Teradata Package for R Function Reference | 17.20 - td_data_robot_predict - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

DataRobotPredict

Description

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

Usage

  td_data_robot_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 Note: The input columns containing Date or Timestamp types should be converted to character type before running `td_data_robot_predict_sqle()`.
`accumulate`	Required Argument. Specifies the name(s) of input tbl_teradata column(s) to copy 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. When a model 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 the completion of function execution. Other functions that use the same model can use it, saving the cost of reloading it into memory. User should overwrite a cached model only when it is 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 user is trying to replace a previously cached model. Using the argument in other cases, including in concurrent queries or multiple times within a short period of time lead to an OOM error. Default behavior: The function does not overwrite cached models. Permitted Values: "true", "t", "yes", "y", "1", "false", "f", "no", "n", "0", "*", "current_cached_model" Types: character OR vector of Strings (character)
`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
`...`	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 character (Strings) "<input.data.arg.name>.hash.column" accepts character or vector of character (Strings) "<input.data.arg.name>.order.column" accepts character or vector of 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_data_robot_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")
    
    # Create table byom_models and save the model with model_id 'dr_iris_rf'.
    # Model file can be found in 'inst/scripts' in tdplyr installation directory.
    # Retrieve the specific model from byom_models table.

    # 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.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.
    Datarobotpredict_1 <- td_data_robot_predict_sqle(newdata=iris_test,
                                                     modeldata=modeldata,
                                                     accumulate=c('id', 'sepal_length', 'petal_length'),
                                                     overwrite.cached.models="*")
    
    # Print the results.
    print(Datarobotpredict_1$result)
    
    # Example 2: Example to show case the trace table usage using
    #            is.debug=TRUE.
    
    # Create the trace table if not present.
    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 tracing for the session.
    DBI::dbExecute(con, "SET SESSION FUNCTION TRACE USING '' FOR TABLE BYOM_Trace;")

    modeldata <- tbl(con, "byom_models") 
    
    # Execute the td_h2o_predict_sqle() function using is.debug=TRUE.
    result <- td_data_robot_predict_sqle(newdata=iris_test,
                                         modeldata=modeldata,
                                         accumulate=c('id', 'sepal_length',
                                                      'petal_length'),
                                         overwrite.cached.models="*",
                                         is.debug=TRUE
                                         )
    
    # Print the results.
    print(result$result)
    
    # View the trace table information.
    trace_df <- dbGetQuery(con, "select * from BYOM_Trace")
    print(trace_df)
    
    # Turn off tracing for the session.
    DBI::dbExecute(con, "SET SESSION FUNCTION TRACE OFF;")