/sas-scoring-wrapper-r

This package facilitates the translation of SAS Model Studio and Visual Text Analytics scoring codes to R.

Primary LanguageRApache License 2.0Apache-2.0

rsct - SAS Scoring Code Wrapper for R

Overview

This package was made to facilitate the translation of scoring codes which SAS outputs from its interfaces, specially Model Studio and Visual Text Analytics. The functions will wrap an DS/DS2 code or make equivalent call using the SWAT package and SAS Viya API. The output is an R code, ready or almost ready to use in any other application, as a good starting point for integration.

What can be translated

This package will translate (or wrap) some Scoring Codes that are export through some of Viya interfaces. All the output R codes are based on the R SWAT package. It’s not necessary for this package to do the translation, but it is necessary to run the output code.

The following table will help you to know which function to user for which kind of code:

  • Interface: Where you can obtain the sas generated score code (not the only place)

  • Code Type: The code type expected by the translator

  • Base File Name: The file which the translator will read from inside the exported zip File. You can also use that to identify which kind of code it is. (dataStep is usually called dmcas_scorecode.sas and DS2 dmcas_epscorecode.sas)

  • Translation Function: Name of the function to translate it properly

  • Output Type: The way the R code is going to be translated to be used outside of the SAS environment

  • Sample File: Sample of the usual name that is exported by the SAS visual interfaces

Interface Code Type Base File Name Translation Function Output Type Sample File
Model Studio DataStep dmcas_scorecode.sas DS_translate() R code that calls a DataStep using dataStep actionset score_code_Logistic Regression.zip
Model Studio DS2 dmcas_epscorecode.sas EPS_translate() R code that calls astore actionSet instead of calling the DS2 wrapper score_code_Gradient Boosting.zip
Visual Text Analytics Sentiment - CAS Procedure scoreCode.sas nlp_sentiment_translate() R code that calls the sentimentAnalysis actionset SentimentScoreCode.zip
Visual Text Analytics Categories - CAS Procedure scoreCode.sas nlp_category_translate() R code that calls textRuleScore actionset with applyCategory action CategoriesScoreCode.zip
Visual Text Analytics Topics - CAS Procedure AstoreScoreCode.sas nlp_topics_translate() R code that calls astore actionSet TopicsScoreCode.zip
Visual Text Analytics Concepts - CAS Procedure ScoreCode.sas nlp_concepts_translate() R code that calls textRuleScore actionset with applyConcept action ConceptsScoreCode.zip

Installation

# Since it's not on CRAN, you will need to use the remotes package to install directly from github
# install.packages("remotes") # uncomment if you don't have it yet

remotes::install_github("sassoftware/sas-scoring-translator-r") 

# loading the package
library("rsct")

Running

For this example here we've used the hmeq dataset available in SAS support examples datasets. You can use this data to generate a model in SAS Model Studio (Gradient Boosting) and export to do this first example, you don't have to unzip the code, but opening it to know which type was exporte may be useful.

## For models that generates DS2 code such as Gradient Boosting

## it will show the place where the file was written to

output_infos <- rsct::EPS_translate(in_file = "path/to/score_code_Gradient Boosting.zip",
                         out_file = "path/to/gb_translated.R",
                         in_caslib = "public", 
                         in_castable = "hmeq", 
                         out_caslib = "casuser",
                         out_castable = "hmeq_scored",
                         copyVars = c("BAD", "LOAN"))
## File successfully written to path/to/gb_translated.R

# there are some info inside `output_infos` object, but no need to show in here

And here is a sample of the generated code, as you see, credentials and servers may require tweaks, other than that, it’s fully functional:

## install swat package from github if needed, uncomment OS version
# install.packages('https://github.com/sassoftware/R-swat/releases/download/v1.6.1/R-swat-1.6.1-linux64.tar.gz',repos=NULL, type='file') ## linux
# install.packages('https://github.com/sassoftware/R-swat/releases/download/v1.6.1/R-swat-1.6.1-win64.tar.gz',repos=NULL, type='file') ## windows
# install.packages('https://github.com/sassoftware/R-swat/releases/download/v1.6.1/R-swat-1.6.1-REST-only-osx64.tar.gz',repos=NULL, type='file') ## osx

## Load library
library("swat")

## Defining tables and models variables
in_caslib <- "public"
in_castable <- "hmeq"
out_caslib <- "casuser"
out_castable <- "hmeq_scored"
astore_name <- "_27BOXIOLWOLETZVC66TN25JAF_ast"
astore_file_name <- "_27BOXIOLWOLETZVC66TN25JAF_ast.sashdat"

## Connecting to SAS Viya
conn <- CAS(hostname = "sasserver.com", ## change if needed
                        port = 8777,
                        protocol='http',  ## change protocol to cas and port to 5570 if using binary connection (unix)
                        username='sasusername', ## use your own credentials
                        password='password') ## we encorage using  .authinfo

## Loading model to memory
cas.table.loadTable(conn,
                      caslib= "Models",
                      path = astore_file_name , #case sensitive
                      casOut = list(name = astore_name,
                                    caslib = "Models")
              )

column_names <- c("BAD", "LOAN")

## loading astore actionset and scoring
loadActionSet(conn, "astore")

cas.astore.score(conn,
                   table = list(caslib= in_caslib, name = in_castable),
                   out = list(caslib = out_caslib, name = out_castable, replace = TRUE),
                   copyVars = column_names,
                   rstore = list(name = astore_name, caslib = "Models")
              )

## Obtaining output/results table
scored_table <- defCasTable(conn,
                            tablename = out_castable,
                            caslib = out_caslib)

head(scored_table)

Each of the functions follows similar structure but have some different variables to have consistant naming, due to that, reference the documentation of the one you want to use to know more about minimun specifications (Using R helper function: ?nlp_sentiment_translate()).

## For models that generates simple DataStep code such as Logistic Regression or Decision Trees
ds <- DS_translate(in_file = "path/to/score_code_Logistic Regression.zip",
                   out_file = "path/to/reg_translated.R",
                   in_caslib = "public", out_caslib = "casuser",
                   in_castable = "hmeq", out_castable = "hmeq_scored")



## For Sentiment model generated from VTA
sentSc <- rsct::sentiment_translate(
                          in_file = "path/to/SentimentScoreCode.zip",
                           out_file = "path/to/sentiment.R",
                           in_caslib = "public", 
                           in_castable = "reports",
                           out_caslib = "public",
                           out_castable_sentiment = "reports_sentiment", 
                           #out_castable_matches, out_castable_features,
                           key_column = "ID", # ID column 
                           document_column = "text", # Text variable column
                           hostname = NULL)

## For Category model generated from VTA

catSc <- rsct::nlp_category_translate(
            in_file = "path/to/CategoriesScoreCode.zip",
            out_file = "path/to/categoryScore.R",
            in_caslib = "public", 
            in_castable = "reports",
            out_caslib = "public",
            out_castable_category = "reports_categories", 
            #out_castable_matches, out_castable_features,
            key_column = "ID", # ID column 
            document_column = "text", # Text variable column
            hostname = NULL)

## For topic model generated from VTA

topSc <- rsct::nlp_topics_translate(in_file = "path/to/TopicsScoreCode.zip",
                           out_file = "path/to/topicsScore.R",
                           in_caslib = "public", out_caslib = "casuser",
                           in_castable = "hmeq",
                           out_castable = "hmeq_scored",
                           copyVars = "ALL")

## For concepts model generated from VTA

conceptSc <- rsct::nlp_concepts_translate(in_file = "path/to/ConceptsScoreCode.zip",
                                    out_file = "path/to/conceptScore.R",
                                    in_caslib = "public", out_caslib = "casuser",
                                    in_castable = "hmeq",
                                    key_column = "ID", # ID column 
                                    document_column = "text", # Text variable column.
                                    out_castable_concepts = "hmeq_scored")

Troubleshooting

Most of the work here assumes that the code is going to be used in the same server that the model was generated, therefore, not needing to upload astores binaries. Some codes generates the uploading code, but it is not fully implemented yet.

Contributing

We welcome your contributions! Please read CONTRIBUTING.md for details on how to submit contributions to this project.

License

This project is licensed under the Apache 2.0 License.

Additional Resources