Teradata Package for R Function Reference | 17.00 - SVMDenseSummary - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

DenseSVMModelPrinter

Description

The DenseSVMModelPrinter function extracts readable information from the model produced by the DenseSVMTrainer (td_svm_dense_mle) function. The function can display either a summary of the model training results or a tbl_teradata containing the weights for each attribute.

Usage

  td_svm_dense_summary_mle (
      object = NULL,
      data = NULL,
      attribute.columns = NULL,
      summary = FALSE,
      data.sequence.column = NULL,
      object.sequence.column = NULL,
      data.order.column = NULL,
      object.order.column = NULL
  )

Arguments

object	Required Argument. Specifies the model tbl_teradata generated by td_svm_dense_mle. This argument can accept either a tbl_teradata or an object of "td_svm_dense_mle" class.
object.order.column	Optional Argument. Specifies Order By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
data	Required Argument. Specifies training dataset input tbl_teradata specified in "data" argument used to produce model by td_svm_dense_mle function, otherwise the result may be incomplete.
data.order.column	Optional Argument. Specifies Order By columns for "data". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
attribute.columns	Required Argument. Specifies the input tbl_teradata columns from "data" argument that contain the attributes of the test samples. Attribute columns must be numeric (int, real, bigint, smallint, or float). Types: character OR vector of Strings (character)
summary	Optional Argument. Specifies whether the function outputs only a summary of the model. If TRUE, the output contains only summary information of the model. If FALSE, the output contains the weight of each attribute in the model. Default Value: FALSE Types: logical
data.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "data". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)
object.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "object". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_svm_dense_summary_mle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using name: result.

Examples

  
    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("svmdense_example", "svm_iris_train")

    # Create object(s) of class "tbl_teradata".
    svm_iris_train <- tbl(con, "svm_iris_train")

    # Generate Radial Basis Model (RBF) Model
    densesvm_iris_rbf_model <- td_svm_dense_mle(data = svm_iris_train,
                                              sample.id.column = "id",
                                              attribute.columns = c('sepal_length', 'sepal_width', 
                                                                    'petal_length', 'petal_width'),
                                              kernel.function = "rbf",
                                              gamma = 0.1,
                                              subspace.dimension = 120,
                                              hash.bits = 512,
                                              label.column = "species",
                                              cost = 1,
                                              bias = 0,
                                              max.step = 100,
                                              seed = 1
                                              )

    # Example 1 - Display the model parameters (weights, attributes etc).
    td_svm_dense_summary_out1 <- td_svm_dense_summary_mle(object = densesvm_iris_rbf_model,
                                                data = svm_iris_train,
                                                attribute.columns=c('sepal_length','sepal_width',
                                                                    'petal_length','petal_width'),
                                                summary = FALSE
                                                )

    # Example 2 - outputs only summary of the model.
    td_svm_dense_summary_out2 <- td_svm_dense_summary_mle(object = densesvm_iris_rbf_model,
                                                data = svm_iris_train,
                                                attribute.columns=c('sepal_length','sepal_width',
                                                                    'petal_length','petal_width'),
                                                summary = TRUE
                                                )