README

ml-playground

This is the repo for AI Lab from Code.org.

Like the Dance Party and AI for Oceans repos, it is a standalone repo that is published and consumed by the main repo.

AI Lab is a highly configurable environment providing a student experience for doing the following:

loading tabular data (from a pre-loaded CSV with JSON metadata, or from a user-uploaded CSV)
choosing which column to predict (the label)
choosing which column(s) to use for the prediction (the features)
handling both categorical and continuous (numerical) columns
viewing per-column graphs and metadata
viewing custom CrossTab tables for comparing categorical columns
viewing scatter plot graphs for comparing continuous columns
using KNN classification or KNN regression to train a model
choosing the subset of data to reserve for validation
measuring accuracy of the resulting trained model using reserved data
using the trained model by doing predictions
saving the trained model to the server for use in App Lab

It can be run standalone for more rapid development, though its ultimate destination is to appear in Code Studio. When run standalone, it does not have all of the styling of Code Studio. It also calls stub functions for completion, saving a trained model, and indicating which Dynamic Instruction should be shown. The standalone runtime does have a dropdown for selecting which set of level parameters are used, in lieu of a level providing these parameters, and it also shows the current Dynamic Instruction identifier.

The standalone runtime is also deployed to https://code-dot-org.github.io/ml-playground/ though we might vary which branch is published there.

Demo recording

https://user-images.githubusercontent.com/2205926/125634349-a30d64f3-6e05-41ac-a2fc-b82ab794b18f.mov

Common operations

First time:

git clone git@github.com:code-dot-org/ml-playground.git
cd ml-playground
nvm install
nvm use
yarn install
yarn start

The app will be running at http://localhost:8080.

Running a local standalone server

Borrowing one step from above:

yarn start

The app will be running at http://localhost:8080.

First time integration with local Code Studio

In this repo:

yarn link

In main repo's apps/ directory:

yarn link @code-dot-org/ml-playground

This will set up a symlink in main repo's apps/node_modules/ to point at your local changes.

Run

yarn run build

in this repo, and then the main repo's apps build should pick the changes up next time it builds.

If you are running yarn start for continuous builds in the main repo, it will pick up the changes once the build in this repo has completed.

Building changes for a local Code Studio

In main repo:

bin/dashboard-server

In main repo's apps/ directory:

yarn start

If you see an error, run yarn before running yarn start again.

In this repo:

yarn run build

Then the local Code Studio apps build will pick up changes.

See AI Lab changes at http://localhost-studio.code.org:3000/s/allthethings/stage/43/puzzle/1.

Note that running yarn start will erase this build, and so for now it seems best to alternate between using yarn start for testing the standalone build, and using yarn run build to make a single build for consumption by the main repo.

Publishing a new version

Once we want the official main repo build to get the latest updates from this repo, we need to publish the changes.

In this repo, modify package.json with the incremented version number.

Then produce a build:

npm run build

Then publish the build, skipping the option to adjust the build version:

yarn publish

Then commit the changed package.json for posterity.

In the main repo, modify package.json to use the new version of ml-playground.

Pick up the new version:

yarn

Then commit the changed package.json and yarn.lock files so that the official build pipeline uses the new version.

Verifying a symlink exists between the main repo and ML playground