162bed2aa2
* test: run tests also in wasm targets * fix: install rand with wasm-bindgen por wasm targets * fix: use actual usize size to access buffer. * fix: do not run functions that create files in wasm. * test: do not run in wasm test that panics. Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
494 lines
17 KiB
Rust
494 lines
17 KiB
Rust
//! # Categorical Naive Bayes
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//!
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//! Categorical Naive Bayes is a variant of [Naive Bayes](../index.html) for the categorically distributed data.
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//! It assumes that each feature has its own categorical 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::categorical::CategoricalNB;
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//!
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//! let x = DenseMatrix::from_2d_array(&[
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//! &[3., 4., 0., 1.],
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//! &[3., 0., 0., 1.],
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//! &[4., 4., 1., 2.],
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//! &[4., 2., 4., 3.],
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//! &[4., 2., 4., 2.],
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//! &[4., 1., 1., 0.],
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//! &[1., 1., 1., 1.],
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//! &[0., 4., 1., 0.],
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//! &[0., 3., 2., 1.],
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//! &[0., 3., 1., 1.],
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//! &[3., 4., 0., 1.],
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//! &[3., 4., 2., 4.],
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//! &[0., 3., 1., 2.],
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//! &[0., 4., 1., 2.],
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//! ]);
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//! let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
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//!
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//! let nb = CategoricalNB::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::BaseVector;
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use crate::linalg::Matrix;
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use crate::math::num::RealNumber;
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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 for categorical features
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug)]
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struct CategoricalNBDistribution<T: RealNumber> {
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/// number of training samples observed in each class
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class_count: Vec<usize>,
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/// class labels known to the classifier
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class_labels: Vec<T>,
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/// probability of each class
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class_priors: Vec<T>,
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coefficients: Vec<Vec<Vec<T>>>,
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/// Number of features of each sample
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n_features: usize,
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/// Number of categories for each feature
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n_categories: Vec<usize>,
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/// Holds arrays of shape (n_classes, n_categories of respective feature)
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/// for each feature. Each array provides the number of samples
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/// encountered for each class and category of the specific feature.
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category_count: Vec<Vec<Vec<usize>>>,
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}
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impl<T: RealNumber> PartialEq for CategoricalNBDistribution<T> {
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fn eq(&self, other: &Self) -> bool {
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if self.class_labels == other.class_labels
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&& self.class_priors == other.class_priors
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&& self.n_features == other.n_features
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&& self.n_categories == other.n_categories
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&& self.class_count == other.class_count
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{
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if self.coefficients.len() != other.coefficients.len() {
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return false;
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}
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for (a, b) in self.coefficients.iter().zip(other.coefficients.iter()) {
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if a.len() != b.len() {
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return false;
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}
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for (a_i, b_i) in a.iter().zip(b.iter()) {
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if a_i.len() != b_i.len() {
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return false;
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}
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for (a_i_j, b_i_j) in a_i.iter().zip(b_i.iter()) {
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if (*a_i_j - *b_i_j).abs() > T::epsilon() {
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return false;
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}
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}
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}
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}
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true
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} else {
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false
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}
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}
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}
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impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for CategoricalNBDistribution<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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if class_index < self.class_labels.len() {
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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).floor().to_usize().unwrap();
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if self.coefficients[feature][class_index].len() > value {
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likelihood += self.coefficients[feature][class_index][value];
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} else {
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return T::zero();
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}
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}
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likelihood
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} else {
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T::zero()
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}
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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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impl<T: RealNumber> CategoricalNBDistribution<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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/// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, alpha: T) -> Result<Self, Failed> {
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if alpha < T::zero() {
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return Err(Failed::fit(&format!(
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"alpha should be >= 0, alpha=[{}]",
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alpha
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)));
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}
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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: Vec<usize> = y
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.to_vec()
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.iter()
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.map(|y_i| y_i.floor().to_usize().unwrap())
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.collect();
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let y_max = y
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.iter()
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.max()
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.ok_or_else(|| Failed::fit(&"Failed to get the labels of y.".to_string()))?;
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let class_labels: Vec<T> = (0..*y_max + 1)
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.map(|label| T::from(label).unwrap())
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.collect();
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let mut class_count = vec![0_usize; class_labels.len()];
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for elem in y.iter() {
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class_count[*elem] += 1;
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}
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let mut n_categories: Vec<usize> = Vec::with_capacity(n_features);
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for feature in 0..n_features {
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let feature_max = x
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.get_col_as_vec(feature)
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.iter()
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.map(|f_i| f_i.floor().to_usize().unwrap())
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.max()
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.ok_or_else(|| {
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Failed::fit(&format!(
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"Failed to get the categories for feature = {}",
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feature
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))
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})?;
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n_categories.push(feature_max + 1);
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}
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let mut coefficients: Vec<Vec<Vec<T>>> = Vec::with_capacity(class_labels.len());
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let mut category_count: Vec<Vec<Vec<usize>>> = Vec::with_capacity(class_labels.len());
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for (feature_index, &n_categories_i) in n_categories.iter().enumerate().take(n_features) {
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let mut coef_i: Vec<Vec<T>> = Vec::with_capacity(n_features);
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let mut category_count_i: Vec<Vec<usize>> = Vec::with_capacity(n_features);
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for (label, &label_count) in class_labels.iter().zip(class_count.iter()) {
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let col = x
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.get_col_as_vec(feature_index)
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.iter()
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.enumerate()
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.filter(|(i, _j)| T::from(y[*i]).unwrap() == *label)
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.map(|(_, j)| *j)
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.collect::<Vec<T>>();
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let mut feat_count: Vec<usize> = vec![0_usize; n_categories_i];
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for row in col.iter() {
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let index = row.floor().to_usize().unwrap();
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feat_count[index] += 1;
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}
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let coef_i_j = feat_count
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.iter()
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.map(|c| {
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((T::from(*c).unwrap() + alpha)
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/ (T::from(label_count).unwrap()
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+ T::from(n_categories_i).unwrap() * alpha))
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.ln()
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})
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.collect::<Vec<T>>();
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category_count_i.push(feat_count);
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coef_i.push(coef_i_j);
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}
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category_count.push(category_count_i);
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coefficients.push(coef_i);
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}
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let class_priors = class_count
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.iter()
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.map(|&count| T::from(count).unwrap() / T::from(n_samples).unwrap())
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.collect::<Vec<T>>();
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Ok(Self {
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class_count,
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class_labels,
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class_priors,
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coefficients,
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n_categories,
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n_features,
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category_count,
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})
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}
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}
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/// `CategoricalNB` 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 CategoricalNBParameters<T: RealNumber> {
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/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub alpha: T,
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}
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impl<T: RealNumber> CategoricalNBParameters<T> {
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/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub fn with_alpha(mut self, alpha: T) -> Self {
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self.alpha = alpha;
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self
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}
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}
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impl<T: RealNumber> Default for CategoricalNBParameters<T> {
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fn default() -> Self {
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Self { alpha: T::one() }
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}
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}
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/// CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data.
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, PartialEq)]
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pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
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inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
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}
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impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, CategoricalNBParameters<T>>
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for CategoricalNB<T, M>
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{
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fn fit(
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x: &M,
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y: &M::RowVector,
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parameters: CategoricalNBParameters<T>,
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) -> Result<Self, Failed> {
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CategoricalNB::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 CategoricalNB<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>> CategoricalNB<T, M> {
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/// Fits CategoricalNB 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 alpha for smoothing
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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: CategoricalNBParameters<T>,
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) -> Result<Self, Failed> {
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let alpha = parameters.alpha;
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let distribution = CategoricalNBDistribution::fit(x, y, alpha)?;
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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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/// Number of features of each sample
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pub fn n_features(&self) -> usize {
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self.inner.distribution.n_features
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}
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/// Number of features of each sample
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pub fn n_categories(&self) -> &Vec<usize> {
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&self.inner.distribution.n_categories
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}
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/// Holds arrays of shape (n_classes, n_categories of respective feature)
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/// for each feature. Each array provides the number of samples
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/// encountered for each class and category of the specific feature.
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pub fn category_count(&self) -> &Vec<Vec<Vec<usize>>> {
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&self.inner.distribution.category_count
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}
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/// Holds arrays of shape (n_classes, n_categories of respective feature)
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/// for each feature. Each array provides the empirical log probability
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/// of categories given the respective feature and class, ``P(x_i|y)``.
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pub fn feature_log_prob(&self) -> &Vec<Vec<Vec<T>>> {
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&self.inner.distribution.coefficients
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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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#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
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#[test]
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fn run_categorical_naive_bayes() {
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let x = DenseMatrix::from_2d_array(&[
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&[0., 2., 1., 0.],
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&[0., 2., 1., 1.],
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&[1., 2., 1., 0.],
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&[2., 1., 1., 0.],
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&[2., 0., 0., 0.],
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&[2., 0., 0., 1.],
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&[1., 0., 0., 1.],
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&[0., 1., 1., 0.],
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&[0., 0., 0., 0.],
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&[2., 1., 0., 0.],
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&[0., 1., 0., 1.],
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&[1., 1., 1., 1.],
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&[1., 2., 0., 0.],
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&[2., 1., 1., 1.],
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]);
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let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
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let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
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// checking parity with scikit
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assert_eq!(cnb.classes(), &[0., 1.]);
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assert_eq!(cnb.class_count(), &[5, 9]);
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assert_eq!(cnb.n_features(), 4);
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assert_eq!(cnb.n_categories(), &[3, 3, 2, 2]);
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assert_eq!(
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cnb.category_count(),
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&vec![
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vec![vec![3, 0, 2], vec![2, 4, 3]],
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vec![vec![1, 2, 2], vec![3, 4, 2]],
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vec![vec![1, 4], vec![6, 3]],
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vec![vec![2, 3], vec![6, 3]]
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]
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);
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assert_eq!(
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cnb.feature_log_prob(),
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&vec![
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vec![
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vec![
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-0.6931471805599453,
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-2.0794415416798357,
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-0.9808292530117262
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],
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vec![
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-1.3862943611198906,
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-0.8754687373538999,
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-1.0986122886681098
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]
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],
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vec![
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vec![
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-1.3862943611198906,
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-0.9808292530117262,
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-0.9808292530117262
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],
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vec![
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-1.0986122886681098,
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-0.8754687373538999,
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-1.3862943611198906
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]
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],
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vec![
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vec![-1.252762968495368, -0.3364722366212129],
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vec![-0.45198512374305727, -1.0116009116784799]
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],
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vec![
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vec![-0.8472978603872037, -0.5596157879354228],
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vec![-0.45198512374305727, -1.0116009116784799]
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]
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]
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);
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let x_test = DenseMatrix::from_2d_array(&[&[0., 2., 1., 0.], &[2., 2., 0., 0.]]);
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let y_hat = cnb.predict(&x_test).unwrap();
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assert_eq!(y_hat, vec![0., 1.]);
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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_categorical_naive_bayes2() {
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let x = DenseMatrix::from_2d_array(&[
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&[3., 4., 0., 1.],
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&[3., 0., 0., 1.],
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&[4., 4., 1., 2.],
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&[4., 2., 4., 3.],
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&[4., 2., 4., 2.],
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&[4., 1., 1., 0.],
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&[1., 1., 1., 1.],
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&[0., 4., 1., 0.],
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&[0., 3., 2., 1.],
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&[0., 3., 1., 1.],
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&[3., 4., 0., 1.],
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&[3., 4., 2., 4.],
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&[0., 3., 1., 2.],
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&[0., 4., 1., 2.],
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]);
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let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
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let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
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let y_hat = cnb.predict(&x).unwrap();
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assert_eq!(
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y_hat,
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vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1.]
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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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#[cfg(feature = "serde")]
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fn serde() {
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let x = DenseMatrix::<f64>::from_2d_array(&[
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&[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);
|
|
}
|
|
}
|