Crate linfa_bayes

Expand description

Naive Bayes

linfa-bayes provides pure Rust implementations of Naive Bayes algorithms for the Linfa toolkit.

The Big Picture

linfa-bayes is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python’s scikit-learn.

Current state

linfa-bayes currently provides an implementation of the following methods:

Gaussian Naive Bayes (GaussianNb)
Multinomial Naive Nayes (MultinomialNb)

Examples

You can find examples in the examples/ directory. To run Gaussian Naive Bayes example, use:

$ cargo run --example winequality --release

Show source code

use linfa::metrics::ToConfusionMatrix;
use linfa::traits::{Fit, Predict};
use linfa_bayes::{GaussianNb, Result};

// Read in the dataset and convert targets to binary data
let (train, valid) = linfa_datasets::winequality()
    .map_targets(|x| if *x > 6 { "good" } else { "bad" })
    .split_with_ratio(0.9);

// Train the model
let model = GaussianNb::params().fit(&train)?;

// Predict the validation dataset
let pred = model.predict(&valid);

// Construct confusion matrix
let cm = pred.confusion_matrix(&valid)?;

// classes    | bad        | good      
// bad        | 130        | 12        
// good       | 7          | 10    
//
// accuracy 0.8805031, MCC 0.45080978
println!("{:?}", cm);
println!("accuracy {}, MCC {}", cm.accuracy(), cm.mcc());

To run Multinomial Naive Bayes example, use:

$ cargo run --example winequality_multinomial --release

Show source code

use linfa::metrics::ToConfusionMatrix;
use linfa::traits::{Fit, Predict};
use linfa_bayes::{MultinomialNb, Result};

// Read in the dataset and convert targets to binary data
let (train, valid) = linfa_datasets::winequality()
    .map_targets(|x| if *x > 6 { "good" } else { "bad" })
    .split_with_ratio(0.9);

// Train the model
let model = MultinomialNb::params().fit(&train)?;

// Predict the validation dataset
let pred = model.predict(&valid);

// Construct confusion matrix
let cm = pred.confusion_matrix(&valid)?;
// classes    | bad        | good      
// bad        | 88         | 54        
// good       | 10         | 7         

// accuracy 0.5974843, MCC 0.02000631
println!("{:?}", cm);
println!("accuracy {}, MCC {}", cm.accuracy(), cm.mcc());

Structs

GaussianNb

Fitted Gaussian Naive Bayes classifier.

GaussianNbParams

A hyper-parameter set during construction for a Gaussian Naive Bayes model.

GaussianNbValidParams

A verified hyper-parameter set ready for the estimation of a Gaussian Naive Bayes model.

MultinomialNb

Fitted Multinomial Naive Bayes classifier.

MultinomialNbParams

A hyper-parameter set during construction for a Multinomial Naive Bayes model.

MultinomialNbValidParams

A verified hyper-parameter set ready for the estimation of a Multinomial Naive Bayes model.

Enums

NaiveBayesError

Error variants from hyper-parameter construction or model estimation

Type Definitions

Result

Simplified Result using NaiveBayesError as error type