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fix: code cleanup, documentation

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This commit is contained in:
Volodymyr Orlov
2020-08-27 11:37:14 -07:00
parent 7a7b0d6875
commit aa458d22fa
10 changed files with 147 additions and 64 deletions
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.gitignore
+1
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@@ -13,6 +13,7 @@ Cargo.lock
.idea
.project
.vscode
smartcore.code-workspace
# OS
.DS_Store
Cargo.toml
+2 -2
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@@ -1,12 +1,12 @@
[package]
name = "smartcore"
version = "0.1.0"
authors = ["Vlad Orlov"]
authors = ["SmartCore Developers"]
edition = "2018"
[dependencies]
ndarray = "0.13"
nalgebra = "0.21.1"
nalgebra = "0.22.0"
num-traits = "0.2.12"
num = "0.3.0"
rand = "0.7.3"
src/algorithm/neighbour/cover_tree.rs
+2 -2
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@@ -66,7 +66,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D> {
}
}
pub fn new_node(&mut self, parent: Option<NodeId>, data: T) -> NodeId {
fn new_node(&mut self, parent: Option<NodeId>, data: T) -> NodeId {
let next_index = self.nodes.len();
let node_id = NodeId { index: next_index };
self.nodes.push(Node {
@@ -300,7 +300,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D> {
}
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub struct NodeId {
struct NodeId {
index: usize,
}
src/algorithm/neighbour/mod.rs
+1 -1
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@@ -1,3 +1,3 @@
pub mod bbd_tree;
pub(crate) mod bbd_tree;
pub mod cover_tree;
pub mod linear_search;
src/common/mod.rs
-18
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@@ -1,18 +0,0 @@
use ndarray::ScalarOperand;
use num_traits::{FromPrimitive, Num, One, ToPrimitive, Zero};
use std::fmt::Debug;
use std::hash::Hash;
pub trait AnyNumber: Num + ScalarOperand + ToPrimitive + FromPrimitive {}
pub trait Nominal:
PartialEq + Zero + One + Eq + Hash + ToPrimitive + FromPrimitive + Debug + 'static + Clone
{
}
impl<T> AnyNumber for T where T: Num + ScalarOperand + ToPrimitive + FromPrimitive {}
impl<T> Nominal for T where
T: PartialEq + Zero + One + Eq + Hash + ToPrimitive + Debug + FromPrimitive + 'static + Clone
{
}
src/lib.rs
+77 -2
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@@ -1,12 +1,87 @@
#![warn(missing_docs)]
#![warn(missing_doc_code_examples)]
//! # SmartCore
//!
//! Welcome to SmartCore library, the most complete machine learning library for Rust!
//!
//! In SmartCore you will find implementation of these ML algorithms:
//! * Regression: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor
//! * Classification: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Unsupervised Nearest Neighbors (KNN)
//! * Clustering: K-Means
//! * Matrix decomposition: PCA, LU, QR, SVD, EVD
//! * Distance Metrics: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis
//! * Evaluation Metrics: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2
//!
//! Most of algorithms implemented in SmartCore operate on n-dimentional arrays. While you can use Rust vectors with all functions defined in this library
//! we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages:
//! * [ndarray](https://docs.rs/ndarray)
//! * [nalgebra](https://docs.rs/nalgebra/)
//!
//! ## Getting Started
//!
//! To start using SmartCore simply add the following to your Cargo.toml file:
//! ```ignore
//! [dependencies]
//! smartcore = "0.1.0"
//! ```
//!
//! All ML algorithms in SmartCore are grouped into these generic categories:
//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition.
//! * [Linear Models](linear/index.html), regression and classification methods where output is assumed to have linear relation to explanatory variables
//! * [Ensemble Models](ensemble/index.html), variety of regression and classification ensemble models
//! * [Tree-based Models](tree/index.html), classification and regression trees
//! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression
//!
//! Each category is assigned to a separate module.
//!
//! For example, KNN classifier is defined in [smartcore::neighbors::knn](neighbors/knn/index.html). To train and run it using standard Rust vectors you will
//! run this code:
//!
//! ```
//! // DenseMatrix defenition
//! use smartcore::linalg::naive::dense_matrix::*;
//! // KNNClassifier
//! use smartcore::neighbors::knn::*;
//! // Various distance metrics
//! use smartcore::math::distance::*;
//!
//! // Turn Rust vectors with samples into a matrix
//! let x = DenseMatrix::from_array(&[
//! &[1., 2.],
//! &[3., 4.],
//! &[5., 6.],
//! &[7., 8.],
//! &[9., 10.]]);
//! // Our classes are defined as a Vector
//! let y = vec![2., 2., 2., 3., 3.];
//!
//! // Train classifier
//! let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
//!
//! // Predict classes
//! let y_hat = knn.predict(&x);
//! ```
/// Various algorithms and helper methods that are used elsewhere in SmartCore
pub mod algorithm;
/// Algorithms for clustering of unlabeled data
pub mod cluster;
pub mod common;
/// Matrix decomposition algorithms
pub mod decomposition;
/// Ensemble methods, including Random Forest classifier and regressor
pub mod ensemble;
/// Diverse collection of linear algebra abstractions and methods that power SmartCore algorithms
pub mod linalg;
/// Supervised classification and regression models that assume linear relationship between dependent and explanatory variables.
pub mod linear;
/// Multitude of helper methods and classes, including definitions of distance metrics
pub mod math;
/// Functions for assessing prediction error.
pub mod metrics;
/// Supervised neighbors-based learning methods
pub mod neighbors;
pub mod optimization;
pub(crate) mod optimization;
/// Supervised tree-based learning methods
pub mod tree;
src/linear/linear_regression.rs
+25 -12
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@@ -6,16 +6,29 @@ use crate::linalg::Matrix;
use crate::math::num::FloatExt;
#[derive(Serialize, Deserialize, Debug)]
pub enum LinearRegressionSolver {
pub enum LinearRegressionSolverName {
QR,
SVD,
}
#[derive(Serialize, Deserialize, Debug)]
pub struct LinearRegressionParameters {
solver: LinearRegressionSolverName,
}
#[derive(Serialize, Deserialize, Debug)]
pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
coefficients: M,
intercept: T,
solver: LinearRegressionSolver,
solver: LinearRegressionSolverName,
}
impl Default for LinearRegressionParameters {
fn default() -> Self {
LinearRegressionParameters {
solver: LinearRegressionSolverName::SVD
}
}
}
impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
@@ -26,7 +39,7 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
}
impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
pub fn fit(x: &M, y: &M::RowVector, solver: LinearRegressionSolver) -> LinearRegression<T, M> {
pub fn fit(x: &M, y: &M::RowVector, parameters: LinearRegressionParameters) -> LinearRegression<T, M> {
let y_m = M::from_row_vector(y.clone());
let b = y_m.transpose();
let (x_nrows, num_attributes) = x.shape();
@@ -38,9 +51,9 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
let a = x.v_stack(&M::ones(x_nrows, 1));
let w = match solver {
LinearRegressionSolver::QR => a.qr_solve_mut(b),
LinearRegressionSolver::SVD => a.svd_solve_mut(b),
let w = match parameters.solver {
LinearRegressionSolverName::QR => a.qr_solve_mut(b),
LinearRegressionSolverName::SVD => a.svd_solve_mut(b),
};
let wights = w.slice(0..num_attributes, 0..1);
@@ -48,7 +61,7 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
LinearRegression {
intercept: w.get(num_attributes, 0),
coefficients: wights,
solver: solver,
solver: parameters.solver,
}
}
@@ -90,9 +103,9 @@ mod tests {
114.2, 115.7, 116.9,
]);
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
assert!(y
.iter()
@@ -130,9 +143,9 @@ mod tests {
114.2, 115.7, 116.9,
];
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
assert!(y
.iter()
@@ -170,7 +183,7 @@ mod tests {
114.2, 115.7, 116.9,
];
let lr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR);
let lr = LinearRegression::fit(&x, &y, Default::default());
let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> =
serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
src/math/mod.rs
+1 -1
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@@ -1,2 +1,2 @@
pub mod distance;
pub mod num;
pub(crate) mod num;
src/neighbors/knn.rs
+36 -23
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@@ -7,44 +7,60 @@ use crate::math::distance::Distance;
use crate::math::num::FloatExt;
#[derive(Serialize, Deserialize, Debug)]
pub struct KNNClassifier<T: FloatExt, D: Distance<Vec<T>, T>> {
classes: Vec<T>,
y: Vec<usize>,
knn_algorithm: KNNAlgorithmV<T, D>,
k: usize,
}
pub enum KNNAlgorithmName {
LinearSearch,
CoverTree,
}
#[derive(Serialize, Deserialize, Debug)]
pub enum KNNAlgorithmV<T: FloatExt, D: Distance<Vec<T>, T>> {
pub struct KNNClassifierParameters {
pub algorithm: KNNAlgorithmName,
pub k: usize
}
#[derive(Serialize, Deserialize, Debug)]
pub struct KNNClassifier<T: FloatExt, D: Distance<Vec<T>, T>> {
classes: Vec<T>,
y: Vec<usize>,
knn_algorithm: KNNAlgorithm<T, D>,
k: usize,
}
#[derive(Serialize, Deserialize, Debug)]
enum KNNAlgorithm<T: FloatExt, D: Distance<Vec<T>, T>> {
LinearSearch(LinearKNNSearch<Vec<T>, T, D>),
CoverTree(CoverTree<Vec<T>, T, D>),
}
impl Default for KNNClassifierParameters {
fn default() -> Self {
KNNClassifierParameters {
algorithm: KNNAlgorithmName::CoverTree,
k: 3
}
}
}
impl KNNAlgorithmName {
fn fit<T: FloatExt, D: Distance<Vec<T>, T>>(
&self,
data: Vec<Vec<T>>,
distance: D,
) -> KNNAlgorithmV<T, D> {
) -> KNNAlgorithm<T, D> {
match *self {
KNNAlgorithmName::LinearSearch => {
KNNAlgorithmV::LinearSearch(LinearKNNSearch::new(data, distance))
KNNAlgorithm::LinearSearch(LinearKNNSearch::new(data, distance))
}
KNNAlgorithmName::CoverTree => KNNAlgorithmV::CoverTree(CoverTree::new(data, distance)),
KNNAlgorithmName::CoverTree => KNNAlgorithm::CoverTree(CoverTree::new(data, distance)),
}
}
}
impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNAlgorithmV<T, D> {
impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNAlgorithm<T, D> {
fn find(&self, from: &Vec<T>, k: usize) -> Vec<usize> {
match *self {
KNNAlgorithmV::LinearSearch(ref linear) => linear.find(from, k),
KNNAlgorithmV::CoverTree(ref cover) => cover.find(from, k),
KNNAlgorithm::LinearSearch(ref linear) => linear.find(from, k),
KNNAlgorithm::CoverTree(ref cover) => cover.find(from, k),
}
}
}
@@ -76,9 +92,8 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
pub fn fit<M: Matrix<T>>(
x: &M,
y: &M::RowVector,
k: usize,
distance: D,
algorithm: KNNAlgorithmName,
parameters: KNNClassifierParameters
) -> KNNClassifier<T, D> {
let y_m = M::from_row_vector(y.clone());
@@ -103,13 +118,13 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
)
);
assert!(k > 1, format!("k should be > 1, k=[{}]", k));
assert!(parameters.k > 1, format!("k should be > 1, k=[{}]", parameters.k));
KNNClassifier {
classes: classes,
y: yi,
k: k,
knn_algorithm: algorithm.fit(data, distance),
k: parameters.k,
knn_algorithm: parameters.algorithm.fit(data, distance),
}
}
@@ -153,9 +168,8 @@ mod tests {
let knn = KNNClassifier::fit(
&x,
&y,
3,
Distances::euclidian(),
KNNAlgorithmName::LinearSearch,
KNNClassifierParameters{k: 3, algorithm: KNNAlgorithmName::LinearSearch}
);
let r = knn.predict(&x);
assert_eq!(5, Vec::len(&r));
@@ -170,9 +184,8 @@ mod tests {
let knn = KNNClassifier::fit(
&x,
&y,
3,
Distances::euclidian(),
KNNAlgorithmName::CoverTree,
Default::default()
);
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
src/optimization/mod.rs
-1
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@@ -6,7 +6,6 @@ pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
#[derive(Debug, PartialEq)]
pub enum FunctionOrder {
FIRST,
SECOND,
THIRD,
}