/AWS1/IF_ML=>CREATEMLMODEL()

About CreateMLModel

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

Method Signature

METHODS /AWS1/IF_ML~CREATEMLMODEL
  IMPORTING
    !IV_MLMODELID TYPE /AWS1/ML_ENTITYID OPTIONAL
    !IV_MLMODELNAME TYPE /AWS1/ML_ENTITYNAME OPTIONAL
    !IV_MLMODELTYPE TYPE /AWS1/ML_MLMODELTYPE OPTIONAL
    !IT_PARAMETERS TYPE /AWS1/CL_ML_TRAININGPARAMS_W=>TT_TRAININGPARAMETERS OPTIONAL
    !IV_TRAININGDATASOURCEID TYPE /AWS1/ML_ENTITYID OPTIONAL
    !IV_RECIPE TYPE /AWS1/ML_RECIPE OPTIONAL
    !IV_RECIPEURI TYPE /AWS1/ML_S3URL OPTIONAL
  RETURNING
    VALUE(OO_OUTPUT) TYPE REF TO /aws1/cl_ml_createmlmodelout
  RAISING
    /AWS1/CX_ML_IDEMPOTENTPRMMIS00
    /AWS1/CX_ML_INTERNALSERVEREX
    /AWS1/CX_ML_INVALIDINPUTEX
    /AWS1/CX_ML_CLIENTEXC
    /AWS1/CX_ML_SERVEREXC
    /AWS1/CX_RT_TECHNICAL_GENERIC
    /AWS1/CX_RT_SERVICE_GENERIC.

IMPORTING

Required arguments:

iv_mlmodelid TYPE /AWS1/ML_ENTITYID /AWS1/ML_ENTITYID

A user-supplied ID that uniquely identifies the MLModel.

iv_mlmodeltype TYPE /AWS1/ML_MLMODELTYPE /AWS1/ML_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.

iv_trainingdatasourceid TYPE /AWS1/ML_ENTITYID /AWS1/ML_ENTITYID

The DataSource that points to the training data.

Optional arguments:

iv_mlmodelname TYPE /AWS1/ML_ENTITYNAME /AWS1/ML_ENTITYNAME

A user-supplied name or description of the MLModel.

it_parameters TYPE /AWS1/CL_ML_TRAININGPARAMS_W=>TT_TRAININGPARAMETERS TT_TRAININGPARAMETERS

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

• sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling 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.

iv_recipe TYPE /AWS1/ML_RECIPE /AWS1/ML_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.

iv_recipeuri TYPE /AWS1/ML_S3URL /AWS1/ML_S3URL

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.

RETURNING

oo_output TYPE REF TO /aws1/cl_ml_createmlmodelout /AWS1/CL_ML_CREATEMLMODELOUT

Domain	/AWS1/RT_ACCOUNT_ID
Primitive Type	NUMC

Examples

Syntax Example

This is an example of the syntax for calling the method. It includes every possible argument and initializes every possible value. The data provided is not necessarily semantically accurate (for example the value "string" may be provided for something that is intended to be an instance ID, or in some cases two arguments may be mutually exclusive). The syntax shows the ABAP syntax for creating the various data structures.

DATA(lo_result) = lo_client->createmlmodel(
  it_parameters = VALUE /aws1/cl_ml_trainingparams_w=>tt_trainingparameters(
    (
      VALUE /aws1/cl_ml_trainingparams_w=>ts_trainingparameters_maprow(
        value = new /aws1/cl_ml_trainingparams_w( |string| )
        key = |string|
      )
    )
  )
  iv_mlmodelid = |string|
  iv_mlmodelname = |string|
  iv_mlmodeltype = |string|
  iv_recipe = |string|
  iv_recipeuri = |string|
  iv_trainingdatasourceid = |string|
).

This is an example of reading all possible response values

lo_result = lo_result.
IF lo_result IS NOT INITIAL.
  lv_entityid = lo_result->get_mlmodelid( ).
ENDIF.