Teradata Package for R Function Reference | 17.20 - OneHotEncodingTransform - 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

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for R
Release Number
17.20
Published
March 2024
Language
English (United States)
Last Update
2024-05-03
dita:id
TeradataR_FxRef_Enterprise_1720
Product Category
Teradata Vantage

OneHotEncodingTransform

Description

Function encodes specified attributes and categorical values as one-hot numeric vectors, using td_one_hot_encoding_fit_sqle() function output.

Usage

  td_one_hot_encoding_transform_sqle (
      data = NULL,
      object = NULL,
      is.input.dense = NULL,
      ...
  )

Arguments

data

Required Argument.
Specifies the input tbl_teradata.
Types: tbl_teradata

object

Required Argument.
Specifies the tbl_teradata containing the encoding parameters generated by td_one_hot_encoding_fit_sqle() function or the instance of td_one_hot_encoding_fit_sqle.
Types: tbl_teradata or td_one_hot_encoding_fit_sqle

is.input.dense

Required Argument.
Specifies whether input is in dense format or sparse format.
Types: logical

...

Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:

persist:
Optional Argument.
Specifies whether to persist the results of the function in a table or not.
When set to TRUE, results are persisted in a table; otherwise, results are garbage collected at the end of the session.
Default Value: FALSE
Types: logical

volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not.
When set to TRUE, results are stored in a volatile table, otherwise not.
Default Value: FALSE
Types: logical

Function allows the user to partition, hash, order or local order the input data. These generic arguments are available for each argument that accepts tbl_teradata as input and can be accessed as:

  • "<input.data.arg.name>.partition.column" accepts character OR vector of Strings (character) (Strings)

  • "<input.data.arg.name>.hash.column" accepts character OR vector of Strings (character) (Strings)

  • "<input.data.arg.name>.order.column" accepts character OR vector of Strings (character) (Strings)

  • "local.order.<input.data.arg.name>" accepts logical

Note:
These generic arguments are supported by tdplyr if the underlying SQL Engine function supports, else an exception is raised.

Value

Function returns an object of class "td_one_hot_encoding_transform_sqle" which is a named list containing object of class "tbl_teradata".
Named list member(s) can be referenced directly with the "$" operator using the name(s):result

Examples

  
    
    # Get the current context/connection.
    con <- td_get_context()$connection
    
    # Load the example data.
    loadExampleData("tdplyr_example", "titanic")
    
    # Create tbl_teradata object.
    titanic_data <- tbl(con, "titanic")
    
    # Check the list of available analytic functions.
    display_analytic_functions()
    
    # Example 1: Transform categorical column "sex" to numerical columns "sex_male", "sex_female",
    #            and "sex_other" using td_one_hot_encoding_fit_sqle()
    #            and td_one_hot_encoding_transform_sqle().
    
    # Generate fit object for column "sex".
    fit_obj <- td_one_hot_encoding_fit_sqle(
                                data=titanic_data,
                                is.input.dense=TRUE,
                                target.column="sex",
                                categorical.values=c("male", "female"),
                                other.column="other")
    
    # Print the result.
    print(fit_obj$result)
    
    # Encode "male" and "female" values of column "sex".
    # Note that tbl_teradata representing the model is passed as
    # input to "object".
    obj <- td_one_hot_encoding_transform_sqle(
                                  data=titanic_data,
                                  object=fit_obj$result,
                                  is.input.dense=TRUE)
    
    # Print the result.
    print(obj$result)
    
    # Example 2: Encode "male" and "female" values of column "sex".
    #            Note that model is passed as instance of td_one_hot_encoding_fit_sqle
    #            to "object".
    obj1 <- td_one_hot_encoding_transform_sqle(
                                   data=titanic_data,
                                   object=fit_obj,
                                   is.input.dense=TRUE)
    
    # Print the result.
    print(obj1$result)
    
    # Alternatively use S3 transform function to run transform on the output of
    # td_fit_sqle() function.
    
    obj1 <- transform(fit_obj,
                      data=titanic_data,
                      is.input.dense=TRUE)
    
    # Print the result.
    print(obj1$result)