Teradata R Package Function Reference | 17.00 - 17.00 - NaiveBayesTextClassifier - Teradata R Package

Teradata® R Package Function Reference
prodname
Teradata R Package
vrm_release
17.00
created_date
September 2020
category
Programming Reference
featnum
B700-4007-090K

Description

The NaiveBayesTextClassifierTrainer function takes training data as input and outputs a model tbl_teradata.

Usage

  td_naivebayes_textclassifier_mle (
      data = NULL,
      data.partition.column = NULL,
      token.column = NULL,
      doc.id.columns = NULL,
      doc.category.column = NULL,
      model.type = "MULTINOMIAL",
      categories.data = NULL,
      category.column = "[0:0]",
      prediction.categories = NULL,
      stopwords.data = NULL,
      stopwords.column = NULL,
      stopwords.list = NULL,
      data.sequence.column = NULL,
      stopwords.data.sequence.column = NULL,
      categories.data.sequence.column = NULL,
      data.order.column = NULL,
      stopwords.data.order.column = NULL,
      categories.data.order.column = NULL
  )

Arguments

data

Required Argument.
Specifies the tbl_teradata defining the training tokens.

data.partition.column

Required Argument.
Specifies Partition By columns for "data".
Values to this argument can be provided as a vector, if multiple columns are used for partition.
Types: character OR vector of Strings (character)

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)

token.column

Required Argument.
Specifies the name of the column in "data" tbl_teradata, that contains the tokens to be classified.
Types: character

doc.id.columns

Optional Argument. Required when "model.type" is 'BERNOULLI'.
Specifies the names of the columns, in "data" tbl_teradata, that contain the document identifier.
Note: This argument should not be provided when "model.type" is 'MULTINOMIAL'.
Otherwise, an exception is raised.
Types: character OR vector of Strings (character)

doc.category.column

Required Argument.
Specifies the name of the column in "data" tbl_teradata, that contains the document category.
Types: character

model.type

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

categories.data

Optional Argument.
Specifies the tbl_teradata defining allowed categories.

categories.data.order.column

Optional Argument.
Specifies Order By columns for "categories.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)

category.column

Optional Argument.
Specifies the name of the column in "categories.data" tbl_teradata, that contains the prediction categories. The default value is the first column of "categories.data" tbl_teradata.
Default Value: "[0:0]"
Types: character

prediction.categories

Optional Argument.
Specifies the prediction categories.
Note: Specify either this argument or the "categories.data" argument, but not both.
Types: character OR vector of characters

stopwords.data

Optional Argument.
Specifies the tbl_teradata defining stop words.

stopwords.data.order.column

Optional Argument.
Specifies Order By columns for "stopwords.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)

stopwords.column

Optional Argument.
Specifies the name of the column in "stopwords.data" tbl_teradata, that contains the stop words. The default value is the first column of "stopwords.data" tbl_teradata.
Types: character

stopwords.list

Optional Argument.
Specifies words to ignore (such as a, an, and the).
Note: Specify either this argument or the "stopwords.data" argument, but not both.
Types: character OR vector of characters

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)

stopwords.data.sequence.column

Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row of the input argument "stopwords.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)

categories.data.sequence.column

Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row of the input argument "categories.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)

Value

Function returns an object of class "td_naivebayes_textclassifier_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_example", "token_table")
    
    # Create object(s) of class "tbl_teradata".
    token_table <- tbl(con, "token_table")
    
    # Example 1 -
    naivebayes_textclassifier_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"
                                           )