Henosis is a cloud-native, lightweight Python-based recommender framework that brings together model training and testing, storage and deployment, and querying under a single framework. Henosis provides Data Scientists with a straight-forward and generalizable environment in which to train, test, store, and deploy categorical machine learning models for making form field recommendations, while also providing software engineers and web developers with a REST API that can be easily queried for recommendations and integrated across different enterprise applications.
Henosis is in active development and is released as alpha software.
Henosis works by acting as a bridge between end users and data scientists which train recommendation (predictive) models. There are several classes that facilitate the interaction between data, scikit-learn models, and a REST API that provides recommendations and other information.
Henosis classes (bold borders) interface with the data scientist or statistician, developers querying for recommendations, and trained models. When queried for recommendations, Henosis references model information in Elasticsearch, loads appropriate models from AWS S3, and serves recommendations by using data available in the query (REST API request).
- Python 3.6+ (untested on lower versions) with the following packages:
- boto3
- dill
- Flask
- Flask-CORS
- Flask-HTTPAuth
- Flask-RESTful
- gevent
- imbalanced-learn
- Jinja2
- jwt
- gevent
- numpy
- pandas
- pymssql
- PyYAML
- requests
- scikit-learn
- A working Amazon Web Services (AWS) S3 bucket, along with:
- AWS key
- AWS secret
- A running Elasticsearch 6+ server (untested on lower versions). You'll need to create an index and specify a mapping (documentation is provided and helpful scripts are available in the scripts directory).
Simply do a pip install and import the Henosis library as follows:
pip install Henosisfrom Henosis import henosisIf you'd like, you can also fork the repository and pull Henosis to a local directory.
The latest Henosis documentation is available here and covers how to use Henosis for modeling and providing recommendations within your form-containing applications.
In October 2017, our data science team at the NASA Jet Propulsion Laboratory (JPL) was approached by the Office of Safety and Mission Success (5X) to improve processes for reporting in the Problem Reporting System (PRS). PRS is an internal tool that allows engineers to submit Problem Failure Reports (PFRs) and Incident Surprise, Anomaly reports (ISAs), which document pre-launch test failures and post-launch operational anomalies experienced by spacecraft. These reports not only serve as a record of past problems but also of past solutions to the problems described.
Despite their value, the reports contained within the PRS are costly to fill out and submit. With dozens of textual, categorical, and other inputs in the forms, the PFRs and ISAs draw valuable time away from mission staff to the annotation of internal forms — time better spent with spacecraft operations and mission work. A solution was needed that would reduce the time needed to file reports in PRS while ensuring ease of use for users already familiar with the current PRS system, such as a recommendation system for form fields. What we needed from a data science and IT operations perspective was a straightforward process to deploy a simple recommendation system for use in enterprise applications containing categorical form inputs (like dropdown menus).
While the initial effort focused on one internal use case, Henosis was developed as a generalized, open-source framework and is freely available for use in other applications.
This project is licensed under the Apache 2.0 license and released by the California Institute of Technology.
- NASA Jet Propulsion Laboratory
- Ian Colwell
- Kyle Hundman
- Paul Ramirez
- parent / sibling models
- support of numpy arrays (no pandas)
- keras support
- collaborative filtering