Intro

openLGD is a Python powered library for the statistical estimation of Credit Risk Loss Given Default models. It can be used both as standalone library and in a federated learning context where data remain in distinct (separate) servers

Key Information

• Author: Open Risk
• License: Apache 2.0
• Mathematical Documentation: Open Risk Manual
• Development website: Github

NB: This is an early alpha release. openLGD is still in active development

Getting started with the demo

• Clone the repo in a local linux environment
• Install the dependencies in a virtual environment
• Fire up a number of flask servers on different shells. Check the Spawn Cluster Script for how to export the environment
• Run the Controller script to perform the demo calculation

Fabric based configuration

Going forward we'll use fabric and yaml to ease deployment. Check Fabfile for preliminary functionality

Dependencies

• The statistical model estimation is currently using scikit-learn / statstmodels components
• The model server is based on the python flask framework.

The complete dependency list in the requirements file

Startup of the model servers:

The demo Model Servers are python/flask based servers

• The model servers should startup on ports http://127.0.0.1:500X/ where X is the serial number
• You can check the server is live by pointing your browser to the port
• or by using curl from the console (curl -v http://127.0.0.1:500X/)

Model Server API endpoints:

The general structure of the simplified API is

• GET http://127.0.0.1:500X/ API Root, indicating the server is live
• GET http://127.0.0.1:500X/start URL to get initial locally estimated parameters (cold start)
• POST http://127.0.0.1:500X/update URL to post current averaged parameters (warm start)