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Added benchmark for Batch

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This commit is contained in:
Anders Olsson
2022-12-19 07:42:08 +01:00
parent 2bdd3d9b89
commit 2b83ee5ef9
8 changed files with 131 additions and 66 deletions
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1
.gitignore vendored
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@@ -1,4 +1,5 @@
/target
/Cargo.lock
.justfile
*.svg

8
Cargo.toml
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@@ -3,10 +3,18 @@ name = "trueskill-tt"
version = "0.1.0"
edition = "2021"
[lib]
bench = false
[[bench]]
name = "batch"
harness = false
[dependencies]
approx = { version = "0.5.1", optional = true }
[dev-dependencies]
criterion = "0.4"
plotters = { version = "0.3.4", default-features = false, features = ["svg_backend", "all_elements", "all_series"] }
plotters-backend = "0.3.4"
time = { version = "0.3.17", features = ["parsing"] }

1
README.md
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@@ -13,6 +13,7 @@ Rust port of [TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillTh
- [x] Implement approx for Gaussian
- [x] Add more tests from `TrueSkillThroughTime.jl`
- [ ] Benchmark Batch::iteration()
- [ ] Time needs to be an enum so we can have multiple states (see `batch::compute_elapsed()`)
- [ ] Add examples (use same TrueSkillThroughTime.(py|jl))
- [ ] Add Observer (see [argmin](https://docs.rs/argmin/latest/argmin/core/trait.Observe.html) for inspiration)

62
benches/batch.rs Normal file
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@@ -0,0 +1,62 @@
use std::collections::HashMap;
use criterion::{criterion_group, criterion_main, Criterion};
use trueskill_tt::{
agent::Agent, batch::Batch, gaussian::Gaussian, player::Player, IndexMap, BETA, GAMMA, MU,
P_DRAW, SIGMA,
};
fn criterion_benchmark(criterion: &mut Criterion) {
let mut index = IndexMap::new();
let a = index.get_or_create("a");
let b = index.get_or_create("b");
let c = index.get_or_create("c");
let agents = {
let mut map = HashMap::new();
map.insert(
a,
Agent {
player: Player::new(Gaussian::new(MU, SIGMA), BETA, GAMMA),
..Default::default()
},
);
map.insert(
b,
Agent {
player: Player::new(Gaussian::new(MU, SIGMA), BETA, GAMMA),
..Default::default()
},
);
map.insert(
c,
Agent {
player: Player::new(Gaussian::new(MU, SIGMA), BETA, GAMMA),
..Default::default()
},
);
map
};
let mut composition = Vec::new();
let mut results = Vec::new();
let mut weights = Vec::new();
for _ in 0..100 {
composition.push(vec![vec![a], vec![b]]);
results.push(vec![1.0, 0.0]);
weights.push(vec![vec![1.0], vec![1.0]]);
}
let mut batch = Batch::new(composition, results, weights, 1, P_DRAW, &agents);
criterion.bench_function("Batch::iteration", |b| {
b.iter(|| batch.iteration(0, &agents))
});
}
criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);

8
src/agent.rs
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@@ -1,10 +1,10 @@
use crate::{gaussian::Gaussian, player::Player, N_INF};
#[derive(Debug)]
pub(crate) struct Agent {
pub(crate) player: Player,
pub(crate) message: Gaussian,
pub(crate) last_time: i64,
pub struct Agent {
pub player: Player,
pub message: Gaussian,
pub last_time: i64,
}
impl Agent {

11
src/batch.rs
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@@ -109,7 +109,7 @@ pub struct Batch {
}
impl Batch {
pub(crate) fn new(
pub fn new(
composition: Vec<Vec<Vec<Index>>>,
results: Vec<Vec<f64>>,
weights: Vec<Vec<Vec<f64>>>,
@@ -269,13 +269,6 @@ impl Batch {
self.iteration(from, agents);
}
// TODO(anders): Use Item::posterior() instead.
pub fn posterior(&self, agent: Index) -> Gaussian {
let skill = &self.skills[&agent];
skill.likelihood * skill.backward * skill.forward
}
pub(crate) fn posteriors(&self) -> HashMap<Index, Gaussian> {
self.skills
.iter()
@@ -283,7 +276,7 @@ impl Batch {
.collect::<HashMap<_, _>>()
}
pub(crate) fn iteration(&mut self, from: usize, agents: &HashMap<Index, Agent>) {
pub fn iteration(&mut self, from: usize, agents: &HashMap<Index, Agent>) {
for event in self.events.iter_mut().skip(from) {
let teams = event.within_priors(false, false, &self.skills, agents);
let result = event.outputs();

98
src/history.rs
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@@ -460,7 +460,7 @@ mod tests {
assert_ulps_eq!(observed, expected, epsilon = 0.000001);
let observed = h.batches[1].posterior(a);
let observed = h.batches[1].skills[&a].posterior();
let p = Game::new(
h.batches[1].events[0].within_priors(false, false, &h.batches[1].skills, &h.agents),
@@ -508,12 +508,12 @@ mod tests {
h1.add_events_with_prior(composition, results, times, vec![], priors);
assert_ulps_eq!(
h1.batches[0].posterior(a),
h1.batches[0].skills[&a].posterior(),
Gaussian::new(22.904409, 6.010330),
epsilon = 1e-6
);
assert_ulps_eq!(
h1.batches[0].posterior(c),
h1.batches[0].skills[&c].posterior(),
Gaussian::new(25.110318, 5.866311),
epsilon = 1e-6
);
@@ -521,12 +521,12 @@ mod tests {
h1.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h1.batches[0].posterior(a),
h1.batches[0].skills[&a].posterior(),
Gaussian::new(25.000000, 5.419212),
epsilon = 1e-6
);
assert_ulps_eq!(
h1.batches[0].posterior(c),
h1.batches[0].skills[&c].posterior(),
Gaussian::new(25.000000, 5.419212),
epsilon = 1e-6
);
@@ -553,12 +553,12 @@ mod tests {
h2.add_events_with_prior(composition, results, times, vec![], priors);
assert_ulps_eq!(
h2.batches[2].posterior(a),
h2.batches[2].skills[&a].posterior(),
Gaussian::new(22.903522, 6.011017),
epsilon = 1e-6
);
assert_ulps_eq!(
h2.batches[2].posterior(c),
h2.batches[2].skills[&c].posterior(),
Gaussian::new(25.110702, 5.866811),
epsilon = 1e-6
);
@@ -566,12 +566,12 @@ mod tests {
h2.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h2.batches[2].posterior(a),
h2.batches[2].skills[&a].posterior(),
Gaussian::new(24.998668, 5.420053),
epsilon = 1e-6
);
assert_ulps_eq!(
h2.batches[2].posterior(c),
h2.batches[2].skills[&c].posterior(),
Gaussian::new(25.000532, 5.419827),
epsilon = 1e-6
);
@@ -658,17 +658,17 @@ mod tests {
assert_eq!(h.batches[2].skills[&c].elapsed, 1);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(25.000267, 5.419381),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(b),
h.batches[0].skills[&b].posterior(),
Gaussian::new(24.999465, 5.419425),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[2].posterior(b),
h.batches[2].skills[&b].posterior(),
Gaussian::new(25.000532, 5.419696),
epsilon = 1e-6
);
@@ -712,8 +712,8 @@ mod tests {
);
assert_ulps_eq!(
h.batches[0].posterior(b).mu,
-1.0 * h.batches[0].posterior(c).mu,
h.batches[0].skills[&b].posterior().mu,
-1.0 * h.batches[0].skills[&c].posterior().mu,
epsilon = 1e-6
);
@@ -732,33 +732,33 @@ mod tests {
assert_ulps_eq!(p_d_m_hat, 0.172432, epsilon = 1e-6);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].posterior(b),
h.batches[0].skills[&a].posterior(),
h.batches[0].skills[&b].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[0].posterior(d),
h.batches[0].skills[&c].posterior(),
h.batches[0].skills[&d].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[1].posterior(e),
h.batches[1].posterior(f),
h.batches[1].skills[&e].posterior(),
h.batches[1].skills[&f].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(4.084902, 5.106919),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[0].skills[&c].posterior(),
Gaussian::new(-0.533029, 5.106919),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[2].posterior(e),
h.batches[2].skills[&e].posterior(),
Gaussian::new(-3.551872, 5.154569),
epsilon = 1e-6
);
@@ -795,17 +795,17 @@ mod tests {
assert_eq!(h.batches[2].skills[&c].elapsed, 1);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(b),
h.batches[0].skills[&b].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[2].posterior(b),
h.batches[2].skills[&b].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
@@ -832,22 +832,22 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[3].posterior(a),
h.batches[3].skills[&a].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[3].posterior(b),
h.batches[3].skills[&b].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[5].posterior(b),
h.batches[5].skills[&b].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
@@ -884,17 +884,17 @@ mod tests {
assert_eq!(h.batches[2].skills[&c].elapsed, 1);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(b),
h.batches[0].skills[&b].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[2].posterior(b),
h.batches[2].skills[&b].posterior(),
Gaussian::new(0.000000, 1.300610),
epsilon = 1e-6
);
@@ -921,22 +921,22 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h.batches[0].posterior(a),
h.batches[0].skills[&a].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[3].posterior(a),
h.batches[3].skills[&a].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[3].posterior(b),
h.batches[3].skills[&b].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[5].posterior(b),
h.batches[5].skills[&b].posterior(),
Gaussian::new(0.000000, 0.931236),
epsilon = 1e-6
);
@@ -1084,20 +1084,20 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h.batches[0].posterior(b),
h.batches[end].posterior(b),
h.batches[0].skills[&b].posterior(),
h.batches[end].skills[&b].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[end].posterior(c),
h.batches[0].skills[&c].posterior(),
h.batches[end].skills[&c].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[0].posterior(b),
h.batches[0].skills[&c].posterior(),
h.batches[0].skills[&b].posterior(),
epsilon = 1e-6
);
@@ -1172,20 +1172,20 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!(
h.batches[0].posterior(b),
h.batches[end].posterior(b),
h.batches[0].skills[&b].posterior(),
h.batches[end].skills[&b].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[end].posterior(c),
h.batches[0].skills[&c].posterior(),
h.batches[end].skills[&c].posterior(),
epsilon = 1e-6
);
assert_ulps_eq!(
h.batches[0].posterior(c),
h.batches[0].posterior(b),
h.batches[0].skills[&c].posterior(),
h.batches[0].skills[&b].posterior(),
epsilon = 1e-6
);
}

8
src/lib.rs
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@@ -4,15 +4,15 @@ use std::collections::HashMap;
use std::f64::consts::{FRAC_1_SQRT_2, FRAC_2_SQRT_PI, SQRT_2};
use std::hash::Hash;
mod agent;
pub mod agent;
#[cfg(feature = "approx")]
mod approx;
mod batch;
pub mod batch;
mod game;
mod gaussian;
pub mod gaussian;
mod history;
mod message;
mod player;
pub mod player;
pub use game::Game;
pub use gaussian::Gaussian;