add benchmark

algorithms/linfa-logistic/Cargo.toml

@@ -39,3 +39,9 @@ linfa-datasets = { version = "0.7.0", path = "../../datasets", features = [
     "winequality",
 ] }
 rmp-serde = "1"
+criterion = "0.4.0"
+rand = "0.8.5"
+
+[[bench]]
+name = "logistic_bench"
+harness = false

algorithms/linfa-logistic/benches/logistic_bench.rs

@@ -0,0 +1,59 @@
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
+use linfa::prelude::*;
+use ndarray::{Array1, Ix1};
+use rand::{Rng, SeedableRng};
+
+const MAX_ITERATIONS: u64 = 2;
+
+fn train_model(
+    dataset: &Dataset<f32, bool, Ix1>,
+) -> linfa_logistic::FittedLogisticRegression<f32, bool> {
+    linfa_logistic::LogisticRegression::default()
+        .max_iterations(MAX_ITERATIONS)
+        .fit(dataset)
+        .unwrap()
+}
+
+fn generate_categorical_data(nfeatures: usize, nsamples: usize) -> Dataset<f32, bool, Ix1> {
+    let mut rng = rand::rngs::SmallRng::seed_from_u64(42);
+    let mut feature_rows: Vec<Vec<f32>> = Vec::new();
+    let mut label_rows: Vec<bool> = Vec::new();
+    for _ in 0..nsamples {
+        let mut features = Vec::new();
+        for _ in 0..nfeatures {
+            let value = if rng.gen() { 1.0 } else { 0.0 };
+            features.push(value);
+        }
+        feature_rows.push(features);
+        label_rows.push(rng.gen());
+    }
+    linfa::Dataset::new(
+        ndarray::Array2::from_shape_vec(
+            (nsamples, nfeatures),
+            feature_rows.into_iter().flatten().collect(),
+        )
+        .unwrap(),
+        Array1::from_shape_vec(label_rows.len(), label_rows).unwrap(),
+    )
+}
+
+fn bench(c: &mut Criterion) {
+    let mut group = c.benchmark_group("Logistic regression");
+    group.measurement_time(std::time::Duration::from_secs(10)).sample_size(10);
+    for nfeatures in [1_000] {
+        for nsamples in [1_000, 10_000, 100_000, 200_000, 500_000, 1_000_000] {
+            let input = generate_categorical_data(nfeatures, nsamples);
+            group.bench_with_input(
+                BenchmarkId::new("train_model", format!("{:e}x{:e}", nfeatures as f64, nsamples as f64)),
+                &input,
+                |b, dataset| {
+                    b.iter(|| train_model(dataset));
+                },
+            );
+        }
+    }
+    group.finish();
+}
+
+criterion_group!(benches, bench);
+criterion_main!(benches);