Crate smartcore

SmartCore

Welcome to SmartCore, the most advanced machine learning library in Rust!

In SmartCore you will find implementation of these ML algorithms:

• Regression: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor, K Nearest Neighbors
• Classification: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Supervised Nearest Neighbors (KNN)
• Clustering: K-Means
• Matrix Decomposition: PCA, LU, QR, SVD, EVD
• Distance Metrics: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis
• Evaluation Metrics: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2

Most of algorithms implemented in SmartCore operate on n-dimentional arrays. While you can use Rust vectors with all functions defined in this library we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages:

• ndarray
• nalgebra

Getting Started

To start using SmartCore simply add the following to your Cargo.toml file:

[dependencies]
smartcore = "0.1.0"

All ML algorithms in SmartCore are grouped into these generic categories:

• Clustering, unsupervised clustering of unlabeled data.
• Martix Decomposition, various methods for matrix decomposition.
• Linear Models, regression and classification methods where output is assumed to have linear relation to explanatory variables
• Ensemble Models, variety of regression and classification ensemble models
• Tree-based Models, classification and regression trees
• Nearest Neighbors, K Nearest Neighbors for classification and regression

Each category is assigned to a separate module.

For example, KNN classifier is defined in smartcore::neighbors::knn_classifier. To train and run it using standard Rust vectors you will run this code:

// DenseMatrix defenition
use smartcore::linalg::naive::dense_matrix::*;
// KNNClassifier
use smartcore::neighbors::knn_classifier::*;
// Various distance metrics
use smartcore::math::distance::*;

// Turn Rust vectors with samples into a matrix
let x = DenseMatrix::from_2d_array(&[
   &[1., 2.],
   &[3., 4.],
   &[5., 6.],
   &[7., 8.],
   &[9., 10.]]);
// Our classes are defined as a Vector
let y = vec![2., 2., 2., 3., 3.];

// Train classifier
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();

// Predict classes
let y_hat = knn.predict(&x).unwrap();

Modules

algorithm	Various algorithms and helper methods that are used elsewhere in SmartCore
cluster	Algorithms for clustering of unlabeled data
dataset	Various datasets Datasets
decomposition	Matrix decomposition algorithms
ensemble	Ensemble methods, including Random Forest classifier and regressor
error	Custom warnings and errors
linalg	Diverse collection of linear algebra abstractions and methods that power SmartCore algorithms
linear	Supervised classification and regression models that assume linear relationship between dependent and explanatory variables.
math	Helper methods and classes, including definitions of distance metrics
metrics	Functions for assessing prediction error.
model_selection	Model Selection methods
neighbors	Supervised neighbors-based learning methods
tree	Supervised tree-based learning methods