How to deploy an R script/model in CML that interacts with DataViz (Visuals/Graphs/Charts)

Shaun goes into great depth on all the capabilities of CML and DataViz in this blog, using a Python model or script.  Here, I'll write the script in R instead of the sample Python script Shaun shares.

There is no need to have an R script with predictive model capabilities.  While an R script with predictive model capabilities is an option, I'll demonstrate deploying a simple R script in CML that adds two columns together.

Step 1: Write your R script

As explained above, this script adds two columns together (assuming both columns are of type integer).  Within DataViz, we'll pass two columns (of type integer).  The cml_model wrapper is necessary with the PBJ runtime, as explained here.

• Notice the "json" is what we'll be returning to DataViz, where we expect the "numbers_added" column
• input[1] and input[2] are representing the two integer columns being passed

  library(cml)
  
      
  add_numbers <- cml_model(function(args) {
      json <-
  '{
      "data": {
          "colnames": [
              "numbers_added","tester"
          ],
          "coltypes": [
              "INT","STRING"
          ],
          "rows": [
          ]
      }
  }
  '
      
    mydf <- fromJSON(json)
    
    for(row in 1:nrow(args$data$rows)) {
          inputs = args$data$rows[row,]
          together <- matrix(list(inputs[1]+inputs[2]))
          mydf$data$rows <- rbind(mydf$data$rows, together)
  
    }
    
    mode(mydf$data$rows) <- "integer"
   
    return(mydf)
      
      
      })

Step 2: Deploy your R script as a Model in CML

• Notice PBJ as the runtime

Step 3: Testing your deployed R script/model via CML and/or Postman

• Below is the JSON input and output:

  {
    "data": {
      "colnames": [
        "week",
        "depttime"
      ],
      "rows": [
        [
          1,
          7
        ],
        [
          2,
          8
        ],
        [
          11,
          55
        ]
      ]
    }
  }

  output:

  {
      "data": {
          "colnames": [
              "numbers_added",
              "tester"
          ],
          "coltypes": [
              "INT",
              "STRING"
          ],
          "rows": [
              [
                  8
              ],
              [
                  10
              ],
              [
                  66
              ]
          ]
      }
  }

  
  

  *The second column "tester" is required from DataViz (where you need more than one column returned)

Step 4: Build a Dataset in DataViz using your deployed model

• Using my deployed model's URL and key, I'll plug this into the built-in function within DataViz, passing two columns: 

  https://modelservice.ml-b74f8940-b97.go01-dem.ylcu-atmi.cloudera.site/model
  {"accessKey":"mxyouraccesskey724mext3y8"}​

•  Within DataViz, I'll upload my CSV file called "people_numbers.csv":

  name,number1,number2
  ryan,1,2
  nicole,3,4
  rupert,5,6
  nigel,7,8

• Creating a new table based on the CSV
  
  
• Dataset is created from the people_numbers table:
  

Step 4: Modify the Dataset with a new column called "numbers_added", which will call the CML R model

•  Clone one of the measure columns (number1 or number2)
  
  
• Edit the "Copy of number#" column
• Change the column name to "numbers_added"
• Go to the expression and enter the following then click "APPLY":

  cviz_rest('{"url":"https://modelservice.ml-b74f8940-b97.go01-dem.ylcu-atmi.cloudera.site/model","accessKey":"withyouraccesskey","colnames":["number1","number2"],"response_colname":"numbers_added"}')

  

• Validate the numbers_added is defined as a Mes and #, then click "SAVE"

Step 5: Add a dashboard with your dataset

• Within the last step 4, click the "NEW DASHBOARD" button
• The new column "numbers_added" shows the results from the CML R model deployed within step 4. 
• We're able to create visuals based on the dimensions and measures we choose

That's it!  Feel free to import your own R script into CML as a model, and build charts/graphs within DataViz!