use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::BaseVector;
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
use serde::{Deserialize, Serialize};
#[derive(Serialize, Deserialize, Debug)]
struct CategoricalNBDistribution<T: RealNumber> {
class_labels: Vec<T>,
class_priors: Vec<T>,
coefficients: Vec<Vec<Vec<T>>>,
}
impl<T: RealNumber> PartialEq for CategoricalNBDistribution<T> {
fn eq(&self, other: &Self) -> bool {
if self.class_labels == other.class_labels && self.class_priors == other.class_priors {
if self.coefficients.len() != other.coefficients.len() {
return false;
}
for (a, b) in self.coefficients.iter().zip(other.coefficients.iter()) {
if a.len() != b.len() {
return false;
}
for (a_i, b_i) in a.iter().zip(b.iter()) {
if a_i.len() != b_i.len() {
return false;
}
for (a_i_j, b_i_j) in a_i.iter().zip(b_i.iter()) {
if (*a_i_j - *b_i_j).abs() > T::epsilon() {
return false;
}
}
}
}
true
} else {
false
}
}
}
impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for CategoricalNBDistribution<T> {
fn prior(&self, class_index: usize) -> T {
if class_index >= self.class_labels.len() {
T::zero()
} else {
self.class_priors[class_index]
}
}
fn log_likelihood(&self, class_index: usize, j: &M::RowVector) -> T {
if class_index < self.class_labels.len() {
let mut likelihood = T::zero();
for feature in 0..j.len() {
let value = j.get(feature).floor().to_usize().unwrap();
if self.coefficients[class_index][feature].len() > value {
likelihood += self.coefficients[class_index][feature][value];
} else {
return T::zero();
}
}
likelihood
} else {
T::zero()
}
}
fn classes(&self) -> &Vec<T> {
&self.class_labels
}
}
impl<T: RealNumber> CategoricalNBDistribution<T> {
pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, alpha: T) -> Result<Self, Failed> {
if alpha < T::zero() {
return Err(Failed::fit(&format!(
"alpha should be >= 0, alpha=[{}]",
alpha
)));
}
let (n_samples, n_features) = x.shape();
let y_samples = y.len();
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 y: Vec<usize> = y
.to_vec()
.iter()
.map(|y_i| y_i.floor().to_usize().unwrap())
.collect();
let y_max = y
.iter()
.max()
.ok_or_else(|| Failed::fit(&"Failed to get the labels of y.".to_string()))?;
let class_labels: Vec<T> = (0..*y_max + 1)
.map(|label| T::from(label).unwrap())
.collect();
let mut classes_count: Vec<T> = vec![T::zero(); class_labels.len()];
for elem in y.iter() {
classes_count[*elem] += T::one();
}
let mut feature_categories: Vec<Vec<T>> = Vec::with_capacity(n_features);
for feature in 0..n_features {
let feature_max = x
.get_col_as_vec(feature)
.iter()
.map(|f_i| f_i.floor().to_usize().unwrap())
.max()
.ok_or_else(|| {
Failed::fit(&format!(
"Failed to get the categories for feature = {}",
feature
))
})?;
let feature_types = (0..feature_max + 1)
.map(|feat| T::from(feat).unwrap())
.collect();
feature_categories.push(feature_types);
}
let mut coefficients: Vec<Vec<Vec<T>>> = Vec::with_capacity(class_labels.len());
for (label, label_count) in class_labels.iter().zip(classes_count.iter()) {
let mut coef_i: Vec<Vec<T>> = Vec::with_capacity(n_features);
for (feature_index, feature_options) in
feature_categories.iter().enumerate().take(n_features)
{
let col = x
.get_col_as_vec(feature_index)
.iter()
.enumerate()
.filter(|(i, _j)| T::from(y[*i]).unwrap() == *label)
.map(|(_, j)| *j)
.collect::<Vec<T>>();
let mut feat_count: Vec<T> = vec![T::zero(); feature_options.len()];
for row in col.iter() {
let index = row.floor().to_usize().unwrap();
feat_count[index] += T::one();
}
let coef_i_j = feat_count
.iter()
.map(|c| {
((*c + alpha)
/ (*label_count + T::from(feature_options.len()).unwrap() * alpha))
.ln()
})
.collect::<Vec<T>>();
coef_i.push(coef_i_j);
}
coefficients.push(coef_i);
}
let class_priors = classes_count
.into_iter()
.map(|count| count / T::from(n_samples).unwrap())
.collect::<Vec<T>>();
Ok(Self {
class_labels,
class_priors,
coefficients,
})
}
}
#[derive(Serialize, Deserialize, Debug, Clone)]
pub struct CategoricalNBParameters<T: RealNumber> {
pub alpha: T,
}
impl<T: RealNumber> CategoricalNBParameters<T> {
pub fn with_alpha(mut self, alpha: T) -> Self {
self.alpha = alpha;
self
}
}
impl<T: RealNumber> Default for CategoricalNBParameters<T> {
fn default() -> Self {
Self { alpha: T::one() }
}
}
#[derive(Serialize, Deserialize, Debug, PartialEq)]
pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
}
impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, CategoricalNBParameters<T>>
for CategoricalNB<T, M>
{
fn fit(
x: &M,
y: &M::RowVector,
parameters: CategoricalNBParameters<T>,
) -> Result<Self, Failed> {
CategoricalNB::fit(x, y, parameters)
}
}
impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for CategoricalNB<T, M> {
fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
self.predict(x)
}
}
impl<T: RealNumber, M: Matrix<T>> CategoricalNB<T, M> {
pub fn fit(
x: &M,
y: &M::RowVector,
parameters: CategoricalNBParameters<T>,
) -> Result<Self, Failed> {
let alpha = parameters.alpha;
let distribution = CategoricalNBDistribution::fit(x, y, alpha)?;
let inner = BaseNaiveBayes::fit(distribution)?;
Ok(Self { inner })
}
pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
self.inner.predict(x)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::DenseMatrix;
#[test]
fn run_categorical_naive_bayes() {
let x = DenseMatrix::from_2d_array(&[
&[0., 2., 1., 0.],
&[0., 2., 1., 1.],
&[1., 2., 1., 0.],
&[2., 1., 1., 0.],
&[2., 0., 0., 0.],
&[2., 0., 0., 1.],
&[1., 0., 0., 1.],
&[0., 1., 1., 0.],
&[0., 0., 0., 0.],
&[2., 1., 0., 0.],
&[0., 1., 0., 1.],
&[1., 1., 1., 1.],
&[1., 2., 0., 0.],
&[2., 1., 1., 1.],
]);
let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
let x_test = DenseMatrix::from_2d_array(&[&[0., 2., 1., 0.], &[2., 2., 0., 0.]]);
let y_hat = cnb.predict(&x_test).unwrap();
assert_eq!(y_hat, vec![0., 1.]);
}
#[test]
fn run_categorical_naive_bayes2() {
let x = DenseMatrix::from_2d_array(&[
&[3., 4., 0., 1.],
&[3., 0., 0., 1.],
&[4., 4., 1., 2.],
&[4., 2., 4., 3.],
&[4., 2., 4., 2.],
&[4., 1., 1., 0.],
&[1., 1., 1., 1.],
&[0., 4., 1., 0.],
&[0., 3., 2., 1.],
&[0., 3., 1., 1.],
&[3., 4., 0., 1.],
&[3., 4., 2., 4.],
&[0., 3., 1., 2.],
&[0., 4., 1., 2.],
]);
let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
let y_hat = cnb.predict(&x).unwrap();
assert_eq!(
y_hat,
vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1.]
);
}
#[test]
fn serde() {
let x = DenseMatrix::<f64>::from_2d_array(&[
&[3., 4., 0., 1.],
&[3., 0., 0., 1.],
&[4., 4., 1., 2.],
&[4., 2., 4., 3.],
&[4., 2., 4., 2.],
&[4., 1., 1., 0.],
&[1., 1., 1., 1.],
&[0., 4., 1., 0.],
&[0., 3., 2., 1.],
&[0., 3., 1., 1.],
&[3., 4., 0., 1.],
&[3., 4., 2., 4.],
&[0., 3., 1., 2.],
&[0., 4., 1., 2.],
]);
let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
let deserialized_cnb: CategoricalNB<f64, DenseMatrix<f64>> =
serde_json::from_str(&serde_json::to_string(&cnb).unwrap()).unwrap();
assert_eq!(cnb, deserialized_cnb);
}
}