Crate automl 

automl with smartcore

AutoML (Automated Machine Learning) streamlines machine learning workflows, making them more accessible and efficient for users of all experience levels. This crate extends the smartcore machine learning framework, providing utilities to quickly train, compare, and deploy models.

Install

Add automl to your Cargo.toml to get started:

Stable Version

automl = "0.3.0"

Latest Development Version

automl = { git = "https://github.com/cmccomb/rust-automl" }

Example Usage

Here’s a quick example to illustrate how AutoML can simplify model training and comparison:

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:

┌────────────────────────────────┬─────────────────────┬───────────────────┬──────────────────┐
│ 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.
csv	Adds methods for predicting/reading data from a .csv using 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

Re-exports

pub use settings::ClassificationSettings;

pub use settings::ClusteringSettings;

pub use settings::RegressionSettings;

pub use algorithms::ClassificationAlgorithm;

pub use algorithms::ClusteringAlgorithm;

pub use algorithms::RegressionAlgorithm;

pub use model::ClassificationModel;

pub use model::ClusteringModel;

pub use model::ModelError;

pub use model::ModelResult;

pub use model::RegressionModel;

pub use model::SupervisedModel;

Modules

algorithms

Algorithm enumerations and helpers. Algorithm enumerations and helpers for model training.

cookbook

A cookbook of common AutoML tasks

metrics

Metric re-exports. Re-exported clustering metrics.

model

Model definitions and implementations. Types and implementations for machine learning models.

settings

Settings customization

utils

Utility functions for the crate.

Structs

DenseMatrix

Dense matrix