CreateMLModelRequest (AWS SDK for Java

java.lang.Object
- com.amazonaws.AmazonWebServiceRequest
- - com.amazonaws.services.machinelearning.model.CreateMLModelRequest

All Implemented Interfaces:: HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
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()

Constructors
Constructor and Description
`CreateMLModelRequest()`

Method Summary

All Methods Instance Methods Concrete Methods
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 object for all fields except the handler context.
`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 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.
    
    Returns:
    
    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 object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.
    
    Overrides:
    
    clone in class AmazonWebServiceRequest
    
    See Also:
    
    Object.clone()

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Class CreateMLModelRequest

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

Methods inherited from class java.lang.Object

Constructor Detail

CreateMLModelRequest

Method Detail

setMLModelId

getMLModelId

withMLModelId

setMLModelName

getMLModelName

withMLModelName

setMLModelType

getMLModelType

withMLModelType

setMLModelType

withMLModelType

getParameters

setParameters

withParameters

addParametersEntry

clearParametersEntries

setTrainingDataSourceId

getTrainingDataSourceId

withTrainingDataSourceId

setRecipe

getRecipe

withRecipe

setRecipeUri

getRecipeUri

withRecipeUri

toString

equals

hashCode

clone