use std::marker::PhantomData;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::basic::arrays::{Array1, Array2};
use crate::metrics::distance::euclidian::Euclidian;
use crate::metrics::distance::{Distance, Distances};
use crate::neighbors::KNNWeightFunction;
use crate::numbers::basenum::Number;
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct KNNClassifierParameters<T: Number, D: Distance<Vec<T>>> {
#[cfg_attr(feature = "serde", serde(default))]
pub distance: D,
#[cfg_attr(feature = "serde", serde(default))]
pub algorithm: KNNAlgorithmName,
#[cfg_attr(feature = "serde", serde(default))]
pub weight: KNNWeightFunction,
#[cfg_attr(feature = "serde", serde(default))]
pub k: usize,
#[cfg_attr(feature = "serde", serde(default))]
t: PhantomData<T>,
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct KNNClassifier<
TX: Number,
TY: Number + Ord,
X: Array2<TX>,
Y: Array1<TY>,
D: Distance<Vec<TX>>,
> {
classes: Option<Vec<TY>>,
y: Option<Vec<usize>>,
knn_algorithm: Option<KNNAlgorithm<TX, D>>,
weight: Option<KNNWeightFunction>,
k: Option<usize>,
_phantom_tx: PhantomData<TX>,
_phantom_x: PhantomData<X>,
_phantom_y: PhantomData<Y>,
}
impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
KNNClassifier<TX, TY, X, Y, D>
{
fn classes(&self) -> &Vec<TY> {
self.classes.as_ref().unwrap()
}
fn y(&self) -> &Vec<usize> {
self.y.as_ref().unwrap()
}
fn knn_algorithm(&self) -> &KNNAlgorithm<TX, D> {
self.knn_algorithm.as_ref().unwrap()
}
fn weight(&self) -> &KNNWeightFunction {
self.weight.as_ref().unwrap()
}
fn k(&self) -> usize {
self.k.unwrap()
}
}
impl<T: Number, D: Distance<Vec<T>>> KNNClassifierParameters<T, D> {
pub fn with_k(mut self, k: usize) -> Self {
self.k = k;
self
}
pub fn with_distance<DD: Distance<Vec<T>>>(
self,
distance: DD,
) -> KNNClassifierParameters<T, DD> {
KNNClassifierParameters {
distance,
algorithm: self.algorithm,
weight: self.weight,
k: self.k,
t: PhantomData,
}
}
pub fn with_algorithm(mut self, algorithm: KNNAlgorithmName) -> Self {
self.algorithm = algorithm;
self
}
pub fn with_weight(mut self, weight: KNNWeightFunction) -> Self {
self.weight = weight;
self
}
}
impl<T: Number> Default for KNNClassifierParameters<T, Euclidian<T>> {
fn default() -> Self {
KNNClassifierParameters {
distance: Distances::euclidian(),
algorithm: KNNAlgorithmName::default(),
weight: KNNWeightFunction::default(),
k: 3,
t: PhantomData,
}
}
}
impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> PartialEq
for KNNClassifier<TX, TY, X, Y, D>
{
fn eq(&self, other: &Self) -> bool {
if self.classes().len() != other.classes().len()
|| self.k() != other.k()
|| self.y().len() != other.y().len()
{
false
} else {
for i in 0..self.classes().len() {
if self.classes()[i] != other.classes()[i] {
return false;
}
}
for i in 0..self.y().len() {
if self.y().get(i) != other.y().get(i) {
return false;
}
}
true
}
}
}
impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
SupervisedEstimator<X, Y, KNNClassifierParameters<TX, D>> for KNNClassifier<TX, TY, X, Y, D>
{
fn new() -> Self {
Self {
classes: Option::None,
y: Option::None,
knn_algorithm: Option::None,
weight: Option::None,
k: Option::None,
_phantom_tx: PhantomData,
_phantom_x: PhantomData,
_phantom_y: PhantomData,
}
}
fn fit(x: &X, y: &Y, parameters: KNNClassifierParameters<TX, D>) -> Result<Self, Failed> {
KNNClassifier::fit(x, y, parameters)
}
}
impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
Predictor<X, Y> for KNNClassifier<TX, TY, X, Y, D>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
KNNClassifier<TX, TY, X, Y, D>
{
pub fn fit(
x: &X,
y: &Y,
parameters: KNNClassifierParameters<TX, D>,
) -> Result<KNNClassifier<TX, TY, X, Y, D>, Failed> {
let y_n = y.shape();
let (x_n, _) = x.shape();
let data = x
.row_iter()
.map(|row| row.iterator(0).copied().collect())
.collect();
let mut yi: Vec<usize> = vec![0; y_n];
let classes = y.unique();
for (i, yi_i) in yi.iter_mut().enumerate().take(y_n) {
let yc = *y.get(i);
*yi_i = classes.iter().position(|c| yc == *c).unwrap();
}
if x_n != y_n {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{x_n}], |y|=[{y_n}]"
)));
}
if parameters.k <= 1 {
return Err(Failed::fit(&format!(
"k should be > 1, k=[{}]",
parameters.k
)));
}
Ok(KNNClassifier {
classes: Some(classes),
y: Some(yi),
k: Some(parameters.k),
knn_algorithm: Some(parameters.algorithm.fit(data, parameters.distance)?),
weight: Some(parameters.weight),
_phantom_tx: PhantomData,
_phantom_x: PhantomData,
_phantom_y: PhantomData,
})
}
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
let mut result = Y::zeros(x.shape().0);
let mut row_vec = vec![TX::zero(); x.shape().1];
for (i, row) in x.row_iter().enumerate() {
row.iterator(0)
.zip(row_vec.iter_mut())
.for_each(|(&s, v)| *v = s);
result.set(i, self.classes()[self.predict_for_row(&row_vec)?]);
}
Ok(result)
}
fn predict_for_row(&self, row: &Vec<TX>) -> Result<usize, Failed> {
let search_result = self.knn_algorithm().find(row, self.k())?;
let weights = self
.weight()
.calc_weights(search_result.iter().map(|v| v.1).collect());
let w_sum: f64 = weights.iter().copied().sum();
let mut c = vec![0f64; self.classes().len()];
let mut max_c = 0f64;
let mut max_i = 0;
for (r, w) in search_result.iter().zip(weights.iter()) {
c[self.y()[r.0]] += *w / w_sum;
if c[self.y()[r.0]] > max_c {
max_c = c[self.y()[r.0]];
max_i = self.y()[r.0];
}
}
Ok(max_i)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn knn_fit_predict() {
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![2, 2, 2, 3, 3];
let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();
let y_hat = knn.predict(&x).unwrap();
assert_eq!(5, Vec::len(&y_hat));
assert_eq!(y.to_vec(), y_hat);
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn knn_fit_predict_weighted() {
let x = DenseMatrix::from_2d_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]);
let y = vec![2, 2, 2, 3, 3];
let knn = KNNClassifier::fit(
&x,
&y,
KNNClassifierParameters::default()
.with_k(5)
.with_algorithm(KNNAlgorithmName::LinearSearch)
.with_weight(KNNWeightFunction::Distance),
)
.unwrap();
let y_hat = knn.predict(&DenseMatrix::from_2d_array(&[&[4.1]])).unwrap();
assert_eq!(vec![3], y_hat);
}
#[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(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![2, 2, 2, 3, 3];
let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
assert_eq!(knn, deserialized_knn);
}
}