R Web API from Dynamics 365 FinOps

Microsoft gives us a fair number of options to seamlessly connect machine learning models to our production code, and I honestly love using them all. AzureML is fantastic for many use cases, and with the Data Factory, Databricks and Data Lakes combo virtually every possible scenario can be covered really nicely.

Except of course if the model you need to use is hosted by a 3rd party which does not support any of these services. Then again, you might want to quickly test a few models first in a POC context before committing to “productizing” these into AzureML. Perhaps you just don’t want all your eggs in one vendor basket, or all your raindrops in one single cloud.

Worse, you might have a requirement to call an R API from D365 FinOps. In this blog post I’ll show you how.

First things first, let’s build a simple R model using the Prophet library from Facebook to do forecasting. This uses a data frame with two columns, y & ds to feed a time series set of values (y) based on time (ds). Prophet supports a lot of parameters for seasonality and such and I suggest reading up on it.

For our example I’ll keep things simple, and assume the R script won’t be doing any munging or wrangling as such. Clean data frame goes in, Prophet predicts, but instead of returning the y-hat values (Ŷ) we’ll make it interesting and return a set of base64 encoded PNG plots containing the forecast and seasonality trends instead.

So there are a number of challenges for us:

  • We need to host this R model as an API
  • We need to grab the resulting plot predictions created by Prophet
  • Encode the plots to base64 and return it from the API as JSON
  • Call and display this all in D365 from a form

The best way I’ve found to host R as an API is to use the Plumber library. So I’ve deployed a Linux environment in my cloud of choice and installed all the required R libraries, including Plumber, and set up NGINX to route incoming traffic on port 80 to Plumber which listens on port 8000. To call this API from D365 you’ll need to install a certificate as only HTTPS will do between D365 and our Linux box.

The R code is shown below, detailing how we grab the plots and encode it to base64. We also receive our data frame as part of the call so we need to URIDecode it. This will do for small data sets; if you want to tackle a large data set, use a different mechanism of passing a reference to the data, perhaps a POST call with the data in the body as JSON. In our case our API returns JSON containing three base64 encoded plots.

library(prophet)
library(dplyr)
library(ggplot2)
library(png)
library(plumber)
library(urltools)

encodeGraphic <- function(g) {
  png(tf1 <- tempfile(fileext = ".png"))
  print(g)
  dev.off()
  encoded <- RCurl::base64Encode(readBin(tf1, "raw", file.info(tf1)[1, "size"]), "txt")
  return(encoded)
}

#* Do a forecast
#* @param data a CSV containing ordered, orderdate
#* @get /forecast
function(data="")
{
  json = '{"forecast":"'
  tmp<-URLdecode(data)
  stats <- read.csv(text=tmp, header=TRUE, sep=',',colClasses = c('numeric','Date'))
  names(stats) <- c("y","ds")
  stats$ds <- as.Date(stats$ds) # coerce to ensure date type

  m <- prophet(stats, yearly.seasonality=TRUE)
  future <- make_future_dataframe(m, periods = 4, freq="m")
  forecast <- predict(m, future)

  g<-plot(m, forecast) +
    xlab("Date") +
    ylab("Data") +
    theme_grey() +
    theme_grey() +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(colour = "black")) +
    ggtitle("Sales Forecast");

  encodedForecast<-encodeGraphic(g)
  json <- paste(json, encodedForecast,sep='')
  g<-prophet_plot_components(m, forecast)
  json <- paste(json, '","trend":"', sep='')
  encodedTrend <- encodeGraphic(g[1])
  json<-paste(json, encodedTrend,sep='')
  json<-paste(json,'","yearly":"', sep='')
  encodedYearly <- encodeGraphic(g[2])
  json<-paste(json, encodedYearly,sep='')
  json<-paste(json, '"}', sep='')
  return(json)
}

 

Next up we’ll create an extensible control in D365 to host our plots. I like wrapping things in extensible controls as it gives me the ability to obfuscate the JavaScript to protect any commercial IP. So I try to keep as little as possible in X++ and as much as possible in JavaScript.

Here is the code for our BuildControl, just a single CSV property is defined:

[FormDesignControlAttribute("Forecast")]
class ForecastControlBuild extends FormBuildControl
{
    str csv = "";

    [FormDesignPropertyAttribute("CSV","Forecast")]
    public str parmCSV(str _csv=csv)
    {
        if (prmIsDefault(_csv))
        {
            csv = _csv;
        }
        return csv;
    }
}

 

Followed by the code for our Control class that contains our CSV property that we will populate from our X++ form.

[FormControlAttribute('Forecast','',classstr(ForecastControlBuild))]
class ForecastControl extends FormTemplateControl
{
    FormProperty csv;

    public void new(FormBuildControl _build, FormRun _formRun)
    {
        super(_build, _formRun);
        this.setTemplateId('Forecast');
        this.setResourceBundleName('/resources/html/Forecast');
        csv = properties.addProperty(methodStr(ForecastControl, parmCSV), Types::String);
    }

    [FormPropertyAttribute(FormPropertyKind::Value, "CSV")]
    public str parmCSV(str _value = csv.parmValue())
    {
        if (!prmIsDefault(_value))
        {
            csv.setValueOrBinding(_value);
        }
        return csv.parmValue();
    }

    public void applyBuild()
    {
        super();
        ForecastControlBuild build = this.build();

        if (build)
        {
            this.parmCSV(build.parmCSV());
        }
    }
}

 

We’ll add a minimal control HTML file to host our image placeholders. Three simple DIV controls with image controls with their ID’s set to forecastImage, trendImage and yearlyImage respectively, so we can get hold of them from our JavaScript code.

Finally the JavaScript for our control containing the actual Ajax call to our R API.

(function () {
    'use strict';
    $dyn.controls.Forecast = function (data, element) {
        $dyn.ui.Control.apply(this, arguments);
        $dyn.ui.applyDefaults(this, data, $dyn.ui.defaults.Forecast);
    };

    $dyn.controls.Forecast.prototype = $dyn.ui.extendPrototype($dyn.ui.Control.prototype, {
        init: function (data, element) {
            var self = this;
            $dyn.ui.Control.prototype.init.apply(this, arguments);
            $dyn.observe(data.CSV, function (csv)
            {
                document.getElementById('forecastImage').style.display = "none";
                document.getElementById('trendImage').style.display = "none";
                document.getElementById('yearlyImage').style.display = "none";
                if (csv.length>0)
                {
                    var url = 'https://yourboxhere.australiaeast.cloudapp.azure.com/forecast?data=' + csv;
                    $.ajax({
                        crossOrigin: true,
                        url: url,
                        success: function (data) {
                            var obj = JSON.parse(data);
                            var forecast = obj.forecast;
                            var trend = obj.trend;
                            var yearly = obj.yearly;

                            document.getElementById('forecastImage').src = 'data:image/png;base64,' + forecast;
                            document.getElementById('trendImage').src = 'data:image/png;base64,' + trend;
                            document.getElementById('yearlyImage').src = 'data:image/png;base64,' + yearly;
                            document.getElementById('forecastImage').style.display = "block";
                            document.getElementById('trendImage').style.display = "block";
                            document.getElementById('yearlyImage').style.display = "block";
                        }
                    });
                }
            })
        }
    });
})();

 

So far it’s all fairly simple, and we can add a demo form in X++ to use our extensible control. We’ll grab some sales orders from D365, URI encode it manually and then send it off to our extensible control to pass to our R API sitting somewhere outside the D365 cloud.

class ForecastFormClass
{
    ///

    ///
    ///

 

    ///
    ///
    [FormControlEventHandler(formControlStr(ForecastForm, FormCommandButtonControl1), FormControlEventType::Clicked)]
    public static void FormCommandButtonControl1_OnClicked(FormControl sender, FormControlEventArgs e)
    {
        FormCommandButtonControl callerButton = sender as FormCommandButtonControl; 
        FormRun form = callerButton.formRun();
        ForecastControl forecastControl;
        forecastControl = form.control(form.controlId("ForecastControl1"));

        SalesLine   SalesLine;
        date        varStartPeriodDT    = mkdate(1, 1, 2015);
        date        varEndPeriodDT      = mkDate(1,7,2016);
        str         csv                 = "ordered%2Corderdate%0D%0A";

        while select sum(QtyOrdered), ShippingDateRequested  from SalesLine group by ShippingDateRequested
            where SalesLine.ShippingDateRequested >= varStartPeriodDT && SalesLine.ShippingDateRequested <= varEndPeriodDT &&  SalesLine.ItemId == 'T0001'
        {
            csv = csv + int2str(SalesLine.QtyOrdered) + "%2C" + date2str(SalesLine.ShippingDateRequested,321,2,3,2,3,4) + "+00%3A00%3A00%0D%0A";
        }
        forecastControl.parmCSV(csv);
    }
}

 

A second or two later and we receive our plots.

AXForecast

Pretty simple stuff. We can extend this further by passing various parameters to the R API, for example, which time-series model we would like to use, whether to return the predicted values (Ŷ) or not, seasonality parameters and anything else we need.

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