Teradata Package for R Function Reference | 17.20 - td_dataiku_predict - 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

DataikuPredict

Description

The td_dataiku_predict_sqle() function is used to score data in Vantage with a model that has been created outside Vantage and exported to Vantage using Dataiku format.

Usage

  td_dataiku_predict_sqle (
      modeldata = NULL,
      newdata = NULL,
      accumulate = NULL,
      model.output.fields = NULL,
      overwrite.cached.models = FALSE,
      is.debug = FALSE,
      ...
  )

Arguments

modeldata

Required Argument.
Specifies the model tbl_teradata to be used for scoring.
Types: tbl_teradata

newdata

Required Argument.
Specifies the input tbl_teradata that contains the data to be scored.
Types: tbl_teradata

accumulate

Required Argument.
Specifies the name(s) of input tbl_teradata column(s) to copy to the output. By default, the function copies all input tbl_teradata columns to the output.
Types: character OR vector of Strings (character)

model.output.fields

Optional Argument.
Specifies the columns of the json output that the user wants to specify as individual columns instead of the entire json report.
Types: character OR vector of Strings (character)

overwrite.cached.models

Optional Argument.
Specifies the model name that needs to be removed from the cache. If a model is loaded into the memory of the node fits in the cache, it stays in the cache until being evicted to make space for another model that needs to be loaded. Therefore, a model can remain in the cache even after completion of function call. Other queries that use the same model can use it, saving the cost of reloading it into memory. User may overwrite a cached model only when it has been updated, to make sure that the Predict function uses the updated model instead of the cached model.
Note:
Do not use the "overwrite.cached.models" argument except when trying to replace a previously cached model. This applies to any model type (PMML, H2O Open Source, DAI, ONNX, and Dataiku). Using this argument in other cases, including in concurrent queries or multiple times within a short period of time, may lead to an OOM error from garbage collection not being fast enough.
Permitted Values: "current_cached_model", "*", "true", "t", "yes", "1", "false", "f", "no", "n", or "0".
Default Values: "false"
Types: logical

is.debug

Optional Argument.
Specifies whether debug statements are added to a trace table or not.
When set to TRUE, debug statements are added to a trace table that must be created beforehand.
Notes:

  • Only available with BYOM version 3.00.00.02 and later.

  • To save logs for debugging, user can create an error log by using the is.debug=TRUE parameter in the predict functions.
    A database trace table is used to collect this information which does impact performance of the function, so using small data input sizes is recommended.

  • To generate this log, user must do the following: 1. Create a global trace table with columns vproc_ID BYTE(2), Sequence INTEGER, Trace_Output VARCHAR(31000) 2. Turn on session function tracing: SET SESSION FUNCTION TRACE USING ” FOR TABLE <trace_table_name_created_in_step_1>; 3. Execute function with "is.debug" set to TRUE. 4. Debug information is logged to the table created in step 1. 5. To turn off the logging, either disconnect from the session or run following SQL: SET SESSION FUNCTION TRACE OFF; The trace table is temporary and the information is deleted if user logs off from the session. If long term persistence is necessary, user can copy the table to a permanent table before leaving the session.

Default Value: FALSE
Types: logical

...

Value

Function returns an object of class "td_dataiku_predict_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 example data.
    loadExampleData("pmmlpredict_example", "iris_test")
    
    # Create tbl_teradata object.
    iris_test <- tbl(con, "iris_test")
    
    # Set install location of BYOM functions.
    options(byom.install.location = "mldb")
    
    # Check the list of available analytic functions.
    display_analytic_functions(type="BYOM")

    # Load model file into Vantage.
    # Create following table on vantage if it does not exist.
    crt_tbl <- "CREATE SET TABLE byom_models(model_id VARCHAR(40), model BLOB)
                PRIMARY INDEX (model_id);"
    DBI::dbExecute(con, sql(crt_tbl))

    # Run the following query through BTEQ or Teradata Studio to load the
    # models. 'load_byom_model.txt' and byom files can be found under
    # 'inst/scripts' in tdplyr installation directory. This file and the byom
    # models to be loaded should be in the same directory.

    # .import vartext file load_byom_model.txt
    # .repeat *
    # USING (c1 VARCHAR(40), c2 BLOB AS DEFERRED BY NAME) INSERT INTO byom_models(:c1, :c2);

    # Retrieve model.
    modeldata <- tbl(con, "byom_models") 

    # Example 1: Score data in Vantage with a model that has
    #            been created outside the Vantage by removing all the
    #            all cached models.
    DataikuPredict_out_1 <- td_dataiku_predict_sqle(
                                          newdata=iris_test,
                                          modeldata=modeldata,
                                          accumulate=c('id', 'sepal_length', 'petal_length'),
                                          overwrite.cached.models="*")
    
    # Print the results.
    print(DataikuPredict_out_1$result)
    
    # Example 2: Example to show case the trace table usage using
    #            is.debug=TRUE.
    
    # Create the trace table.
    crt_tbl_query <- 'CREATE GLOBAL TEMPORARY TRACE TABLE BYOM_Trace \
                    (vproc_ID	BYTE(2) \
                    ,Sequence	INTEGER \
                    ,Trace_Output VARCHAR(31000) CHARACTER SET LATIN NOT CASESPECIFIC) \
                    ON COMMIT PRESERVE ROWS;'
    DBI::dbExecute(con, sql(crt_tbl_query))
    
    # Turn on the session function.
    DBI::dbExecute(con, sql("SET SESSION FUNCTION TRACE USING '' FOR TABLE BYOM_Trace;"))
    
    # Execute the td_dataiku_predict_sqle() function using is.debug=TRUE.
    DataikuPredict_out_2 <- td_dataiku_predict_sqle(
                                          newdata=iris_test,
                                          modeldata=modeldata,
                                          accumulate=c('id', 'sepal_length', 'petal_length'),
                                          overwrite.cached.models="*",
                                          is.debug=TRUE)
    
    # Print the results.
    print(DataikuPredict_out_2$result)
    
    # View the trace table information.
    trace_df <- dbGetQuery(con, "select * from BYOM_Trace")
    print(trace_df)
    
    # Turn off the session function.
    DBI::dbExecute(con, sql("SET SESSION FUNCTION TRACE OFF;"))