com.amazonaws.services.machinelearning.model

Class CreateMLModelRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.machinelearning.model.CreateMLModelRequest

All Implemented Interfaces:

ReadLimitInfo, Serializable, Cloneable

public class CreateMLModelRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable

See Also:

Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreateMLModelRequest()

Method Summary

Modifier and Type	Method and Description
CreateMLModelRequest	addParametersEntry(String key, String value)
CreateMLModelRequest	clearParametersEntries() Removes all the entries added into Parameters.
CreateMLModelRequest	clone() Creates a shallow clone of this request.
boolean	equals(Object obj)
String	getMLModelId() A user-supplied ID that uniquely identifies the MLModel.
String	getMLModelName() A user-supplied name or description of the MLModel.
String	getMLModelType() The category of supervised learning that this MLModel will address.
Map<String,String>	getParameters() A list of the training parameters in the MLModel.
String	getRecipe() The data recipe for creating the MLModel.
String	getRecipeUri() The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe.
String	getTrainingDataSourceId() The DataSource that points to the training data.
int	hashCode()
void	setMLModelId(String mLModelId) A user-supplied ID that uniquely identifies the MLModel.
void	setMLModelName(String mLModelName) A user-supplied name or description of the MLModel.
void	setMLModelType(MLModelType mLModelType) The category of supervised learning that this MLModel will address.
void	setMLModelType(String mLModelType) The category of supervised learning that this MLModel will address.
void	setParameters(Map<String,String> parameters) A list of the training parameters in the MLModel.
void	setRecipe(String recipe) The data recipe for creating the MLModel.
void	setRecipeUri(String recipeUri) The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe.
void	setTrainingDataSourceId(String trainingDataSourceId) The DataSource that points to the training data.
String	toString() Returns a string representation of this object; useful for testing and debugging.
CreateMLModelRequest	withMLModelId(String mLModelId) A user-supplied ID that uniquely identifies the MLModel.
CreateMLModelRequest	withMLModelName(String mLModelName) A user-supplied name or description of the MLModel.
CreateMLModelRequest	withMLModelType(MLModelType mLModelType) The category of supervised learning that this MLModel will address.
CreateMLModelRequest	withMLModelType(String mLModelType) The category of supervised learning that this MLModel will address.
CreateMLModelRequest	withParameters(Map<String,String> parameters) A list of the training parameters in the MLModel.
CreateMLModelRequest	withRecipe(String recipe) The data recipe for creating the MLModel.
CreateMLModelRequest	withRecipeUri(String recipeUri) The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe.
CreateMLModelRequest	withTrainingDataSourceId(String trainingDataSourceId) The DataSource that points to the training data.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreateMLModelRequest

public CreateMLModelRequest()

Method Detail

setMLModelId

public void setMLModelId(String mLModelId)

A user-supplied ID that uniquely identifies the MLModel.

Parameters:

mLModelId - A user-supplied ID that uniquely identifies the MLModel.

getMLModelId

public String getMLModelId()

A user-supplied ID that uniquely identifies the MLModel.

Returns:

A user-supplied ID that uniquely identifies the MLModel.

withMLModelId

public CreateMLModelRequest withMLModelId(String mLModelId)

A user-supplied ID that uniquely identifies the MLModel.

Parameters:

mLModelId - A user-supplied ID that uniquely identifies the MLModel.

Returns:

Returns a reference to this object so that method calls can be chained together.

setMLModelName

public void setMLModelName(String mLModelName)

A user-supplied name or description of the MLModel.

Parameters:

mLModelName - A user-supplied name or description of the MLModel.

getMLModelName

public String getMLModelName()

A user-supplied name or description of the MLModel.

Returns:

A user-supplied name or description of the MLModel.

withMLModelName

public CreateMLModelRequest withMLModelName(String mLModelName)

A user-supplied name or description of the MLModel.

Parameters:

mLModelName - A user-supplied name or description of the MLModel.

Returns:

Returns a reference to this object so that method calls can be chained together.

setMLModelType

public void setMLModelType(String mLModelType)

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Parameters:

mLModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See Also:

MLModelType

getMLModelType

public String getMLModelType()

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns:

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See Also:

MLModelType

withMLModelType

public CreateMLModelRequest withMLModelType(String mLModelType)

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Parameters:

mLModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

MLModelType

setMLModelType

public void setMLModelType(MLModelType mLModelType)

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Parameters:

mLModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See Also:

MLModelType

withMLModelType

public CreateMLModelRequest withMLModelType(MLModelType mLModelType)

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Parameters:

mLModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.
• Choose BINARY if the MLModel result has two possible values.
• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

MLModelType

getParameters

public Map<String,String> getParameters()

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 the 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 the 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.

setParameters

public void setParameters(Map<String,String> parameters)

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 the 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.

Parameters:

parameters - 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 the 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.

withParameters

public CreateMLModelRequest withParameters(Map<String,String> parameters)

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 the 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.

Parameters:

parameters - 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 the 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:

Returns a reference to this object so that method calls can be chained together.

addParametersEntry

public CreateMLModelRequest addParametersEntry(String key,
                                               String value)

clearParametersEntries

public CreateMLModelRequest clearParametersEntries()

Removes all the entries added into Parameters. <p> Returns a reference to this object so that method calls can be chained together.

setTrainingDataSourceId

public void setTrainingDataSourceId(String trainingDataSourceId)

The DataSource that points to the training data.

Parameters:

trainingDataSourceId - The DataSource that points to the training data.

getTrainingDataSourceId

public String getTrainingDataSourceId()

The DataSource that points to the training data.

Returns:

The DataSource that points to the training data.

withTrainingDataSourceId

public CreateMLModelRequest withTrainingDataSourceId(String trainingDataSourceId)

The DataSource that points to the training data.

Parameters:

trainingDataSourceId - The DataSource that points to the training data.

Returns:

Returns a reference to this object so that method calls can be chained together.

setRecipe

public void setRecipe(String recipe)

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Parameters:

recipe - The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

getRecipe

public String getRecipe()

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Returns:

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

withRecipe

public CreateMLModelRequest withRecipe(String recipe)

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Parameters:

recipe - The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Returns:

Returns a reference to this object so that method calls can be chained together.

setRecipeUri

public void setRecipeUri(String recipeUri)

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Parameters:

recipeUri - The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

getRecipeUri

public String getRecipeUri()

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Returns:

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

withRecipeUri

public CreateMLModelRequest withRecipeUri(String recipeUri)

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Parameters:

recipeUri - The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public String toString()

Returns a string representation of this object; useful for testing and debugging.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CreateMLModelRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this request. Explicitly does not clone the deep structure of the request object.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()