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

SVMSparsePredict

Description

The SVMSparsePredict function takes the model output by the function SVMSparse (td_svm_sparse_mle) and a set of test samples (in sparse format) and outputs a prediction for each sample.
Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

  td_svm_sparse_predict_mle (
      object = NULL,
      newdata = NULL,
      sample.id.column = NULL,
      attribute.column = NULL,
      value.column = NULL,
      accumulate.label = NULL,
      output.class.num = NULL,
      output.response.probdist = TRUE,
      output.responses = NULL,
      newdata.sequence.column = NULL,
      object.sequence.column = NULL,
      newdata.partition.column = NULL,
      newdata.order.column = NULL,
      object.order.column = NULL
  )

Arguments

`object`	Required Argument. Specifies the model tbl_teradata generated by `td_svm_sparse_mle`. This argument can accept either a tbl_teradata or an object of "td_svm_sparse_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)
`newdata`	Required Argument. Specifies the tbl_teradata containing the input test data.
`newdata.partition.column`	Required Argument. Specifies Partition By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
`newdata.order.column`	Optional Argument. Specifies Order By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
`sample.id.column`	Required Argument. Specifies the name of the input tbl_teradata column that contains the identifiers of the test samples. The input tbl_teradata 'newdata' must be partitioned by this column. Types: character
`attribute.column`	Required Argument. Specifies the name of the input tbl_teradata column that contains the attributes of the test samples. Types: character
`value.column`	Optional Argument. Specifies the name of the input tbl_teradata column that contains the attribute values. By default, each attribute has the value 1. Types: character
`accumulate.label`	Optional Argument. Specifies the names of the input tbl_teradata columns to copy to the output tbl_teradata. Types: character OR vector of Strings (character)
`output.class.num`	Optional Argument. Valid only for multiple-class models. Specifies the number of class labels to appear in the output tbl_teradata, with its corresponding prediction confidence. Note: With Vantage version prior to 1.1.1, the argument defaults to the value 1. "output.class.num" cannot be specified along with "output.responses". Types: integer
`output.response.probdist`	Optional Argument. Specifies whether to display output probability for the predicted category. Note: "output.response.probdist" argument support is only available when tdplyr is connected to Vantage 1.1.1 or later versions. Default Value: TRUE Types: logical
`output.responses`	Optional Argument. Specifies responses in the input tbl_teradata object. Note: "output.responses" argument support is only available when tdplyr is connected to Vantage 1.1.1 or later versions. "output.responses" cannot be specified along with "output.class.num". The argument "output.response.probdist" must be set to TRUE to use this argument. Types: character OR vector of characters
`newdata.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "newdata". 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_sparse_predict_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("svmsparsepredict_example", "svm_iris_input_test", "svm_iris_input_train")

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

    # Example 1 -
    # Generate SparseSVMTrainer model based on train data "svm_iris_input_train".
    svm_iris_model <- td_svm_sparse_mle(data = svm_iris_input_train,
                                        sample.id.column = "id",
                                        attribute.column = "attribute",
                                        value.column = "value1",
                                        label.column = "species",
                                        max.step = 150,
                                        seed = 0
                                        )

    # Use the generated model to predict the 'species' on the test data "svm_iris_input_test".
    td_svm_sparse_predict_mle_out <- td_svm_sparse_predict_mle(object = svm_iris_model,
                                                               newdata = svm_iris_input_test,
                                                               newdata.partition.column = c("id"),
                                                               attribute.column = "attribute",
                                                               sample.id.column = "id",
                                                               value.column = "value1",
                                                               accumulate.label = "species"
                                                               )