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:

  • Constructor Details

    • CreateMLModelRequest

      public CreateMLModelRequest()

  • Method Details

    • 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:

    • 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:

    • 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:

    • 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:

    • 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:

    • 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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

      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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

    • 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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

      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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

    • 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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

      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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.

      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 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 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 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 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 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 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:

    • 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: