bdeboe / integratedml-demo

Repository with scripts and datasets for the IntegratedML beta program

IntegratedML Demo Kit

This repo contains a few simple datasets to demonstrate InterSystems IRIS IntegratedML. Instructions below describe how to use IntegratedML on Docker, Linux/Mac or Windows platforms.

How to install/build on Docker

The included Dockerfile will pull an InterSystems IRIS IntegratedML Community Edition image from the Docker Hub and set up a few simple datasets. Full Enterprise Edition container images are available from the InterSystems Container Repository.

Use the following command to build the demo image:

docker build --tag integratedml-demo .

To start your container, use the following command (or your favourite equivalent, as this one will drop your container after stopping)

docker run --rm -d -p 9091:1972 -p 9092:52773 --name integratedml integratedml-demo

The IRIS password is initialized as SYS, but you can get in directly through the following command, the SMP or connecting through a SQL client such as DBeaver

docker exec -it integratedml iris sql IRIS

How to use IntegratedML

Using IntegratedML takes only three simple commands:

CREATE MODEL Flowers PREDICTING (Species) FROM DataMining.IrisDataset;
TRAIN MODEL Flowers FROM DataMining.IrisDataset;
SELECT TOP 20 PREDICT(Flowers) AS PredictedSpecies, Species AS ActualSpecies FROM DataMining.IrisDataset;

Note that the semicolons at the end are for use in a multiline-style client such as DBeaver or SQuirreL and not part of regular IRIS SQL.

More Resources

• IntegratedML User Guide
• Learn IntegratedML from InterSystems Learning Services

Included datasets

These are broadly available datasets, but we may not have permission to re-distribute them, so keep this repo to yourself:

• DataMining.IrisDataset: Iris (the flower, not the Gartner MQ ODBMS leader!), as in the classic SAMPLES namespace. You can predict the Species (categorization) or any of the width / length columns (regression)
• Titanic.Passenger: List of all the passengers of the ill-fated bathtub, with a "Survived" column that makes a fun demo target.
• [SQLUser.]LoanPerformance: as used at Global Summit, with a "LoanDefault" column that works well for a more serious demo. Derived from a Kaggle dataset
• [SQLUser.]Campaign: as used in the campaign showcase in the ML Toolkit. The target column to put your crosshairs on is RESPONSE
• [SQLUser.]BreastCancer
• [SQLUser.]appointments
• NLP.HateSpeech - anticipating some work on the NLP end, taking a text classification challenge from https://github.com/aitor-garcia-p/hate-speech-dataset