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

Description

The Decision Tree Predict (td_decision_tree_predict_mle) function applies a tree model to input data, to output predicted labels for each data point.
Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

  td_decision_tree_predict_mle (
      object = NULL,
      newdata = NULL,
      attr.table.groupby.columns = NULL,
      attr.table.pid.columns = NULL,
      attr.table.val.column = NULL,
      output.response.probdist = FALSE,
      accumulate = NULL,
      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 name of the object that contains the decision tree model which is the output of function `td_decision_tree_mle`.
`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 name of the tbl_teradata object containing the attribute names and the values. It contains test data and has same schema as that of input tbl_teradata of `td_decision_tree_mle` function.
`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)
`attr.table.groupby.columns`	Required Argument. Specifies the names of the columns on which attribute tbl_teradata used in `td_decision_tree_mle` is partitioned. Each partition contains one attribute of the input data. Types: character OR vector of Strings (character)
`attr.table.pid.columns`	Required Argument. Specifies the names of the columns that define the data point identifiers. Types: character OR vector of Strings (character)
`attr.table.val.column`	Required Argument. Specifies the name of the column that contains the input values. Types: character
`output.response.probdist`	Optional Argument. Specifies whether to output probabilities. Note: With Vantage version prior to 1.1.1, when this argument is set to TRUE, the argument 'output.responses' must also be specified. It can be set to TRUE only when the `td_decision_tree_mle` function call used to generate the model had output.response.probdist to TRUE. Default Value: FALSE Types: logical
`accumulate`	Optional Argument. Specifies the names of input tbl_teradata columns to copy to the output tbl_teradata. Types: character OR vector of Strings (character)
`output.responses`	Optional Argument. Specifies all responses in input tbl_teradata. This argument requires the output.response.probdist argument to be set to TRUE. 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_decision_tree_predict_mle" which is a named list containing Teradata tbl object. 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("decisiontreepredict_example", "iris_attribute_test", "iris_attribute_output")
    loadExampleData("decision_tree_example", "iris_attribute_train", "iris_response_train", "iris_altinput")
    
    # Create remote tibble objects.
    iris_attribute_train <- tbl(con, "iris_attribute_train")
    iris_response_train <- tbl(con, "iris_response_train")
    iris_attribute_test <- tbl(con, "iris_attribute_test")
    
    # Example - 1
    # First train the data, i.e. create a Model
    decision_tree_out <- td_decision_tree_mle(attribute.table = iris_attribute_train,
                               response.table = iris_response_train,
                               attribute.name.columns = c("attribute"),
                               attribute.value.column = "attrvalue",
                               id.columns = c("pid"),
                               response.column = "response",
                               num.splits = 3,
                               approx.splits = FALSE,
                               nodesize = 10,
                               max.depth = 10,
                               split.measure = "gini"
                               )
    # Run predict on the output of td_decision_tree_mle
    td_decision_tree_predict_out <- td_decision_tree_predict_mle(object = decision_tree_out,
                                       newdata = iris_attribute_test,
                                       newdata.partition.column = c("pid"),
                                       newdata.order.column = c("attribute"),
                                       attr.table.groupby.columns = c("attribute"),
                                       attr.table.pid.columns = c("pid"),
                                       attr.table.val.column = "attrvalue",
                                       accumulate = c("attrvalue")
                                       )
    # Example - 2                                  
    # set output.response.probdist to TRUE to output the probability distributions.                 
    decision_tree_out2 <- td_decision_tree_mle(attribute.table=iris_attribute_train,
                                response.table=iris_response_train,
                                attribute.name.columns='attribute',
                                id.columns='pid',
                                attribute.value.column='attrvalue',
                                response.column='response',
                                num.splits=3,
                                nodesize=5,
                                max.depth=10,
                                split.measure='gini',
                                approx.splits=FALSE,
                                output.response.probdist=TRUE,
                                response.probdist.type = "Laplace"
                              )
                          
    # use output.response.probdist, output.responses.    
    td_decision_tree_predict_out <- td_decision_tree_predict_mle(newdata=iris_attribute_test,
                                       newdata.partition.column='pid',
                                       newdata.order.column='attribute',
                                       object=decision_tree_out2,
                                       attr.table.groupby.columns='attribute',
                                       attr.table.pid.columns='pid',
                                       attr.table.val.column='attrvalue',
                                       accumulate='attrvalue',
                                       output.response.probdist=TRUE,
                                       output.responses=c('1','2','3')
                                       )