Simple code example demonstrating the use of DeployR as a real-time, R analytics scoring engine.

A more complete js-example-fraud-score example application can be found here.

• About The Example
• Example R Analytics
• Example Application
• Running The Example
• DeployR Integration Details
• License

This example demonstrates the use of DeployR as a real-time, R analytics scoring engine. The example scenario mimics a real world application where employees at a fictitious bank can request fraud scores for bank account records to help detect fraudulent account activity.

This example is built using the DeployR RBroker Framework, the simplest way to integrate R analytics inside any Java, JavaScript or .NET application. This example consists of two distinct parts:

1. Example R Analytics
2. Example Application

The final section of this document provides additional details regarding the DeployR integration implemented for this example.

Source: analytics/*

This example uses an R model built to score fictitious bank account data to help uncover fraudulent account activity. The model used is found here:

analytics/fraudModel.rData

The model uses three variables associated with individual bank accounts:

1. The number of transactions on the account
2. The account balance
3. The credit line on the account

This example makes use of a scoring function that uses the model to help determine the likelihood of fraud on a given account based on these data inputs. The scoring function is found here:

analytics/ccFraudScore.R

The R scripts and data models used by this example application are bundled by default within the DeployR repository, inside the example-fraud-score directory owned by testuser.

However, if for any reason your DeployR repository does not contain these files you can add them using the DeployR Repository Manager as follows:

1. Log in as testuser into the Repository Manager
2. Create a new repository directory called example-fraud-score
3. Upload analytics/fraudModel.rData to the example-fraud-score directory
4. Upload analytics/ccFraudScore.R to the example-fraud-score directory

Source:

fraud-score.js

The example application is implemented as a simple Node.js application, using the RBroker Framework for integration with DeployR.

The application works as follows:

1. Creates an instance of RBroker, representing a pool of R sessions on the DeployR server.
2. Preloads the fraud-score model into the workspace for each R session in the pool.
3. Registers asynchronous callback listeners for task completion and error events in the application.
4. Once RBroker instance initialized, submits sample fraud-score tasks for execution.
5. Demonstrates task completion and error handling.
6. Releases the instance of RBroker and exits.

We recommend reading the comments provided within the fraud-score.js source file for further details.

Use the DeployR CLI to download and run the js-example-fraud-score-basics example. You can observe the console output generated by the application in your terminal window.

Alternatively you can run it inline:

1. npm install
2. Edit /config/broker.json to set your DeployR server endpoint host and user credentials. 3.$ node fraud-score.js
3. View stdout for logging statements

DeployR-powered applications typically depend on repository-managed R analytics scripts, models and/or data files. See the DeployR Repository Manager for details on how best to manage your own R analytics dependencies.

This example depends on two repository-managed files:

1. /testuser/example-fraud-score/fraudModel.rData
2. /testuser/example-fraud-score/ccFraudScore.R

Both files, an R model and scoring function respectively, are owned by testuser and can be found in the example-fraud-score repository-managed directory owned by testuser.

These example file dependencies ship, pre-deployed in the DeployR repository so there is no further action for you to take in order for this example to use them. Typically a data scientist would first develop and then provide these analytics files to an application developer for integration.

This examples uses the RBroker Framework to integrate DeployR real-time scoring capabilities inside the example application.

Specifically, this example uses the Pooled Task Runtime provided by the RBroker Framework.

We recommend experimenting with the size of the pool and the number of tasks executed and observe the effects on throughput. To change the size of the pool used by the example application update the maxConcurrentTaskLimit propety in the config/broker.json file. To change the number of tasks executed by the example application update the tasksize property in the config/app.json file.

See the following sections of the RBroker Framework tutorial for related details:

• Client Application Profiling
• Grid Resource Management

Copyright (C) 2010-2016, Microsoft Corporation

This program is licensed to you under the terms of Version 2.0 of the Apache License. This program is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING THOSE OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Please refer to the Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0) for more details.