764309e313
* add seed param to search params * make default params available to serde * lints * create defaults for enums * lint
417 lines
13 KiB
Rust
417 lines
13 KiB
Rust
//! # Gaussian Naive Bayes
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//!
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//! Gaussian Naive Bayes is a variant of [Naive Bayes](../index.html) for the data that follows Gaussian distribution and
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//! it supports continuous valued features conforming to a normal distribution.
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//!
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//! Example:
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//!
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//! ```
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//! use smartcore::linalg::naive::dense_matrix::*;
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//! use smartcore::naive_bayes::gaussian::GaussianNB;
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//!
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//! let x = DenseMatrix::from_2d_array(&[
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//! &[-1., -1.],
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//! &[-2., -1.],
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//! &[-3., -2.],
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//! &[ 1., 1.],
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//! &[ 2., 1.],
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//! &[ 3., 2.],
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//! ]);
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//! let y = vec![1., 1., 1., 2., 2., 2.];
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//!
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//! let nb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
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//! let y_hat = nb.predict(&x).unwrap();
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//! ```
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use crate::api::{Predictor, SupervisedEstimator};
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use crate::error::Failed;
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use crate::linalg::row_iter;
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use crate::linalg::BaseVector;
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use crate::linalg::Matrix;
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use crate::math::num::RealNumber;
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use crate::math::vector::RealNumberVector;
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use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
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#[cfg(feature = "serde")]
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use serde::{Deserialize, Serialize};
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/// Naive Bayes classifier using Gaussian distribution
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, PartialEq)]
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struct GaussianNBDistribution<T: RealNumber> {
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/// class labels known to the classifier
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class_labels: Vec<T>,
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/// number of training samples observed in each class
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class_count: Vec<usize>,
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/// probability of each class.
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class_priors: Vec<T>,
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/// variance of each feature per class
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var: Vec<Vec<T>>,
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/// mean of each feature per class
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theta: Vec<Vec<T>>,
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}
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impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for GaussianNBDistribution<T> {
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fn prior(&self, class_index: usize) -> T {
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if class_index >= self.class_labels.len() {
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T::zero()
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} else {
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self.class_priors[class_index]
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}
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}
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fn log_likelihood(&self, class_index: usize, j: &M::RowVector) -> T {
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let mut likelihood = T::zero();
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for feature in 0..j.len() {
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let value = j.get(feature);
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let mean = self.theta[class_index][feature];
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let variance = self.var[class_index][feature];
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likelihood += self.calculate_log_probability(value, mean, variance);
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}
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likelihood
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}
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fn classes(&self) -> &Vec<T> {
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&self.class_labels
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}
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}
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/// `GaussianNB` parameters. Use `Default::default()` for default values.
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct GaussianNBParameters<T: RealNumber> {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
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pub priors: Option<Vec<T>>,
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}
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impl<T: RealNumber> GaussianNBParameters<T> {
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/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
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pub fn with_priors(mut self, priors: Vec<T>) -> Self {
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self.priors = Some(priors);
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self
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}
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}
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impl<T: RealNumber> Default for GaussianNBParameters<T> {
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fn default() -> Self {
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Self { priors: None }
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}
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}
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/// GaussianNB grid search parameters
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct GaussianNBSearchParameters<T: RealNumber> {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
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pub priors: Vec<Option<Vec<T>>>,
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}
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/// GaussianNB grid search iterator
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pub struct GaussianNBSearchParametersIterator<T: RealNumber> {
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gaussian_nb_search_parameters: GaussianNBSearchParameters<T>,
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current_priors: usize,
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}
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impl<T: RealNumber> IntoIterator for GaussianNBSearchParameters<T> {
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type Item = GaussianNBParameters<T>;
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type IntoIter = GaussianNBSearchParametersIterator<T>;
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fn into_iter(self) -> Self::IntoIter {
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GaussianNBSearchParametersIterator {
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gaussian_nb_search_parameters: self,
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current_priors: 0,
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}
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}
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}
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impl<T: RealNumber> Iterator for GaussianNBSearchParametersIterator<T> {
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type Item = GaussianNBParameters<T>;
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fn next(&mut self) -> Option<Self::Item> {
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if self.current_priors == self.gaussian_nb_search_parameters.priors.len() {
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return None;
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}
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let next = GaussianNBParameters {
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priors: self.gaussian_nb_search_parameters.priors[self.current_priors].clone(),
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};
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self.current_priors += 1;
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Some(next)
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}
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}
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impl<T: RealNumber> Default for GaussianNBSearchParameters<T> {
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fn default() -> Self {
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let default_params = GaussianNBParameters::default();
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GaussianNBSearchParameters {
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priors: vec![default_params.priors],
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}
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}
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}
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impl<T: RealNumber> GaussianNBDistribution<T> {
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/// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
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/// * `x` - training data.
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/// * `y` - vector with target values (classes) of length N.
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/// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
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/// priors are adjusted according to the data.
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pub fn fit<M: Matrix<T>>(
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x: &M,
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y: &M::RowVector,
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priors: Option<Vec<T>>,
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) -> Result<Self, Failed> {
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let (n_samples, n_features) = x.shape();
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let y_samples = y.len();
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if y_samples != n_samples {
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return Err(Failed::fit(&format!(
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"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
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n_samples, y_samples
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)));
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}
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if n_samples == 0 {
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return Err(Failed::fit(&format!(
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"Size of x and y should greater than 0; |x|=[{}]",
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n_samples
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)));
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}
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let y = y.to_vec();
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let (class_labels, indices) = <Vec<T> as RealNumberVector<T>>::unique_with_indices(&y);
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let mut class_count = vec![0_usize; class_labels.len()];
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let mut subdataset: Vec<Vec<Vec<T>>> = vec![vec![]; class_labels.len()];
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for (row, class_index) in row_iter(x).zip(indices.iter()) {
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class_count[*class_index] += 1;
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subdataset[*class_index].push(row);
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}
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let class_priors = if let Some(class_priors) = priors {
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if class_priors.len() != class_labels.len() {
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return Err(Failed::fit(
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"Size of priors provided does not match the number of classes of the data.",
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));
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}
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class_priors
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} else {
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class_count
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.iter()
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.map(|&c| T::from(c).unwrap() / T::from(n_samples).unwrap())
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.collect()
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};
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let subdataset: Vec<M> = subdataset
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.into_iter()
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.map(|v| {
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let mut m = M::zeros(v.len(), n_features);
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for (row_i, v_i) in v.iter().enumerate() {
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for (col_j, v_i_j) in v_i.iter().enumerate().take(n_features) {
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m.set(row_i, col_j, *v_i_j);
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}
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}
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m
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})
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.collect();
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let (var, theta): (Vec<Vec<T>>, Vec<Vec<T>>) = subdataset
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.iter()
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.map(|data| (data.var(0), data.mean(0)))
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.unzip();
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Ok(Self {
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class_labels,
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class_count,
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class_priors,
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var,
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theta,
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})
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}
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/// Calculate probability of x equals to a value of a Gaussian distribution given its mean and its
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/// variance.
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fn calculate_log_probability(&self, value: T, mean: T, variance: T) -> T {
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let pi = T::from(std::f64::consts::PI).unwrap();
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-((value - mean).powf(T::two()) / (T::two() * variance))
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- (T::two() * pi).ln() / T::two()
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- (variance).ln() / T::two()
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}
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}
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/// GaussianNB implements the naive Bayes algorithm for data that follows the Gaussian
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/// distribution.
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, PartialEq)]
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pub struct GaussianNB<T: RealNumber, M: Matrix<T>> {
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inner: BaseNaiveBayes<T, M, GaussianNBDistribution<T>>,
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}
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impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, GaussianNBParameters<T>>
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for GaussianNB<T, M>
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{
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fn fit(x: &M, y: &M::RowVector, parameters: GaussianNBParameters<T>) -> Result<Self, Failed> {
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GaussianNB::fit(x, y, parameters)
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}
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}
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impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for GaussianNB<T, M> {
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fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
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self.predict(x)
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}
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}
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impl<T: RealNumber, M: Matrix<T>> GaussianNB<T, M> {
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/// Fits GaussianNB with given data
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/// * `x` - training data of size NxM where N is the number of samples and M is the number of
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/// features.
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/// * `y` - vector with target values (classes) of length N.
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/// * `parameters` - additional parameters like class priors.
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pub fn fit(
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x: &M,
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y: &M::RowVector,
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parameters: GaussianNBParameters<T>,
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) -> Result<Self, Failed> {
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let distribution = GaussianNBDistribution::fit(x, y, parameters.priors)?;
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let inner = BaseNaiveBayes::fit(distribution)?;
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Ok(Self { inner })
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}
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/// Estimates the class labels for the provided data.
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/// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
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/// Returns a vector of size N with class estimates.
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pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
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self.inner.predict(x)
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}
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/// Class labels known to the classifier.
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/// Returns a vector of size n_classes.
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pub fn classes(&self) -> &Vec<T> {
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&self.inner.distribution.class_labels
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}
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/// Number of training samples observed in each class.
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/// Returns a vector of size n_classes.
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pub fn class_count(&self) -> &Vec<usize> {
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&self.inner.distribution.class_count
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}
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/// Probability of each class
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/// Returns a vector of size n_classes.
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pub fn class_priors(&self) -> &Vec<T> {
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&self.inner.distribution.class_priors
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}
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/// Mean of each feature per class
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/// Returns a 2d vector of shape (n_classes, n_features).
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pub fn theta(&self) -> &Vec<Vec<T>> {
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&self.inner.distribution.theta
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}
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/// Variance of each feature per class
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/// Returns a 2d vector of shape (n_classes, n_features).
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pub fn var(&self) -> &Vec<Vec<T>> {
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&self.inner.distribution.var
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::linalg::naive::dense_matrix::DenseMatrix;
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#[test]
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fn search_parameters() {
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let parameters = GaussianNBSearchParameters {
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priors: vec![Some(vec![1.]), Some(vec![2.])],
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..Default::default()
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};
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let mut iter = parameters.into_iter();
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let next = iter.next().unwrap();
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assert_eq!(next.priors, Some(vec![1.]));
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let next = iter.next().unwrap();
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assert_eq!(next.priors, Some(vec![2.]));
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assert!(iter.next().is_none());
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}
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#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
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#[test]
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fn run_gaussian_naive_bayes() {
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let x = DenseMatrix::from_2d_array(&[
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&[-1., -1.],
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&[-2., -1.],
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&[-3., -2.],
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&[1., 1.],
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&[2., 1.],
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&[3., 2.],
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]);
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let y = vec![1., 1., 1., 2., 2., 2.];
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let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
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let y_hat = gnb.predict(&x).unwrap();
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assert_eq!(y_hat, y);
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assert_eq!(gnb.classes(), &[1., 2.]);
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assert_eq!(gnb.class_count(), &[3, 3]);
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assert_eq!(
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gnb.var(),
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&[
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&[0.666666666666667, 0.22222222222222232],
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&[0.666666666666667, 0.22222222222222232]
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]
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);
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assert_eq!(gnb.class_priors(), &[0.5, 0.5]);
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assert_eq!(
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gnb.theta(),
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&[&[-2., -1.3333333333333333], &[2., 1.3333333333333333]]
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);
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}
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#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
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#[test]
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fn run_gaussian_naive_bayes_with_priors() {
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let x = DenseMatrix::from_2d_array(&[
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&[-1., -1.],
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&[-2., -1.],
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&[-3., -2.],
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&[1., 1.],
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&[2., 1.],
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&[3., 2.],
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]);
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let y = vec![1., 1., 1., 2., 2., 2.];
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let priors = vec![0.3, 0.7];
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let parameters = GaussianNBParameters::default().with_priors(priors.clone());
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let gnb = GaussianNB::fit(&x, &y, parameters).unwrap();
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assert_eq!(gnb.class_priors(), &priors);
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}
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#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
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#[test]
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#[cfg(feature = "serde")]
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fn serde() {
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let x = DenseMatrix::<f64>::from_2d_array(&[
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&[-1., -1.],
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&[-2., -1.],
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&[-3., -2.],
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&[1., 1.],
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&[2., 1.],
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&[3., 2.],
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]);
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let y = vec![1., 1., 1., 2., 2., 2.];
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let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
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let deserialized_gnb: GaussianNB<f64, DenseMatrix<f64>> =
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serde_json::from_str(&serde_json::to_string(&gnb).unwrap()).unwrap();
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assert_eq!(gnb, deserialized_gnb);
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}
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}
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