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

Description

The DenseSVMTrainer (td_svm_dense_mle) function takes training data in dense format and outputs a predictive model in binary format, which is the input to the functions DenseSVMPredictor (td_svm_dense_predict_mle) and DenseSVMModelPrinter (td_svm_dense_summary_mle).

Usage

  td_svm_dense_mle (
      data = NULL,
      sample.id.column = NULL,
      attribute.columns = NULL,
      kernel.function = "LINEAR",
      gamma = 1.0,
      constant = 1.0,
      degree = 2,
      subspace.dimension = 256,
      hash.bits = 256,
      label.column = NULL,
      cost = 1,
      bias = 0,
      class.weights = NULL,
      max.step = 100,
      epsilon = 0.01,
      seed = 0,
      data.sequence.column = NULL
  )

Arguments

`data`	Required Argument. Name of the tbl_teradata containing the training samples. Each row consists of a sample id, a set of attribute values, and a corresponding label.
`sample.id.column`	Required Argument. Name of the column in the data that contains the identifier of the training samples.
`attribute.columns`	Required Argument. Specifies the names of the columns in the data argument that contain the attributes, which must have numeric data types.
`kernel.function`	Optional Argument. Specifies the distribution exponential family used to compute the hash function. For function linear, a Pegasos algorithm is used to solve the linear SVM. For function polynomial, RBF, or sigmoid, a Hash-SVM algorithm is used. Each sample is represented by compact hash bits, over which an inner product is defined to serve as the surrogate of the original nonlinear kernels. Default Value: "LINEAR" Permitted Values: LINEAR, POLYNOMIAL, RBF, SIGMOID
`gamma`	Optional Argument. Only used when kernel.function is polynomial, RBF, or sigmoid. Must be a positive double. The minimum value is 0.0. Default Value: 1.0
`constant`	Optional Argument. Specifies double value. This argument is used only when kernel.function is polynomial or sigmoid. If kernel.function is polynomial, the minimum value is 0.0. Default Value: 1.0
`degree`	Optional Argument. Only used when kernel.function is polynomial. A positive integer that specifies the degree (d) of the polynomial kernel. The input value must be greater than 0. Default Value: 2
`subspace.dimension`	Optional Argument. Only valid if kernel.function is polynomial, RBF, or sigmoid. A positive integer that specifies the random subspace dimension of the basis matrix V obtained by the Gram-Schmidt process. Since the Gram-Schmidt process cannot be parallelized, this dimension cannot be too large. Accuracy will increase with higher values of this number, but computation costs will also increase. The input value must be in the range [1, 2048]. Default Value: 256
`hash.bits`	Optional Argument. Only valid if kernel.function is polynomial, RBF, or sigmoid. A positive integer that specifies the number of compact hash bits used to represent a data point. Accuracy will increase with higher values of this number, but computation costs will also increase. The input value must be in the range [8, 8192]. Default Value: 256
`label.column`	Required Argument. Specifies the column that identifies the class of the corresponding sample. Must be an integer or a string.
`cost`	Optional Argument. Specifies the regularization parameter in the SVM soft-margin loss function. Cost must be greater than 0.0. Default Value: 1
`bias`	Optional Argument. Specifies a non-negative value. If the value is greater than zero, each sample (x) in the training set will be converted to (x, b); that is, it will add another dimension containing the bias value b. This argument addresses situations where not all samples center at 0. Default Value: 0
`class.weights`	Optional Argument. Specifies the weights for different classes. The format should be: "classlabel m:weight m, classlabel n:weight n". If weight for a class is given, the cost parameter for this class will be weight * cost. A weight larger than 1 often increases the accuracy of the corresponding class; however, it may decrease global accuracy. Classes not assigned a weight in this argument will be assigned a weight of 1.0.
`max.step`	Optional Argument. A positive integer value that specifies the maximum number of iterations of the training process. One step means that each sample is seen once by the trainer. The input value must be in the range (0, 10000]. Default Value: 100
`epsilon`	Optional Argument. Termination criterion. When the difference between the values of the loss function in two sequential iterations is less than this number, the function stops. Must be greater than 0.0. Default Value: 0.01
`seed`	Optional Argument. Specifies an integer value used to order the training set randomly and consistently. This value can be used to ensure that the same model will be generated if the function is run multiple times in a given database with the same arguments. Default Value: 0
`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.

Value

Function returns an object of class "td_svm_dense_mle" which is a named list containing Teradata tbl objects. Named list members can be referenced directly with the "$" operator using following names:

model.table
output

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("svmdense_example", "svm_iris_train")
    
    # Create remote tibble objects.
    svm_iris_train <- tbl(con, "svm_iris_train")
    
    # Example 1 -  Linear Model
    td_svm_dense_out <- 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 = "linear",
                                     label.column = "species",
                                     cost = 1,
                                     bias = 0,
                                     max.step = 100,
                                     seed = 1
                                     )
    
    # Example 2 - Polynomial Model
    td_svm_dense_out <- 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 = "polynomial",
                                     gamma = 0.1,
                                     degree = 2,
                                     subspace.dimension = 120,
                                     hash.bits = 512,
                                     label.column = "species",
                                     cost = 1,
                                     bias = 0,
                                     max.step = 100,
                                     seed = 1
                                     )
    
    # Example 3 - Radial Basis Model (RBF) Model
    td_svm_dense_out <- 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 4 - Sigmoid Model
    td_svm_dense_out <- 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 = "sigmoid",
                                     gamma = 0.1,
                                     subspace.dimension = 120,
                                     hash.bits = 512,
                                     label.column = "species",
                                     cost = 1,
                                     bias = 0,
                                     max.step = 30,
                                     seed = 1
                                     )