589 lines
20 KiB
Rust
589 lines
20 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::basic::matrix::DenseMatrix;
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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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//! ]).unwrap();
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//! let y: Vec<u32> = 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 std::fmt;
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use num_traits::Unsigned;
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use crate::api::{Predictor, SupervisedEstimator};
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use crate::error::Failed;
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use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
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use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
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use crate::numbers::basenum::Number;
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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, Clone)]
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struct CategoricalNBDistribution<T: Number + Unsigned> {
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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<f64>,
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coefficients: Vec<Vec<Vec<f64>>>,
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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: Number + Ord + Unsigned> fmt::Display for CategoricalNBDistribution<T> {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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writeln!(
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f,
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"CategoricalNBDistribution: n_features: {:?}",
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self.n_features
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)?;
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writeln!(f, "class_labels: {:?}", self.class_labels)?;
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Ok(())
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}
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}
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impl<T: Number + Unsigned> 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() > f64::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: Number + Unsigned> NBDistribution<T, T> for CategoricalNBDistribution<T> {
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fn prior(&self, class_index: usize) -> f64 {
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if class_index >= self.class_labels.len() {
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0f64
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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<'a>(&'a self, class_index: usize, j: &'a Box<dyn ArrayView1<T> + 'a>) -> f64 {
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if class_index < self.class_labels.len() {
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let mut likelihood = 0f64;
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for feature in 0..j.shape() {
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let value = j.get(feature).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 0f64;
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}
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}
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likelihood
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} else {
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0f64
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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: Number + Unsigned, X: Array2<T>, Y: Array1<T>> fmt::Display for CategoricalNB<T, X, Y> {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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writeln!(
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f,
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"CategoricalNB:\ninner: {:?}",
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self.inner.as_ref().unwrap()
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)?;
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Ok(())
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}
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}
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impl<T: Number + Unsigned> 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<X: Array2<T>, Y: Array1<T>>(x: &X, y: &Y, alpha: f64) -> Result<Self, Failed> {
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if alpha < 0f64 {
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return Err(Failed::fit(&format!(
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"alpha should be >= 0, alpha=[{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.shape();
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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|=[{n_samples}], |y|=[{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|=[{n_samples}]"
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)));
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}
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let y: Vec<usize> = y.iterator(0).map(|y_i| y_i.to_usize().unwrap()).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."))?;
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let class_labels: Vec<T> = (0..*y_max + 1)
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.map(|label| T::from_usize(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(feature)
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.iterator(0)
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.map(|f_i| f_i.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 = {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<f64>>> = 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<f64>> = 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(feature_index)
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.iterator(0)
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.enumerate()
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.filter(|(i, _j)| T::from_usize(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.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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((c as f64 + alpha) / (label_count as f64 + n_categories_i as f64 * alpha))
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.ln()
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})
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.collect::<Vec<f64>>();
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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| count as f64 / n_samples as f64)
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.collect::<Vec<f64>>();
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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_features,
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n_categories,
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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 {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub alpha: f64,
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}
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impl CategoricalNBParameters {
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/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub fn with_alpha(mut self, alpha: f64) -> 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 Default for CategoricalNBParameters {
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fn default() -> Self {
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Self { alpha: 1f64 }
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}
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}
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/// CategoricalNB 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 CategoricalNBSearchParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
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pub alpha: Vec<f64>,
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}
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/// CategoricalNB grid search iterator
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pub struct CategoricalNBSearchParametersIterator {
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categorical_nb_search_parameters: CategoricalNBSearchParameters,
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current_alpha: usize,
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}
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impl IntoIterator for CategoricalNBSearchParameters {
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type Item = CategoricalNBParameters;
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type IntoIter = CategoricalNBSearchParametersIterator;
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fn into_iter(self) -> Self::IntoIter {
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CategoricalNBSearchParametersIterator {
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categorical_nb_search_parameters: self,
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current_alpha: 0,
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}
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}
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}
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impl Iterator for CategoricalNBSearchParametersIterator {
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type Item = CategoricalNBParameters;
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fn next(&mut self) -> Option<Self::Item> {
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if self.current_alpha == self.categorical_nb_search_parameters.alpha.len() {
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return None;
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}
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let next = CategoricalNBParameters {
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alpha: self.categorical_nb_search_parameters.alpha[self.current_alpha],
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};
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self.current_alpha += 1;
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Some(next)
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}
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}
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impl Default for CategoricalNBSearchParameters {
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fn default() -> Self {
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let default_params = CategoricalNBParameters::default();
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CategoricalNBSearchParameters {
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alpha: vec![default_params.alpha],
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}
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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: Number + Unsigned, X: Array2<T>, Y: Array1<T>> {
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inner: Option<BaseNaiveBayes<T, T, X, Y, CategoricalNBDistribution<T>>>,
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}
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impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>>
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SupervisedEstimator<X, Y, CategoricalNBParameters> for CategoricalNB<T, X, Y>
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{
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fn new() -> Self {
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Self {
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inner: Option::None,
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}
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}
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fn fit(x: &X, y: &Y, parameters: CategoricalNBParameters) -> 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: Number + Unsigned, X: Array2<T>, Y: Array1<T>> Predictor<X, Y> for CategoricalNB<T, X, Y> {
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fn predict(&self, x: &X) -> Result<Y, Failed> {
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self.predict(x)
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}
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}
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impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> CategoricalNB<T, X, Y> {
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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(x: &X, y: &Y, parameters: CategoricalNBParameters) -> 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: Some(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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///
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/// Returns a vector of size N with class estimates.
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pub fn predict(&self, x: &X) -> Result<Y, Failed> {
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self.inner.as_ref().unwrap().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.as_ref().unwrap().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.as_ref().unwrap().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.as_ref().unwrap().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.as_ref().unwrap().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.as_ref().unwrap().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<f64>>> {
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&self.inner.as_ref().unwrap().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::basic::matrix::DenseMatrix;
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#[test]
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fn search_parameters() {
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let parameters = CategoricalNBSearchParameters {
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alpha: vec![1., 2.],
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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.alpha, 1.);
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let next = iter.next().unwrap();
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assert_eq!(next.alpha, 2.);
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assert!(iter.next().is_none());
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}
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#[cfg_attr(
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all(target_arch = "wasm32", not(target_os = "wasi")),
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wasm_bindgen_test::wasm_bindgen_test
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)]
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#[test]
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fn run_categorical_naive_bayes() {
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let x = DenseMatrix::<u32>::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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.unwrap();
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let y: Vec<u32> = 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!(
|
|
cnb.feature_log_prob(),
|
|
&vec![
|
|
vec![
|
|
vec![
|
|
-0.6931471805599453,
|
|
-2.0794415416798357,
|
|
-0.9808292530117262
|
|
],
|
|
vec![
|
|
-1.3862943611198906,
|
|
-0.8754687373538999,
|
|
-1.0986122886681098
|
|
]
|
|
],
|
|
vec![
|
|
vec![
|
|
-1.3862943611198906,
|
|
-0.9808292530117262,
|
|
-0.9808292530117262
|
|
],
|
|
vec![
|
|
-1.0986122886681098,
|
|
-0.8754687373538999,
|
|
-1.3862943611198906
|
|
]
|
|
],
|
|
vec![
|
|
vec![-1.252762968495368, -0.3364722366212129],
|
|
vec![-0.45198512374305727, -1.0116009116784799]
|
|
],
|
|
vec![
|
|
vec![-0.8472978603872037, -0.5596157879354228],
|
|
vec![-0.45198512374305727, -1.0116009116784799]
|
|
]
|
|
]
|
|
);
|
|
|
|
let x_test = DenseMatrix::from_2d_array(&[&[0, 2, 1, 0], &[2, 2, 0, 0]]).unwrap();
|
|
let y_hat = cnb.predict(&x_test).unwrap();
|
|
assert_eq!(y_hat, vec![0, 1]);
|
|
}
|
|
|
|
#[cfg_attr(
|
|
all(target_arch = "wasm32", not(target_os = "wasi")),
|
|
wasm_bindgen_test::wasm_bindgen_test
|
|
)]
|
|
#[test]
|
|
fn run_categorical_naive_bayes2() {
|
|
let x = DenseMatrix::<u32>::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],
|
|
])
|
|
.unwrap();
|
|
let y: Vec<u32> = 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]);
|
|
|
|
println!("{}", &cnb);
|
|
}
|
|
|
|
#[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::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],
|
|
])
|
|
.unwrap();
|
|
|
|
let y: Vec<u32> = 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<u32, DenseMatrix<u32>, Vec<u32>> =
|
|
serde_json::from_str(&serde_json::to_string(&cnb).unwrap()).unwrap();
|
|
|
|
assert_eq!(cnb, deserialized_cnb);
|
|
}
|
|
}
|