use std::fmt;
use num_traits::Unsigned;
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
use crate::numbers::basenum::Number;
use crate::numbers::realnum::RealNumber;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, PartialEq, Clone)]
struct GaussianNBDistribution<T: Number> {
class_labels: Vec<T>,
class_count: Vec<usize>,
class_priors: Vec<f64>,
var: Vec<Vec<f64>>,
theta: Vec<Vec<f64>>,
}
impl<T: Number + Ord + Unsigned> fmt::Display for GaussianNBDistribution<T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(
f,
"GaussianNBDistribution: class_count: {:?}",
self.class_count
)?;
writeln!(f, "class_labels: {:?}", self.class_labels)?;
Ok(())
}
}
impl<X: Number + RealNumber, Y: Number + Ord + Unsigned> NBDistribution<X, Y>
for GaussianNBDistribution<Y>
{
fn prior(&self, class_index: usize) -> f64 {
if class_index >= self.class_labels.len() {
0f64
} else {
self.class_priors[class_index]
}
}
fn log_likelihood<'a>(&self, class_index: usize, j: &'a Box<dyn ArrayView1<X> + 'a>) -> f64 {
let mut likelihood = 0f64;
for feature in 0..j.shape() {
let value = X::to_f64(j.get(feature)).unwrap();
let mean = self.theta[class_index][feature];
let variance = self.var[class_index][feature];
likelihood += self.calculate_log_probability(value, mean, variance);
}
likelihood
}
fn classes(&self) -> &Vec<Y> {
&self.class_labels
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Default, Clone)]
pub struct GaussianNBParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub priors: Option<Vec<f64>>,
}
impl GaussianNBParameters {
pub fn with_priors(mut self, priors: Vec<f64>) -> Self {
self.priors = Some(priors);
self
}
}
impl GaussianNBParameters {
fn default() -> Self {
Self {
priors: Option::None,
}
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct GaussianNBSearchParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub priors: Vec<Option<Vec<f64>>>,
}
pub struct GaussianNBSearchParametersIterator {
gaussian_nb_search_parameters: GaussianNBSearchParameters,
current_priors: usize,
}
impl IntoIterator for GaussianNBSearchParameters {
type Item = GaussianNBParameters;
type IntoIter = GaussianNBSearchParametersIterator;
fn into_iter(self) -> Self::IntoIter {
GaussianNBSearchParametersIterator {
gaussian_nb_search_parameters: self,
current_priors: 0,
}
}
}
impl Iterator for GaussianNBSearchParametersIterator {
type Item = GaussianNBParameters;
fn next(&mut self) -> Option<Self::Item> {
if self.current_priors == self.gaussian_nb_search_parameters.priors.len() {
return None;
}
let next = GaussianNBParameters {
priors: self.gaussian_nb_search_parameters.priors[self.current_priors].clone(),
};
self.current_priors += 1;
Some(next)
}
}
impl Default for GaussianNBSearchParameters {
fn default() -> Self {
let default_params = GaussianNBParameters::default();
GaussianNBSearchParameters {
priors: vec![default_params.priors],
}
}
}
impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
x: &X,
y: &Y,
priors: Option<Vec<f64>>,
) -> Result<Self, Failed> {
let (n_samples, _) = x.shape();
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
n_samples, y_samples
)));
}
if n_samples == 0 {
return Err(Failed::fit(&format!(
"Size of x and y should greater than 0; |x|=[{}]",
n_samples
)));
}
let (class_labels, indices) = y.unique_with_indices();
let mut class_count = vec![0_usize; class_labels.len()];
let mut subdataset: Vec<Vec<Box<dyn ArrayView1<TX>>>> =
(0..class_labels.len()).map(|_| vec![]).collect();
for (row, class_index) in x.row_iter().zip(indices.iter()) {
class_count[*class_index] += 1;
subdataset[*class_index].push(row);
}
let class_priors = if let Some(class_priors) = priors {
if class_priors.len() != class_labels.len() {
return Err(Failed::fit(
"Size of priors provided does not match the number of classes of the data.",
));
}
class_priors
} else {
class_count
.iter()
.map(|&c| c as f64 / n_samples as f64)
.collect()
};
let subdataset: Vec<X> = subdataset
.iter()
.map(|v| {
X::concatenate_1d(
&v.iter()
.map(|v| v.as_ref())
.collect::<Vec<&dyn ArrayView1<TX>>>(),
0,
)
})
.collect();
let (var, theta): (Vec<Vec<f64>>, Vec<Vec<f64>>) = subdataset
.iter()
.map(|data| (data.variance(0), data.mean_by(0)))
.unzip();
Ok(Self {
class_labels,
class_count,
class_priors,
var,
theta,
})
}
fn calculate_log_probability(&self, value: f64, mean: f64, variance: f64) -> f64 {
let pi = std::f64::consts::PI;
-((value - mean).powf(2.0) / (2.0 * variance))
- (2.0 * pi).ln() / 2.0
- (variance).ln() / 2.0
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, PartialEq)]
pub struct GaussianNB<
TX: Number + RealNumber + RealNumber,
TY: Number + Ord + Unsigned,
X: Array2<TX>,
Y: Array1<TY>,
> {
inner: Option<BaseNaiveBayes<TX, TY, X, Y, GaussianNBDistribution<TY>>>,
}
impl<
TX: Number + RealNumber + RealNumber,
TY: Number + Ord + Unsigned,
X: Array2<TX>,
Y: Array1<TY>,
> fmt::Display for GaussianNB<TX, TY, X, Y>
{
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(f, "GaussianNB:\ninner: {:?}", self.inner.as_ref().unwrap())?;
Ok(())
}
}
impl<
TX: Number + RealNumber + RealNumber,
TY: Number + Ord + Unsigned,
X: Array2<TX>,
Y: Array1<TY>,
> SupervisedEstimator<X, Y, GaussianNBParameters> for GaussianNB<TX, TY, X, Y>
{
fn new() -> Self {
Self {
inner: Option::None,
}
}
fn fit(x: &X, y: &Y, parameters: GaussianNBParameters) -> Result<Self, Failed> {
GaussianNB::fit(x, y, parameters)
}
}
impl<
TX: Number + RealNumber + RealNumber,
TY: Number + Ord + Unsigned,
X: Array2<TX>,
Y: Array1<TY>,
> Predictor<X, Y> for GaussianNB<TX, TY, X, Y>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
GaussianNB<TX, TY, X, Y>
{
pub fn fit(x: &X, y: &Y, parameters: GaussianNBParameters) -> Result<Self, Failed> {
let distribution = GaussianNBDistribution::fit(x, y, parameters.priors)?;
let inner = BaseNaiveBayes::fit(distribution)?;
Ok(Self { inner: Some(inner) })
}
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
self.inner.as_ref().unwrap().predict(x)
}
pub fn classes(&self) -> &Vec<TY> {
&self.inner.as_ref().unwrap().distribution.class_labels
}
pub fn class_count(&self) -> &Vec<usize> {
&self.inner.as_ref().unwrap().distribution.class_count
}
pub fn class_priors(&self) -> &Vec<f64> {
&self.inner.as_ref().unwrap().distribution.class_priors
}
pub fn theta(&self) -> &Vec<Vec<f64>> {
&self.inner.as_ref().unwrap().distribution.theta
}
pub fn var(&self) -> &Vec<Vec<f64>> {
&self.inner.as_ref().unwrap().distribution.var
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
#[test]
fn search_parameters() {
let parameters = GaussianNBSearchParameters {
priors: vec![Some(vec![1.]), Some(vec![2.])],
..Default::default()
};
let mut iter = parameters.into_iter();
let next = iter.next().unwrap();
assert_eq!(next.priors, Some(vec![1.]));
let next = iter.next().unwrap();
assert_eq!(next.priors, Some(vec![2.]));
assert!(iter.next().is_none());
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn run_gaussian_naive_bayes() {
let x = DenseMatrix::from_2d_array(&[
&[-1., -1.],
&[-2., -1.],
&[-3., -2.],
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
let y_hat = gnb.predict(&x).unwrap();
assert_eq!(y_hat, y);
assert_eq!(gnb.classes(), &[1, 2]);
assert_eq!(gnb.class_count(), &[3, 3]);
assert_eq!(
gnb.var(),
&[
&[0.666666666666667, 0.22222222222222232],
&[0.666666666666667, 0.22222222222222232]
]
);
assert_eq!(gnb.class_priors(), &[0.5, 0.5]);
assert_eq!(
gnb.theta(),
&[&[-2., -1.3333333333333333], &[2., 1.3333333333333333]]
);
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn run_gaussian_naive_bayes_with_priors() {
let x = DenseMatrix::from_2d_array(&[
&[-1., -1.],
&[-2., -1.],
&[-3., -2.],
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let priors = vec![0.3, 0.7];
let parameters = GaussianNBParameters::default().with_priors(priors.clone());
let gnb = GaussianNB::fit(&x, &y, parameters).unwrap();
assert_eq!(gnb.class_priors(), &priors);
println!("{}", &gnb);
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
#[cfg(feature = "serde")]
fn serde() {
let x = DenseMatrix::<f64>::from_2d_array(&[
&[-1., -1.],
&[-2., -1.],
&[-3., -2.],
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
let deserialized_gnb: GaussianNB<f64, u32, DenseMatrix<f64>, Vec<u32>> =
serde_json::from_str(&serde_json::to_string(&gnb).unwrap()).unwrap();
assert_eq!(gnb, deserialized_gnb);
}
}