Merge 4e70e868fdcd3250c27a29df4c764f8f25464116 into aeaf93638050524c8251e0a6d3a2a6913753d1c8

benches/benchmarks.rs

@@ -1,33 +1,15 @@
 // Combined benchmarks for rustframe
 use chrono::NaiveDate;
 use criterion::{criterion_group, criterion_main, Criterion};
-// Import Duration for measurement_time and warm_up_time
 use rustframe::{
     frame::{Frame, RowIndex},
     matrix::{BoolMatrix, Matrix},
     utils::{BDateFreq, BDatesList},
 };
-use std::time::Duration;
-
-// You can define a custom Criterion configuration function
-// This will be passed to the criterion_group! macro
-pub fn for_short_runs() -> Criterion {
-    Criterion::default()
-        // (samples != total iterations)
-        // limits the number of statistical data points.
-        .sample_size(50)
-        // measurement time per sample
-        .measurement_time(Duration::from_millis(250))
-        // reduce warm-up time as well for faster overall run
-        .warm_up_time(Duration::from_millis(50))
-    // You could also make it much shorter if needed, e.g., 50ms measurement, 100ms warm-up
-    // .measurement_time(Duration::from_millis(50))
-    // .warm_up_time(Duration::from_millis(100))
-}
 
 fn bool_matrix_operations_benchmark(c: &mut Criterion) {
-    let sizes = [1, 100, 1000];
+    // let sizes = [1, 100, 1000];
-    // let sizes = [1000];
+    let sizes = [1000];
 
     for &size in &sizes {
         let data1: Vec<bool> = (0..size * size).map(|x| x % 2 == 0).collect();
@@ -62,8 +44,8 @@ fn bool_matrix_operations_benchmark(c: &mut Criterion) {
 }
 
 fn matrix_boolean_operations_benchmark(c: &mut Criterion) {
-    let sizes = [1, 100, 1000];
+    // let sizes = [1, 100, 1000];
-    // let sizes = [1000];
+    let sizes = [1000];
 
     for &size in &sizes {
         let data1: Vec<bool> = (0..size * size).map(|x| x % 2 == 0).collect();
@@ -98,8 +80,8 @@ fn matrix_boolean_operations_benchmark(c: &mut Criterion) {
 }
 
 fn matrix_operations_benchmark(c: &mut Criterion) {
-    let sizes = [1, 100, 1000];
+    // let sizes = [1, 100, 1000];
-    // let sizes = [1000];
+    let sizes = [1000];
 
     for &size in &sizes {
         let data: Vec<f64> = (0..size * size).map(|x| x as f64).collect();
@@ -164,21 +146,17 @@ fn matrix_operations_benchmark(c: &mut Criterion) {
 }
 
 fn benchmark_frame_operations(c: &mut Criterion) {
-    let n_periods = 1000;
+    let n_periods = 4;
-    let n_cols = 1000;
     let dates: Vec<NaiveDate> =
         BDatesList::from_n_periods("2024-01-02".to_string(), BDateFreq::Daily, n_periods)
             .unwrap()
             .list()
             .unwrap();
 
-    // let col_names= str(i) for i in range(1, 1000)
+    let col_names: Vec<String> = vec!["a".to_string(), "b".to_string()];
-    let col_names: Vec<String> = (1..=n_cols).map(|i| format!("col_{}", i)).collect();
 
-    let data1: Vec<f64> = (0..n_periods * n_cols).map(|x| x as f64).collect();
+    let ma = Matrix::from_cols(vec![vec![1.0, 2.0, 3.0, 4.0], vec![5.0, 6.0, 7.0, 8.0]]);
-    let data2: Vec<f64> = (0..n_periods * n_cols).map(|x| (x + 1) as f64).collect();
+    let mb = Matrix::from_cols(vec![vec![4.0, 3.0, 2.0, 1.0], vec![8.0, 7.0, 6.0, 5.0]]);
-    let ma = Matrix::from_vec(data1.clone(), n_periods, n_cols);
-    let mb = Matrix::from_vec(data2.clone(), n_periods, n_cols);
 
     let fa = Frame::new(
         ma.clone(),
@@ -187,18 +165,16 @@ fn benchmark_frame_operations(c: &mut Criterion) {
     );
     let fb = Frame::new(mb, col_names, Some(RowIndex::Date(dates)));
 
-    c.bench_function("frame element-wise multiply (1000x1000)", |b| {
+    c.bench_function("frame element-wise multiply", |b| {
         b.iter(|| {
             let _result = &fa * &fb;
         });
     });
 }
 
-// Define the criterion group and pass the custom configuration function
 criterion_group!(
-    name = combined_benches;
+    combined_benches,
-    config = for_short_runs(); // Use the custom configuration here
+    bool_matrix_operations_benchmark,
-    targets = bool_matrix_operations_benchmark,
     matrix_boolean_operations_benchmark,
     matrix_operations_benchmark,
     benchmark_frame_operations