Add benches for GNB (#33)

* Add benches for GNB * use [black_box](https://github.com/bheisler/criterion.rs/blob/master/book/src/faq.md#when-should-i-use-criterionblack_box)
2020-12-04 20:46:36 -04:00
parent f0b348dd6e
commit 2650416235
2 changed files with 78 additions and 0 deletions
@@ -36,3 +36,8 @@ bincode = "1.3.1"
 [[bench]]
 name = "distance"
 harness = false
+
+[[bench]]
+name = "naive_bayes"
+harness = false
+required-features = ["ndarray-bindings", "nalgebra-bindings"]
@@ -0,0 +1,73 @@
+use criterion::BenchmarkId;
+use criterion::{black_box, criterion_group, criterion_main, Criterion};
+
+use nalgebra::DMatrix;
+use ndarray::Array2;
+use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+use smartcore::linalg::BaseMatrix;
+use smartcore::linalg::BaseVector;
+use smartcore::naive_bayes::GaussianNB;
+
+pub fn gaussian_naive_bayes_fit_benchmark(c: &mut Criterion) {
+    let mut group = c.benchmark_group("GaussianNB::fit");
+
+    for n_samples in [100_usize, 1000_usize, 10000_usize].iter() {
+        for n_features in [10_usize, 100_usize, 1000_usize].iter() {
+            let x = DenseMatrix::<f64>::rand(*n_samples, *n_features);
+            let y: Vec<f64> = (0..*n_samples)
+                .map(|i| (i % *n_samples / 5_usize) as f64)
+                .collect::<Vec<f64>>();
+            group.bench_with_input(
+                BenchmarkId::from_parameter(format!(
+                    "n_samples: {}, n_features: {}",
+                    n_samples, n_features
+                )),
+                n_samples,
+                |b, _| {
+                    b.iter(|| {
+                        GaussianNB::fit(black_box(&x), black_box(&y), Default::default()).unwrap();
+                    })
+                },
+            );
+        }
+    }
+    group.finish();
+}
+
+pub fn gaussian_naive_matrix_datastructure(c: &mut Criterion) {
+    let mut group = c.benchmark_group("GaussianNB");
+    let classes = (0..10000).map(|i| (i % 25) as f64).collect::<Vec<f64>>();
+
+    group.bench_function("DenseMatrix", |b| {
+        let x = DenseMatrix::<f64>::rand(10000, 500);
+        let y = <DenseMatrix<f64> as BaseMatrix<f64>>::RowVector::from_array(&classes);
+
+        b.iter(|| {
+            GaussianNB::fit(black_box(&x), black_box(&y), Default::default()).unwrap();
+        })
+    });
+
+    group.bench_function("ndarray", |b| {
+        let x = Array2::<f64>::rand(10000, 500);
+        let y = <Array2<f64> as BaseMatrix<f64>>::RowVector::from_array(&classes);
+
+        b.iter(|| {
+            GaussianNB::fit(black_box(&x), black_box(&y), Default::default()).unwrap();
+        })
+    });
+
+    group.bench_function("ndalgebra", |b| {
+        let x = DMatrix::<f64>::rand(10000, 500);
+        let y = <DMatrix<f64> as BaseMatrix<f64>>::RowVector::from_array(&classes);
+
+        b.iter(|| {
+            GaussianNB::fit(black_box(&x), black_box(&y), Default::default()).unwrap();
+        })
+    });
+}
+criterion_group!(
+    benches,
+    gaussian_naive_bayes_fit_benchmark,
+    gaussian_naive_matrix_datastructure
+);
+criterion_main!(benches);