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feat: refactoring, adds Result to most public API

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This commit is contained in:
Volodymyr Orlov
2020-09-18 15:20:32 -07:00
parent 4921ae76f5
commit a9db970195
24 changed files with 389 additions and 298 deletions
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src/neighbors/knn_classifier.rs
+31 -28
View File
@@ -25,8 +25,8 @@
//! &[9., 10.]]);
//! let y = vec![2., 2., 2., 3., 3.]; //your class labels
//!
//! let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
//! let y_hat = knn.predict(&x);
//! let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
//! let y_hat = knn.predict(&x).unwrap();
//! ```
//!
//! variable `y_hat` will hold a vector with estimates of class labels
@@ -34,6 +34,7 @@
use serde::{Deserialize, Serialize};
use crate::error::Failed;
use crate::linalg::{row_iter, Matrix};
use crate::math::distance::Distance;
use crate::math::num::RealNumber;
@@ -106,7 +107,7 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
y: &M::RowVector,
distance: D,
parameters: KNNClassifierParameters,
) -> KNNClassifier<T, D> {
) -> Result<KNNClassifier<T, D>, Failed> {
let y_m = M::from_row_vector(y.clone());
let (_, y_n) = y_m.shape();
@@ -122,43 +123,44 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
yi[i] = classes.iter().position(|c| yc == *c).unwrap();
}
assert!(
x_n == y_n,
format!(
if x_n != y_n {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
x_n, y_n
)
);
)));
}
assert!(
parameters.k > 1,
format!("k should be > 1, k=[{}]", parameters.k)
);
if parameters.k <= 1 {
return Err(Failed::fit(&format!(
"k should be > 1, k=[{}]",
parameters.k
)));
}
KNNClassifier {
Ok(KNNClassifier {
classes: classes,
y: yi,
k: parameters.k,
knn_algorithm: parameters.algorithm.fit(data, distance),
knn_algorithm: parameters.algorithm.fit(data, distance)?,
weight: parameters.weight,
}
})
}
/// 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<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let mut result = M::zeros(1, x.shape().0);
row_iter(x)
.enumerate()
.for_each(|(i, x)| result.set(0, i, self.classes[self.predict_for_row(x)]));
for (i, x) in row_iter(x).enumerate() {
result.set(0, i, self.classes[self.predict_for_row(x)?]);
}
result.to_row_vector()
Ok(result.to_row_vector())
}
fn predict_for_row(&self, x: Vec<T>) -> usize {
let search_result = self.knn_algorithm.find(&x, self.k);
fn predict_for_row(&self, x: Vec<T>) -> Result<usize, Failed> {
let search_result = self.knn_algorithm.find(&x, self.k)?;
let weights = self
.weight
@@ -176,7 +178,7 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
}
}
max_i
Ok(max_i)
}
}
@@ -191,8 +193,8 @@ mod tests {
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, Distances::euclidian(), Default::default());
let y_hat = knn.predict(&x);
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
let y_hat = knn.predict(&x).unwrap();
assert_eq!(5, Vec::len(&y_hat));
assert_eq!(y.to_vec(), y_hat);
}
@@ -210,8 +212,9 @@ mod tests {
algorithm: KNNAlgorithmName::LinearSearch,
weight: KNNWeightFunction::Distance,
},
);
let y_hat = knn.predict(&DenseMatrix::from_2d_array(&[&[4.1]]));
)
.unwrap();
let y_hat = knn.predict(&DenseMatrix::from_2d_array(&[&[4.1]])).unwrap();
assert_eq!(vec![3.0], y_hat);
}
@@ -221,7 +224,7 @@ mod tests {
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, Distances::euclidian(), Default::default());
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
src/neighbors/knn_regressor.rs
+31 -28
View File
@@ -27,8 +27,8 @@
//! &[5., 5.]]);
//! let y = vec![1., 2., 3., 4., 5.]; //your target values
//!
//! let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());
//! let y_hat = knn.predict(&x);
//! let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
//! let y_hat = knn.predict(&x).unwrap();
//! ```
//!
//! variable `y_hat` will hold predicted value
@@ -36,6 +36,7 @@
//!
use serde::{Deserialize, Serialize};
use crate::error::Failed;
use crate::linalg::{row_iter, BaseVector, Matrix};
use crate::math::distance::Distance;
use crate::math::num::RealNumber;
@@ -99,7 +100,7 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
y: &M::RowVector,
distance: D,
parameters: KNNRegressorParameters,
) -> KNNRegressor<T, D> {
) -> Result<KNNRegressor<T, D>, Failed> {
let y_m = M::from_row_vector(y.clone());
let (_, y_n) = y_m.shape();
@@ -107,42 +108,43 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
let data = row_iter(x).collect();
assert!(
x_n == y_n,
format!(
if x_n != y_n {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
x_n, y_n
)
);
)));
}
assert!(
parameters.k > 1,
format!("k should be > 1, k=[{}]", parameters.k)
);
if parameters.k <= 1 {
return Err(Failed::fit(&format!(
"k should be > 1, k=[{}]",
parameters.k
)));
}
KNNRegressor {
Ok(KNNRegressor {
y: y.to_vec(),
k: parameters.k,
knn_algorithm: parameters.algorithm.fit(data, distance),
knn_algorithm: parameters.algorithm.fit(data, distance)?,
weight: parameters.weight,
}
})
}
/// Predict the target 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 estimates.
pub fn predict<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let mut result = M::zeros(1, x.shape().0);
row_iter(x)
.enumerate()
.for_each(|(i, x)| result.set(0, i, self.predict_for_row(x)));
for (i, x) in row_iter(x).enumerate() {
result.set(0, i, self.predict_for_row(x)?);
}
result.to_row_vector()
Ok(result.to_row_vector())
}
fn predict_for_row(&self, x: Vec<T>) -> T {
let search_result = self.knn_algorithm.find(&x, self.k);
fn predict_for_row(&self, x: Vec<T>) -> Result<T, Failed> {
let search_result = self.knn_algorithm.find(&x, self.k)?;
let mut result = T::zero();
let weights = self
@@ -154,7 +156,7 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
result = result + self.y[r.0] * (*w / w_sum);
}
result
Ok(result)
}
}
@@ -179,8 +181,9 @@ mod tests {
algorithm: KNNAlgorithmName::LinearSearch,
weight: KNNWeightFunction::Distance,
},
);
let y_hat = knn.predict(&x);
)
.unwrap();
let y_hat = knn.predict(&x).unwrap();
assert_eq!(5, Vec::len(&y_hat));
for i in 0..y_hat.len() {
assert!((y_hat[i] - y_exp[i]).abs() < std::f64::EPSILON);
@@ -193,8 +196,8 @@ mod tests {
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
let y_exp = vec![2., 2., 3., 4., 4.];
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());
let y_hat = knn.predict(&x);
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
let y_hat = knn.predict(&x).unwrap();
assert_eq!(5, Vec::len(&y_hat));
for i in 0..y_hat.len() {
assert!((y_hat[i] - y_exp[i]).abs() < 1e-7);
@@ -207,7 +210,7 @@ mod tests {
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![1., 2., 3., 4., 5.];
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default()).unwrap();
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
src/neighbors/mod.rs
+7 -4
View File
@@ -34,6 +34,7 @@
use crate::algorithm::neighbour::cover_tree::CoverTree;
use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
use crate::error::Failed;
use crate::math::distance::Distance;
use crate::math::num::RealNumber;
use serde::{Deserialize, Serialize};
@@ -93,18 +94,20 @@ impl KNNAlgorithmName {
&self,
data: Vec<Vec<T>>,
distance: D,
) -> KNNAlgorithm<T, D> {
) -> Result<KNNAlgorithm<T, D>, Failed> {
match *self {
KNNAlgorithmName::LinearSearch => {
KNNAlgorithm::LinearSearch(LinearKNNSearch::new(data, distance))
LinearKNNSearch::new(data, distance).map(|a| KNNAlgorithm::LinearSearch(a))
}
KNNAlgorithmName::CoverTree => {
CoverTree::new(data, distance).map(|a| KNNAlgorithm::CoverTree(a))
}
KNNAlgorithmName::CoverTree => KNNAlgorithm::CoverTree(CoverTree::new(data, distance)),
}
}
}
impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNAlgorithm<T, D> {
fn find(&self, from: &Vec<T>, k: usize) -> Vec<(usize, T)> {
fn find(&self, from: &Vec<T>, k: usize) -> Result<Vec<(usize, T)>, Failed> {
match *self {
KNNAlgorithm::LinearSearch(ref linear) => linear.find(from, k),
KNNAlgorithm::CoverTree(ref cover) => cover.find(from, k),