Add another pairwise distance algorithm

* Handle kernel serialization
* Do not use typetag in WASM
* enable tests for serialization
* Update serde feature deps

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
Co-authored-by: Lorenzo <tunedconsulting@gmail.com>

.github/workflows/ci.yml

@@ -19,13 +19,14 @@ jobs:
             { os: "ubuntu", target: "i686-unknown-linux-gnu" },
             { os: "ubuntu", target: "wasm32-unknown-unknown" },
             { os: "macos", target: "aarch64-apple-darwin" },
+            { os: "ubuntu", target: "wasm32-wasi" },
           ]
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - name: Cache .cargo and target
-        uses: actions/cache@v4
+        uses: actions/cache@v2
         with:
           path: |
             ~/.cargo
@@ -35,13 +36,16 @@ jobs:
       - name: Install Rust toolchain
         uses: actions-rs/toolchain@v1
         with:
-          toolchain: stable
+          toolchain: 1.81 # 1.82 seems to break wasm32 tests https://github.com/rustwasm/wasm-bindgen/issues/4274
           target: ${{ matrix.platform.target }}
           profile: minimal
           default: true
       - name: Install test runner for wasm
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
+      - name: Install test runner for wasi
+        if: matrix.platform.target == 'wasm32-wasi'
+        run: curl https://wasmtime.dev/install.sh -sSf | bash
       - name: Stable Build with all features
         uses: actions-rs/cargo@v1
         with:
@@ -61,6 +65,12 @@ jobs:
       - name: Tests in WASM
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: wasm-pack test --node -- --all-features
+      - name: Tests in WASI
+        if: matrix.platform.target == 'wasm32-wasi'
+        run: |
+          export WASMTIME_HOME="$HOME/.wasmtime"
+          export PATH="$WASMTIME_HOME/bin:$PATH"
+          cargo install cargo-wasi && cargo wasi test
 
   check_features:
     runs-on: "${{ matrix.platform.os }}-latest"
@@ -71,9 +81,9 @@ jobs:
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - name: Cache .cargo and target
-        uses: actions/cache@v4
+        uses: actions/cache@v2
         with:
           path: |
             ~/.cargo

.github/workflows/coverage.yml

@@ -12,9 +12,9 @@ jobs:
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v2
       - name: Cache .cargo
-        uses: actions/cache@v4
+        uses: actions/cache@v2
         with:
           path: |
             ~/.cargo

.github/workflows/lint.yml

@@ -14,7 +14,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache .cargo and target
-        uses: actions/cache@v4
+        uses: actions/cache@v2
         with:
           path: |
             ~/.cargo

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.1"
+version = "0.4.0"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"

README.md

@@ -18,4 +18,4 @@
 -----
 [![CI](https://github.com/smartcorelib/smartcore/actions/workflows/ci.yml/badge.svg)](https://github.com/smartcorelib/smartcore/actions/workflows/ci.yml)
 
-To start getting familiar with the new smartcore v0.4 API, there is now available a [**Jupyter Notebook environment repository**](https://github.com/smartcorelib/smartcore-jupyter). Please see instructions there, contributions welcome see [CONTRIBUTING](.github/CONTRIBUTING.md).
+To start getting familiar with the new smartcore v0.3 API, there is now available a [**Jupyter Notebook environment repository**](https://github.com/smartcorelib/smartcore-jupyter). Please see instructions there, contributions welcome see [CONTRIBUTING](.github/CONTRIBUTING.md).

src/algorithm/neighbour/eppstein.rs

@@ -0,0 +1,219 @@
+//! This module provides FastPair, a data-structure for efficiently tracking the dynamic
+//! closest pairs in a set of points, with an example usage in hierarchical clustering.[2][3][5]
+//!
+//! ## Purpose
+//!
+//! FastPair allows quick retrieval of the nearest neighbor for each data point by maintaining
+//! a "conga line" of closest pairs. Each point retains a link to its known nearest neighbor,
+//! and updates in the data structure propagate accordingly. This can be leveraged in
+//! agglomerative clustering steps, where merging or insertion of new points must be reflected
+//! in nearest-neighbor relationships.
+//!
+//! ## Example
+//!
+//! ```
+//! use smartcore::metrics::distance::PairwiseDistance;
+//! use smartcore::linalg::basic::matrix::DenseMatrix;
+//! use smartcore::algorithm::neighbour::fastpair::FastPair;
+//!
+//! let x = DenseMatrix::from_2d_array(&[
+//!     &[5.1, 3.5, 1.4, 0.2],
+//!     &[4.9, 3.0, 1.4, 0.2],
+//!     &[4.7, 3.2, 1.3, 0.2],
+//!     &[4.6, 3.1, 1.5, 0.2],
+//!     &[5.0, 3.6, 1.4, 0.2],
+//!     &[5.4, 3.9, 1.7, 0.4],
+//! ]).unwrap();
+//!
+//! let fastpair = FastPair::new(&x).unwrap();
+//! let closest = fastpair.closest_pair();
+//! println!("Closest pair: {:?}", closest);
+//! ```
+use std::collections::HashMap;
+
+use num::Bounded;
+
+use crate::error::{Failed, FailedError};
+use crate::linalg::basic::arrays::{Array, Array1, Array2};
+use crate::metrics::distance::euclidian::Euclidian;
+use crate::metrics::distance::PairwiseDistance;
+use crate::numbers::floatnum::FloatNumber;
+use crate::numbers::realnum::RealNumber;
+
+/// Eppstein dynamic closet-pair structure
+/// 'M' can be a matrix-like trait that provides row access
+#[derive(Debug)]
+pub struct EppsteinDCP<'a, T: RealNumber + FloatNumber, M: Array2<T>> {
+    samples: &'a M,
+    // "buckets" store, for each row, a small structure recording potential neighbors
+    neighbors: HashMap<usize, PairwiseDistance<T>>,
+}
+
+impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> EppsteinDCP<'a, T, M> {
+    /// Creates a new EppsteinDCP instance with the given data
+    pub fn new(m: &'a M) -> Result<Self, Failed> {
+        if m.shape().0 < 3 {
+            return Err(Failed::because(
+                FailedError::FindFailed,
+                "min number of rows should be 3",
+            ));
+        }
+
+        let mut this = Self {
+            samples: m,
+            neighbors: HashMap::with_capacity(m.shape().0),
+        };
+        this.initialize();
+        Ok(this)
+    }
+
+    /// Build an initial "conga line" or chain of potential neighbors
+    /// akin to Eppstein’s technique[2].
+    fn initialize(&mut self) {
+        let n = self.samples.shape().0;
+        if n < 2 {
+            return;
+        }
+        // Assign each row i some large distance by default
+        for i in 0..n {
+            self.neighbors.insert(
+                i,
+                PairwiseDistance {
+                    node: i,
+                    neighbour: None,
+                    distance: Some(<T as Bounded>::max_value()),
+                },
+            );
+        }
+        // Example: link each i to the next, forming a chain
+        // (depending on the actual Eppstein approach, can refine)
+        for i in 0..(n - 1) {
+            let dist = self.compute_dist(i, i + 1);
+            self.neighbors.entry(i).and_modify(|pd| {
+                pd.neighbour = Some(i + 1);
+                pd.distance = Some(dist);
+            });
+        }
+        // Potential refinement steps omitted for brevity
+    }
+
+    /// Insert a point into the structure.
+    pub fn insert(&mut self, row_idx: usize) {
+        // Expand data, find neighbor to link with
+        // For example, link row_idx to nearest among existing
+        let mut best_neighbor = None;
+        let mut best_d = <T as Bounded>::max_value();
+        for (i, _) in &self.neighbors {
+            let d = self.compute_dist(*i, row_idx);
+            if d < best_d {
+                best_d = d;
+                best_neighbor = Some(*i);
+            }
+        }
+        self.neighbors.insert(
+            row_idx,
+            PairwiseDistance {
+                node: row_idx,
+                neighbour: best_neighbor,
+                distance: Some(best_d),
+            },
+        );
+        // For the best_neighbor, you might want to see if row_idx becomes closer
+        if let Some(kn) = best_neighbor {
+            let dist = self.compute_dist(row_idx, kn);
+            let entry = self.neighbors.get_mut(&kn).unwrap();
+            if dist < entry.distance.unwrap() {
+                entry.neighbour = Some(row_idx);
+                entry.distance = Some(dist);
+            }
+        }
+    }
+
+    /// For hierarchical clustering, discover minimal pairs, then merge
+    pub fn closest_pair(&self) -> Option<PairwiseDistance<T>> {
+        let mut min_pair: Option<PairwiseDistance<T>> = None;
+        for (_, pd) in &self.neighbors {
+            if let Some(d) = pd.distance {
+                if min_pair.is_none() || d < min_pair.as_ref().unwrap().distance.unwrap() {
+                    min_pair = Some(pd.clone());
+                }
+            }
+        }
+        min_pair
+    }
+
+    fn compute_dist(&self, i: usize, j: usize) -> T {
+        // Example: Euclidean
+        let row_i = self.samples.get_row(i);
+        let row_j = self.samples.get_row(j);
+        row_i
+            .iterator(0)
+            .zip(row_j.iterator(0))
+            .map(|(a, b)| (*a - *b) * (*a - *b))
+            .sum()
+    }
+}
+
+/// Simple usage
+#[cfg(test)]
+mod tests_eppstein {
+    use super::*;
+    use crate::linalg::basic::matrix::DenseMatrix;
+
+    #[test]
+    fn test_eppstein() {
+        let matrix =
+            DenseMatrix::from_2d_array(&[&vec![1.0, 2.0], &vec![2.0, 2.0], &vec![5.0, 3.0]])
+                .unwrap();
+        let mut dcp = EppsteinDCP::new(&matrix).unwrap();
+        dcp.insert(2);
+        let cp = dcp.closest_pair();
+        assert!(cp.is_some());
+    }
+
+    #[test]
+    fn compare_fastpair_eppstein() {
+        use crate::algorithm::neighbour::fastpair::FastPair;
+        // Assuming EppsteinDCP is implemented in a similar module
+        use crate::algorithm::neighbour::eppstein::EppsteinDCP;
+
+        // Create a static example matrix
+        let x = DenseMatrix::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+        ])
+        .unwrap();
+
+        // Build FastPair
+        let fastpair = FastPair::new(&x).unwrap();
+        let pair_fastpair = fastpair.closest_pair();
+
+        // Build EppsteinDCP
+        let eppstein = EppsteinDCP::new(&x).unwrap();
+        let pair_eppstein = eppstein.closest_pair();
+
+        // Compare the results
+        assert_eq!(pair_fastpair.node, pair_eppstein.as_ref().unwrap().node);
+        assert_eq!(
+            pair_fastpair.neighbour.unwrap(),
+            pair_eppstein.as_ref().unwrap().neighbour.unwrap()
+        );
+
+        // Use a small epsilon for floating-point comparison
+        let epsilon = 1e-9;
+        let diff: f64 =
+            pair_fastpair.distance.unwrap() - pair_eppstein.as_ref().unwrap().distance.unwrap();
+        assert!(diff.abs() < epsilon);
+
+        println!("FastPair result: {:?}", pair_fastpair);
+        println!("EppsteinDCP result: {:?}", pair_eppstein);
+    }
+}

src/algorithm/neighbour/mod.rs

@@ -41,7 +41,9 @@ use serde::{Deserialize, Serialize};
 pub(crate) mod bbd_tree;
 /// tree data structure for fast nearest neighbor search
 pub mod cover_tree;
-/// fastpair closest neighbour algorithm
+/// eppstein pairwise closest neighbour algorithm
+pub mod eppstein;
+/// fastpair pairwise closest neighbour algorithm
 pub mod fastpair;
 /// very simple algorithm that sequentially checks each element of the list until a match is found or the whole list has been searched.
 pub mod linear_search;

src/linalg/basic/matrix.rs

@@ -663,7 +663,6 @@ mod tests {
     #[test]
     fn test_instantiate_err_view3() {
         let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        #[allow(clippy::reversed_empty_ranges)]
         let v = DenseMatrixView::new(&x, 0..3, 4..3);
         assert!(v.is_err());
     }

src/naive_bayes/bernoulli.rs

@@ -257,7 +257,8 @@ 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.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
+    ///     priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     /// * `binarize` - Threshold for binarizing.
     fn fit<TX: Number + PartialOrd, X: Array2<TX>, Y: Array1<TY>>(

src/naive_bayes/gaussian.rs

@@ -174,7 +174,8 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<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.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
+    ///     priors are adjusted according to the data.
     pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
         y: &Y,

src/naive_bayes/multinomial.rs

@@ -207,7 +207,8 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<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.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
+    ///     priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     pub fn fit<TX: Number + Unsigned, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,

src/preprocessing/categorical.rs

@@ -24,7 +24,7 @@
 //! //    &[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0]
 //! //    &[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0]
 //! ```
-use std::iter::repeat_n;
+use std::iter;
 
 use crate::error::Failed;
 use crate::linalg::basic::arrays::Array2;
@@ -75,7 +75,11 @@ fn find_new_idxs(num_params: usize, cat_sizes: &[usize], cat_idxs: &[usize]) ->
     let offset = (0..1).chain(offset_);
 
     let new_param_idxs: Vec<usize> = (0..num_params)
-        .zip(repeats.zip(offset).flat_map(|(r, o)| repeat_n(o, r)))
+        .zip(
+            repeats
+                .zip(offset)
+                .flat_map(|(r, o)| iter::repeat(o).take(r)),
+        )
         .map(|(idx, ofst)| idx + ofst)
         .collect();
     new_param_idxs
@@ -120,7 +124,7 @@ impl OneHotEncoder {
                 let (nrows, _) = data.shape();
 
                 // col buffer to avoid allocations
-                let mut col_buf: Vec<T> = repeat_n(T::zero(), nrows).collect();
+                let mut col_buf: Vec<T> = iter::repeat(T::zero()).take(nrows).collect();
 
                 let mut res: Vec<CategoryMapper<CategoricalFloat>> = Vec::with_capacity(idxs.len());