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.

Definition at line 708 of file GetMLModelResult.h.

◆ AddTrainingParameters() [7/7]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::AddTrainingParameters ( const char *  key,
const char *  value 
)
inline

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.

Definition at line 776 of file GetMLModelResult.h.

◆ GetComputeTime()

long long Aws::MachineLearning::Model::GetMLModelResult::GetComputeTime ( ) const
inline

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.

Definition at line 1027 of file GetMLModelResult.h.

◆ GetCreatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::GetMLModelResult::GetCreatedAt ( ) const
inline

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

Definition at line 179 of file GetMLModelResult.h.

◆ GetCreatedByIamUser()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetCreatedByIamUser ( ) const
inline

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.

Definition at line 130 of file GetMLModelResult.h.

◆ GetEndpointInfo()

const RealtimeEndpointInfo& Aws::MachineLearning::Model::GetMLModelResult::GetEndpointInfo ( ) const
inline

The current endpoint of the MLModel

Definition at line 347 of file GetMLModelResult.h.

◆ GetFinishedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::GetMLModelResult::GetFinishedAt ( ) const
inline

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.

Definition at line 1052 of file GetMLModelResult.h.

◆ GetInputDataLocationS3()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetInputDataLocationS3 ( ) const
inline

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

Definition at line 783 of file GetMLModelResult.h.

◆ GetLastUpdatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::GetMLModelResult::GetLastUpdatedAt ( ) const
inline

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

Definition at line 210 of file GetMLModelResult.h.

◆ GetLogUri()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetLogUri ( ) const
inline

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

Definition at line 939 of file GetMLModelResult.h.

◆ GetMessage()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetMessage ( ) const
inline

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

Definition at line 982 of file GetMLModelResult.h.

◆ GetMLModelId()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetMLModelId ( ) const
inline

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

Definition at line 50 of file GetMLModelResult.h.

◆ GetMLModelType()

const MLModelType& Aws::MachineLearning::Model::GetMLModelResult::GetMLModelType ( ) const
inline

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?"

Definition at line 830 of file GetMLModelResult.h.

◆ GetName()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetName ( ) const
inline

A user-supplied name or description of the MLModel.

Definition at line 240 of file GetMLModelResult.h.

◆ GetRecipe()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetRecipe ( ) const
inline

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.

Definition at line 1130 of file GetMLModelResult.h.

◆ GetSchema()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetSchema ( ) const
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1192 of file GetMLModelResult.h.

◆ GetScoreThreshold()

double Aws::MachineLearning::Model::GetMLModelResult::GetScoreThreshold ( ) const
inline

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.

Definition at line 881 of file GetMLModelResult.h.

◆ GetScoreThresholdLastUpdatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::GetMLModelResult::GetScoreThresholdLastUpdatedAt ( ) const
inline

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

Definition at line 908 of file GetMLModelResult.h.

◆ GetSizeInBytes()

long long Aws::MachineLearning::Model::GetMLModelResult::GetSizeInBytes ( ) const
inline

Definition at line 335 of file GetMLModelResult.h.

◆ GetStartedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::GetMLModelResult::GetStartedAt ( ) const
inline

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

Definition at line 1092 of file GetMLModelResult.h.

◆ GetStatus()

const EntityStatus& Aws::MachineLearning::Model::GetMLModelResult::GetStatus ( ) const
inline

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.

Definition at line 283 of file GetMLModelResult.h.

◆ GetTrainingDataSourceId()

const Aws::String& Aws::MachineLearning::Model::GetMLModelResult::GetTrainingDataSourceId ( ) const
inline

The ID of the training DataSource.

Definition at line 92 of file GetMLModelResult.h.

◆ GetTrainingParameters()

const Aws::Map<Aws::String, Aws::String>& Aws::MachineLearning::Model::GetMLModelResult::GetTrainingParameters ( ) const
inline

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.

Definition at line 402 of file GetMLModelResult.h.

◆ operator=()

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::operator= ( const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &  result)

◆ SetComputeTime()

void Aws::MachineLearning::Model::GetMLModelResult::SetComputeTime ( long long  value)
inline

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.

Definition at line 1035 of file GetMLModelResult.h.

◆ SetCreatedAt() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetCreatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 191 of file GetMLModelResult.h.

◆ SetCreatedAt() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetCreatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 185 of file GetMLModelResult.h.

◆ SetCreatedByIamUser() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetCreatedByIamUser ( Aws::String &&  value)
inline

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.

Definition at line 144 of file GetMLModelResult.h.

◆ SetCreatedByIamUser() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetCreatedByIamUser ( const Aws::String value)
inline

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.

Definition at line 137 of file GetMLModelResult.h.

◆ SetCreatedByIamUser() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetCreatedByIamUser ( const char *  value)
inline

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.

Definition at line 151 of file GetMLModelResult.h.

◆ SetEndpointInfo() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetEndpointInfo ( const RealtimeEndpointInfo value)
inline

The current endpoint of the MLModel

Definition at line 352 of file GetMLModelResult.h.

◆ SetEndpointInfo() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetEndpointInfo ( RealtimeEndpointInfo &&  value)
inline

The current endpoint of the MLModel

Definition at line 357 of file GetMLModelResult.h.

◆ SetFinishedAt() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetFinishedAt ( Aws::Utils::DateTime &&  value)
inline

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.

Definition at line 1068 of file GetMLModelResult.h.

◆ SetFinishedAt() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetFinishedAt ( const Aws::Utils::DateTime value)
inline

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.

Definition at line 1060 of file GetMLModelResult.h.

◆ SetInputDataLocationS3() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetInputDataLocationS3 ( Aws::String &&  value)
inline

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

Definition at line 795 of file GetMLModelResult.h.

◆ SetInputDataLocationS3() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetInputDataLocationS3 ( const Aws::String value)
inline

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

Definition at line 789 of file GetMLModelResult.h.

◆ SetInputDataLocationS3() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetInputDataLocationS3 ( const char *  value)
inline

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

Definition at line 801 of file GetMLModelResult.h.

◆ SetLastUpdatedAt() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 222 of file GetMLModelResult.h.

◆ SetLastUpdatedAt() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 216 of file GetMLModelResult.h.

◆ SetLogUri() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetLogUri ( Aws::String &&  value)
inline

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

Definition at line 951 of file GetMLModelResult.h.

◆ SetLogUri() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetLogUri ( const Aws::String value)
inline

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

Definition at line 945 of file GetMLModelResult.h.

◆ SetLogUri() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetLogUri ( const char *  value)
inline

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

Definition at line 957 of file GetMLModelResult.h.

◆ SetMessage() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMessage ( Aws::String &&  value)
inline

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

Definition at line 994 of file GetMLModelResult.h.

◆ SetMessage() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMessage ( const Aws::String value)
inline

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

Definition at line 988 of file GetMLModelResult.h.

◆ SetMessage() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMessage ( const char *  value)
inline

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

Definition at line 1000 of file GetMLModelResult.h.

◆ SetMLModelId() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMLModelId ( Aws::String &&  value)
inline

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

Definition at line 62 of file GetMLModelResult.h.

◆ SetMLModelId() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMLModelId ( const Aws::String value)
inline

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

Definition at line 56 of file GetMLModelResult.h.

◆ SetMLModelId() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetMLModelId ( const char *  value)
inline

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

Definition at line 68 of file GetMLModelResult.h.

◆ SetMLModelType() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetMLModelType ( const MLModelType value)
inline

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?"

Definition at line 840 of file GetMLModelResult.h.

◆ SetMLModelType() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetMLModelType ( MLModelType &&  value)
inline

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?"

Definition at line 850 of file GetMLModelResult.h.

◆ SetName() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 250 of file GetMLModelResult.h.

◆ SetName() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 245 of file GetMLModelResult.h.

◆ SetName() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 255 of file GetMLModelResult.h.

◆ SetRecipe() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetRecipe ( Aws::String &&  value)
inline

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.

Definition at line 1148 of file GetMLModelResult.h.

◆ SetRecipe() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetRecipe ( const Aws::String value)
inline

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.

Definition at line 1139 of file GetMLModelResult.h.

◆ SetRecipe() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetRecipe ( const char *  value)
inline

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.

Definition at line 1157 of file GetMLModelResult.h.

◆ SetSchema() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetSchema ( Aws::String &&  value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1206 of file GetMLModelResult.h.

◆ SetSchema() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetSchema ( const Aws::String value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1199 of file GetMLModelResult.h.

◆ SetSchema() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetSchema ( const char *  value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1213 of file GetMLModelResult.h.

◆ SetScoreThreshold()

void Aws::MachineLearning::Model::GetMLModelResult::SetScoreThreshold ( double  value)
inline

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.

Definition at line 891 of file GetMLModelResult.h.

◆ SetScoreThresholdLastUpdatedAt() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetScoreThresholdLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 920 of file GetMLModelResult.h.

◆ SetScoreThresholdLastUpdatedAt() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetScoreThresholdLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 914 of file GetMLModelResult.h.

◆ SetSizeInBytes()

void Aws::MachineLearning::Model::GetMLModelResult::SetSizeInBytes ( long long  value)
inline

Definition at line 338 of file GetMLModelResult.h.

◆ SetStartedAt() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetStartedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 1106 of file GetMLModelResult.h.

◆ SetStartedAt() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetStartedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 1099 of file GetMLModelResult.h.

◆ SetStatus() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetStatus ( const EntityStatus value)
inline

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.

Definition at line 295 of file GetMLModelResult.h.

◆ SetStatus() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetStatus ( EntityStatus &&  value)
inline

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.

Definition at line 307 of file GetMLModelResult.h.

◆ SetTrainingDataSourceId() [1/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetTrainingDataSourceId ( Aws::String &&  value)
inline

The ID of the training DataSource.

Definition at line 102 of file GetMLModelResult.h.

◆ SetTrainingDataSourceId() [2/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetTrainingDataSourceId ( const Aws::String value)
inline

The ID of the training DataSource.

Definition at line 97 of file GetMLModelResult.h.

◆ SetTrainingDataSourceId() [3/3]

void Aws::MachineLearning::Model::GetMLModelResult::SetTrainingDataSourceId ( const char *  value)
inline

The ID of the training DataSource.

Definition at line 107 of file GetMLModelResult.h.

◆ SetTrainingParameters() [1/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetTrainingParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 470 of file GetMLModelResult.h.

◆ SetTrainingParameters() [2/2]

void Aws::MachineLearning::Model::GetMLModelResult::SetTrainingParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 436 of file GetMLModelResult.h.

◆ WithComputeTime()

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithComputeTime ( long long  value)
inline

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.

Definition at line 1043 of file GetMLModelResult.h.

◆ WithCreatedAt() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithCreatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 203 of file GetMLModelResult.h.

◆ WithCreatedAt() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithCreatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 197 of file GetMLModelResult.h.

◆ WithCreatedByIamUser() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithCreatedByIamUser ( Aws::String &&  value)
inline

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.

Definition at line 165 of file GetMLModelResult.h.

◆ WithCreatedByIamUser() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithCreatedByIamUser ( const Aws::String value)
inline

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.

Definition at line 158 of file GetMLModelResult.h.

◆ WithCreatedByIamUser() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithCreatedByIamUser ( const char *  value)
inline

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.

Definition at line 172 of file GetMLModelResult.h.

◆ WithEndpointInfo() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithEndpointInfo ( const RealtimeEndpointInfo value)
inline

The current endpoint of the MLModel

Definition at line 362 of file GetMLModelResult.h.

◆ WithEndpointInfo() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithEndpointInfo ( RealtimeEndpointInfo &&  value)
inline

The current endpoint of the MLModel

Definition at line 367 of file GetMLModelResult.h.

◆ WithFinishedAt() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithFinishedAt ( Aws::Utils::DateTime &&  value)
inline

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.

Definition at line 1084 of file GetMLModelResult.h.

◆ WithFinishedAt() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithFinishedAt ( const Aws::Utils::DateTime value)
inline

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.

Definition at line 1076 of file GetMLModelResult.h.

◆ WithInputDataLocationS3() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithInputDataLocationS3 ( Aws::String &&  value)
inline

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

Definition at line 813 of file GetMLModelResult.h.

◆ WithInputDataLocationS3() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithInputDataLocationS3 ( const Aws::String value)
inline

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

Definition at line 807 of file GetMLModelResult.h.

◆ WithInputDataLocationS3() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithInputDataLocationS3 ( const char *  value)
inline

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

Definition at line 819 of file GetMLModelResult.h.

◆ WithLastUpdatedAt() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 234 of file GetMLModelResult.h.

◆ WithLastUpdatedAt() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 228 of file GetMLModelResult.h.

◆ WithLogUri() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithLogUri ( Aws::String &&  value)
inline

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

Definition at line 969 of file GetMLModelResult.h.

◆ WithLogUri() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithLogUri ( const Aws::String value)
inline

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

Definition at line 963 of file GetMLModelResult.h.

◆ WithLogUri() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithLogUri ( const char *  value)
inline

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

Definition at line 975 of file GetMLModelResult.h.

◆ WithMessage() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMessage ( Aws::String &&  value)
inline

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

Definition at line 1012 of file GetMLModelResult.h.

◆ WithMessage() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMessage ( const Aws::String value)
inline

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

Definition at line 1006 of file GetMLModelResult.h.

◆ WithMessage() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMessage ( const char *  value)
inline

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

Definition at line 1018 of file GetMLModelResult.h.

◆ WithMLModelId() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMLModelId ( Aws::String &&  value)
inline

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

Definition at line 80 of file GetMLModelResult.h.

◆ WithMLModelId() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMLModelId ( const Aws::String value)
inline

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

Definition at line 74 of file GetMLModelResult.h.

◆ WithMLModelId() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMLModelId ( const char *  value)
inline

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

Definition at line 86 of file GetMLModelResult.h.

◆ WithMLModelType() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMLModelType ( const MLModelType value)
inline

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?"

Definition at line 860 of file GetMLModelResult.h.

◆ WithMLModelType() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithMLModelType ( MLModelType &&  value)
inline

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?"

Definition at line 870 of file GetMLModelResult.h.

◆ WithName() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 265 of file GetMLModelResult.h.

◆ WithName() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 260 of file GetMLModelResult.h.

◆ WithName() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 270 of file GetMLModelResult.h.

◆ WithRecipe() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithRecipe ( Aws::String &&  value)
inline

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.

Definition at line 1175 of file GetMLModelResult.h.

◆ WithRecipe() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithRecipe ( const Aws::String value)
inline

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.

Definition at line 1166 of file GetMLModelResult.h.

◆ WithRecipe() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithRecipe ( const char *  value)
inline

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.

Definition at line 1184 of file GetMLModelResult.h.

◆ WithSchema() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithSchema ( Aws::String &&  value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1227 of file GetMLModelResult.h.

◆ WithSchema() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithSchema ( const Aws::String value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1220 of file GetMLModelResult.h.

◆ WithSchema() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithSchema ( const char *  value)
inline

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

Note: This parameter is provided as part of the verbose format.

Definition at line 1234 of file GetMLModelResult.h.

◆ WithScoreThreshold()

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithScoreThreshold ( double  value)
inline

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.

Definition at line 901 of file GetMLModelResult.h.

◆ WithScoreThresholdLastUpdatedAt() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithScoreThresholdLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 932 of file GetMLModelResult.h.

◆ WithScoreThresholdLastUpdatedAt() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithScoreThresholdLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 926 of file GetMLModelResult.h.

◆ WithSizeInBytes()

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithSizeInBytes ( long long  value)
inline

Definition at line 341 of file GetMLModelResult.h.

◆ WithStartedAt() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithStartedAt ( Aws::Utils::DateTime &&  value)
inline

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

Definition at line 1120 of file GetMLModelResult.h.

◆ WithStartedAt() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithStartedAt ( const Aws::Utils::DateTime value)
inline

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

Definition at line 1113 of file GetMLModelResult.h.

◆ WithStatus() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithStatus ( const EntityStatus value)
inline

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.

Definition at line 319 of file GetMLModelResult.h.

◆ WithStatus() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithStatus ( EntityStatus &&  value)
inline

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.

Definition at line 331 of file GetMLModelResult.h.

◆ WithTrainingDataSourceId() [1/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithTrainingDataSourceId ( Aws::String &&  value)
inline

The ID of the training DataSource.

Definition at line 117 of file GetMLModelResult.h.

◆ WithTrainingDataSourceId() [2/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithTrainingDataSourceId ( const Aws::String value)
inline

The ID of the training DataSource.

Definition at line 112 of file GetMLModelResult.h.

◆ WithTrainingDataSourceId() [3/3]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithTrainingDataSourceId ( const char *  value)
inline

The ID of the training DataSource.

Definition at line 122 of file GetMLModelResult.h.

◆ WithTrainingParameters() [1/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithTrainingParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 538 of file GetMLModelResult.h.

◆ WithTrainingParameters() [2/2]

GetMLModelResult& Aws::MachineLearning::Model::GetMLModelResult::WithTrainingParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 504 of file GetMLModelResult.h.


The documentation for this class was generated from the following file: