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Merge potential next release v0.4 (#187) Breaking Changes

Browse Source 
* First draft of the new n-dimensional arrays + NB use case
* Improves default implementation of multiple Array methods
* Refactors tree methods
* Adds matrix decomposition routines
* Adds matrix decomposition methods to ndarray and nalgebra bindings
* Refactoring + linear regression now uses array2
* Ridge & Linear regression
* LBFGS optimizer & logistic regression
* LBFGS optimizer & logistic regression
* Changes linear methods, metrics and model selection methods to new n-dimensional arrays
* Switches KNN and clustering algorithms to new n-d array layer
* Refactors distance metrics
* Optimizes knn and clustering methods
* Refactors metrics module
* Switches decomposition methods to n-dimensional arrays
* Linalg refactoring - cleanup rng merge (#172)
* Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure.
* Exclude AUC metrics. Needs reimplementation
* Improve developers walkthrough

New traits system in place at `src/numbers` and `src/linalg`
Co-authored-by: Lorenzo <tunedconsulting@gmail.com>

* Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021
* Implement getters to use as_ref() in src/neighbors
* Implement getters to use as_ref() in src/naive_bayes
* Implement getters to use as_ref() in src/linear
* Add Clone to src/naive_bayes
* Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021
* Implement ndarray-bindings. Remove FloatNumber from implementations
* Drop nalgebra-bindings support (as decided in conf-call to go for ndarray)
* Remove benches. Benches will have their own repo at smartcore-benches
* Implement SVC
* Implement SVC serialization. Move search parameters in dedicated module
* Implement SVR. Definitely too slow
* Fix compilation issues for wasm (#202)

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
* Fix tests (#203)

* Port linalg/traits/stats.rs
* Improve methods naming
* Improve Display for DenseMatrix

Co-authored-by: Montana Low <montanalow@users.noreply.github.com>
Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
This commit is contained in:
Lorenzo
2022-10-31 10:44:57 +00:00
committed by GitHub
parent bb71656137
commit 52eb6ce023
110 changed files with 10327 additions and 9107 deletions
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src/naive_bayes/bernoulli.rs
+189 -155
View File
@@ -6,7 +6,7 @@
//! Example:
//!
//! ```
//! use smartcore::linalg::naive::dense_matrix::*;
//! use smartcore::linalg::basic::matrix::DenseMatrix;
//! use smartcore::naive_bayes::bernoulli::BernoulliNB;
//!
//! // Training data points are:
@@ -14,56 +14,55 @@
//! // Chinese Chinese Shanghai (class: China)
//! // Chinese Macao (class: China)
//! // Tokyo Japan Chinese (class: Japan)
//! 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 x = DenseMatrix::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 y: Vec<u32> = vec![0, 0, 0, 1];
//!
//! let nb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
//!
//! // Testing data point is:
//! // Chinese Chinese Chinese Tokyo Japan
//! let x_test = DenseMatrix::<f64>::from_2d_array(&[&[0., 1., 1., 0., 0., 1.]]);
//! let x_test = DenseMatrix::from_2d_array(&[&[0, 1, 1, 0, 0, 1]]);
//! let y_hat = nb.predict(&x_test).unwrap();
//! ```
//!
//! ## References:
//!
//! * ["Introduction to Information Retrieval", Manning C. D., Raghavan P., Schutze H., 2009, Chapter 13 ](https://nlp.stanford.edu/IR-book/information-retrieval-book.html)
use num_traits::Unsigned;
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::row_iter;
use crate::linalg::BaseVector;
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
use crate::math::vector::RealNumberVector;
use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
use crate::numbers::basenum::Number;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
/// Naive Bayes classifier for Bearnoulli features
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
struct BernoulliNBDistribution<T: RealNumber> {
#[derive(Debug, Clone)]
struct BernoulliNBDistribution<T: Number + Ord + Unsigned> {
/// class labels known to the classifier
class_labels: Vec<T>,
/// number of training samples observed in each class
class_count: Vec<usize>,
/// probability of each class
class_priors: Vec<T>,
class_priors: Vec<f64>,
/// Number of samples encountered for each (class, feature)
feature_count: Vec<Vec<usize>>,
/// probability of features per class
feature_log_prob: Vec<Vec<T>>,
feature_log_prob: Vec<Vec<f64>>,
/// Number of features of each sample
n_features: usize,
}
impl<T: RealNumber> PartialEq for BernoulliNBDistribution<T> {
impl<T: Number + Ord + Unsigned> PartialEq for BernoulliNBDistribution<T> {
fn eq(&self, other: &Self) -> bool {
if self.class_labels == other.class_labels
&& self.class_count == other.class_count
@@ -76,7 +75,7 @@ impl<T: RealNumber> PartialEq for BernoulliNBDistribution<T> {
.iter()
.zip(other.feature_log_prob.iter())
{
if !a.approximate_eq(b, T::epsilon()) {
if !a.iter().zip(b.iter()).all(|(a, b)| (a - b).abs() < 1e-4) {
return false;
}
}
@@ -87,25 +86,27 @@ impl<T: RealNumber> PartialEq for BernoulliNBDistribution<T> {
}
}
impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for BernoulliNBDistribution<T> {
fn prior(&self, class_index: usize) -> T {
impl<X: Number + PartialOrd, Y: Number + Ord + Unsigned> NBDistribution<X, Y>
for BernoulliNBDistribution<Y>
{
fn prior(&self, class_index: usize) -> f64 {
self.class_priors[class_index]
}
fn log_likelihood(&self, class_index: usize, j: &M::RowVector) -> T {
let mut likelihood = T::zero();
for feature in 0..j.len() {
let value = j.get(feature);
if value == T::one() {
fn log_likelihood<'a>(&'a self, class_index: usize, j: &'a Box<dyn ArrayView1<X> + 'a>) -> f64 {
let mut likelihood = 0f64;
for feature in 0..j.shape() {
let value = *j.get(feature);
if value == X::one() {
likelihood += self.feature_log_prob[class_index][feature];
} else {
likelihood += (T::one() - self.feature_log_prob[class_index][feature].exp()).ln();
likelihood += (1f64 - self.feature_log_prob[class_index][feature].exp()).ln();
}
}
likelihood
}
fn classes(&self) -> &Vec<T> {
fn classes(&self) -> &Vec<Y> {
&self.class_labels
}
}
@@ -113,26 +114,26 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for BernoulliNBDistributi
/// `BernoulliNB` parameters. Use `Default::default()` for default values.
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct BernoulliNBParameters<T: RealNumber> {
pub struct BernoulliNBParameters<T: Number> {
#[cfg_attr(feature = "serde", serde(default))]
/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
pub alpha: T,
pub alpha: f64,
#[cfg_attr(feature = "serde", serde(default))]
/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
pub priors: Option<Vec<T>>,
pub priors: Option<Vec<f64>>,
#[cfg_attr(feature = "serde", serde(default))]
/// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
pub binarize: Option<T>,
}
impl<T: RealNumber> BernoulliNBParameters<T> {
impl<T: Number + PartialOrd> BernoulliNBParameters<T> {
/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
pub fn with_alpha(mut self, alpha: T) -> Self {
pub fn with_alpha(mut self, alpha: f64) -> Self {
self.alpha = alpha;
self
}
/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
pub fn with_priors(mut self, priors: Vec<T>) -> Self {
pub fn with_priors(mut self, priors: Vec<f64>) -> Self {
self.priors = Some(priors);
self
}
@@ -143,11 +144,11 @@ impl<T: RealNumber> BernoulliNBParameters<T> {
}
}
impl<T: RealNumber> Default for BernoulliNBParameters<T> {
impl<T: Number + PartialOrd> Default for BernoulliNBParameters<T> {
fn default() -> Self {
Self {
alpha: T::one(),
priors: None,
alpha: 1f64,
priors: Option::None,
binarize: Some(T::zero()),
}
}
@@ -156,27 +157,27 @@ impl<T: RealNumber> Default for BernoulliNBParameters<T> {
/// BernoulliNB grid search parameters
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct BernoulliNBSearchParameters<T: RealNumber> {
pub struct BernoulliNBSearchParameters<T: Number> {
#[cfg_attr(feature = "serde", serde(default))]
/// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
pub alpha: Vec<T>,
pub alpha: Vec<f64>,
#[cfg_attr(feature = "serde", serde(default))]
/// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
pub priors: Vec<Option<Vec<T>>>,
pub priors: Vec<Option<Vec<f64>>>,
#[cfg_attr(feature = "serde", serde(default))]
/// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
pub binarize: Vec<Option<T>>,
}
/// BernoulliNB grid search iterator
pub struct BernoulliNBSearchParametersIterator<T: RealNumber> {
pub struct BernoulliNBSearchParametersIterator<T: Number> {
bernoulli_nb_search_parameters: BernoulliNBSearchParameters<T>,
current_alpha: usize,
current_priors: usize,
current_binarize: usize,
}
impl<T: RealNumber> IntoIterator for BernoulliNBSearchParameters<T> {
impl<T: Number> IntoIterator for BernoulliNBSearchParameters<T> {
type Item = BernoulliNBParameters<T>;
type IntoIter = BernoulliNBSearchParametersIterator<T>;
@@ -190,7 +191,7 @@ impl<T: RealNumber> IntoIterator for BernoulliNBSearchParameters<T> {
}
}
impl<T: RealNumber> Iterator for BernoulliNBSearchParametersIterator<T> {
impl<T: Number> Iterator for BernoulliNBSearchParametersIterator<T> {
type Item = BernoulliNBParameters<T>;
fn next(&mut self) -> Option<Self::Item> {
@@ -226,9 +227,9 @@ impl<T: RealNumber> Iterator for BernoulliNBSearchParametersIterator<T> {
}
}
impl<T: RealNumber> Default for BernoulliNBSearchParameters<T> {
impl<T: Number + std::cmp::PartialOrd> Default for BernoulliNBSearchParameters<T> {
fn default() -> Self {
let default_params = BernoulliNBParameters::default();
let default_params = BernoulliNBParameters::<T>::default();
BernoulliNBSearchParameters {
alpha: vec![default_params.alpha],
@@ -238,7 +239,7 @@ impl<T: RealNumber> Default for BernoulliNBSearchParameters<T> {
}
}
impl<T: RealNumber> BernoulliNBDistribution<T> {
impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
/// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
/// * `x` - training data.
/// * `y` - vector with target values (classes) of length N.
@@ -246,14 +247,14 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
/// priors are adjusted according to the data.
/// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
/// * `binarize` - Threshold for binarizing.
pub fn fit<M: Matrix<T>>(
x: &M,
y: &M::RowVector,
alpha: T,
priors: Option<Vec<T>>,
fn fit<TX: Number + PartialOrd, X: Array2<TX>, Y: Array1<TY>>(
x: &X,
y: &Y,
alpha: f64,
priors: Option<Vec<f64>>,
) -> Result<Self, Failed> {
let (n_samples, n_features) = x.shape();
let y_samples = y.len();
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
@@ -267,16 +268,15 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
n_samples
)));
}
if alpha < T::zero() {
if alpha < 0f64 {
return Err(Failed::fit(&format!(
"Alpha should be greater than 0; |alpha|=[{}]",
alpha
)));
}
let y = y.to_vec();
let (class_labels, indices) = y.unique_with_indices();
let (class_labels, indices) = <Vec<T> as RealNumberVector<T>>::unique_with_indices(&y);
let mut class_count = vec![0_usize; class_labels.len()];
for class_index in indices.iter() {
@@ -293,14 +293,14 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
} else {
class_count
.iter()
.map(|&c| T::from(c).unwrap() / T::from(n_samples).unwrap())
.map(|&c| c as f64 / (n_samples as f64))
.collect()
};
let mut feature_in_class_counter = vec![vec![0_usize; n_features]; class_labels.len()];
for (row, class_index) in row_iter(x).zip(indices) {
for (idx, row_i) in row.iter().enumerate().take(n_features) {
for (row, class_index) in x.row_iter().zip(indices) {
for (idx, row_i) in row.iterator(0).enumerate().take(n_features) {
feature_in_class_counter[class_index][idx] +=
row_i.to_usize().ok_or_else(|| {
Failed::fit(&format!(
@@ -318,9 +318,8 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
feature_count
.iter()
.map(|&count| {
((T::from(count).unwrap() + alpha)
/ (T::from(class_count[class_index]).unwrap() + alpha * T::two()))
.ln()
((count as f64 + alpha) / (class_count[class_index] as f64 + alpha * 2f64))
.ln()
})
.collect()
})
@@ -341,40 +340,52 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
/// distribution.
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, PartialEq)]
pub struct BernoulliNB<T: RealNumber, M: Matrix<T>> {
inner: BaseNaiveBayes<T, M, BernoulliNBDistribution<T>>,
binarize: Option<T>,
pub struct BernoulliNB<
TX: Number + PartialOrd,
TY: Number + Ord + Unsigned,
X: Array2<TX>,
Y: Array1<TY>,
> {
inner: Option<BaseNaiveBayes<TX, TY, X, Y, BernoulliNBDistribution<TY>>>,
binarize: Option<TX>,
}
impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, BernoulliNBParameters<T>>
for BernoulliNB<T, M>
impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
SupervisedEstimator<X, Y, BernoulliNBParameters<TX>> for BernoulliNB<TX, TY, X, Y>
{
fn fit(x: &M, y: &M::RowVector, parameters: BernoulliNBParameters<T>) -> Result<Self, Failed> {
fn new() -> Self {
Self {
inner: Option::None,
binarize: Option::None,
}
}
fn fit(x: &X, y: &Y, parameters: BernoulliNBParameters<TX>) -> Result<Self, Failed> {
BernoulliNB::fit(x, y, parameters)
}
}
impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for BernoulliNB<T, M> {
fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
Predictor<X, Y> for BernoulliNB<TX, TY, X, Y>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
impl<T: RealNumber, M: Matrix<T>> BernoulliNB<T, M> {
impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
BernoulliNB<TX, TY, X, Y>
{
/// Fits BernoulliNB with given data
/// * `x` - training data of size NxM where N is the number of samples and M is the number of
/// features.
/// * `y` - vector with target values (classes) of length N.
/// * `parameters` - additional parameters like class priors, alpha for smoothing and
/// binarizing threshold.
pub fn fit(
x: &M,
y: &M::RowVector,
parameters: BernoulliNBParameters<T>,
) -> Result<Self, Failed> {
pub fn fit(x: &X, y: &Y, parameters: BernoulliNBParameters<TX>) -> Result<Self, Failed> {
let distribution = if let Some(threshold) = parameters.binarize {
BernoulliNBDistribution::fit(
&(x.binarize(threshold)),
&Self::binarize(x, threshold),
y,
parameters.alpha,
parameters.priors,
@@ -385,7 +396,7 @@ impl<T: RealNumber, M: Matrix<T>> BernoulliNB<T, M> {
let inner = BaseNaiveBayes::fit(distribution)?;
Ok(Self {
inner,
inner: Some(inner),
binarize: parameters.binarize,
})
}
@@ -393,51 +404,73 @@ impl<T: RealNumber, M: Matrix<T>> BernoulliNB<T, M> {
/// Estimates the class labels for the provided data.
/// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
/// Returns a vector of size N with class estimates.
pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
if let Some(threshold) = self.binarize {
self.inner.predict(&(x.binarize(threshold)))
self.inner
.as_ref()
.unwrap()
.predict(&Self::binarize(x, threshold))
} else {
self.inner.predict(x)
self.inner.as_ref().unwrap().predict(x)
}
}
/// Class labels known to the classifier.
/// Returns a vector of size n_classes.
pub fn classes(&self) -> &Vec<T> {
&self.inner.distribution.class_labels
pub fn classes(&self) -> &Vec<TY> {
&self.inner.as_ref().unwrap().distribution.class_labels
}
/// Number of training samples observed in each class.
/// Returns a vector of size n_classes.
pub fn class_count(&self) -> &Vec<usize> {
&self.inner.distribution.class_count
&self.inner.as_ref().unwrap().distribution.class_count
}
/// Number of features of each sample
pub fn n_features(&self) -> usize {
self.inner.distribution.n_features
self.inner.as_ref().unwrap().distribution.n_features
}
/// Number of samples encountered for each (class, feature)
/// Returns a 2d vector of shape (n_classes, n_features)
pub fn feature_count(&self) -> &Vec<Vec<usize>> {
&self.inner.distribution.feature_count
&self.inner.as_ref().unwrap().distribution.feature_count
}
/// Empirical log probability of features given a class
pub fn feature_log_prob(&self) -> &Vec<Vec<T>> {
&self.inner.distribution.feature_log_prob
pub fn feature_log_prob(&self) -> &Vec<Vec<f64>> {
&self.inner.as_ref().unwrap().distribution.feature_log_prob
}
fn binarize_mut(x: &mut X, threshold: TX) {
let (nrows, ncols) = x.shape();
for row in 0..nrows {
for col in 0..ncols {
if *x.get((row, col)) > threshold {
x.set((row, col), TX::one());
} else {
x.set((row, col), TX::zero());
}
}
}
}
fn binarize(x: &X, threshold: TX) -> X {
let mut new_x = x.clone();
Self::binarize_mut(&mut new_x, threshold);
new_x
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::DenseMatrix;
use crate::linalg::basic::matrix::DenseMatrix;
#[test]
fn search_parameters() {
let parameters = BernoulliNBSearchParameters {
let parameters: BernoulliNBSearchParameters<f64> = BernoulliNBSearchParameters {
alpha: vec![1., 2.],
..Default::default()
};
@@ -462,16 +495,18 @@ mod tests {
// Chinese Chinese Shanghai (class: China)
// Chinese Macao (class: China)
// Tokyo Japan Chinese (class: Japan)
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 x = DenseMatrix::from_2d_array(&[
&[1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
&[0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
&[0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0],
]);
let y = vec![0., 0., 0., 1.];
let y: Vec<u32> = vec![0, 0, 0, 1];
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
assert_eq!(bnb.inner.distribution.class_priors, &[0.75, 0.25]);
let distribution = bnb.inner.clone().unwrap().distribution;
assert_eq!(&distribution.class_priors, &[0.75, 0.25]);
assert_eq!(
bnb.feature_log_prob(),
&[
@@ -496,38 +531,38 @@ mod tests {
// Testing data point is:
// Chinese Chinese Chinese Tokyo Japan
let x_test = DenseMatrix::<f64>::from_2d_array(&[&[0., 1., 1., 0., 0., 1.]]);
let x_test = DenseMatrix::from_2d_array(&[&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0]]);
let y_hat = bnb.predict(&x_test).unwrap();
assert_eq!(y_hat, &[1.]);
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 x = DenseMatrix::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 y: Vec<u32> = 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.classes(), &[0, 1, 2]);
assert_eq!(bnb.class_count(), &[7, 3, 5]);
assert_eq!(bnb.n_features(), 10);
assert_eq!(
@@ -539,48 +574,47 @@ mod tests {
]
);
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(
let distribution = bnb.inner.clone().unwrap().distribution;
assert_eq!(
&distribution.class_priors,
&vec!(0.4666666666666667, 0.2, 0.3333333333333333)
);
assert_eq!(
&bnb.feature_log_prob()[1],
&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
));
-0.2231435513142097,
-0.2231435513142097,
-0.2231435513142097,
-0.916290731874155,
-0.2231435513142097,
-0.5108256237659907,
-0.2231435513142097,
-0.5108256237659907,
-0.5108256237659907,
-0.2231435513142097
]
);
assert_eq!(y_hat, vec!(2, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0));
}
#[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.];
// TODO: implement serialization
// #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
// #[test]
// #[cfg(feature = "serde")]
// fn serde() {
// let x = DenseMatrix::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<u32> = 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();
// let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
// let deserialized_bnb: BernoulliNB<i32, u32, DenseMatrix<i32>, Vec<u32>> =
// serde_json::from_str(&serde_json::to_string(&bnb).unwrap()).unwrap();
assert_eq!(bnb, deserialized_bnb);
}
// assert_eq!(bnb, deserialized_bnb);
// }
}