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

Teradata® Package for R Function Reference
Product
Teradata Package for R
Release Number
17.00
Published
July 2021
Language
English (United States)
Last Update
2023-08-08
dita:id
B700-4007
NMT
no
Product Category
Teradata Vantage
NaiveBayesTextClassifierPredict

Description

The NaiveBayesTextClassifierPredict function uses the model output by the NaiveBayesTextClassifierTrainer (td_naivebayes_textclassifier_mle) function to predict outcomes for test data.
Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

  td_naivebayes_textclassifier_predict_mle (
      object = NULL,
      newdata = NULL,
      input.token.column = NULL,
      doc.id.columns = NULL,
      model.type = "MULTINOMIAL",
      top.k = NULL,
      model.token.column = NULL,
      model.category.column = NULL,
      model.prob.column = NULL,
      terms = NULL,
      output.prob = FALSE,
      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 tbl_teradata which contains the model generated by td_naivebayes_textclassifier_mle.
This argument can accept either a tbl_teradata object or an object of "td_naivebayes_textclassifier_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 defining the tokens for prediction.

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)

input.token.column

Required Argument.
Specifies the name of the column in the input argument "newdata" that contains the tokens.
Types: character

doc.id.columns

Required Argument.
Specifies the names of the columns in the input argument "newdata" that contain the document identifier.
Types: character OR vector of Strings (character)

model.type

Optional Argument.
Specifies the model type of the text classifier.
Default Value: "MULTINOMIAL"
Permitted Values: MULTINOMIAL, BERNOULLI
Types: character

top.k

Optional Argument.
Specifies the number of most likely prediction categories to output with their log-likelihood values (for example, the top 10 most likely prediction categories). The default is all prediction categories.
Note: "top.k" cannot be specified along with "output.responses".
Types: integer

model.token.column

Optional Argument.
Specifies the name of the column in the input argument "object" that contains the tokens. The default value is the first column of the model.
Note: This argument must be specified along with "model.category.column" and "model.prob.column".
Types: character

model.category.column

Optional Argument.
Specifies the name of the column in the input argument "object" that contains the prediction categories. The default value is the second column of the model.
Note: This argument must be specified along with "model.token.column" and "model.prob.column".
Types: character

model.prob.column

Optional Argument.
Specifies the name of the column in the input argument "object" that contains the token counts. The default value is the third column of the model.
Note: This argument must be specified along with "model.token.column" and "model.category.column".
Types: character

terms

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.prob

Optional Argument.
Specifies whether to output probabilities.
Default Value: FALSE
Types: logical

output.responses

Optional Argument.
Specifies the labels for which to output the probabilities.
Note:

  1. "output.responses" argument support is only available when tdplyr is connected to Vantage 1.1.1 or later versions.

  2. "output.responses" cannot be specified along with "top.k".

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_naivebayes_textclassifier_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("naivebayes_textclassifier_predict_example", "token_table",
                    "complaints_tokens_test")
    
    # Create object(s) of class "tbl_teradata".
    token_table <- tbl(con, "token_table")
    complaints_tokens_test <- tbl(con,"complaints_tokens_test")
    
    # Example -
    # Run NaiveBayesTextClassifier on the train data (token_table).
    nbt_out <- td_naivebayes_textclassifier_mle(data = token_table,
                                                data.partition.column = c("category"),
                                                token.column = "token",
                                                doc.id.columns = c("doc_id"),
                                                doc.category.column = "category",
                                                model.type = "Bernoulli"
                                               )
    
    # Use the generated model to predict the tokens on the test data 'complaints_tokens_test'
    # by using nbt_out model.
    nbt_predict_out <- td_naivebayes_textclassifier_predict_mle(newdata = complaints_tokens_test,
                                                              object = nbt_out,
                                                              newdata.partition.column = "doc_id",
                                                              input.token.column = "token",
                                                              doc.id.columns = c("doc_id"),
                                                              model.type = "Bernoulli",
                                                              top.k = 1
                                                             )
   
   # Use the argument "output.responses" to predict the tokens on the test data 
   # 'complaints_tokens_test' by using nbt_out model generated by 
   # td_naivebayes_textclassifier_mle() function.
   nbt_predict_out1 <- td_naivebayes_textclassifier_predict_mle(newdata=complaints_tokens_test,
                                                          newdata.partition.column='doc_id',
                                                          object=nbt_out,
                                                          input.token.column='token',
                                                          doc.id.columns='doc_id',
                                                          model.token.column='token',
                                                          model.category.column='category',
                                                          model.prob.column='prob',
                                                          model.type='Bernoulli',
                                                          terms = 'token',
                                                          output.prob = TRUE,
                                                          output.responses = c('crash','no_crash'),
                                                          newdata.sequence.column = 'token',
                                                          object.sequence.column = 'token',
                                                          newdata.order.column = 'doc_id',
                                                          object.order.column = 'category'
                                                         )