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https://github.com/cmccomb/rust-automl

Automated Machine Learning in Rust 🦀
https://github.com/cmccomb/rust-automl

automl machine-learning ml rust rust-crate smartcore

Last synced: 15 days ago
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Automated Machine Learning in Rust 🦀

Host: GitHub
URL: https://github.com/cmccomb/rust-automl
Owner: cmccomb
License: mit
Created: 2021-11-11T15:28:59.000Z (almost 3 years ago)
Default Branch: master
Last Pushed: 2023-07-11T21:14:24.000Z (over 1 year ago)
Last Synced: 2024-10-13T14:12:54.195Z (29 days ago)
Topics: automl, machine-learning, ml, rust, rust-crate, smartcore
Language: Rust
Homepage: https://crates.io/crates/automl
Size: 810 KB
Stars: 32
Watchers: 3
Forks: 4
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

[![Github CI](https://github.com/cmccomb/rust-automl/actions/workflows/tests.yml/badge.svg)](https://github.com/cmccomb/automl/actions)
[![Crates.io](https://img.shields.io/crates/v/automl.svg)](https://crates.io/crates/automl)
[![docs.rs](https://img.shields.io/docsrs/automl/latest?logo=rust)](https://docs.rs/automl)

# AutoML with SmartCore

AutoML is _Automated Machine Learning_, referring to processes and methods to make machine learning more accessible for
a general audience. This crate builds on top of the [smartcore](https://docs.rs/smartcore/) machine learning framework,
and provides some utilities to quickly train and compare models.

# Install

To use the latest released version of `AutoML`, add this to your `Cargo.toml`:
```toml
automl = "0.3.0"
```
To use the bleeding edge instead, add this:
```toml
automl = { git = "https://github.com/cmccomb/rust-automl" }
```

# Usage

Running the following:

```rust
let dataset = smartcore::dataset::breast_cancer::load_dataset();
let settings = automl::Settings::default_classification();
let mut classifier = automl::SupervisedModel::new(dataset, settings);
classifier.train();
```

will perform a comparison of classifier models using cross-validation. Printing the classifier object will yield:

```text
┌────────────────────────────────┬─────────────────────┬───────────────────┬──────────────────┐
│ Model │ Time │ Training Accuracy │ Testing Accuracy │
╞════════════════════════════════╪═════════════════════╪═══════════════════╪══════════════════╡
│ Random Forest Classifier │ 835ms 393us 583ns │ 1.00 │ 0.96 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Logistic Regression Classifier │ 620ms 714us 583ns │ 0.97 │ 0.95 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Gaussian Naive Bayes │ 6ms 529us │ 0.94 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Categorical Naive Bayes │ 2ms 922us 250ns │ 0.96 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Decision Tree Classifier │ 15ms 404us 750ns │ 1.00 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ KNN Classifier │ 28ms 874us 208ns │ 0.96 │ 0.92 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Support Vector Classifier │ 4s 187ms 61us 708ns │ 0.57 │ 0.57 │
└────────────────────────────────┴─────────────────────┴───────────────────┴──────────────────┘
```

You can then perform inference using the best model with the `predict` method.

## Features

This crate has several features that add some additional methods.

| Feature | Description |
| :------ | :------------------------------------------------------------------------------------------------------ |
| `nd` | Adds methods for predicting/reading data using [`ndarray`](https://crates.io/crates/ndarray). |
| `csv` | Adds methods for predicting/reading data from a .csv using [`polars`](https://crates.io/crates/polars). |

## Capabilities

- Feature Engineering
- PCA
- SVD
- Interaction terms
- Polynomial terms
- Regression
- Decision Tree Regression
- KNN Regression
- Random Forest Regression
- Linear Regression
- Ridge Regression
- LASSO
- Elastic Net
- Support Vector Regression
- Classification
- Random Forest Classification
- Decision Tree Classification
- Support Vector Classification
- Logistic Regression
- KNN Classification
- Gaussian Naive Bayes
- Meta-learning
- Blending
- Save and load settings
- Save and load models