Intelligently Assisted Form Fields with Henosis

Online forms are the worst. The often-long, sometimes multi-page forms can be a time-consuming and laborious process to fill out. Almost any other task is more enjoyable, even with the occasional prize drawing or other form of incentive. While large forms can and often do provide valuable data, they certainly have not won any awards in regards to providing a well-regarded user experience.

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).

Here’s a simple modeling workflow example. First, we import Henosis from some directory (a pip install is planned) and the libraries pandas or scikit-learn.

Data prep. Here, we create the Henosis Data object d and load data from a local csv file into the object. This allows Henosis to create testing and training splits of your data, and upsample or downsample your data (using the imbalanced-learn library) if you need it.

Define the model. Here, we can pass any scikit-learn classification model into Henosis to use for providing recommendations.

Once your model m is defined, use it to train and test against your data.

Happy with the results? You can then store the model in S3 and update the entry in Elasticsearch with simple call from the model, m. First, you’ll need to load a Henosis server object s using a local config.yaml file. Then, you can call the model and store it on AWS S3 and Elasticsearch using the settings specified in object s.

While I spare readers the details of configuring Henosis from this article, you can find the information you need in the Github repository (and soon to come documentation).

Deployment is easy too, and just one call away. Once you’re satisfied with model results, you can deploy that model for use in the recommender system by calling m.deploy().

Running a Henosis instance to serve your deployed recommendations is easy, too. Here, we show how one can spin up a Henosis instance for serving recommendations. Using an identically configured config.yaml, one can start a Henosis instance in a Python script which spins up a simple Flask server.

See? Not so bad. We recommend using Docker and a container manager such as Kubernetes for scalable deployment, redundancy, and minimizing downtime. But you can just as easily host a Henosis instance using any service that will deploy and serve Python applications.

Modeling and REST API documentation is in the works and will soon be released for the community to use in their own applications. We’re taking the time to make sure the documentation is complete and accurate prior to release. In the meantime, development of Henosis continues and you’ll see pushes to Github every now and then adding additional features or capability (a pip install is coming soon, too)! We’re excited to share this simple but useful capability with the general public and beyond the confines of the laboratory.

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