f4fd4d2239
* add seed param to search params * make default params available to serde * lints * create defaults for enums * lint
587 lines
20 KiB
Rust
587 lines
20 KiB
Rust
//! # Bernoulli Naive Bayes
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//!
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//! Bernoulli Naive Bayes classifier is a variant of [Naive Bayes](../index.html) for the data that is distributed according to multivariate Bernoulli distribution.
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//! It is used for discrete data with binary features. One example of a binary feature is a word that occurs in the text or not.
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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::bernoulli::BernoulliNB;
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//!
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//! // Training data points are:
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//! // Chinese Beijing Chinese (class: China)
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//! // Chinese Chinese Shanghai (class: China)
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//! // Chinese Macao (class: China)
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//! // Tokyo Japan Chinese (class: Japan)
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//! let x = DenseMatrix::<f64>::from_2d_array(&[
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//! &[1., 1., 0., 0., 0., 0.],
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//! &[0., 1., 0., 0., 1., 0.],
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//! &[0., 1., 0., 1., 0., 0.],
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//! &[0., 1., 1., 0., 0., 1.],
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//! ]);
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//! let y = vec![0., 0., 0., 1.];
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//!
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//! let nb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
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//!
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//! // Testing data point is:
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//! // Chinese Chinese Chinese Tokyo Japan
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//! let x_test = DenseMatrix::<f64>::from_2d_array(&[&[0., 1., 1., 0., 0., 1.]]);
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//! let y_hat = nb.predict(&x_test).unwrap();
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//! ```
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//!
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//! ## References:
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//!
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//! * ["Introduction to Information Retrieval", Manning C. D., Raghavan P., Schutze H., 2009, Chapter 13 ](https://nlp.stanford.edu/IR-book/information-retrieval-book.html)
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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 for Bearnoulli features
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug)]
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struct BernoulliNBDistribution<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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/// Number of samples encountered for each (class, feature)
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feature_count: Vec<Vec<usize>>,
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/// probability of features per class
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feature_log_prob: Vec<Vec<T>>,
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/// Number of features of each sample
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n_features: usize,
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}
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impl<T: RealNumber> PartialEq for BernoulliNBDistribution<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_count == other.class_count
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&& self.class_priors == other.class_priors
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&& self.feature_count == other.feature_count
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&& self.n_features == other.n_features
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{
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for (a, b) in self
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.feature_log_prob
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.iter()
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.zip(other.feature_log_prob.iter())
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{
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if !a.approximate_eq(b, T::epsilon()) {
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return false;
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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 BernoulliNBDistribution<T> {
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fn prior(&self, class_index: usize) -> T {
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self.class_priors[class_index]
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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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if value == T::one() {
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likelihood += self.feature_log_prob[class_index][feature];
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} else {
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likelihood += (T::one() - self.feature_log_prob[class_index][feature].exp()).ln();
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}
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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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/// `BernoulliNB` 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 BernoulliNBParameters<T: RealNumber> {
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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: T,
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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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#[cfg_attr(feature = "serde", serde(default))]
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/// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
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pub binarize: Option<T>,
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}
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impl<T: RealNumber> BernoulliNBParameters<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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/// 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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/// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
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pub fn with_binarize(mut self, binarize: T) -> Self {
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self.binarize = Some(binarize);
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self
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}
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}
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impl<T: RealNumber> Default for BernoulliNBParameters<T> {
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fn default() -> Self {
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Self {
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alpha: T::one(),
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priors: None,
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binarize: Some(T::zero()),
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}
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}
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}
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/// BernoulliNB 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 BernoulliNBSearchParameters<T: RealNumber> {
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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<T>,
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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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#[cfg_attr(feature = "serde", serde(default))]
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/// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
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pub binarize: Vec<Option<T>>,
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}
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/// BernoulliNB grid search iterator
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pub struct BernoulliNBSearchParametersIterator<T: RealNumber> {
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bernoulli_nb_search_parameters: BernoulliNBSearchParameters<T>,
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current_alpha: usize,
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current_priors: usize,
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current_binarize: usize,
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}
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impl<T: RealNumber> IntoIterator for BernoulliNBSearchParameters<T> {
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type Item = BernoulliNBParameters<T>;
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type IntoIter = BernoulliNBSearchParametersIterator<T>;
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fn into_iter(self) -> Self::IntoIter {
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BernoulliNBSearchParametersIterator {
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bernoulli_nb_search_parameters: self,
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current_alpha: 0,
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current_priors: 0,
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current_binarize: 0,
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}
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}
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}
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impl<T: RealNumber> Iterator for BernoulliNBSearchParametersIterator<T> {
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type Item = BernoulliNBParameters<T>;
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fn next(&mut self) -> Option<Self::Item> {
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if self.current_alpha == self.bernoulli_nb_search_parameters.alpha.len()
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&& self.current_priors == self.bernoulli_nb_search_parameters.priors.len()
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&& self.current_binarize == self.bernoulli_nb_search_parameters.binarize.len()
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{
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return None;
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}
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let next = BernoulliNBParameters {
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alpha: self.bernoulli_nb_search_parameters.alpha[self.current_alpha],
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priors: self.bernoulli_nb_search_parameters.priors[self.current_priors].clone(),
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binarize: self.bernoulli_nb_search_parameters.binarize[self.current_binarize],
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};
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if self.current_alpha + 1 < self.bernoulli_nb_search_parameters.alpha.len() {
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self.current_alpha += 1;
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} else if self.current_priors + 1 < self.bernoulli_nb_search_parameters.priors.len() {
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self.current_alpha = 0;
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self.current_priors += 1;
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} else if self.current_binarize + 1 < self.bernoulli_nb_search_parameters.binarize.len() {
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self.current_alpha = 0;
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self.current_priors = 0;
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self.current_binarize += 1;
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} else {
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self.current_alpha += 1;
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self.current_priors += 1;
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self.current_binarize += 1;
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}
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Some(next)
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}
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}
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impl<T: RealNumber> Default for BernoulliNBSearchParameters<T> {
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fn default() -> Self {
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let default_params = BernoulliNBParameters::default();
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BernoulliNBSearchParameters {
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alpha: vec![default_params.alpha],
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priors: vec![default_params.priors],
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binarize: vec![default_params.binarize],
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}
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}
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}
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impl<T: RealNumber> BernoulliNBDistribution<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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/// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
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/// * `binarize` - Threshold for binarizing.
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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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alpha: T,
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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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if alpha < T::zero() {
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return Err(Failed::fit(&format!(
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"Alpha should be greater than 0; |alpha|=[{}]",
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alpha
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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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for class_index in indices.iter() {
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class_count[*class_index] += 1;
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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 mut feature_in_class_counter = vec![vec![0_usize; n_features]; class_labels.len()];
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for (row, class_index) in row_iter(x).zip(indices) {
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for (idx, row_i) in row.iter().enumerate().take(n_features) {
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feature_in_class_counter[class_index][idx] +=
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row_i.to_usize().ok_or_else(|| {
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Failed::fit(&format!(
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"Elements of the matrix should be 1.0 or 0.0 |found|=[{}]",
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row_i
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))
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})?;
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}
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}
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let feature_log_prob = feature_in_class_counter
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.iter()
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.enumerate()
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.map(|(class_index, feature_count)| {
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feature_count
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.iter()
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.map(|&count| {
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((T::from(count).unwrap() + alpha)
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/ (T::from(class_count[class_index]).unwrap() + alpha * T::two()))
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.ln()
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})
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.collect()
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})
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.collect();
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Ok(Self {
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class_labels,
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class_priors,
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class_count,
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feature_count: feature_in_class_counter,
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feature_log_prob,
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n_features,
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})
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}
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}
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/// BernoulliNB implements the naive Bayes algorithm for data that follows the Bernoulli
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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 BernoulliNB<T: RealNumber, M: Matrix<T>> {
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inner: BaseNaiveBayes<T, M, BernoulliNBDistribution<T>>,
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binarize: Option<T>,
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}
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impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, BernoulliNBParameters<T>>
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for BernoulliNB<T, M>
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{
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fn fit(x: &M, y: &M::RowVector, parameters: BernoulliNBParameters<T>) -> Result<Self, Failed> {
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BernoulliNB::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 BernoulliNB<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>> BernoulliNB<T, M> {
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/// Fits BernoulliNB 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, alpha for smoothing and
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/// binarizing threshold.
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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: BernoulliNBParameters<T>,
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) -> Result<Self, Failed> {
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let distribution = if let Some(threshold) = parameters.binarize {
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BernoulliNBDistribution::fit(
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&(x.binarize(threshold)),
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y,
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parameters.alpha,
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parameters.priors,
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)?
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} else {
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BernoulliNBDistribution::fit(x, y, parameters.alpha, parameters.priors)?
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};
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let inner = BaseNaiveBayes::fit(distribution)?;
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Ok(Self {
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inner,
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binarize: parameters.binarize,
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})
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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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if let Some(threshold) = self.binarize {
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self.inner.predict(&(x.binarize(threshold)))
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} else {
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self.inner.predict(x)
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}
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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 samples encountered for each (class, feature)
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/// Returns a 2d vector of shape (n_classes, n_features)
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pub fn feature_count(&self) -> &Vec<Vec<usize>> {
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&self.inner.distribution.feature_count
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}
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/// Empirical log probability of features given a class
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pub fn feature_log_prob(&self) -> &Vec<Vec<T>> {
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&self.inner.distribution.feature_log_prob
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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 = BernoulliNBSearchParameters {
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alpha: vec![1., 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.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(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
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#[test]
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fn run_bernoulli_naive_bayes() {
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// Tests that BernoulliNB when alpha=1.0 gives the same values as
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// those given for the toy example in Manning, Raghavan, and
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// Schuetze's "Introduction to Information Retrieval" book:
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// https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
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// Training data points are:
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// Chinese Beijing Chinese (class: China)
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// Chinese Chinese Shanghai (class: China)
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// Chinese Macao (class: China)
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// Tokyo Japan Chinese (class: Japan)
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let x = DenseMatrix::<f64>::from_2d_array(&[
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&[1., 1., 0., 0., 0., 0.],
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&[0., 1., 0., 0., 1., 0.],
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&[0., 1., 0., 1., 0., 0.],
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&[0., 1., 1., 0., 0., 1.],
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]);
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let y = vec![0., 0., 0., 1.];
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let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
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|
|
assert_eq!(bnb.inner.distribution.class_priors, &[0.75, 0.25]);
|
|
assert_eq!(
|
|
bnb.feature_log_prob(),
|
|
&[
|
|
&[
|
|
-0.916290731874155,
|
|
-0.2231435513142097,
|
|
-1.6094379124341003,
|
|
-0.916290731874155,
|
|
-0.916290731874155,
|
|
-1.6094379124341003
|
|
],
|
|
&[
|
|
-1.0986122886681098,
|
|
-0.40546510810816444,
|
|
-0.40546510810816444,
|
|
-1.0986122886681098,
|
|
-1.0986122886681098,
|
|
-0.40546510810816444
|
|
]
|
|
]
|
|
);
|
|
|
|
// Testing data point is:
|
|
// Chinese Chinese Chinese Tokyo Japan
|
|
let x_test = DenseMatrix::<f64>::from_2d_array(&[&[0., 1., 1., 0., 0., 1.]]);
|
|
let y_hat = bnb.predict(&x_test).unwrap();
|
|
|
|
assert_eq!(y_hat, &[1.]);
|
|
}
|
|
|
|
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
|
#[test]
|
|
fn bernoulli_nb_scikit_parity() {
|
|
let x = DenseMatrix::<f64>::from_2d_array(&[
|
|
&[2., 4., 0., 0., 2., 1., 2., 4., 2., 0.],
|
|
&[3., 4., 0., 2., 1., 0., 1., 4., 0., 3.],
|
|
&[1., 4., 2., 4., 1., 0., 1., 2., 3., 2.],
|
|
&[0., 3., 3., 4., 1., 0., 3., 1., 1., 1.],
|
|
&[0., 2., 1., 4., 3., 4., 1., 2., 3., 1.],
|
|
&[3., 2., 4., 1., 3., 0., 2., 4., 0., 2.],
|
|
&[3., 1., 3., 0., 2., 0., 4., 4., 3., 4.],
|
|
&[2., 2., 2., 0., 1., 1., 2., 1., 0., 1.],
|
|
&[3., 3., 2., 2., 0., 2., 3., 2., 2., 3.],
|
|
&[4., 3., 4., 4., 4., 2., 2., 0., 1., 4.],
|
|
&[3., 4., 2., 2., 1., 4., 4., 4., 1., 3.],
|
|
&[3., 0., 1., 4., 4., 0., 0., 3., 2., 4.],
|
|
&[2., 0., 3., 3., 1., 2., 0., 2., 4., 1.],
|
|
&[2., 4., 0., 4., 2., 4., 1., 3., 1., 4.],
|
|
&[0., 2., 2., 3., 4., 0., 4., 4., 4., 4.],
|
|
]);
|
|
let y = vec![2., 2., 0., 0., 0., 2., 1., 1., 0., 1., 0., 0., 2., 0., 2.];
|
|
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
|
|
|
|
let y_hat = bnb.predict(&x).unwrap();
|
|
|
|
assert_eq!(bnb.classes(), &[0., 1., 2.]);
|
|
assert_eq!(bnb.class_count(), &[7, 3, 5]);
|
|
assert_eq!(bnb.n_features(), 10);
|
|
assert_eq!(
|
|
bnb.feature_count(),
|
|
&[
|
|
&[5, 6, 6, 7, 6, 4, 6, 7, 7, 7],
|
|
&[3, 3, 3, 1, 3, 2, 3, 2, 2, 3],
|
|
&[4, 4, 3, 4, 5, 2, 4, 5, 3, 4]
|
|
]
|
|
);
|
|
|
|
assert!(bnb
|
|
.inner
|
|
.distribution
|
|
.class_priors
|
|
.approximate_eq(&vec!(0.46, 0.2, 0.33), 1e-2));
|
|
assert!(bnb.feature_log_prob()[1].approximate_eq(
|
|
&vec![
|
|
-0.22314355,
|
|
-0.22314355,
|
|
-0.22314355,
|
|
-0.91629073,
|
|
-0.22314355,
|
|
-0.51082562,
|
|
-0.22314355,
|
|
-0.51082562,
|
|
-0.51082562,
|
|
-0.22314355
|
|
],
|
|
1e-1
|
|
));
|
|
assert!(y_hat.approximate_eq(
|
|
&vec!(2.0, 2.0, 0.0, 0.0, 0.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
|
|
1e-5
|
|
));
|
|
}
|
|
|
|
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
|
#[test]
|
|
#[cfg(feature = "serde")]
|
|
fn serde() {
|
|
let x = DenseMatrix::<f64>::from_2d_array(&[
|
|
&[1., 1., 0., 0., 0., 0.],
|
|
&[0., 1., 0., 0., 1., 0.],
|
|
&[0., 1., 0., 1., 0., 0.],
|
|
&[0., 1., 1., 0., 0., 1.],
|
|
]);
|
|
let y = vec![0., 0., 0., 1.];
|
|
|
|
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
|
|
let deserialized_bnb: BernoulliNB<f64, DenseMatrix<f64>> =
|
|
serde_json::from_str(&serde_json::to_string(&bnb).unwrap()).unwrap();
|
|
|
|
assert_eq!(bnb, deserialized_bnb);
|
|
}
|
|
}
|