Upgraded sodlib performance

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "single_algebra"
-version = "0.7.0"
+version = "0.8.3"
 edition = "2021"
 license-file = "LICENSE.md"
 description = "A linear algebra convenience library for the single-rust library. Can be used externally as well."
@@ -37,7 +37,7 @@ simba = { version = "0.9.0", optional = true }
 smartcore = { version = "0.4", features = ["ndarray-bindings"], optional = true }
 single-svdlib = { version = "1.0.4" }
 rand = "0.9.0"
-single-utilities = "0.6.0"
+single-utilities = "0.7.0"
 
 [dev-dependencies]
 criterion = "0.5.1"

src/sparse/csc.rs

@@ -2,11 +2,10 @@ use nalgebra_sparse::CscMatrix;
 use num_traits::{Float, NumCast, PrimInt, Unsigned, Zero};
 use single_utilities::types::Direction;
 use std::collections::{HashMap, HashSet};
-use std::hash::Hash;
 use std::iter::Sum;
-use std::ops::Add;
 use std::ops::AddAssign;
 
+use crate::sparse::MatrixNTop;
 use crate::utils::Normalize;
 
 use super::{
@@ -359,8 +358,8 @@ where
 
     fn var_col<I, T>(&self) -> anyhow::Result<Vec<T>>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + std::iter::Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + std::iter::Sum + Send + Sync,
         Self::Item: NumCast,
     {
         let sum: Vec<T> = self.sum_col()?;
@@ -385,8 +384,8 @@ where
 
     fn var_row<I, T>(&self) -> anyhow::Result<Vec<T>>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + std::iter::Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + std::iter::Sum + Send + Sync,
         Self::Item: NumCast,
     {
         let sum: Vec<T> = self.sum_row()?;
@@ -410,8 +409,8 @@ where
 
     fn var_col_chunk<I, T>(&self, reference: &mut [T]) -> anyhow::Result<()>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + std::iter::Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + std::iter::Sum + Send + Sync,
         Self::Item: NumCast,
     {
         // Validate input slice length matches number of columns
@@ -449,8 +448,8 @@ where
 
     fn var_row_chunk<I, T>(&self, reference: &mut [T]) -> anyhow::Result<()>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + std::iter::Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + std::iter::Sum + Send + Sync,
         Self::Item: NumCast,
     {
         // Validate input slice length matches number of rows
@@ -488,8 +487,8 @@ where
 
     fn var_col_masked<I, T>(&self, mask: &[bool]) -> anyhow::Result<Vec<T>>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + Sum + Send + Sync,
     {
         // Validate mask length
         if mask.len() < self.nrows() {
@@ -537,8 +536,8 @@ where
 
     fn var_row_masked<I, T>(&self, mask: &[bool]) -> anyhow::Result<Vec<T>>
     where
-        I: PrimInt + Unsigned + Zero + AddAssign + Into<T>,
-        T: Float + NumCast + AddAssign + Sum,
+        I: PrimInt + Unsigned + Zero + AddAssign + Into<T> + Send + Sync,
+        T: Float + NumCast + AddAssign + Sum + Send + Sync
     {
         // Validate mask length
         if mask.len() < self.ncols() {
@@ -590,7 +589,7 @@ impl<M: NumCast + Copy + PartialOrd + NumericOps> MatrixMinMax for CscMatrix<M>
 
     fn min_max_col<Item>(&self) -> anyhow::Result<(Vec<Item>, Vec<Item>)>
     where
-        Item: NumCast + Copy + PartialOrd + NumericOps,
+        Item: NumCast + Copy + PartialOrd + NumericOps + Send + Sync,
     {
         let mut min: Vec<Item> = vec![Item::max_value(); self.ncols()];
         let mut max: Vec<Item> = vec![Item::min_value(); self.ncols()];
@@ -601,7 +600,7 @@ impl<M: NumCast + Copy + PartialOrd + NumericOps> MatrixMinMax for CscMatrix<M>
 
     fn min_max_row<Item>(&self) -> anyhow::Result<(Vec<Item>, Vec<Item>)>
     where
-        Item: NumCast + Copy + PartialOrd + NumericOps,
+        Item: NumCast + Copy + PartialOrd + NumericOps + Send + Sync,
     {
         let mut min: Vec<Item> = vec![Item::max_value(); self.nrows()];
         let mut max: Vec<Item> = vec![Item::min_value(); self.nrows()];
@@ -1027,6 +1026,41 @@ impl<M: NumericOps + NumCast> BatchMatrixMean for CscMatrix<M> {
     }
 }
 
+impl<M: NumericOps + NumCast> MatrixNTop for CscMatrix<M> {
+    type Item = M;
+
+    fn sum_row_n_top<T>(&self, n: usize) -> anyhow::Result<Vec<T>>
+    where
+        T: Float + NumCast + AddAssign + Sum {
+        let mut result = vec![T::zero(); self.nrows()];
+
+        let mut row_values: Vec<Vec<T>> = vec![Vec::new(); self.nrows()];
+
+        for col_idx in 0..self.ncols() {
+            let col_start = self.col_offsets()[col_idx];
+            let col_end = self.col_offsets()[col_idx + 1];
+
+            for idx in col_start..col_end {
+                let row_idx = self.row_indices()[idx];
+                if let Some(val) = T::from(self.values()[idx]) {
+                    row_values[row_idx].push(val);
+                }
+            }
+        }
+
+        for (row_idx, mut values) in row_values.into_iter().enumerate() {
+            if values.len() <= n {
+                result[row_idx] = values.into_iter().sum();
+            } else {
+                values.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
+                result[row_idx] = values.into_iter().take(n).sum();
+            }
+        }
+
+        Ok(result)
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use Direction;