Add browser WASM feature support

.github/workflows/checking.yml

@@ -40,3 +40,24 @@ jobs:
 
       - name: Run cargo check (with serde)
         run: cargo check --workspace --all-targets --features "linfa-clustering/serde linfa-ica/serde linfa-kernel/serde linfa-reduction/serde linfa-svm/serde linfa-elasticnet/serde linfa-pls/serde linfa-trees/serde linfa-nn/serde linfa-linear/serde linfa-preprocessing/serde linfa-bayes/serde linfa-logistic/serde linfa-ftrl/serde"
+
+  check-wasm-browser:
+    name: check-wasm-browser
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout sources
+        uses: actions/checkout@master
+
+      - name: Install toolchain
+        uses: dtolnay/rust-toolchain@master
+        with:
+          toolchain: stable
+          targets: wasm32-unknown-unknown
+
+      - name: Run browser WASM checks
+        run: |
+          cargo check -p linfa --lib --target wasm32-unknown-unknown --features wasm-bindgen
+          cargo check -p linfa-linear --lib --target wasm32-unknown-unknown --features wasm-bindgen
+          cargo check -p linfa-logistic --lib --target wasm32-unknown-unknown --features wasm-bindgen
+          cargo check -p linfa-ftrl --lib --target wasm32-unknown-unknown --features wasm-bindgen

Cargo.toml

@@ -34,11 +34,13 @@ intel-mkl-system = ["blas", "ndarray-linalg/intel-mkl-system"]
 blas = ["ndarray/blas"]
 
 serde = ["serde_crate", "ndarray/serde"]
+wasm-bindgen = ["dep:getrandom"]
 
 [dependencies]
 num-traits = "0.2"
 rand = { version = "0.8", features = ["small_rng"] }
 approx = "0.5"
+getrandom = { version = "0.2", optional = true, features = ["js"] }
 
 ndarray = { version = "0.16", features = ["approx"] }
 ndarray-linalg = { version = "0.17", optional = true }
@@ -75,6 +77,7 @@ pprof = { version = "0.15", features = [
 
 [workspace]
 members = ["algorithms/*", "datasets"]
+resolver = "2"
 
 [profile.release]
 opt-level = 3

README.md

@@ -53,6 +53,10 @@ We believe that only a significant community effort can nurture, build, and sust
 
 If this strikes a chord with you, please take a look at the [roadmap](https://github.com/rust-ml/linfa/issues/7) and get involved!
 
+## Browser WASM
+
+For browser-style WASM on `wasm32-unknown-unknown`, enable `linfa`'s `wasm-bindgen` feature.
+
 ## BLAS/Lapack backend
 
 Some algorithm crates need to use an external library for linear algebra routines. By default, we use a pure-Rust implementation. However, you can also choose an external BLAS/LAPACK backend library instead, by enabling the `blas` feature and a feature corresponding to your BLAS backend. Currently you can choose between the following BLAS/LAPACK backends: `openblas`, `netblas` or `intel-mkl`.

algorithms/linfa-clustering/src/dbscan/algorithm.rs

@@ -131,7 +131,7 @@ impl<F: Float, D: Data<Elem = F>, DF: Distance<F>, N: NearestNeighbour>
                 continue;
             }
             neighbors.iter().for_each(|&n| search_found[n] = true);
-            search_queue.extend(neighbors.into_iter());
+            search_queue.extend(neighbors);
 
             // Now go over the neighbours adding them to the cluster
             cluster_memberships[i] = Some(current_cluster_id);

algorithms/linfa-clustering/src/optics/algorithm.rs

@@ -198,15 +198,15 @@ impl<F: Float, D: Distance<F>, N: NearestNeighbour>
                 index += 1;
                 continue;
             }
-            let mut expected = if processed.is_empty() { 0 } else { index };
             let mut points_index = index;
             // Look for next point to process starting from lowest possible unprocessed index
-            for index in processed.range(index..) {
+            for (expected, index) in
+                (if processed.is_empty() { 0 } else { index }..).zip(processed.range(index..))
+            {
                 if expected != *index {
                     points_index = expected;
                     break;
                 }
-                expected += 1;
             }
             index += 1;
             let neighbors = self.find_neighbors(&*nn, observations.row(points_index));

algorithms/linfa-ftrl/Cargo.toml

@@ -15,7 +15,7 @@ categories = ["algorithms", "mathematics", "science"]
 
 [features]
 serde = ["serde_crate", "linfa/serde", "ndarray/serde", "argmin/serde1"]
-wasm-bindgen = ["argmin/wasm-bindgen"]
+wasm-bindgen = ["argmin/wasm-bindgen", "linfa/wasm-bindgen"]
 
 [dependencies.serde_crate]
 package = "serde"

algorithms/linfa-hierarchical/examples/irisflower.rs

@@ -16,7 +16,7 @@ fn main() -> Result<(), Box<dyn Error>> {
         .num_clusters(3)
         .transform(kernel)?;
 
-    for (id, target) in kernel.targets().iter().zip(dataset.targets().into_iter()) {
+    for (id, target) in kernel.targets().iter().zip(dataset.targets()) {
         let name = match *target {
             0 => "setosa",
             1 => "versicolor",

algorithms/linfa-hierarchical/src/lib.rs

@@ -141,10 +141,7 @@ impl<F: Float> Transformer<Kernel<F>, DatasetBase<Kernel<F>, Vec<usize>>>
             .map(|x| (x, vec![x]))
             .collect::<HashMap<_, _>>();
 
-        // counter for new clusters, which are formed as unions of previous ones
-        let mut ct = num_observations;
-
-        for step in res.steps() {
+        for (ct, step) in (num_observations..).zip(res.steps().iter()) {
             let should_stop = match self.stopping {
                 Criterion::NumClusters(max_clusters) => clusters.len() <= max_clusters,
                 Criterion::Distance(dis) => step.dissimilarity >= dis,
@@ -164,7 +161,6 @@ impl<F: Float> Transformer<Kernel<F>, DatasetBase<Kernel<F>, Vec<usize>>>
 
             // insert into hashmap and increase counter
             clusters.insert(ct, ids);
-            ct += 1;
         }
 
         // flatten resulting clusters and reverse index

algorithms/linfa-linear/Cargo.toml

@@ -19,7 +19,7 @@ categories = ["algorithms", "mathematics", "science"]
 [features]
 blas = ["ndarray-linalg", "linfa/ndarray-linalg"]
 serde = ["serde_crate", "linfa/serde", "ndarray/serde", "argmin/serde1"]
-wasm-bindgen = ["argmin/wasm-bindgen"]
+wasm-bindgen = ["argmin/wasm-bindgen", "linfa/wasm-bindgen"]
 
 [dependencies.serde_crate]
 package = "serde"

algorithms/linfa-logistic/Cargo.toml

@@ -15,7 +15,7 @@ categories = ["algorithms", "mathematics", "science"]
 
 [features]
 serde = ["serde_crate", "linfa/serde", "ndarray/serde", "argmin/serde1"]
-wasm-bindgen = ["argmin/wasm-bindgen"]
+wasm-bindgen = ["argmin/wasm-bindgen", "linfa/wasm-bindgen"]
 
 [dependencies.serde_crate]
 package = "serde"

datasets/src/dataset.rs

@@ -2,6 +2,12 @@ use std::io::Read;
 
 use csv::ReaderBuilder;
 use flate2::read::GzDecoder;
+#[cfg(any(
+    feature = "iris",
+    feature = "diabetes",
+    feature = "winequality",
+    feature = "linnerud"
+))]
 use linfa::Dataset;
 use ndarray::prelude::*;
 use ndarray_csv::{Array2Reader, ReadError};

src/composing/multi_class_model.rs

@@ -46,7 +46,7 @@ impl<L: Clone + Default, F, D: Data<Elem = F>> PredictInplace<ArrayBase<D, Ix2>,
             // if probability is higher
             res = res
                 .into_iter()
-                .zip(pairs.into_iter())
+                .zip(pairs)
                 .map(|(c, d)| if d.1 > c.1 { d } else { c })
                 .collect();
         }