modelfox

ModelFox makes it easy to train, deploy, and monitor machine learning models.

Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser.

# ModelFox [Discord](https://discord.gg/jT9ZGp3TK2) ModelFox makes it easy to train, deploy, and monitor machine learning models. - Run `modelfox train` to train a model from a CSV file on the command line. - Make predictions with libraries for [Elixir](https://hex.pm/packages/modelfox), [Go](https://pkg.go.dev/github.com/modelfoxdotdev/modelfox-go), [JavaScript](https://www.npmjs.com/package/@modelfoxdotdev/modelfox), [PHP](https://packagist.org/packages/modelfox/modelfox), [Python](https://pypi.org/project/modelfox), [Ruby](https://rubygems.org/gems/modelfox), and [Rust](https://lib.rs/crates/modelfox). - Run `modelfox app` to learn more about your models and monitor them in production. ### Install You can install the modelfox CLI by either downloading the binary from the [latest github release](https://github.com/modelfoxdotdev/modelfox/releases/tag/v0.8.0), or by building from source. ### Train Train a machine learning model by running `modelfox train` with the path to a CSV file and the name of the column you want to predict. ``` $ modelfox train --file heart_disease.csv --target diagnosis --output heart_disease.modelfox ✅ Loading data. ✅ Computing features. Training model 1 of 8. [==========================================> ] ``` The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a `.modelfox` file. If you want more control, you can provide a config file. ### Predict Make predictions with libraries for [Elixir](https://hex.pm/packages/modelfox), [Go](https://pkg.go.dev/github.com/modelfoxdotdev/modelfox-go), [JavaScript](https://www.npmjs.com/package/@modelfoxdotdev/modelfox), [PHP](https://packagist.org/packages/modelfox/modelfox), [Python](https://pypi.org/project/modelfox), [Ruby](https://rubygems.org/gems/modelfox), and [Rust](https://lib.rs/modelfox). ```javascript let modelfox = require("@modelfoxdotdev/modelfox") let model = new modelfox.Model("./heart_disease.modelfox") let input = { age: 63, gender: "male", // ... } let output = model.predict(input) console.log(output) ``` ```javascript { className: 'Negative', probability: 0.9381780624389648 } ``` ### Inspect Run `modelfox app`, open your browser to http://localhost:8080, and upload the model you trained. - View stats and metrics. - Tune your model to get the best performance. - Make example predictions and get detailed explanations. ### Monitor Once your model is deployed, make sure that it performs as well in production as it did in training. Opt in to logging by calling `logPrediction`. ```javascript // Log the prediction. model.logPrediction({ identifier: "6c955d4f-be61-4ca7-bba9-8fe32d03f801", input, options, output, }) ``` Later on, if you find out the true value for a prediction, call `logTrueValue`. ```javascript // Later on, if we get an official diagnosis for the patient, log the true value. model.logTrueValue({ identifier: "6c955d4f-be61-4ca7-bba9-8fe32d03f801", trueValue: "Positive", }) ``` Now you can: - Look up any prediction by its identifier and get a detailed explanation. - Get alerts if your data drifts or metrics dip. - Track production accuracy, precision, recall, etc. ## Building from Source This repository is a Cargo workspace, and does not require anything other than the latest nightly Rust toolchain to get started with. 1. Install [Rust](rust-lang.org) on Linux, macOS, or Windows. 2. Clone this repo and `cd` into it. 3. Run `cargo run` to run a debug build of the CLI. If you are working on the app, run `scripts/app/dev`. This rebuilds and reruns the CLI with the `app` subcommand as you make changes. To install all dependencies necessary to work on the language libraries and build releases, install [Nix](https://nixos.org) with [flake support](https://nixos.wiki/wiki/Flakes), then run `nix develop` or set up [direnv](https://github.com/direnv/direnv). If you want to submit a pull request, please run `scripts/fmt` and `scripts/check` at the root of the repository to confirm that your changes are formatted correctly and do not have any errors. ## License All of this repository is MIT licensed, except for the `crates/app` directory, which is source available and free to use for testing, but requires a paid license to use in production.