From 55c0b20f5d259924f081f4235ca5734ab928219a Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Sat, 9 May 2026 16:21:24 -0400 Subject: [PATCH 01/24] wip rust port --- Cargo.lock | 8 + crates/cs2kz-api/src/bin/server/cli.rs | 9 - crates/cs2kz/Cargo.toml | 3 + crates/cs2kz/src/config/points.rs | 9 +- crates/cs2kz/src/context.rs | 25 +-- crates/cs2kz/src/lib.rs | 3 - crates/cs2kz/src/points.rs | 14 +- crates/cs2kz/src/points/calculator.rs | 288 ++++++++++++++++--------- crates/cs2kz/src/points/daemon.rs | 281 ++++++++++++++++-------- crates/cs2kz/src/python.rs | 219 ------------------- crates/cs2kz/src/records.rs | 19 +- crates/nig/Cargo.toml | 11 + crates/nig/src/bessel.rs | 173 +++++++++++++++ crates/nig/src/distribution.rs | 158 ++++++++++++++ crates/nig/src/fitting.rs | 283 ++++++++++++++++++++++++ crates/nig/src/lib.rs | 8 + crates/nig/src/params.rs | 16 ++ cs2kz-api.example.toml | 4 - 18 files changed, 1057 insertions(+), 474 deletions(-) delete mode 100644 crates/cs2kz/src/python.rs create mode 100644 crates/nig/Cargo.toml create mode 100644 crates/nig/src/bessel.rs create mode 100644 crates/nig/src/distribution.rs create mode 100644 crates/nig/src/fitting.rs create mode 100644 crates/nig/src/lib.rs create mode 100644 crates/nig/src/params.rs diff --git a/Cargo.lock b/Cargo.lock index 05bb512f..3074263e 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -765,6 +765,7 @@ dependencies = [ "futures-util", "lettre", "md-5", + "nig", "pin-project", "rust-s3", "semver", @@ -1885,6 +1886,13 @@ dependencies = [ "windows-sys 0.52.0", ] +[[package]] +name = "nig" +version = "0.1.0" +dependencies = [ + "serde", +] + [[package]] name = "nom" version = "7.1.3" diff --git a/crates/cs2kz-api/src/bin/server/cli.rs b/crates/cs2kz-api/src/bin/server/cli.rs index 2cdb2ffe..49f57fe0 100644 --- a/crates/cs2kz-api/src/bin/server/cli.rs +++ b/crates/cs2kz-api/src/bin/server/cli.rs @@ -33,10 +33,6 @@ pub struct Args { /// Path to the `DepotDownloader` executable the API should use. #[arg(long)] pub depot_downloader_path: Option, - - /// Path to a directory containing the `calc_filter.py` and `calc_run.py` scripts. - #[arg(long)] - pub scripts_path: Option, } impl Args { @@ -53,10 +49,5 @@ impl Args { if let Some(ref path) = self.depot_downloader_path { config.depot_downloader.exe_path = path.clone(); } - - if let Some(ref path) = self.scripts_path { - config.cs2kz.points.calc_filter_path = Some(path.join("calc_filter.py")); - config.cs2kz.points.calc_run_path = Some(path.join("calc_run.py")); - } } } diff --git a/crates/cs2kz/Cargo.toml b/crates/cs2kz/Cargo.toml index 8a25eb29..c86c07a0 100644 --- a/crates/cs2kz/Cargo.toml +++ b/crates/cs2kz/Cargo.toml @@ -47,6 +47,9 @@ futures-util.workspace = true lettre.workspace = true sqlx.workspace = true +[dependencies.nig] +path = "../nig" + [dependencies.steam-id] path = "../steam-id" features = ["serde"] diff --git a/crates/cs2kz/src/config/points.rs b/crates/cs2kz/src/config/points.rs index d4e277a0..f6bac24b 100644 --- a/crates/cs2kz/src/config/points.rs +++ b/crates/cs2kz/src/config/points.rs @@ -1,10 +1,11 @@ -use std::path::PathBuf; - use serde::Deserialize; +/// Configuration for the points system. +/// +/// All distribution fitting and recalculation is now done in pure Rust +/// (see `crate::points::calculator`), so no external script paths are needed. #[derive(Debug, Default, Deserialize)] #[serde(default, rename_all = "kebab-case", deny_unknown_fields)] pub struct PointsConfig { - pub calc_filter_path: Option, - pub calc_run_path: Option, + // (no fields needed — kept for future extensibility) } diff --git a/crates/cs2kz/src/context.rs b/crates/cs2kz/src/context.rs index d2caad8a..e37aca1b 100644 --- a/crates/cs2kz/src/context.rs +++ b/crates/cs2kz/src/context.rs @@ -1,6 +1,6 @@ use std::sync::Arc; use std::time::Duration; -use std::{fmt, io}; +use std::fmt; use tokio::task; use tokio_util::sync::CancellationToken; @@ -25,7 +25,6 @@ mod inner { pub(super) database: Database, pub(super) shutdown_token: CancellationToken, pub(super) tasks: TaskTracker, - pub(super) points_calculator: Option, pub(super) points_daemon: points::daemon::PointsDaemonHandle, } } @@ -41,9 +40,6 @@ pub enum InitializeContextError { #[display("failed to run database migrations: {_0}")] RunDatabaseMigrations(sqlx::migrate::MigrateError), - - #[display("failed to initialize points calculator: {_0}")] - InitializePointsCalculator(io::Error), } impl Context { @@ -71,20 +67,6 @@ impl Context { database::MIGRATIONS.run(database.as_ref()).await?; let tasks = TaskTracker::new(); - let points_calculator = points::calculator::PointsCalculator::new(&config) - .await? - .map(|calc| { - let handle = calc.handle(); - let cancellation_token = shutdown_token.child_token(); - let task = tasks.track_future(calc.run(cancellation_token)); - - task::Builder::new() - .name("cs2kz::points_calculator") - .spawn(task) - .expect("failed to spawn tokio task"); - - handle - }); let points_daemon = points::daemon::PointsDaemonHandle::new(); Ok(Self(Arc::new(inner::Context { @@ -92,7 +74,6 @@ impl Context { database, shutdown_token, tasks, - points_calculator, points_daemon, }))) } @@ -105,10 +86,6 @@ impl Context { &self.0.database } - pub fn points_calculator(&self) -> Option<&points::calculator::PointsCalculatorHandle> { - self.0.points_calculator.as_ref() - } - pub fn points_daemon(&self) -> &points::daemon::PointsDaemonHandle { &self.0.points_daemon } diff --git a/crates/cs2kz/src/lib.rs b/crates/cs2kz/src/lib.rs index 03f0b653..601aa90b 100644 --- a/crates/cs2kz/src/lib.rs +++ b/crates/cs2kz/src/lib.rs @@ -70,8 +70,5 @@ pub mod steam; pub mod styles; pub mod time; -// mod python; -mod python; - mod fmt; mod num; diff --git a/crates/cs2kz/src/points.rs b/crates/cs2kz/src/points.rs index c2952332..f910f36a 100644 --- a/crates/cs2kz/src/points.rs +++ b/crates/cs2kz/src/points.rs @@ -3,24 +3,14 @@ use crate::maps::courses::Tier; pub mod daemon; pub mod calculator; +pub use nig::NigParams; + /// The maximum points for any record. pub const MAX: f64 = 10_000.0; /// Threshold for what counts as a "small" leaderboard. pub const SMALL_LEADERBOARD_THRESHOLD: u64 = 50; -/// [Normal-inverse Gaussian distribution][norminvgauss] parameters. -/// -/// [norminvgauss]: https://en.wikipedia.org/wiki/Normal-inverse_Gaussian_distribution -#[derive(Debug, Clone, Copy, serde::Serialize)] -pub struct DistributionParameters { - pub a: f64, - pub b: f64, - pub loc: f64, - pub scale: f64, - pub top_scale: f64, -} - /// "Completes" pre-calculated distribution points cached in the database. /// /// # Panics diff --git a/crates/cs2kz/src/points/calculator.rs b/crates/cs2kz/src/points/calculator.rs index 31fe8e5f..99f562a6 100644 --- a/crates/cs2kz/src/points/calculator.rs +++ b/crates/cs2kz/src/points/calculator.rs @@ -1,34 +1,5 @@ -use std::io; -use std::time::Duration; - -use futures_util::TryFutureExt as _; -use tokio::sync::{mpsc, oneshot}; -use tokio::time::sleep; -use tokio_util::sync::CancellationToken; - -use crate::Config; use crate::maps::courses::Tier; -use crate::points::DistributionParameters; -use crate::python::Python; - -type Message = (Request, oneshot::Sender); - -#[derive(Debug)] -pub struct PointsCalculator { - python: Python, - chan: (mpsc::Sender, mpsc::Receiver), -} - -#[derive(Debug, Clone)] -pub struct PointsCalculatorHandle { - chan: mpsc::Sender, -} - -#[derive(Debug, Display, Error)] -pub enum Error { - #[display("python error")] - Python(io::Error), -} +use crate::points::{self, NigParams}; #[derive(Debug, Clone, serde::Serialize)] pub struct Request { @@ -43,16 +14,9 @@ pub struct Response { pub pro_fraction: Option, } -#[derive(Debug, Display, Error)] -#[display("failed to calculate points ({_variant})")] -pub enum CalculatePointsError { - #[display("calculator unavailable")] - CalculatorUnavailable, -} - #[derive(Debug, Clone, serde::Serialize)] pub struct LeaderboardData { - pub dist_params: Option, + pub dist_params: Option, #[serde(serialize_with = "Tier::serialize_as_integer")] pub tier: Tier, pub leaderboard_size: u64, @@ -60,76 +24,204 @@ pub struct LeaderboardData { pub top_time: f64, } -impl PointsCalculator { - pub async fn new(config: &Config) -> io::Result> { - let Some(script_path) = config.points.calc_run_path.as_deref() else { - tracing::warn!( - "no `points.calc-run-path` configured; points calculator will be disabled" - ); - return Ok(None); - }; +pub fn calculate(request: &Request) -> Response { + let nub_fraction = dist_points_portion(request.time, &request.nub_data); - let python = Python::new(script_path.to_owned(), config.database.url.clone()).await?; - let chan = mpsc::channel(128); + let pro_fraction = request.pro_data.as_ref().map(|data| { + let pro_fraction = dist_points_portion(request.time, data); + pro_fraction.max(nub_fraction) + }); - Ok(Some(Self { python, chan })) - } + Response { nub_fraction, pro_fraction } +} - pub fn handle(&self) -> PointsCalculatorHandle { - PointsCalculatorHandle { chan: self.chan.0.clone() } +fn dist_points_portion(time: f64, data: &LeaderboardData) -> f64 { + if data.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD || data.dist_params.is_none() { + return points::for_small_leaderboard(data.tier, data.top_time, time); } - #[tracing::instrument(skip_all)] - pub async fn run(mut self, cancellation_token: CancellationToken) -> Result<(), Error> { - loop { - select! { - () = cancellation_token.cancelled() => { - tracing::debug!("cancelled"); - break Ok(()); - }, - - Some((request, response_tx)) = self.chan.1.recv() => { - let mut attempts = 0; - - if let Err(_err) = loop { - match self.python.send_request(&request).await { - Ok(response) => { - _ = response_tx.send(response); - break Ok(()); - }, - Err(err) => { - tracing::error!(%err, "failed to execute python request"); - self.python.reset().map_err(Error::Python).await?; - - attempts += 1; - - if attempts == 3 { - break Err(err); - } - - sleep(Duration::from_secs(1)).await; - }, - } - } { - tracing::error!("giving up after 3 failed attempts"); - } - }, - }; + let dist = data.dist_params.expect("checked above"); + let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; + (nig::nig_survival(dist.a, dist.b, dist.loc, dist.scale, time) / top_scale).clamp(0.0, 1.0) +} + + +/// Input record data for batch recalculation. +#[derive(Debug, Clone)] +pub struct BestRecordData { + pub record_id: [u8; 16], + pub time: f64, +} + +/// Output record with recalculated distribution points fraction. +#[derive(Debug, Clone)] +pub struct RecordPoints { + pub record_id: [u8; 16], + pub points: f64, +} + +/// Result of a batch filter recalculation. +#[derive(Debug, Clone)] +pub struct RecalculateFilterResult { + pub nub_records: Vec, + pub pro_records: Vec, + pub nub_params: NigParams, + pub pro_params: NigParams, + pub nub_fitted: bool, + pub pro_fitted: bool, +} + +/// Recompute point fractions for all records in a filter +pub fn recalculate_filter( + nub_records: &[BestRecordData], + pro_records: &[BestRecordData], + nub_tier: Tier, + pro_tier: Tier, + prev_nub_params: Option<&NigParams>, + prev_pro_params: Option<&NigParams>, +) -> RecalculateFilterResult { + let zero_params = + NigParams { a: 0.0, b: 0.0, loc: 0.0, scale: 0.0, top_scale: 0.0 }; + + let nub_times: Vec = nub_records.iter().map(|r| r.time).collect(); + let (nub_params, nub_fitted) = if nub_times.len() >= 50 { + let prev = prev_nub_params.copied(); + let result = nig::fit_nig(&nub_times, prev.as_ref()); + if result.valid { + (result.params, true) + } else { + (zero_params, false) + } + } else { + (zero_params, false) + }; + + let nub_wr = nub_times.first().copied().unwrap_or(0.0); + let nub_leaderboard = LeaderboardData { + dist_params: if nub_fitted { Some(nub_params) } else { None }, + tier: nub_tier, + leaderboard_size: nub_records.len() as u64, + top_time: nub_wr, + }; + + let new_nub_records: Vec = nub_records + .iter() + .map(|r| RecordPoints { + record_id: r.record_id, + points: dist_points_portion(r.time, &nub_leaderboard), + }) + .collect(); + + let pro_times: Vec = pro_records.iter().map(|r| r.time).collect(); + let (pro_params, pro_fitted) = if pro_times.len() >= 50 { + let prev = prev_pro_params.copied(); + let result = nig::fit_nig(&pro_times, prev.as_ref()); + if result.valid { + (result.params, true) + } else { + (zero_params, false) } + } else { + (zero_params, false) + }; + + let pro_wr = pro_times.first().copied().unwrap_or(0.0); + let pro_leaderboard = LeaderboardData { + dist_params: if pro_fitted { Some(pro_params) } else { None }, + tier: pro_tier, + leaderboard_size: pro_records.len() as u64, + top_time: pro_wr, + }; + + let new_pro_records: Vec = pro_records + .iter() + .map(|r| { + let pro_fraction = dist_points_portion(r.time, &pro_leaderboard); + let nub_fraction = dist_points_portion(r.time, &nub_leaderboard); + RecordPoints { + record_id: r.record_id, + points: pro_fraction.max(nub_fraction), + } + }) + .collect(); + + RecalculateFilterResult { + nub_records: new_nub_records, + pro_records: new_pro_records, + nub_params, + pro_params, + nub_fitted, + pro_fitted, } } -impl PointsCalculatorHandle { - pub async fn calculate(&self, request: Request) -> Result { - let (response_tx, response_rx) = oneshot::channel::(); +#[cfg(test)] +mod tests { + use super::*; - if let Err(_send_err) = self.chan.send((request, response_tx)).await { - return Err(CalculatePointsError::CalculatorUnavailable); - } + fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { + let abs_error = (actual - expected).abs(); + assert!( + abs_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", + ); + } - match response_rx.await { - Ok(response) => Ok(response), - Err(_recv_err) => Err(CalculatePointsError::CalculatorUnavailable), - } + #[test] + fn calculate_points_matches_python_example() { + let request = Request { + time: 8.609375, + nub_data: LeaderboardData { + dist_params: Some(NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + top_scale: 0.9979285278452101, + }), + tier: Tier::VeryEasy, + leaderboard_size: 224, + top_time: 7.6484375, + }, + pro_data: Some(LeaderboardData { + dist_params: Some(NigParams { + a: 2.6294814553333743, + b: 2.511121972118702, + loc: 8.713014153227697, + scale: 2.2226724397990805, + top_scale: 0.9952929135343108, + }), + tier: Tier::VeryEasy, + leaderboard_size: 165, + top_time: 7.6484375, + }), + }; + + let response = calculate(&request); + assert_abs_close(response.nub_fraction, 0.9745534941686896, 1e-3); + assert_abs_close(response.pro_fraction.expect("pro fraction"), 0.9760910013054752, 1e-3); + } + + #[test] + fn pro_fraction_is_never_less_than_nub_fraction() { + let request = Request { + time: 8.0, + nub_data: LeaderboardData { + dist_params: None, + tier: Tier::VeryEasy, + leaderboard_size: 30, + top_time: 7.0, + }, + pro_data: Some(LeaderboardData { + dist_params: None, + tier: Tier::Death, + leaderboard_size: 30, + top_time: 7.0, + }), + }; + + let response = calculate(&request); + assert!(response.pro_fraction.is_some()); + assert!(response.pro_fraction.unwrap() >= response.nub_fraction); } } diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index b44733e2..512ee864 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -1,17 +1,16 @@ -use std::io; use std::sync::Arc; use std::time::Duration; use futures_util::TryFutureExt as _; +use sqlx::Row as _; use tokio::sync::Notify; -use tokio::time::{interval, sleep}; +use tokio::time::interval; use tokio_util::sync::CancellationToken; use crate::maps::CourseFilterId; -use crate::maps::courses::filters::GetCourseFiltersError; use crate::mode::Mode; -use crate::python::Python; -use crate::records::GetRecordsError; +use crate::points::calculator; +use crate::points::NigParams; use crate::{Context, database, players}; #[derive(Debug, Clone)] @@ -39,10 +38,8 @@ struct Notifications { #[derive(Debug, Display, Error, From)] pub enum Error { - GetCourseFilter(GetCourseFiltersError), - GetRecords(GetRecordsError), DetermineFilterToRecalculate(DetermineFilterToRecalculateError), - Python(io::Error), + ProcessFilter(database::Error), } #[derive(Debug, Display, Error, From)] @@ -50,54 +47,8 @@ pub enum Error { #[from(forward)] pub struct DetermineFilterToRecalculateError(database::Error); -#[derive(Debug, serde::Serialize)] -struct PythonRequest { - filter_id: CourseFilterId, -} - -#[derive(Debug, serde::Deserialize)] -struct PythonResponse { - #[expect(dead_code, reason = "included in tracing events")] - filter_id: CourseFilterId, - - #[expect(dead_code, reason = "included in tracing events")] - timings: PythonTimings, -} - -#[derive(Debug, serde::Deserialize)] -#[expect(dead_code, reason = "included in tracing events")] -struct PythonTimings { - #[serde(rename = "db_query_ms", deserialize_with = "deserialize_millis")] - db_query: Duration, - - #[serde(rename = "nub_fit_ms", deserialize_with = "deserialize_millis")] - nub_fit: Duration, - - #[serde(rename = "nub_compute_ms", deserialize_with = "deserialize_millis")] - nub_compute: Duration, - - #[serde(rename = "pro_fit_ms", deserialize_with = "deserialize_millis")] - pro_fit: Duration, - - #[serde(rename = "pro_compute_ms", deserialize_with = "deserialize_millis")] - pro_compute: Duration, - - #[serde(rename = "db_write_ms", deserialize_with = "deserialize_millis")] - db_write: Duration, -} - #[tracing::instrument(skip_all, err)] pub async fn run(cx: Context, cancellation_token: CancellationToken) -> Result<(), Error> { - let Some(script_path) = cx.config().points.calc_filter_path.as_deref() else { - tracing::warn!("no `points.calc-filter-path` configured; points daemon will be disabled"); - return Ok(()); - }; - - let mut python = Python::::new( - script_path.to_owned(), - cx.config().database.url.clone(), - ) - .await?; let mut recalc_ratings_interval = interval(Duration::from_secs(10)); loop { @@ -114,7 +65,7 @@ pub async fn run(cx: Context, cancellation_token: CancellationToken) -> Result<( res = determine_filter_to_recalculate(&cx) => { let (filter_id, priority) = res?; - process_filter(&mut python, &cancellation_token, filter_id).await?; + process_filter(&cx, &cancellation_token, filter_id).await?; update_filters_to_recalculate(&cx, filter_id, priority).await; }, }; @@ -184,42 +135,198 @@ async fn update_filters_to_recalculate( } } -#[tracing::instrument(skip(python))] +/// Holds DB-fetched record data for native recalculation. +#[derive(Debug)] +struct DbRecord { + record_id: [u8; 16], + time: f64, +} + +#[tracing::instrument(skip(cx, cancellation_token))] async fn process_filter( - python: &mut Python, + cx: &Context, cancellation_token: &CancellationToken, filter_id: CourseFilterId, -) -> Result<(), Error> { - let request = PythonRequest { filter_id }; +) -> Result<(), database::Error> { + tracing::debug!(%filter_id, "recalculating filter natively"); - loop { - tracing::debug!(?request); - match cancellation_token - .run_until_cancelled(python.send_request(&request)) - .await - { - None => { - tracing::debug!("cancelled"); - break Ok(()); - }, - Some(Ok(response)) => { - tracing::debug!(?response); - break Ok(()); - }, - Some(Err(err)) => { - tracing::error!(%err, "failed to execute python request"); - python.reset().map_err(Error::Python).await?; - sleep(Duration::from_secs(1)).await; - }, - } + let db = cx.database().as_ref(); + + // Nub records (sorted by time ASC) + let nub_rows = sqlx::query( + "SELECT record_id, time FROM BestNubRecords WHERE filter_id = ? ORDER BY time ASC" + ) + .bind(filter_id) + .fetch_all(db) + .await?; + + // Pro records (sorted by time ASC) + let pro_rows = sqlx::query( + "SELECT record_id, time FROM BestProRecords WHERE filter_id = ? ORDER BY time ASC" + ) + .bind(filter_id) + .fetch_all(db) + .await?; + + // Filter tiers + let tiers_row = sqlx::query( + "SELECT nub_tier, pro_tier FROM CourseFilters WHERE id = ?" + ) + .bind(filter_id) + .fetch_optional(db) + .await?; + + let Some(tiers_row) = tiers_row else { + tracing::warn!(%filter_id, "filter not found in CourseFilters"); + return Ok(()); + }; + + let nub_tier: crate::maps::courses::Tier = tiers_row.try_get(0)?; + let pro_tier: crate::maps::courses::Tier = tiers_row.try_get(1)?; + + // Previous distribution parameters for warm start + let prev_nub_row = sqlx::query( + "SELECT a, b, loc, scale, top_scale + FROM PointDistributionData + WHERE filter_id = ? AND (NOT is_pro_leaderboard)" + ) + .bind(filter_id) + .fetch_optional(db) + .await?; + + let prev_nub_params = prev_nub_row.map(|row| NigParams { + a: row.get(0), + b: row.get(1), + loc: row.get(2), + scale: row.get(3), + top_scale: row.get(4), + }); + + let prev_pro_row = sqlx::query( + "SELECT a, b, loc, scale, top_scale + FROM PointDistributionData + WHERE filter_id = ? AND is_pro_leaderboard" + ) + .bind(filter_id) + .fetch_optional(db) + .await?; + + let prev_pro_params = prev_pro_row.map(|row| NigParams { + a: row.get(0), + b: row.get(1), + loc: row.get(2), + scale: row.get(3), + top_scale: row.get(4), + }); + + let nub_records: Vec = nub_rows.iter().map(|row| { + let bytes: &[u8] = row.get(0); + let mut id = [0u8; 16]; + id.copy_from_slice(bytes); + DbRecord { record_id: id, time: row.get(1) } + }).collect(); + let pro_records: Vec = pro_rows.iter().map(|row| { + let bytes: &[u8] = row.get(0); + let mut id = [0u8; 16]; + id.copy_from_slice(bytes); + DbRecord { record_id: id, time: row.get(1) } + }).collect(); + + let nub_recs: Vec = nub_records.iter().map(|r| + calculator::BestRecordData { record_id: r.record_id, time: r.time } + ).collect(); + let pro_recs: Vec = pro_records.iter().map(|r| + calculator::BestRecordData { record_id: r.record_id, time: r.time } + ).collect(); + + // heavy calcs on blocking thread + let result = tokio::task::spawn_blocking(move || { + calculator::recalculate_filter( + &nub_recs, + &pro_recs, + nub_tier, + pro_tier, + prev_nub_params.as_ref(), + prev_pro_params.as_ref(), + ) + }); + + let result = cancellation_token + .run_until_cancelled(result) + .await + .ok_or_else(|| { + database::Error::decode(std::io::Error::other("points recalculation cancelled")) + }) + .and_then(|res| res.map_err(database::Error::decode))?; + + tracing::debug!( + %filter_id, + nub_fitted = result.nub_fitted, + pro_fitted = result.pro_fitted, + "recalculation complete, writing to DB" + ); + + for record in &result.nub_records { + sqlx::query("UPDATE BestNubRecords SET points = ? WHERE record_id = ?") + .bind(record.points) + .bind(record.record_id.as_slice()) + .execute(db) + .await?; + } + + for record in &result.pro_records { + sqlx::query("UPDATE BestProRecords SET points = ? WHERE record_id = ?") + .bind(record.points) + .bind(record.record_id.as_slice()) + .execute(db) + .await?; + } + + if result.nub_fitted { + sqlx::query( + "INSERT INTO PointDistributionData ( + filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale + ) + VALUES (?, FALSE, ?, ?, ?, ?, ?) + ON DUPLICATE KEY UPDATE + a = VALUES(a), + b = VALUES(b), + loc = VALUES(loc), + scale = VALUES(scale), + top_scale = VALUES(top_scale)" + ) + .bind(filter_id) + .bind(result.nub_params.a) + .bind(result.nub_params.b) + .bind(result.nub_params.loc) + .bind(result.nub_params.scale) + .bind(result.nub_params.top_scale) + .execute(db) + .await?; + } + + if result.pro_fitted { + sqlx::query( + "INSERT INTO PointDistributionData ( + filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale + ) + VALUES (?, TRUE, ?, ?, ?, ?, ?) + ON DUPLICATE KEY UPDATE + a = VALUES(a), + b = VALUES(b), + loc = VALUES(loc), + scale = VALUES(scale), + top_scale = VALUES(top_scale)" + ) + .bind(filter_id) + .bind(result.pro_params.a) + .bind(result.pro_params.b) + .bind(result.pro_params.loc) + .bind(result.pro_params.scale) + .bind(result.pro_params.top_scale) + .execute(db) + .await?; } -} -fn deserialize_millis<'de, D>(deserializer: D) -> Result -where - D: serde::Deserializer<'de>, -{ - >::deserialize(deserializer) - .map(|millis| millis / 1000.0) - .map(Duration::from_secs_f64) + Ok(()) } diff --git a/crates/cs2kz/src/python.rs b/crates/cs2kz/src/python.rs deleted file mode 100644 index affb9821..00000000 --- a/crates/cs2kz/src/python.rs +++ /dev/null @@ -1,219 +0,0 @@ -use std::bstr::ByteStr; -use std::ffi::OsStr; -use std::marker::PhantomData; -use std::path::{Path, PathBuf}; -use std::process::Stdio; -use std::time::Duration; -use std::{fmt, io, mem}; - -use futures_util::FutureExt as _; -use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader}; -use tokio::process::{Child, ChildStderr, ChildStdout, Command}; -use tokio::sync::OnceCell; -use tokio::task; -use tokio_util::task::AbortOnDropHandle; -use tokio_util::time::FutureExt as _; -use tracing::Instrument as _; -use url::Url; - -#[derive(Debug)] -pub struct Python { - script_path: PathBuf, - database_url: Url, - process: Child, - process_stdout: BufReader, - process_stderr_reader_task: AbortOnDropHandle>, - _request: PhantomData Request>, - _response: PhantomData Response>, -} - -#[derive(Debug, serde::Deserialize)] -#[serde(untagged)] -enum PythonResponse { - Success(T), - Error { - #[serde(rename = "error")] - message: String, - }, -} - -impl Python { - pub async fn new(script_path: PathBuf, database_url: Url) -> io::Result { - let (mut process, process_stderr_reader_task) = - spawn_script(&script_path, &database_url).await?; - - let process_stdout = process - .stdout - .take() - .map(BufReader::new) - .expect("we only take stdout once"); - - Ok(Self { - script_path, - database_url, - process, - process_stdout, - process_stderr_reader_task: AbortOnDropHandle::new(process_stderr_reader_task), - _request: PhantomData, - _response: PhantomData, - }) - } - - #[tracing::instrument(skip(self), err)] - pub async fn send_request(&mut self, request: &Request) -> io::Result - where - Request: fmt::Debug + serde::Serialize, - Response: for<'de> serde::Deserialize<'de>, - { - let mut serialized_request = - serde_json::to_vec(request).expect("requests should serialize to JSON"); - serialized_request.push(b'\n'); - - let mut serialized_response = Vec::with_capacity(128); - - 'outer: loop { - serialized_response.clear(); - - if let Some(exit_status) = self.process.try_wait()? { - tracing::warn!(?exit_status, "python process exited"); - self.reset().await?; - continue; - } - - tracing::trace!( - request = str::from_utf8(&serialized_request).unwrap(), - "writing request to python stdin" - ); - { - let stdin = self.process.stdin.as_mut().expect("we never close stdin"); - stdin.write_all(&serialized_request[..]).await?; - stdin.flush().await?; - } - - tracing::trace!("reading response from python stdout"); - for _ in 0..3 { - match self - .process_stdout - .read_until(b'\n', &mut serialized_response) - .timeout(Duration::from_secs(10)) - .await - { - Ok(Ok(_)) => break, - Ok(Err(err)) => { - tracing::error!(%err, "failed to read from stdout"); - self.reset().await?; - continue 'outer; - }, - Err(_elapsed) => { - tracing::warn!( - stdout = ?ByteStr::new(self.process_stdout.buffer()), - response_buf = ?ByteStr::new(&serialized_response), - "still waiting for response", - ); - }, - } - } - - break if serialized_response.is_empty() { - Err(io::Error::new(io::ErrorKind::TimedOut, "did not complete request in time")) - } else { - serde_json::from_slice::>(&serialized_response[..]) - .map_err(|err| io::Error::new(io::ErrorKind::InvalidData, err)) - .and_then(|response| match response { - PythonResponse::Success(res) => Ok(res), - PythonResponse::Error { message } => Err(io::Error::other(message)), - }) - }; - } - } - - pub async fn reset(&mut self) -> io::Result<()> { - let (process, process_stderr_reader_task) = - spawn_script(&self.script_path, &self.database_url).await?; - self.process = process; - self.process_stdout = self - .process - .stdout - .take() - .map(BufReader::new) - .expect("we only take stdout once"); - - let old_process_stderr_reader_task = mem::replace( - &mut self.process_stderr_reader_task, - AbortOnDropHandle::new(process_stderr_reader_task), - ); - - if let Some(Ok(Err(err))) = old_process_stderr_reader_task.now_or_never() { - tracing::error!(%err, "python stderr task encountered an error"); - } - - Ok(()) - } -} - -async fn spawn_script( - path: &Path, - database_url: &Url, -) -> io::Result<(Child, task::JoinHandle>)> { - let span = tracing::debug_span!("python", script_path = %path.display()); - let executable_name = resolve_executable_name().await?; - let mut child = Command::new(executable_name) - .arg(path) - .env("DATABASE_URL", database_url.as_str()) - .stdin(Stdio::piped()) - .stdout(Stdio::piped()) - .stderr(Stdio::piped()) - .spawn()?; - - let stderr = child.stderr.take().expect("we only take stderr once"); - let task = task::spawn(read_from_stderr(stderr).instrument(span)); - - Ok((child, task)) -} - -async fn resolve_executable_name() -> io::Result<&'static OsStr> { - #[cfg(unix)] - const EXECUTABLE_NAMES: &[&str] = &["python3", "python", "py"]; - - #[cfg(windows)] - const EXECUTABLE_NAMES: &[&str] = &["python3.exe", "python.exe", "py.exe"]; - - static EXECUTABLE_NAME: OnceCell<&OsStr> = OnceCell::const_new(); - - EXECUTABLE_NAME - .get_or_try_init(async || { - for name in EXECUTABLE_NAMES { - if Command::new(name) - .arg("--version") - .output() - .await? - .status - .success() - { - return Ok(OsStr::new(name)); - } - } - - Err(io::Error::other("failed to find suitable python executable")) - }) - .await - .copied() -} - -async fn read_from_stderr(stderr: ChildStderr) -> io::Result<()> { - let mut stderr = BufReader::new(stderr); - let mut line = String::new(); - - while let 1.. = stderr.read_line(&mut line).await? { - if let Some(c) = line.pop() - && c != '\n' - { - line.push(c); - } - - tracing::debug!(line); - line.clear(); - } - - Ok(()) -} diff --git a/crates/cs2kz/src/records.rs b/crates/cs2kz/src/records.rs index 6624b865..378eee08 100644 --- a/crates/cs2kz/src/records.rs +++ b/crates/cs2kz/src/records.rs @@ -14,12 +14,8 @@ use crate::num::AsF64; use crate::pagination::{Limit, Offset, Paginated}; use crate::players::{CalculateRatingError, PlayerId, PlayerInfo}; use crate::plugin::PluginVersionId; -use crate::points::calculator::{ - CalculatePointsError, - Request as CalculatePointsRequest, - Response as CalculatePointsResponse, -}; -use crate::points::{self, DistributionParameters}; +use crate::points::calculator::{self, Request as CalculatePointsRequest, Response as CalculatePointsResponse}; +use crate::points::{self, NigParams}; use crate::servers::{ServerId, ServerInfo}; use crate::styles::{ClientStyleInfo, Styles}; use crate::time::Seconds; @@ -193,10 +189,6 @@ pub struct SubmittedPB { #[derive(Debug, Display, Error, From)] pub enum SubmitRecordError { - #[display("{_0}")] - #[from] - CalculatePoints(CalculatePointsError), - #[display("{_0}")] #[from] CalculateRating(CalculateRatingError), @@ -284,7 +276,7 @@ pub async fn submit( .await?; let nub_dist = sqlx::query_as!( - DistributionParameters, + NigParams, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? @@ -337,7 +329,7 @@ pub async fn submit( let pro_data = if teleports == 0 { let pro_dist = sqlx::query_as!( - DistributionParameters, + NigParams, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? @@ -384,7 +376,6 @@ pub async fn submit( let points = if nub_leaderboard_size > points::SMALL_LEADERBOARD_THRESHOLD && nub_data.dist_params.is_some() - && let Some(calc) = cx.points_calculator() { let request = CalculatePointsRequest { time: time.as_f64(), @@ -392,7 +383,7 @@ pub async fn submit( pro_data: pro_data.clone().filter(|data| data.dist_params.is_some()), }; - let mut response = calc.calculate(request.clone()).await?; + let mut response = calculator::calculate(&request); if let Some(ref pro_data) = pro_data && request.pro_data.is_none() { response.pro_fraction = Some(points::for_small_leaderboard( diff --git a/crates/nig/Cargo.toml b/crates/nig/Cargo.toml new file mode 100644 index 00000000..979c35e9 --- /dev/null +++ b/crates/nig/Cargo.toml @@ -0,0 +1,11 @@ +[package] +name = "nig" +version = "0.1.0" +authors = ["Praetor"] +edition = 2026 +homepage.workspace = true +repository.workspace = true +license = "AGPL-3.0-only" + +[dependencies] +serde = { workspace = true, features = ["derive"] } diff --git a/crates/nig/src/bessel.rs b/crates/nig/src/bessel.rs new file mode 100644 index 00000000..df2d40c1 --- /dev/null +++ b/crates/nig/src/bessel.rs @@ -0,0 +1,173 @@ +/// Evaluates the rational function P(z)/Q(z) using Horner's method. +/// +pub(crate) fn evaluate_rational(p: &[f64], q: &[f64], z: f64) -> f64 { + if p.is_empty() || q.is_empty() { + return 0.0; + } + + let mut pn = p[p.len() - 1]; + for i in (0..p.len() - 1).rev() { + pn = pn * z + p[i]; + } + + let mut qn = q[q.len() - 1]; + for i in (0..q.len() - 1).rev() { + qn = qn * z + q[i]; + } + + pn / qn +} + +/// Bessel K1 functions adapted from Boost +/// https://github.com/boostorg/math/blob/develop/include/boost/math/special_functions/detail/bessel_k1.hpp +pub(crate) fn bessel_k1(x: f64) -> f64 { + if x <= 0.0 { + return f64::INFINITY; + } + + if x <= 1.0 { + bessel_k1_small(x) + } else { + bessel_k1_large(x) + } +} + +fn bessel_k1_small(x: f64) -> f64 { + const Y: f64 = 8.69547128677368164e-02; + + const P1: [f64; 4] = [ + -3.62137953440350228e-03, + 7.11842087490330300e-03, + 1.00302560256614306e-05, + 1.77231085381040811e-06, + ]; + const Q1: [f64; 4] = [ + 1.00000000000000000e+00, + -4.80414794429043831e-02, + 9.85972641934416525e-04, + -8.91196859397070326e-06, + ]; + + const P2: [f64; 4] = [ + -3.07965757829206184e-01, + -7.80929703673074907e-02, + -2.70619343754051620e-03, + -2.49549522229072008e-05, + ]; + const Q2: [f64; 4] = [ + 1.00000000000000000e+00, + -2.36316836412163098e-02, + 2.64524577525962719e-04, + -1.49749618004162787e-06, + ]; + + let a = x * x / 4.0; + let log_term = ((evaluate_rational(&P1, &Q1, a) + Y) * a * a + a / 2.0 + 1.0) * x / 2.0; + + evaluate_rational(&P2, &Q2, x * x) * x + 1.0 / x + x.ln() * log_term +} + +fn bessel_k1_large(x: f64) -> f64 { + const Y: f64 = 1.45034217834472656e+00; + + const P: [f64; 9] = [ + -1.97028041029226295e-01, + -2.32408961548087617e+00, + -7.98269784507699938e+00, + -2.39968410774221632e+00, + 3.28314043780858713e+01, + 5.67713761158496058e+01, + 3.30907788466509823e+01, + 6.62582288933739787e+00, + 3.08851840645286691e-01, + ]; + const Q: [f64; 9] = [ + 1.00000000000000000e+00, + 1.41811409298826118e+01, + 7.35979466317556420e+01, + 1.77821793937080859e+02, + 2.11014501598705982e+02, + 1.19425262951064454e+02, + 2.88448064302447607e+01, + 2.27912927104139732e+00, + 2.50358186953478678e-02, + ]; + + let rat = evaluate_rational(&P, &Q, 1.0 / x) + Y; + if x < 709.0 { + return rat * (-x).exp() / x.sqrt(); + } + + let exp_half = (-x / 2.0).exp(); + rat * exp_half / x.sqrt() * exp_half +} + +pub(crate) fn bessel_k1_scaled(x: f64) -> f64 { + if x <= 0.0 { + return f64::INFINITY; + } + + if x <= 1.0 { + return bessel_k1(x) * x.exp(); + } + + const Y: f64 = 1.45034217834472656e+00; + + const P: [f64; 9] = [ + -1.97028041029226295e-01, + -2.32408961548087617e+00, + -7.98269784507699938e+00, + -2.39968410774221632e+00, + 3.28314043780858713e+01, + 5.67713761158496058e+01, + 3.30907788466509823e+01, + 6.62582288933739787e+00, + 3.08851840645286691e-01, + ]; + const Q: [f64; 9] = [ + 1.00000000000000000e+00, + 1.41811409298826118e+01, + 7.35979466317556420e+01, + 1.77821793937080859e+02, + 2.11014501598705982e+02, + 1.19425262951064454e+02, + 2.88448064302447607e+01, + 2.27912927104139732e+00, + 2.50358186953478678e-02, + ]; + + (evaluate_rational(&P, &Q, 1.0 / x) + Y) / x.sqrt() +} + +#[cfg(test)] +mod tests { + use super::*; + + fn assert_rel_close(actual: f64, expected: f64, tolerance: f64) { + let rel_error = if expected == 0.0 { + actual.abs() + } else { + (actual - expected).abs() / expected.abs() + }; + assert!( + rel_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, rel error {rel_error:.2e}", + ); + } + + #[test] + fn bessel_k1_matches_reference_values() { + for (x, expected) in [ + (0.001, 9.999962381560855e+02), + (0.1, 9.853844780870606e+00), + (1.0, 6.019072301972346e-01), + (2.0, 1.398658818165225e-01), + (5.0, 4.044613445452163e-03), + (20.0, 5.883057969557038e-10), + (100.0, 4.679853735636910e-45), + (709.0, 5.730317612554602e-310), + ] { + assert_rel_close(bessel_k1(x), expected, 1e-14); + } + } +} diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs new file mode 100644 index 00000000..a43973ee --- /dev/null +++ b/crates/nig/src/distribution.rs @@ -0,0 +1,158 @@ +use crate::bessel::bessel_k1_scaled; + + +pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { + if a <= 0.0 || delta <= 0.0 || b.abs() >= a { + return 0.0; + } + + let gamma = (a * a - b * b).sqrt(); + let z = (x - mu) / delta; + let sqrt_z2p1 = (z * z + 1.0).sqrt(); + let y = a * sqrt_z2p1; + let scaled_bessel = bessel_k1_scaled(y); + + if scaled_bessel <= 0.0 { + return 0.0; + } + + let net_exp = gamma + b * z - y; + let log_pdf = a.ln() + - std::f64::consts::PI.ln() + - delta.ln() + - sqrt_z2p1.ln() + + net_exp + + scaled_bessel.ln(); + + if log_pdf < -745.0 { + // exp(-745) underflows to 0 in f64 + return 0.0; + } + + log_pdf.exp() +} + +// Adapted from C code from https://en.wikipedia.org/wiki/Adaptive_Simpson%27s_method +pub(crate) fn adaptive_simpson(f: impl Fn(f64) -> f64 + Copy, a: f64, b: f64, eps: f64, max_depth: u32) -> Option { + if a == b { + return Some(0.0); + } + + let h = b - a; + let fa = f(a); + let fb = f(b); + let fm = f((a + b) / 2.0); + let whole = (h / 6.0) * (fa + 4.0 * fm + fb); + adaptive_simpson_rec(f, a, b, eps, whole, fa, fb, fm, max_depth) +} + +fn adaptive_simpson_rec( + f: impl Fn(f64) -> f64 + Copy, + a: f64, + b: f64, + eps: f64, + whole: f64, + fa: f64, + fb: f64, + fm: f64, + depth: u32, +) -> Option { + let m = (a + b) / 2.0; + let lm = (a + m) / 2.0; + let rm = (m + b) / 2.0; + + // Numerical trouble: epsilon underflow or interval collapsed + if eps / 2.0 == eps || a == lm { + return None; + } + + let flm = f(lm); + let frm = f(rm); + let h = (b - a) / 2.0; + let left = (h / 6.0) * (fa + 4.0 * flm + fm); + let right = (h / 6.0) * (fm + 4.0 * frm + fb); + let delta = left + right - whole; + + if depth == 0 || delta.abs() <= 15.0 * eps { + return Some(left + right + delta / 15.0); + } + + Some( + adaptive_simpson_rec(f, a, m, eps / 2.0, left, fa, fm, flm, depth - 1)? + + adaptive_simpson_rec(f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, + ) +} + +pub fn nig_survival(a: f64, b: f64, loc: f64, scale: f64, x: f64) -> f64 { + if a <= 0.0 || scale <= 0.0 || b.abs() >= a { + return 0.0; + } + + let gamma = (a * a - b * b).sqrt().max(1e-10); + let mean = loc + scale * b / gamma; + let stddev = (scale * a * a / (gamma * gamma * gamma)).sqrt(); + + let mut upper = mean + 20.0 * stddev; + if upper < x + scale { + upper = x + 10.0 * scale; + } + + adaptive_simpson(|t| nig_pdf(a, b, loc, scale, t), x, upper, 1e-12, 64) + .unwrap_or(0.0) + .clamp(0.0, 1.0) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn assert_rel_close(actual: f64, expected: f64, tolerance: f64) { + let rel_error = if expected == 0.0 { + actual.abs() + } else { + (actual - expected).abs() / expected.abs() + }; + assert!( + rel_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, rel error {rel_error:.2e}", + ); + } + + fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { + let abs_error = (actual - expected).abs(); + assert!( + abs_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", + ); + } + + #[test] + fn nig_pdf_matches_reference_values() { + let (a, b, loc, scale) = + (33.53900289787477, 33.52140111667502, 6.3663207368487065, 0.4480388195262859); + + for (x, expected) in [ + (7.648, 9.314339782198335e-03), + (8.0, 2.138356268395934e-02), + (10.0, 7.240000069597700e-02), + (20.0, 3.070336727949191e-02), + ] { + assert_rel_close(nig_pdf(a, b, loc, scale, x), expected, 1e-10); + } + } + + #[test] + fn nig_survival_matches_reference_values() { + let (a, b, loc, scale) = + (33.53900289787477, 33.52140111667502, 6.3663207368487065, 0.4480388195262859); + + for (x, expected) in [ + (7.0, 9.999892785756547e-01), + (7.648, 9.979326056403205e-01), + (10.0, 8.873317615160712e-01), + (20.0, 3.429376452167427e-01), + ] { + assert_abs_close(nig_survival(a, b, loc, scale, x), expected, 1e-4); + } + } +} diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs new file mode 100644 index 00000000..e7149635 --- /dev/null +++ b/crates/nig/src/fitting.rs @@ -0,0 +1,283 @@ +use crate::bessel::bessel_k1; +use crate::distribution::nig_survival; +use crate::params::{FitResult, NigParams}; + +pub(crate) fn central_diff f64>(f: F, x0: f64) -> f64 { + let eps = f64::EPSILON; + let h = (eps.cbrt() * x0.abs()).max(eps); + let x_plus = x0 + h; + let x_minus = x0 - h; + let denom = x_plus - x_minus; // NOT 2*h — preserves floating-point nuance + (f(x_plus) - f(x_minus)) / denom +} + +pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> FitResult { + let (a, b, loc, scale); + + if let Some(prev) = prev_params.filter(|p| p.a > 0.0) { + a = prev.a; + b = prev.b; + loc = prev.loc; + scale = prev.scale; + } else { + loc = estimate_location(times); + scale = estimate_scale(times, loc); + a = estimate_alpha(times, loc, scale); + b = estimate_beta(times, loc, scale, a); + } + + let (a, b, loc, scale) = optimize_nig(times, a, b, loc, scale); + + let top_scale = { + let sf = nig_survival(a, b, loc, scale, times[0]); + if sf <= 0.0 { 1.0 } else { sf } + }; + + FitResult { params: NigParams { a, b, loc, scale, top_scale }, valid: true } +} + +/// Median-based location estimate. +pub(crate) fn estimate_location(times: &[f64]) -> f64 { + let n = times.len(); + if n % 2 == 0 { + (times[n / 2 - 1] + times[n / 2]) / 2.0 + } else { + times[n / 2] + } +} + +/// Standard-deviation-based scale estimate. +pub(crate) fn estimate_scale(times: &[f64], loc: f64) -> f64 { + let n = times.len() as f64; + let variance = times.iter().map(|&t| (t - loc).powi(2)).sum::() / n; + variance.max(1e-10).sqrt() +} + +/// Kurtosis-based estimate +pub(crate) fn estimate_alpha(times: &[f64], loc: f64, _scale: f64) -> f64 { + let n = times.len() as f64; + let (mut m2, mut m4) = (0.0, 0.0); + for &t in times { + let d = t - loc; + m2 += d * d; + m4 += d * d * d * d; + } + m2 /= n; + m4 /= n; + + if m2 < 1e-10 { + return 1.0; + } + + let kurtosis = m4 / (m2 * m2); + 1.0_f64.max(3.0 / (kurtosis - 3.0).max(0.1)) +} + +/// Skewness-based estimate +pub(crate) fn estimate_beta(times: &[f64], loc: f64, _scale: f64, alpha: f64) -> f64 { + let n = times.len() as f64; + let (mut m2, mut m3) = (0.0, 0.0); + for &t in times { + let d = t - loc; + m2 += d * d; + m3 += d * d * d; + } + m2 /= n; + m3 /= n; + + if m2 < 1e-10 { + return 0.0; + } + + let skewness = m3 / m2.powf(1.5); + let mut beta = skewness * alpha * 0.5; + if beta.abs() >= alpha { + beta = 0.9 * alpha * beta.signum(); + } + beta +} + +pub(crate) fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: f64) -> f64 { + if a <= 0.0 || scale <= 0.0 || b.abs() >= a { + return f64::INFINITY; + } + + let delta = scale; + let mu = loc; + let gamma = (a * a - b * b).sqrt(); + + let mut nll = 0.0; + for &x in times { + let z = (x - mu) / delta; + let sqrt_z2p1 = (z * z + 1.0).sqrt(); + let bessel = bessel_k1(a * sqrt_z2p1); + if bessel <= 0.0 { + return f64::INFINITY; + } + + let log_pdf = a.ln() + - std::f64::consts::PI.ln() + - delta.ln() + + gamma + + b * z + + bessel.ln() + - sqrt_z2p1.ln(); + nll -= log_pdf; + } + nll +} + +pub(crate) fn optimize_nig(times: &[f64], mut a: f64, mut b: f64, mut loc: f64, mut scale: f64) -> (f64, f64, f64, f64) { + const MAX_ITER: usize = 200; + const TOL: f64 = 1e-8; + const INIT_LR: f64 = 0.01; + + let mut lr = INIT_LR; + let mut best_ll = neg_log_likelihood(times, a, b, loc, scale); + + for _iter in 0..MAX_ITER { + // Compute gradients via central differences on each parameter + let da = central_diff(|x| neg_log_likelihood(times, x, b, loc, scale), a); + let db = central_diff(|x| neg_log_likelihood(times, a, x, loc, scale), b); + let dloc = central_diff(|x| neg_log_likelihood(times, a, b, x, scale), loc); + let dscale = central_diff(|x| neg_log_likelihood(times, a, b, loc, x), scale); + + let mut new_a = a - lr * da; + let mut new_b = b - lr * db; + let new_loc = loc - lr * dloc; + let mut new_scale = scale - lr * dscale; + + // Enforce constraints + if new_a < 0.01 { + new_a = 0.01; + } + if new_b.abs() >= new_a { + new_b = 0.99 * new_a * new_b.signum(); + } + if new_scale < 1e-6 { + new_scale = 1e-6; + } + + let new_ll = neg_log_likelihood(times, new_a, new_b, new_loc, new_scale); + if new_ll < best_ll { + if best_ll - new_ll < TOL { + a = new_a; + b = new_b; + loc = new_loc; + scale = new_scale; + break; + } + a = new_a; + b = new_b; + loc = new_loc; + scale = new_scale; + best_ll = new_ll; + // Gradually restore learning rate on success + if lr < INIT_LR { + lr *= 1.2; + if lr > INIT_LR { + lr = INIT_LR; + } + } + } else { + lr *= 0.5; + if lr < 1e-10 { + break; + } + } + } + + (a, b, loc, scale) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { + let abs_error = (actual - expected).abs(); + assert!( + abs_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", + ); + } + + #[test] + fn neg_log_likelihood_returns_inf_for_invalid_params() { + let times = [7.0, 8.0, 9.0, 10.0]; + assert!(neg_log_likelihood(×, 0.0, 1.0, 5.0, 1.0).is_infinite()); + assert!(neg_log_likelihood(×, 2.0, 0.0, 5.0, 0.0).is_infinite()); + assert!(neg_log_likelihood(×, 1.0, 1.0, 5.0, 1.0).is_infinite()); + assert!(neg_log_likelihood(×, 1.0, -1.1, 5.0, 1.0).is_infinite()); + } + + #[test] + fn neg_log_likelihood_returns_finite_for_valid_params() { + let times = [7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0]; + let nll = neg_log_likelihood(×, 5.0, 2.0, 8.0, 1.0); + assert!(nll.is_finite()); + assert!(nll > 0.0); + } + + #[test] + fn estimate_location_is_median() { + let mut times = vec![1.0, 2.0, 3.0, 4.0, 5.0]; + times.sort_by(f64::total_cmp); + assert_abs_close(estimate_location(×), 3.0, 1e-12); + } + + #[test] + fn estimate_scale_is_positive() { + let mut times = vec![8.0, 8.5, 9.0, 9.5, 10.0]; + times.sort_by(f64::total_cmp); + let loc = estimate_location(×); + let scale = estimate_scale(×, loc); + assert!(scale > 0.0); + } + + #[test] + fn estimate_alpha_is_at_least_one() { + let mut times: Vec = (0..100).map(|i| 7.0 + i as f64 * 0.01).collect(); + times.sort_by(f64::total_cmp); + let loc = estimate_location(×); + let scale = estimate_scale(×, loc); + let alpha = estimate_alpha(×, loc, scale); + assert!(alpha >= 1.0); + } + + #[test] + fn estimate_beta_constrained_by_alpha() { + let mut times: Vec = (0..100).map(|i| 7.0 + i as f64 * 0.01).collect(); + times.sort_by(f64::total_cmp); + let loc = estimate_location(×); + let scale = estimate_scale(×, loc); + let alpha = estimate_alpha(×, loc, scale); + let beta = estimate_beta(×, loc, scale, alpha); + assert!(beta.abs() < alpha); + } + + #[test] + fn fit_nig_converges_on_synthetic_data() { + let times: Vec = (0..200) + .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) + .collect(); + let result = fit_nig(×, None); + assert!(result.valid); + assert!(result.params.a > 0.01); + assert!(result.params.b.abs() < result.params.a); + assert!(result.params.scale > 1e-6); + assert!(result.params.top_scale > 0.0); + assert!(result.params.top_scale <= 1.01); + } + + #[test] + fn fit_nig_warm_start_works() { + let times: Vec = (0..200) + .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) + .collect(); + let cold_result = fit_nig(×, None); + let warm_result = fit_nig(×, Some(&cold_result.params)); + assert!(warm_result.valid); + assert_abs_close(warm_result.params.a, cold_result.params.a, 1.0); + } +} diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs new file mode 100644 index 00000000..fc8fdec9 --- /dev/null +++ b/crates/nig/src/lib.rs @@ -0,0 +1,8 @@ +mod bessel; +mod distribution; +mod fitting; +mod params; + +pub use distribution::nig_survival; +pub use fitting::fit_nig; +pub use params::{FitResult, NigParams}; diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs new file mode 100644 index 00000000..49f3e7eb --- /dev/null +++ b/crates/nig/src/params.rs @@ -0,0 +1,16 @@ +use serde::Serialize; + +#[derive(Debug, Clone, Copy, Serialize)] +pub struct NigParams { + pub a: f64, // alpha + pub b: f64, // beta + pub loc: f64, // mu + pub scale: f64, // delta + pub top_scale: f64, // used to normalize the pdf +} + +#[derive(Debug, Clone)] +pub struct FitResult { + pub params: NigParams, + pub valid: bool, +} diff --git a/cs2kz-api.example.toml b/cs2kz-api.example.toml index e722fcf7..b6e34e55 100644 --- a/cs2kz-api.example.toml +++ b/cs2kz-api.example.toml @@ -106,7 +106,3 @@ min-connections = 1 # # 0 will let the API choose an amount max-connections = 0 - -[points] -calc-filter-path = "scripts/calc_filter.py" -calc-run-path = "scripts/calc_run.py" From 62834392e17de364f03c20a86f2a4f6a032beb33 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Sun, 10 May 2026 16:14:30 -0400 Subject: [PATCH 02/24] apply some of the requested changes --- crates/cs2kz-api/src/bin/generator/main.rs | 3 +- crates/cs2kz/src/config/mod.rs | 6 - crates/cs2kz/src/config/points.rs | 11 -- crates/cs2kz/src/points/calculator.rs | 56 +++++---- crates/cs2kz/src/points/daemon.rs | 140 ++++++++++----------- crates/nig/Cargo.toml | 2 +- crates/nig/src/distribution.rs | 16 ++- crates/nig/src/fitting.rs | 6 +- 8 files changed, 115 insertions(+), 125 deletions(-) delete mode 100644 crates/cs2kz/src/config/points.rs diff --git a/crates/cs2kz-api/src/bin/generator/main.rs b/crates/cs2kz-api/src/bin/generator/main.rs index a8e53652..a9963b66 100644 --- a/crates/cs2kz-api/src/bin/generator/main.rs +++ b/crates/cs2kz-api/src/bin/generator/main.rs @@ -79,8 +79,7 @@ async fn main() -> anyhow::Result<()> { url, min_connections: 1, max_connections: Some(NonZero::::MIN), - }, - points: Default::default(), + } })?; let cx = Context::new(cfg).await?; diff --git a/crates/cs2kz/src/config/mod.rs b/crates/cs2kz/src/config/mod.rs index 4524878f..95f78b6a 100644 --- a/crates/cs2kz/src/config/mod.rs +++ b/crates/cs2kz/src/config/mod.rs @@ -1,15 +1,9 @@ mod database; pub use database::DatabaseConfig; -mod points; -pub use points::PointsConfig; - #[derive(Debug, Default, serde::Deserialize)] #[serde(default, rename_all = "kebab-case", deny_unknown_fields)] pub struct Config { #[serde(default)] pub database: DatabaseConfig, - - #[serde(default)] - pub points: PointsConfig, } diff --git a/crates/cs2kz/src/config/points.rs b/crates/cs2kz/src/config/points.rs deleted file mode 100644 index f6bac24b..00000000 --- a/crates/cs2kz/src/config/points.rs +++ /dev/null @@ -1,11 +0,0 @@ -use serde::Deserialize; - -/// Configuration for the points system. -/// -/// All distribution fitting and recalculation is now done in pure Rust -/// (see `crate::points::calculator`), so no external script paths are needed. -#[derive(Debug, Default, Deserialize)] -#[serde(default, rename_all = "kebab-case", deny_unknown_fields)] -pub struct PointsConfig { - // (no fields needed — kept for future extensibility) -} diff --git a/crates/cs2kz/src/points/calculator.rs b/crates/cs2kz/src/points/calculator.rs index 99f562a6..64179a4b 100644 --- a/crates/cs2kz/src/points/calculator.rs +++ b/crates/cs2kz/src/points/calculator.rs @@ -1,5 +1,6 @@ use crate::maps::courses::Tier; use crate::points::{self, NigParams}; +use crate::records::RecordId; #[derive(Debug, Clone, serde::Serialize)] pub struct Request { @@ -24,39 +25,17 @@ pub struct LeaderboardData { pub top_time: f64, } -pub fn calculate(request: &Request) -> Response { - let nub_fraction = dist_points_portion(request.time, &request.nub_data); - - let pro_fraction = request.pro_data.as_ref().map(|data| { - let pro_fraction = dist_points_portion(request.time, data); - pro_fraction.max(nub_fraction) - }); - - Response { nub_fraction, pro_fraction } -} - -fn dist_points_portion(time: f64, data: &LeaderboardData) -> f64 { - if data.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD || data.dist_params.is_none() { - return points::for_small_leaderboard(data.tier, data.top_time, time); - } - - let dist = data.dist_params.expect("checked above"); - let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; - (nig::nig_survival(dist.a, dist.b, dist.loc, dist.scale, time) / top_scale).clamp(0.0, 1.0) -} - - /// Input record data for batch recalculation. #[derive(Debug, Clone)] pub struct BestRecordData { - pub record_id: [u8; 16], + pub record_id: RecordId, pub time: f64, } /// Output record with recalculated distribution points fraction. #[derive(Debug, Clone)] pub struct RecordPoints { - pub record_id: [u8; 16], + pub record_id: RecordId, pub points: f64, } @@ -71,6 +50,29 @@ pub struct RecalculateFilterResult { pub pro_fitted: bool, } +pub fn calculate(request: &Request) -> Response { + let nub_fraction = dist_points_portion(request.time, &request.nub_data); + + let pro_fraction = request.pro_data.as_ref().map(|data| { + let pro_fraction = dist_points_portion(request.time, data); + f64::max(pro_fraction, nub_fraction) + }); + + Response { nub_fraction, pro_fraction } +} + +fn dist_points_portion(time: f64, data: &LeaderboardData) -> f64 { + if data.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD { + return points::for_small_leaderboard(data.tier, data.top_time, time); + } + + let Some(dist) = data.dist_params else { + return points::for_small_leaderboard(data.tier, data.top_time, time); + }; + let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; + (nig::nig_survival(dist.a, dist.b, dist.loc, dist.scale, time) / top_scale).clamp(0.0, 1.0) +} + /// Recompute point fractions for all records in a filter pub fn recalculate_filter( nub_records: &[BestRecordData], @@ -98,7 +100,7 @@ pub fn recalculate_filter( let nub_wr = nub_times.first().copied().unwrap_or(0.0); let nub_leaderboard = LeaderboardData { - dist_params: if nub_fitted { Some(nub_params) } else { None }, + dist_params: nub_fitted.then_some(nub_params), tier: nub_tier, leaderboard_size: nub_records.len() as u64, top_time: nub_wr, @@ -127,7 +129,7 @@ pub fn recalculate_filter( let pro_wr = pro_times.first().copied().unwrap_or(0.0); let pro_leaderboard = LeaderboardData { - dist_params: if pro_fitted { Some(pro_params) } else { None }, + dist_params: pro_fitted.then_some(pro_params), tier: pro_tier, leaderboard_size: pro_records.len() as u64, top_time: pro_wr, @@ -140,7 +142,7 @@ pub fn recalculate_filter( let nub_fraction = dist_points_portion(r.time, &nub_leaderboard); RecordPoints { record_id: r.record_id, - points: pro_fraction.max(nub_fraction), + points: f64::max(pro_fraction, nub_fraction), } }) .collect(); diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 512ee864..5f8c62ef 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -1,4 +1,5 @@ use std::sync::Arc; +use std::thread; use std::time::Duration; use futures_util::TryFutureExt as _; @@ -8,9 +9,9 @@ use tokio::time::interval; use tokio_util::sync::CancellationToken; use crate::maps::CourseFilterId; +use crate::maps::courses::Tier; use crate::mode::Mode; -use crate::points::calculator; -use crate::points::NigParams; +use crate::points::{NigParams, calculator}; use crate::{Context, database, players}; #[derive(Debug, Clone)] @@ -65,7 +66,7 @@ pub async fn run(cx: Context, cancellation_token: CancellationToken) -> Result<( res = determine_filter_to_recalculate(&cx) => { let (filter_id, priority) = res?; - process_filter(&cx, &cancellation_token, filter_id).await?; + process_filter(&cx, filter_id).await?; update_filters_to_recalculate(&cx, filter_id, priority).await; }, }; @@ -135,60 +136,73 @@ async fn update_filters_to_recalculate( } } -/// Holds DB-fetched record data for native recalculation. -#[derive(Debug)] -struct DbRecord { - record_id: [u8; 16], - time: f64, -} - -#[tracing::instrument(skip(cx, cancellation_token))] -async fn process_filter( - cx: &Context, - cancellation_token: &CancellationToken, - filter_id: CourseFilterId, -) -> Result<(), database::Error> { - tracing::debug!(%filter_id, "recalculating filter natively"); +#[tracing::instrument(skip(cx))] +async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), database::Error> { + tracing::debug!(%filter_id, "recalculating filter"); let db = cx.database().as_ref(); // Nub records (sorted by time ASC) let nub_rows = sqlx::query( - "SELECT record_id, time FROM BestNubRecords WHERE filter_id = ? ORDER BY time ASC" + "SELECT record_id, time + FROM BestNubRecords + WHERE filter_id = ? + ORDER BY time ASC", ) .bind(filter_id) .fetch_all(db) .await?; + let nub_recs: Vec = nub_rows + .iter() + .map(|row| { + Ok(calculator::BestRecordData { + record_id: row.try_get(0)?, + time: row.try_get(1)?, + }) + }) + .collect::>()?; + // Pro records (sorted by time ASC) let pro_rows = sqlx::query( - "SELECT record_id, time FROM BestProRecords WHERE filter_id = ? ORDER BY time ASC" + "SELECT record_id, time + FROM BestProRecords + WHERE filter_id = ? + ORDER BY time ASC", ) .bind(filter_id) .fetch_all(db) .await?; + let pro_recs: Vec = pro_rows + .iter() + .map(|row| { + Ok(calculator::BestRecordData { + record_id: row.try_get(0)?, + time: row.try_get(1)?, + }) + }) + .collect::>()?; + // Filter tiers - let tiers_row = sqlx::query( - "SELECT nub_tier, pro_tier FROM CourseFilters WHERE id = ?" - ) - .bind(filter_id) - .fetch_optional(db) - .await?; + let tiers_row = sqlx::query("SELECT nub_tier, pro_tier FROM CourseFilters WHERE id = ?") + .bind(filter_id) + .fetch_optional(db) + .await?; let Some(tiers_row) = tiers_row else { tracing::warn!(%filter_id, "filter not found in CourseFilters"); return Ok(()); }; - let nub_tier: crate::maps::courses::Tier = tiers_row.try_get(0)?; - let pro_tier: crate::maps::courses::Tier = tiers_row.try_get(1)?; + let nub_tier: Tier = tiers_row.try_get(0)?; + let pro_tier: Tier = tiers_row.try_get(1)?; // Previous distribution parameters for warm start let prev_nub_row = sqlx::query( "SELECT a, b, loc, scale, top_scale FROM PointDistributionData - WHERE filter_id = ? AND (NOT is_pro_leaderboard)" + WHERE filter_id = ? AND (NOT is_pro_leaderboard)", ) .bind(filter_id) .fetch_optional(db) @@ -205,7 +219,7 @@ async fn process_filter( let prev_pro_row = sqlx::query( "SELECT a, b, loc, scale, top_scale FROM PointDistributionData - WHERE filter_id = ? AND is_pro_leaderboard" + WHERE filter_id = ? AND is_pro_leaderboard", ) .bind(filter_id) .fetch_optional(db) @@ -219,45 +233,31 @@ async fn process_filter( top_scale: row.get(4), }); - let nub_records: Vec = nub_rows.iter().map(|row| { - let bytes: &[u8] = row.get(0); - let mut id = [0u8; 16]; - id.copy_from_slice(bytes); - DbRecord { record_id: id, time: row.get(1) } - }).collect(); - let pro_records: Vec = pro_rows.iter().map(|row| { - let bytes: &[u8] = row.get(0); - let mut id = [0u8; 16]; - id.copy_from_slice(bytes); - DbRecord { record_id: id, time: row.get(1) } - }).collect(); - - let nub_recs: Vec = nub_records.iter().map(|r| - calculator::BestRecordData { record_id: r.record_id, time: r.time } - ).collect(); - let pro_recs: Vec = pro_records.iter().map(|r| - calculator::BestRecordData { record_id: r.record_id, time: r.time } - ).collect(); - - // heavy calcs on blocking thread - let result = tokio::task::spawn_blocking(move || { - calculator::recalculate_filter( - &nub_recs, - &pro_recs, - nub_tier, - pro_tier, - prev_nub_params.as_ref(), - prev_pro_params.as_ref(), - ) + // heavy calcs on dedicated thread + let (tx, rx) = tokio::sync::oneshot::channel(); + + thread::spawn({ + let nub_recs = nub_recs.clone(); + let pro_recs = pro_recs.clone(); + let prev_nub_params = prev_nub_params; + let prev_pro_params = prev_pro_params; + + move || { + let result = calculator::recalculate_filter( + &nub_recs, + &pro_recs, + nub_tier, + pro_tier, + prev_nub_params.as_ref(), + prev_pro_params.as_ref(), + ); + let _ = tx.send(result); + } }); - let result = cancellation_token - .run_until_cancelled(result) - .await - .ok_or_else(|| { - database::Error::decode(std::io::Error::other("points recalculation cancelled")) - }) - .and_then(|res| res.map_err(database::Error::decode))?; + let result = rx.await.map_err(|_| { + database::Error::decode(std::io::Error::other("points recalculation thread panicked")) + })?; tracing::debug!( %filter_id, @@ -269,7 +269,7 @@ async fn process_filter( for record in &result.nub_records { sqlx::query("UPDATE BestNubRecords SET points = ? WHERE record_id = ?") .bind(record.points) - .bind(record.record_id.as_slice()) + .bind(record.record_id) .execute(db) .await?; } @@ -277,7 +277,7 @@ async fn process_filter( for record in &result.pro_records { sqlx::query("UPDATE BestProRecords SET points = ? WHERE record_id = ?") .bind(record.points) - .bind(record.record_id.as_slice()) + .bind(record.record_id) .execute(db) .await?; } @@ -293,7 +293,7 @@ async fn process_filter( b = VALUES(b), loc = VALUES(loc), scale = VALUES(scale), - top_scale = VALUES(top_scale)" + top_scale = VALUES(top_scale)", ) .bind(filter_id) .bind(result.nub_params.a) @@ -316,7 +316,7 @@ async fn process_filter( b = VALUES(b), loc = VALUES(loc), scale = VALUES(scale), - top_scale = VALUES(top_scale)" + top_scale = VALUES(top_scale)", ) .bind(filter_id) .bind(result.pro_params.a) diff --git a/crates/nig/Cargo.toml b/crates/nig/Cargo.toml index 979c35e9..f0fb1ee6 100644 --- a/crates/nig/Cargo.toml +++ b/crates/nig/Cargo.toml @@ -2,7 +2,7 @@ name = "nig" version = "0.1.0" authors = ["Praetor"] -edition = 2026 +edition.workspace = true homepage.workspace = true repository.workspace = true license = "AGPL-3.0-only" diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs index a43973ee..452fe9de 100644 --- a/crates/nig/src/distribution.rs +++ b/crates/nig/src/distribution.rs @@ -33,7 +33,13 @@ pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { } // Adapted from C code from https://en.wikipedia.org/wiki/Adaptive_Simpson%27s_method -pub(crate) fn adaptive_simpson(f: impl Fn(f64) -> f64 + Copy, a: f64, b: f64, eps: f64, max_depth: u32) -> Option { +pub(crate) fn adaptive_simpson( + mut f: impl FnMut(f64) -> f64, + a: f64, + b: f64, + eps: f64, + max_depth: u32, +) -> Option { if a == b { return Some(0.0); } @@ -43,11 +49,11 @@ pub(crate) fn adaptive_simpson(f: impl Fn(f64) -> f64 + Copy, a: f64, b: f64, ep let fb = f(b); let fm = f((a + b) / 2.0); let whole = (h / 6.0) * (fa + 4.0 * fm + fb); - adaptive_simpson_rec(f, a, b, eps, whole, fa, fb, fm, max_depth) + adaptive_simpson_rec(&mut f, a, b, eps, whole, fa, fb, fm, max_depth) } fn adaptive_simpson_rec( - f: impl Fn(f64) -> f64 + Copy, + f: &mut impl FnMut(f64) -> f64, a: f64, b: f64, eps: f64, @@ -78,8 +84,8 @@ fn adaptive_simpson_rec( } Some( - adaptive_simpson_rec(f, a, m, eps / 2.0, left, fa, fm, flm, depth - 1)? - + adaptive_simpson_rec(f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, + adaptive_simpson_rec(&mut *f, a, m, eps / 2.0, left, fa, fm, flm, depth - 1)? + + adaptive_simpson_rec(&mut *f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, ) } diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs index e7149635..b6d4ba17 100644 --- a/crates/nig/src/fitting.rs +++ b/crates/nig/src/fitting.rs @@ -2,12 +2,12 @@ use crate::bessel::bessel_k1; use crate::distribution::nig_survival; use crate::params::{FitResult, NigParams}; -pub(crate) fn central_diff f64>(f: F, x0: f64) -> f64 { +pub(crate) fn central_diff(mut f: impl FnMut(f64) -> f64, x0: f64) -> f64 { let eps = f64::EPSILON; - let h = (eps.cbrt() * x0.abs()).max(eps); + let h = eps.cbrt() * x0.abs().max(1.0); // https://docs.sciml.ai/FiniteDiff/dev/epsilons/ let x_plus = x0 + h; let x_minus = x0 - h; - let denom = x_plus - x_minus; // NOT 2*h — preserves floating-point nuance + let denom = x_plus - x_minus; // representable floating point nb (not same as 2h) (f(x_plus) - f(x_minus)) / denom } From d615909756b9b67baf1d1c4f05d238ccba038bf8 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Tue, 12 May 2026 03:37:57 +0200 Subject: [PATCH 03/24] use sqlx macros + ugly batched upsert --- README.md | 5 +- crates/cs2kz/src/points/daemon.rs | 276 +++++++++++++++++++----------- 2 files changed, 180 insertions(+), 101 deletions(-) diff --git a/README.md b/README.md index ade0fcd3..d74c2a16 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ Questions and feedback are appreciated! Feel free to open an issue or join [our The API uses a configuration file called `cs2kz-api.toml`. An example configuration file is provided with all the default values filled in, -copy and modify it as you see fit. `.example.env` and `.docker.example.env` +copy and modify it as you see fit. `.example.env` and `.example.docker.env` should be copied to `.env` and `.docker.env` respectively. Again, change the default values as you see fit. @@ -32,6 +32,9 @@ Install docker and run the following command: ```sh docker compose up -d database + +# required for SQLx compile-time query checking +sqlx migrate run --source crates/cs2kz/migrations ``` To compile the API itself, you can use `cargo`: diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 5f8c62ef..4b44596a 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -3,7 +3,6 @@ use std::thread; use std::time::Duration; use futures_util::TryFutureExt as _; -use sqlx::Row as _; use tokio::sync::Notify; use tokio::time::interval; use tokio_util::sync::CancellationToken; @@ -11,9 +10,30 @@ use tokio_util::sync::CancellationToken; use crate::maps::CourseFilterId; use crate::maps::courses::Tier; use crate::mode::Mode; +use crate::players::PlayerId; use crate::points::{NigParams, calculator}; +use crate::records::RecordId; use crate::{Context, database, players}; +const UPSERT_CHUNK_SIZE: usize = 5_000; // should prob put this somewhe + +#[derive(Debug, Clone, Copy)] +struct BestRecordRow { + filter_id: CourseFilterId, + player_id: PlayerId, + record_id: RecordId, + time: f64, +} + +#[derive(Debug, Clone, Copy)] +struct BestRecordUpsertRow { + filter_id: CourseFilterId, + player_id: PlayerId, + record_id: RecordId, + points: f64, + time: f64, +} + #[derive(Debug, Clone)] pub struct PointsDaemonHandle { notifications: Arc, @@ -142,51 +162,62 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let db = cx.database().as_ref(); - // Nub records (sorted by time ASC) - let nub_rows = sqlx::query( - "SELECT record_id, time + let nub_rows = sqlx::query_as!( + BestRecordRow, + "SELECT + filter_id AS `filter_id: CourseFilterId`, + player_id AS `player_id: PlayerId`, + record_id AS `record_id: RecordId`, + time FROM BestNubRecords WHERE filter_id = ? ORDER BY time ASC", + filter_id, ) - .bind(filter_id) .fetch_all(db) .await?; - let nub_recs: Vec = nub_rows + let nub_recs = nub_rows .iter() - .map(|row| { - Ok(calculator::BestRecordData { - record_id: row.try_get(0)?, - time: row.try_get(1)?, - }) + .map(|row| calculator::BestRecordData { + record_id: row.record_id, + time: row.time, }) - .collect::>()?; + .collect::>(); // Pro records (sorted by time ASC) - let pro_rows = sqlx::query( - "SELECT record_id, time + let pro_rows = sqlx::query_as!( + BestRecordRow, + "SELECT + filter_id AS `filter_id: CourseFilterId`, + player_id AS `player_id: PlayerId`, + record_id AS `record_id: RecordId`, + time FROM BestProRecords WHERE filter_id = ? ORDER BY time ASC", + filter_id, ) - .bind(filter_id) .fetch_all(db) .await?; - let pro_recs: Vec = pro_rows + let pro_recs = pro_rows .iter() - .map(|row| { - Ok(calculator::BestRecordData { - record_id: row.try_get(0)?, - time: row.try_get(1)?, - }) + .map(|row| calculator::BestRecordData { + record_id: row.record_id, + time: row.time, }) - .collect::>()?; + .collect::>(); // Filter tiers - let tiers_row = sqlx::query("SELECT nub_tier, pro_tier FROM CourseFilters WHERE id = ?") - .bind(filter_id) + let tiers_row = sqlx::query!( + "SELECT + nub_tier AS `nub_tier: Tier`, + pro_tier AS `pro_tier: Tier` + FROM CourseFilters + WHERE id = ?", + filter_id, + ) .fetch_optional(db) .await?; @@ -195,44 +226,30 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d return Ok(()); }; - let nub_tier: Tier = tiers_row.try_get(0)?; - let pro_tier: Tier = tiers_row.try_get(1)?; + let nub_tier = tiers_row.nub_tier; + let pro_tier = tiers_row.pro_tier; // Previous distribution parameters for warm start - let prev_nub_row = sqlx::query( + let prev_nub_params = sqlx::query_as!( + NigParams, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? AND (NOT is_pro_leaderboard)", + filter_id, ) - .bind(filter_id) .fetch_optional(db) .await?; - let prev_nub_params = prev_nub_row.map(|row| NigParams { - a: row.get(0), - b: row.get(1), - loc: row.get(2), - scale: row.get(3), - top_scale: row.get(4), - }); - - let prev_pro_row = sqlx::query( + let prev_pro_params = sqlx::query_as!( + NigParams, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? AND is_pro_leaderboard", + filter_id, ) - .bind(filter_id) .fetch_optional(db) .await?; - let prev_pro_params = prev_pro_row.map(|row| NigParams { - a: row.get(0), - b: row.get(1), - loc: row.get(2), - scale: row.get(3), - top_scale: row.get(4), - }); - // heavy calcs on dedicated thread let (tx, rx) = tokio::sync::oneshot::channel(); @@ -266,66 +283,125 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d "recalculation complete, writing to DB" ); - for record in &result.nub_records { - sqlx::query("UPDATE BestNubRecords SET points = ? WHERE record_id = ?") - .bind(record.points) - .bind(record.record_id) - .execute(db) - .await?; - } + let nub_upsert_rows = build_upsert_rows(nub_rows, &result.nub_records)?; + let pro_upsert_rows = build_upsert_rows(pro_rows, &result.pro_records)?; - for record in &result.pro_records { - sqlx::query("UPDATE BestProRecords SET points = ? WHERE record_id = ?") - .bind(record.points) - .bind(record.record_id) - .execute(db) - .await?; - } - - if result.nub_fitted { - sqlx::query( - "INSERT INTO PointDistributionData ( - filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale - ) - VALUES (?, FALSE, ?, ?, ?, ?, ?) - ON DUPLICATE KEY UPDATE - a = VALUES(a), - b = VALUES(b), - loc = VALUES(loc), - scale = VALUES(scale), - top_scale = VALUES(top_scale)", + cx.database_transaction(async move |conn| -> Result<_, database::Error> { + upsert_best_records( + conn, + "INSERT INTO BestNubRecords (filter_id, player_id, record_id, points, time)", + &nub_upsert_rows, ) - .bind(filter_id) - .bind(result.nub_params.a) - .bind(result.nub_params.b) - .bind(result.nub_params.loc) - .bind(result.nub_params.scale) - .bind(result.nub_params.top_scale) - .execute(db) .await?; - } - if result.pro_fitted { - sqlx::query( - "INSERT INTO PointDistributionData ( - filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale - ) - VALUES (?, TRUE, ?, ?, ?, ?, ?) - ON DUPLICATE KEY UPDATE - a = VALUES(a), - b = VALUES(b), - loc = VALUES(loc), - scale = VALUES(scale), - top_scale = VALUES(top_scale)", + upsert_best_records( + conn, + "INSERT INTO BestProRecords (filter_id, player_id, record_id, points, time)", + &pro_upsert_rows, ) - .bind(filter_id) - .bind(result.pro_params.a) - .bind(result.pro_params.b) - .bind(result.pro_params.loc) - .bind(result.pro_params.scale) - .bind(result.pro_params.top_scale) - .execute(db) .await?; + + if result.nub_fitted { + sqlx::query!( + "INSERT INTO PointDistributionData ( + filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale + ) + VALUES (?, FALSE, ?, ?, ?, ?, ?) + ON DUPLICATE KEY UPDATE + a = VALUES(a), + b = VALUES(b), + loc = VALUES(loc), + scale = VALUES(scale), + top_scale = VALUES(top_scale)", + filter_id, + result.nub_params.a, + result.nub_params.b, + result.nub_params.loc, + result.nub_params.scale, + result.nub_params.top_scale, + ) + .execute(&mut *conn) + .await?; + } + + if result.pro_fitted { + sqlx::query!( + "INSERT INTO PointDistributionData ( + filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale + ) + VALUES (?, TRUE, ?, ?, ?, ?, ?) + ON DUPLICATE KEY UPDATE + a = VALUES(a), + b = VALUES(b), + loc = VALUES(loc), + scale = VALUES(scale), + top_scale = VALUES(top_scale)", + filter_id, + result.pro_params.a, + result.pro_params.b, + result.pro_params.loc, + result.pro_params.scale, + result.pro_params.top_scale, + ) + .execute(&mut *conn) + .await?; + } + + Ok(()) + }) + .await?; + + Ok(()) +} + +fn build_upsert_rows( + rows: Vec, + recalculated_records: &[calculator::RecordPoints], +) -> Result, database::Error> { + if rows.len() != recalculated_records.len() { // should never happen ? + return Err(database::Error::decode(std::io::Error::other( + "recalculated record count does not match fetched best record rows", + ))); + } + + rows.into_iter() // deterministic order because both queries have ORDER BY time ASC + .zip(recalculated_records) + .map(|(row, recalculated_record)| { + if row.record_id != recalculated_record.record_id { // should never happen ? + return Err(database::Error::decode(std::io::Error::other( + "recalculated record order no longer matches fetched best record rows", + ))); + } + + Ok(BestRecordUpsertRow { + filter_id: row.filter_id, + player_id: row.player_id, + record_id: row.record_id, + points: recalculated_record.points, + time: row.time, + }) + }) + .collect() +} + +async fn upsert_best_records( + conn: &mut database::Connection, + insert_prefix: &'static str, + rows: &[BestRecordUpsertRow], +) -> Result<(), database::Error> { + for chunk in rows.chunks(UPSERT_CHUNK_SIZE) { + let mut query = database::QueryBuilder::new(insert_prefix); + + query.push_values(chunk, |mut query, row| { + query.push_bind(row.filter_id); + query.push_bind(row.player_id); + query.push_bind(row.record_id); + query.push_bind(row.points); + query.push_bind(row.time); + }); + + query.push(" ON DUPLICATE KEY UPDATE points = VALUES(points)"); + query.build().execute(&mut *conn).await?; } Ok(()) From c1f5c8178940a13ba4b6b24064f750a44a59699e Mon Sep 17 00:00:00 2001 From: samayala22 Date: Tue, 12 May 2026 03:40:18 +0200 Subject: [PATCH 04/24] remove intermediate materialization --- crates/cs2kz/src/points/daemon.rs | 60 ++++++++++--------------------- 1 file changed, 19 insertions(+), 41 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 4b44596a..76bd31eb 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -25,15 +25,6 @@ struct BestRecordRow { time: f64, } -#[derive(Debug, Clone, Copy)] -struct BestRecordUpsertRow { - filter_id: CourseFilterId, - player_id: PlayerId, - record_id: RecordId, - points: f64, - time: f64, -} - #[derive(Debug, Clone)] pub struct PointsDaemonHandle { notifications: Arc, @@ -283,21 +274,20 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d "recalculation complete, writing to DB" ); - let nub_upsert_rows = build_upsert_rows(nub_rows, &result.nub_records)?; - let pro_upsert_rows = build_upsert_rows(pro_rows, &result.pro_records)?; - cx.database_transaction(async move |conn| -> Result<_, database::Error> { upsert_best_records( conn, "INSERT INTO BestNubRecords (filter_id, player_id, record_id, points, time)", - &nub_upsert_rows, + &nub_rows, + &result.nub_records, ) .await?; upsert_best_records( conn, "INSERT INTO BestProRecords (filter_id, player_id, record_id, points, time)", - &pro_upsert_rows, + &pro_rows, + &result.pro_records, ) .await?; @@ -354,49 +344,37 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d Ok(()) } -fn build_upsert_rows( - rows: Vec, +async fn upsert_best_records( + conn: &mut database::Connection, + insert_prefix: &'static str, + rows: &[BestRecordRow], recalculated_records: &[calculator::RecordPoints], -) -> Result, database::Error> { - if rows.len() != recalculated_records.len() { // should never happen ? +) -> Result<(), database::Error> { + if rows.len() != recalculated_records.len() { return Err(database::Error::decode(std::io::Error::other( "recalculated record count does not match fetched best record rows", ))); } - rows.into_iter() // deterministic order because both queries have ORDER BY time ASC - .zip(recalculated_records) - .map(|(row, recalculated_record)| { - if row.record_id != recalculated_record.record_id { // should never happen ? + for (row_chunk, recalculated_chunk) in rows + .chunks(UPSERT_CHUNK_SIZE) + .zip(recalculated_records.chunks(UPSERT_CHUNK_SIZE)) + { + for (row, recalculated_record) in row_chunk.iter().zip(recalculated_chunk.iter()) { + if row.record_id != recalculated_record.record_id { return Err(database::Error::decode(std::io::Error::other( "recalculated record order no longer matches fetched best record rows", ))); } + } - Ok(BestRecordUpsertRow { - filter_id: row.filter_id, - player_id: row.player_id, - record_id: row.record_id, - points: recalculated_record.points, - time: row.time, - }) - }) - .collect() -} - -async fn upsert_best_records( - conn: &mut database::Connection, - insert_prefix: &'static str, - rows: &[BestRecordUpsertRow], -) -> Result<(), database::Error> { - for chunk in rows.chunks(UPSERT_CHUNK_SIZE) { let mut query = database::QueryBuilder::new(insert_prefix); - query.push_values(chunk, |mut query, row| { + query.push_values(row_chunk.iter().zip(recalculated_chunk.iter()), |mut query, (row, recalculated_record)| { query.push_bind(row.filter_id); query.push_bind(row.player_id); query.push_bind(row.record_id); - query.push_bind(row.points); + query.push_bind(recalculated_record.points); query.push_bind(row.time); }); From 1ad64c631330f2e1efbbc2119c36201382a2fdc3 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 00:28:13 +0200 Subject: [PATCH 05/24] cleanups --- crates/cs2kz-api/src/bin/generator/main.rs | 2 +- crates/cs2kz/src/context.rs | 2 +- crates/cs2kz/src/points/calculator.rs | 246 ++++++++------------- crates/cs2kz/src/points/daemon.rs | 85 ++++--- crates/cs2kz/src/records.rs | 45 ++-- crates/nig/src/distribution.rs | 8 +- crates/nig/src/fitting.rs | 20 +- crates/nig/src/params.rs | 8 +- 8 files changed, 165 insertions(+), 251 deletions(-) diff --git a/crates/cs2kz-api/src/bin/generator/main.rs b/crates/cs2kz-api/src/bin/generator/main.rs index a9963b66..a872445b 100644 --- a/crates/cs2kz-api/src/bin/generator/main.rs +++ b/crates/cs2kz-api/src/bin/generator/main.rs @@ -79,7 +79,7 @@ async fn main() -> anyhow::Result<()> { url, min_connections: 1, max_connections: Some(NonZero::::MIN), - } + }, })?; let cx = Context::new(cfg).await?; diff --git a/crates/cs2kz/src/context.rs b/crates/cs2kz/src/context.rs index e37aca1b..4ada1bc5 100644 --- a/crates/cs2kz/src/context.rs +++ b/crates/cs2kz/src/context.rs @@ -1,6 +1,6 @@ +use std::fmt; use std::sync::Arc; use std::time::Duration; -use std::fmt; use tokio::task; use tokio_util::sync::CancellationToken; diff --git a/crates/cs2kz/src/points/calculator.rs b/crates/cs2kz/src/points/calculator.rs index 64179a4b..676606e1 100644 --- a/crates/cs2kz/src/points/calculator.rs +++ b/crates/cs2kz/src/points/calculator.rs @@ -2,19 +2,6 @@ use crate::maps::courses::Tier; use crate::points::{self, NigParams}; use crate::records::RecordId; -#[derive(Debug, Clone, serde::Serialize)] -pub struct Request { - pub time: f64, - pub nub_data: LeaderboardData, - pub pro_data: Option, -} - -#[derive(Debug, serde::Deserialize)] -pub struct Response { - pub nub_fraction: f64, - pub pro_fraction: Option, -} - #[derive(Debug, Clone, serde::Serialize)] pub struct LeaderboardData { pub dist_params: Option, @@ -27,7 +14,7 @@ pub struct LeaderboardData { /// Input record data for batch recalculation. #[derive(Debug, Clone)] -pub struct BestRecordData { +pub struct RecordTime { pub record_id: RecordId, pub time: f64, } @@ -39,121 +26,78 @@ pub struct RecordPoints { pub points: f64, } -/// Result of a batch filter recalculation. +/// Result of a leaderboard recalculation. #[derive(Debug, Clone)] -pub struct RecalculateFilterResult { - pub nub_records: Vec, - pub pro_records: Vec, - pub nub_params: NigParams, - pub pro_params: NigParams, - pub nub_fitted: bool, - pub pro_fitted: bool, +pub struct RecalculatedLeaderboard { + pub leaderboard: LeaderboardData, + pub records: Vec, + pub params: NigParams, + pub fitted: bool, } -pub fn calculate(request: &Request) -> Response { - let nub_fraction = dist_points_portion(request.time, &request.nub_data); - - let pro_fraction = request.pro_data.as_ref().map(|data| { - let pro_fraction = dist_points_portion(request.time, data); - f64::max(pro_fraction, nub_fraction) - }); - - Response { nub_fraction, pro_fraction } -} - -fn dist_points_portion(time: f64, data: &LeaderboardData) -> f64 { - if data.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD { - return points::for_small_leaderboard(data.tier, data.top_time, time); +pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { + if leaderboard.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD { + return points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); } - let Some(dist) = data.dist_params else { - return points::for_small_leaderboard(data.tier, data.top_time, time); + let Some(dist) = leaderboard.dist_params else { + return points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); }; + let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; (nig::nig_survival(dist.a, dist.b, dist.loc, dist.scale, time) / top_scale).clamp(0.0, 1.0) } -/// Recompute point fractions for all records in a filter -pub fn recalculate_filter( - nub_records: &[BestRecordData], - pro_records: &[BestRecordData], - nub_tier: Tier, - pro_tier: Tier, - prev_nub_params: Option<&NigParams>, - prev_pro_params: Option<&NigParams>, -) -> RecalculateFilterResult { - let zero_params = - NigParams { a: 0.0, b: 0.0, loc: 0.0, scale: 0.0, top_scale: 0.0 }; - - let nub_times: Vec = nub_records.iter().map(|r| r.time).collect(); - let (nub_params, nub_fitted) = if nub_times.len() >= 50 { - let prev = prev_nub_params.copied(); - let result = nig::fit_nig(&nub_times, prev.as_ref()); - if result.valid { - (result.params, true) - } else { - (zero_params, false) - } - } else { - (zero_params, false) - }; - - let nub_wr = nub_times.first().copied().unwrap_or(0.0); - let nub_leaderboard = LeaderboardData { - dist_params: nub_fitted.then_some(nub_params), - tier: nub_tier, - leaderboard_size: nub_records.len() as u64, - top_time: nub_wr, +/// Recompute point fractions for a single leaderboard. +pub fn recalculate_leaderboard( + records: &[RecordTime], + tier: Tier, + prev_params: Option<&NigParams>, +) -> RecalculatedLeaderboard { + let times: Vec = records.iter().map(|record| record.time).collect(); + let (params, fitted) = fit_distribution(×, prev_params); + + let leaderboard = LeaderboardData { + dist_params: fitted.then_some(params), + tier, + leaderboard_size: records.len() as u64, + top_time: times.first().copied().unwrap_or(0.0), }; - let new_nub_records: Vec = nub_records + let recalculated_records = records .iter() - .map(|r| RecordPoints { - record_id: r.record_id, - points: dist_points_portion(r.time, &nub_leaderboard), + .map(|record| RecordPoints { + record_id: record.record_id, + points: calculate_fraction(record.time, &leaderboard), }) .collect(); - let pro_times: Vec = pro_records.iter().map(|r| r.time).collect(); - let (pro_params, pro_fitted) = if pro_times.len() >= 50 { - let prev = prev_pro_params.copied(); - let result = nig::fit_nig(&pro_times, prev.as_ref()); - if result.valid { - (result.params, true) - } else { - (zero_params, false) - } - } else { - (zero_params, false) - }; + RecalculatedLeaderboard { + leaderboard, + records: recalculated_records, + params, + fitted, + } +} - let pro_wr = pro_times.first().copied().unwrap_or(0.0); - let pro_leaderboard = LeaderboardData { - dist_params: pro_fitted.then_some(pro_params), - tier: pro_tier, - leaderboard_size: pro_records.len() as u64, - top_time: pro_wr, +fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> (NigParams, bool) { + let zero_params = NigParams { + a: 0.0, + b: 0.0, + loc: 0.0, + scale: 0.0, + top_scale: 0.0, }; - let new_pro_records: Vec = pro_records - .iter() - .map(|r| { - let pro_fraction = dist_points_portion(r.time, &pro_leaderboard); - let nub_fraction = dist_points_portion(r.time, &nub_leaderboard); - RecordPoints { - record_id: r.record_id, - points: f64::max(pro_fraction, nub_fraction), - } - }) - .collect(); + if times.len() < 50 { + return (zero_params, false); + } - RecalculateFilterResult { - nub_records: new_nub_records, - pro_records: new_pro_records, - nub_params, - pro_params, - nub_fitted, - pro_fitted, + let result = nig::fit_nig(times, prev_params); + if result.valid { + (result.params, true) + } else { + (zero_params, false) } } @@ -170,60 +114,50 @@ mod tests { } #[test] - fn calculate_points_matches_python_example() { - let request = Request { - time: 8.609375, - nub_data: LeaderboardData { - dist_params: Some(NigParams { - a: 33.53900289787477, - b: 33.52140111667502, - loc: 6.3663207368487065, - scale: 0.4480388195262859, - top_scale: 0.9979285278452101, - }), - tier: Tier::VeryEasy, - leaderboard_size: 224, - top_time: 7.6484375, - }, - pro_data: Some(LeaderboardData { - dist_params: Some(NigParams { - a: 2.6294814553333743, - b: 2.511121972118702, - loc: 8.713014153227697, - scale: 2.2226724397990805, - top_scale: 0.9952929135343108, - }), - tier: Tier::VeryEasy, - leaderboard_size: 165, - top_time: 7.6484375, + fn calculate_fraction_matches_python_example() { + let time = 8.609375; + let nub_data = LeaderboardData { + dist_params: Some(NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + top_scale: 0.9979285278452101, }), + tier: Tier::VeryEasy, + leaderboard_size: 224, + top_time: 7.6484375, + }; + let pro_data = LeaderboardData { + dist_params: Some(NigParams { + a: 2.6294814553333743, + b: 2.511121972118702, + loc: 8.713014153227697, + scale: 2.2226724397990805, + top_scale: 0.9952929135343108, + }), + tier: Tier::VeryEasy, + leaderboard_size: 165, + top_time: 7.6484375, }; - let response = calculate(&request); - assert_abs_close(response.nub_fraction, 0.9745534941686896, 1e-3); - assert_abs_close(response.pro_fraction.expect("pro fraction"), 0.9760910013054752, 1e-3); + assert_abs_close(calculate_fraction(time, &nub_data), 0.9745534941686896, 1e-3); + assert_abs_close(calculate_fraction(time, &pro_data), 0.9760910013054752, 1e-3); } #[test] - fn pro_fraction_is_never_less_than_nub_fraction() { - let request = Request { - time: 8.0, - nub_data: LeaderboardData { - dist_params: None, - tier: Tier::VeryEasy, - leaderboard_size: 30, - top_time: 7.0, - }, - pro_data: Some(LeaderboardData { - dist_params: None, - tier: Tier::Death, - leaderboard_size: 30, - top_time: 7.0, - }), + fn calculate_fraction_falls_back_to_small_leaderboard_formula() { + let leaderboard = LeaderboardData { + dist_params: None, + tier: Tier::VeryEasy, + leaderboard_size: 30, + top_time: 7.0, }; - let response = calculate(&request); - assert!(response.pro_fraction.is_some()); - assert!(response.pro_fraction.unwrap() >= response.nub_fraction); + assert_abs_close( + calculate_fraction(8.0, &leaderboard), + points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, 8.0), + 1e-12, + ); } } diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 76bd31eb..603cb42a 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -170,10 +170,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let nub_recs = nub_rows .iter() - .map(|row| calculator::BestRecordData { - record_id: row.record_id, - time: row.time, - }) + .map(|row| calculator::RecordTime { record_id: row.record_id, time: row.time }) .collect::>(); // Pro records (sorted by time ASC) @@ -194,10 +191,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let pro_recs = pro_rows .iter() - .map(|row| calculator::BestRecordData { - record_id: row.record_id, - time: row.time, - }) + .map(|row| calculator::RecordTime { record_id: row.record_id, time: row.time }) .collect::>(); // Filter tiers @@ -209,8 +203,8 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d WHERE id = ?", filter_id, ) - .fetch_optional(db) - .await?; + .fetch_optional(db) + .await?; let Some(tiers_row) = tiers_row else { tracing::warn!(%filter_id, "filter not found in CourseFilters"); @@ -251,26 +245,28 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let prev_pro_params = prev_pro_params; move || { - let result = calculator::recalculate_filter( - &nub_recs, - &pro_recs, - nub_tier, - pro_tier, - prev_nub_params.as_ref(), - prev_pro_params.as_ref(), - ); - let _ = tx.send(result); + let nub_result = calculator::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); + + let mut pro_result = calculator::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); + + for (record, recalculated_record) in pro_recs.iter().zip(pro_result.records.iter_mut()) + { + let nub_fraction = calculator::calculate_fraction(record.time, &nub_result.leaderboard); + recalculated_record.points = recalculated_record.points.max(nub_fraction); + } + + let _ = tx.send((nub_result, pro_result)); } }); - let result = rx.await.map_err(|_| { + let (nub_result, pro_result) = rx.await.map_err(|_| { database::Error::decode(std::io::Error::other("points recalculation thread panicked")) })?; tracing::debug!( %filter_id, - nub_fitted = result.nub_fitted, - pro_fitted = result.pro_fitted, + nub_fitted = nub_result.fitted, + pro_fitted = pro_result.fitted, "recalculation complete, writing to DB" ); @@ -279,7 +275,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d conn, "INSERT INTO BestNubRecords (filter_id, player_id, record_id, points, time)", &nub_rows, - &result.nub_records, + &nub_result.records, ) .await?; @@ -287,11 +283,11 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d conn, "INSERT INTO BestProRecords (filter_id, player_id, record_id, points, time)", &pro_rows, - &result.pro_records, + &pro_result.records, ) .await?; - if result.nub_fitted { + if nub_result.fitted { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale @@ -304,17 +300,17 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d scale = VALUES(scale), top_scale = VALUES(top_scale)", filter_id, - result.nub_params.a, - result.nub_params.b, - result.nub_params.loc, - result.nub_params.scale, - result.nub_params.top_scale, + nub_result.params.a, + nub_result.params.b, + nub_result.params.loc, + nub_result.params.scale, + nub_result.params.top_scale, ) .execute(&mut *conn) .await?; } - if result.pro_fitted { + if pro_result.fitted { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale @@ -327,11 +323,11 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d scale = VALUES(scale), top_scale = VALUES(top_scale)", filter_id, - result.pro_params.a, - result.pro_params.b, - result.pro_params.loc, - result.pro_params.scale, - result.pro_params.top_scale, + pro_result.params.a, + pro_result.params.b, + pro_result.params.loc, + pro_result.params.scale, + pro_result.params.top_scale, ) .execute(&mut *conn) .await?; @@ -370,13 +366,16 @@ async fn upsert_best_records( let mut query = database::QueryBuilder::new(insert_prefix); - query.push_values(row_chunk.iter().zip(recalculated_chunk.iter()), |mut query, (row, recalculated_record)| { - query.push_bind(row.filter_id); - query.push_bind(row.player_id); - query.push_bind(row.record_id); - query.push_bind(recalculated_record.points); - query.push_bind(row.time); - }); + query.push_values( + row_chunk.iter().zip(recalculated_chunk.iter()), + |mut query, (row, recalculated_record)| { + query.push_bind(row.filter_id); + query.push_bind(row.player_id); + query.push_bind(row.record_id); + query.push_bind(recalculated_record.points); + query.push_bind(row.time); + }, + ); query.push(" ON DUPLICATE KEY UPDATE points = VALUES(points)"); query.build().execute(&mut *conn).await?; diff --git a/crates/cs2kz/src/records.rs b/crates/cs2kz/src/records.rs index 378eee08..bf926223 100644 --- a/crates/cs2kz/src/records.rs +++ b/crates/cs2kz/src/records.rs @@ -14,12 +14,17 @@ use crate::num::AsF64; use crate::pagination::{Limit, Offset, Paginated}; use crate::players::{CalculateRatingError, PlayerId, PlayerInfo}; use crate::plugin::PluginVersionId; -use crate::points::calculator::{self, Request as CalculatePointsRequest, Response as CalculatePointsResponse}; -use crate::points::{self, NigParams}; +use crate::points::{self, NigParams, calculator}; use crate::servers::{ServerId, ServerInfo}; use crate::styles::{ClientStyleInfo, Styles}; use crate::time::Seconds; +#[derive(Debug, Clone, Copy)] +struct CalculatedPoints { + nub_fraction: f64, + pro_fraction: Option, +} + define_id_type! { /// A unique identifier for records. pub struct RecordId(Uuid); @@ -374,36 +379,12 @@ pub async fn submit( None }; - let points = if nub_leaderboard_size > points::SMALL_LEADERBOARD_THRESHOLD - && nub_data.dist_params.is_some() - { - let request = CalculatePointsRequest { - time: time.as_f64(), - nub_data, - pro_data: pro_data.clone().filter(|data| data.dist_params.is_some()), - }; - - let mut response = calculator::calculate(&request); - - if let Some(ref pro_data) = pro_data && request.pro_data.is_none() { - response.pro_fraction = Some(points::for_small_leaderboard( - pro_data.tier, - pro_data.top_time, - time.as_f64(), - )); - } - - response - } else { - let nub_fraction = - points::for_small_leaderboard(nub_data.tier, nub_data.top_time, time.as_f64()); - - let pro_fraction = pro_data.clone().map(|pro_data| { - points::for_small_leaderboard(pro_data.tier, pro_data.top_time, time.as_f64()) - }); - - CalculatePointsResponse { nub_fraction, pro_fraction } - }; + let nub_fraction = calculator::calculate_fraction(time.as_f64(), &nub_data); + let pro_fraction = pro_data + .as_ref() + .map(|leaderboard| calculator::calculate_fraction(time.as_f64(), leaderboard)) + .map(|fraction| fraction.max(nub_fraction)); + let points = CalculatedPoints { nub_fraction, pro_fraction }; let is_nub_pb = nub_pb_time.is_none_or(|nub_pb_time| nub_pb_time > time.as_f64()); diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs index 452fe9de..f6e86cbe 100644 --- a/crates/nig/src/distribution.rs +++ b/crates/nig/src/distribution.rs @@ -1,6 +1,5 @@ use crate::bessel::bessel_k1_scaled; - pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { if a <= 0.0 || delta <= 0.0 || b.abs() >= a { return 0.0; @@ -17,10 +16,7 @@ pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { } let net_exp = gamma + b * z - y; - let log_pdf = a.ln() - - std::f64::consts::PI.ln() - - delta.ln() - - sqrt_z2p1.ln() + let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() - sqrt_z2p1.ln() + net_exp + scaled_bessel.ln(); @@ -85,7 +81,7 @@ fn adaptive_simpson_rec( Some( adaptive_simpson_rec(&mut *f, a, m, eps / 2.0, left, fa, fm, flm, depth - 1)? - + adaptive_simpson_rec(&mut *f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, + + adaptive_simpson_rec(&mut *f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, ) } diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs index b6d4ba17..6c6f5e98 100644 --- a/crates/nig/src/fitting.rs +++ b/crates/nig/src/fitting.rs @@ -33,7 +33,10 @@ pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> FitResult { if sf <= 0.0 { 1.0 } else { sf } }; - FitResult { params: NigParams { a, b, loc, scale, top_scale }, valid: true } + FitResult { + params: NigParams { a, b, loc, scale, top_scale }, + valid: true, + } } /// Median-based location estimate. @@ -115,19 +118,20 @@ pub(crate) fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: return f64::INFINITY; } - let log_pdf = a.ln() - - std::f64::consts::PI.ln() - - delta.ln() - + gamma - + b * z - + bessel.ln() + let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() + gamma + b * z + bessel.ln() - sqrt_z2p1.ln(); nll -= log_pdf; } nll } -pub(crate) fn optimize_nig(times: &[f64], mut a: f64, mut b: f64, mut loc: f64, mut scale: f64) -> (f64, f64, f64, f64) { +pub(crate) fn optimize_nig( + times: &[f64], + mut a: f64, + mut b: f64, + mut loc: f64, + mut scale: f64, +) -> (f64, f64, f64, f64) { const MAX_ITER: usize = 200; const TOL: f64 = 1e-8; const INIT_LR: f64 = 0.01; diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs index 49f3e7eb..e74cce7b 100644 --- a/crates/nig/src/params.rs +++ b/crates/nig/src/params.rs @@ -2,10 +2,10 @@ use serde::Serialize; #[derive(Debug, Clone, Copy, Serialize)] pub struct NigParams { - pub a: f64, // alpha - pub b: f64, // beta - pub loc: f64, // mu - pub scale: f64, // delta + pub a: f64, // alpha + pub b: f64, // beta + pub loc: f64, // mu + pub scale: f64, // delta pub top_scale: f64, // used to normalize the pdf } From 224df288b6e3da5a0c7b350924166fbfca047022 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 00:40:24 +0200 Subject: [PATCH 06/24] merge calculator and points --- crates/cs2kz/src/points.rs | 78 ---------------- crates/cs2kz/src/points/daemon.rs | 16 ++-- .../src/points/{calculator.rs => mod.rs} | 89 +++++++++++++++++-- crates/cs2kz/src/records.rs | 10 +-- 4 files changed, 96 insertions(+), 97 deletions(-) delete mode 100644 crates/cs2kz/src/points.rs rename crates/cs2kz/src/points/{calculator.rs => mod.rs} (62%) diff --git a/crates/cs2kz/src/points.rs b/crates/cs2kz/src/points.rs deleted file mode 100644 index f910f36a..00000000 --- a/crates/cs2kz/src/points.rs +++ /dev/null @@ -1,78 +0,0 @@ -use crate::maps::courses::Tier; - -pub mod daemon; -pub mod calculator; - -pub use nig::NigParams; - -/// The maximum points for any record. -pub const MAX: f64 = 10_000.0; - -/// Threshold for what counts as a "small" leaderboard. -pub const SMALL_LEADERBOARD_THRESHOLD: u64 = 50; - -/// "Completes" pre-calculated distribution points cached in the database. -/// -/// # Panics -/// -/// This function will panic if tier > [Tier::Death]. -pub fn complete(tier: Tier, is_pro_leaderboard: bool, rank: u32, dist_points: f64) -> f64 { - let for_tier = for_tier(tier, is_pro_leaderboard); - let remaining = MAX - for_tier; - let for_rank = 0.125 * remaining * for_rank(rank); - let from_dist = 0.875 * remaining * dist_points; - - for_tier + for_rank + from_dist -} - -/// Calculates the amount of points to award for completing a difficult course. -/// -/// # Panics -/// -/// This function will panic if tier > [Tier::Death]. -pub const fn for_tier(tier: Tier, is_pro_leaderboard: bool) -> f64 { - const POINTS_BY_TIER: [f64; 8] = [ - 0.0, 500.0, 2_000.0, 3_500.0, 5_000.0, 6_500.0, 8_000.0, 9_500.0, - ]; - - const POINTS_BY_TIER_PRO: [f64; 8] = [ - 1_000.0, 1_450.0, 2_800.0, 4_150.0, 5_500.0, 6_850.0, 8_200.0, 9_550.0, - ]; - - [POINTS_BY_TIER, POINTS_BY_TIER_PRO][is_pro_leaderboard as usize][(tier as usize) - 1] -} - -/// Calculates the amount of points to award for achieving a high rank on the leaderboard. -pub fn for_rank(rank: u32) -> f64 { - let mut points = 0.0; - - if rank < 100 { - points += ((100 - rank) as f64) * 0.004; - } - - if rank < 20 { - points += ((20 - rank) as f64) * 0.02; - } - - if let Some(&extra) = [0.2, 0.12, 0.09, 0.06, 0.02].get(rank as usize) { - points += extra; - } - - points -} - -/// Calculates the amount of points to award for completing a course only few others have also -/// completed. -/// -/// The threshold for a "small" leaderboard is [`SMALL_LEADERBOARD_THRESHOLD`]. -pub fn for_small_leaderboard(tier: Tier, top_time: f64, time: f64) -> f64 { - // no idea what any of this means; consult zer0.k - - assert!(top_time <= time); - - let x = 2.1 - 0.25 * (tier as u8 as f64); - let y = 1.0 + (x * -0.5).exp(); - let z = 1.0 + (x * (time / top_time - 1.5)).exp(); - - y / z -} diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 603cb42a..c766a35a 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -11,7 +11,7 @@ use crate::maps::CourseFilterId; use crate::maps::courses::Tier; use crate::mode::Mode; use crate::players::PlayerId; -use crate::points::{NigParams, calculator}; +use crate::points::{self, NigParams}; use crate::records::RecordId; use crate::{Context, database, players}; @@ -170,7 +170,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let nub_recs = nub_rows .iter() - .map(|row| calculator::RecordTime { record_id: row.record_id, time: row.time }) + .map(|row| points::RecordTime { record_id: row.record_id, time: row.time }) .collect::>(); // Pro records (sorted by time ASC) @@ -191,7 +191,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let pro_recs = pro_rows .iter() - .map(|row| calculator::RecordTime { record_id: row.record_id, time: row.time }) + .map(|row| points::RecordTime { record_id: row.record_id, time: row.time }) .collect::>(); // Filter tiers @@ -245,13 +245,15 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let prev_pro_params = prev_pro_params; move || { - let nub_result = calculator::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); + let nub_result = + points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); - let mut pro_result = calculator::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); + let mut pro_result = + points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); for (record, recalculated_record) in pro_recs.iter().zip(pro_result.records.iter_mut()) { - let nub_fraction = calculator::calculate_fraction(record.time, &nub_result.leaderboard); + let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); recalculated_record.points = recalculated_record.points.max(nub_fraction); } @@ -344,7 +346,7 @@ async fn upsert_best_records( conn: &mut database::Connection, insert_prefix: &'static str, rows: &[BestRecordRow], - recalculated_records: &[calculator::RecordPoints], + recalculated_records: &[points::RecordPoints], ) -> Result<(), database::Error> { if rows.len() != recalculated_records.len() { return Err(database::Error::decode(std::io::Error::other( diff --git a/crates/cs2kz/src/points/calculator.rs b/crates/cs2kz/src/points/mod.rs similarity index 62% rename from crates/cs2kz/src/points/calculator.rs rename to crates/cs2kz/src/points/mod.rs index 676606e1..d9b10995 100644 --- a/crates/cs2kz/src/points/calculator.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -1,7 +1,10 @@ use crate::maps::courses::Tier; -use crate::points::{self, NigParams}; use crate::records::RecordId; +pub mod daemon; + +pub use nig::NigParams; + #[derive(Debug, Clone, serde::Serialize)] pub struct LeaderboardData { pub dist_params: Option, @@ -35,13 +38,85 @@ pub struct RecalculatedLeaderboard { pub fitted: bool, } +/// The maximum points for any record. +pub const MAX: f64 = 10_000.0; + +/// Threshold for what counts as a "small" leaderboard. +pub const SMALL_LEADERBOARD_THRESHOLD: u64 = 50; + +/// "Completes" pre-calculated distribution points cached in the database. +/// +/// # Panics +/// +/// This function will panic if tier > [Tier::Death]. +pub fn complete(tier: Tier, is_pro_leaderboard: bool, rank: u32, dist_points: f64) -> f64 { + let for_tier = for_tier(tier, is_pro_leaderboard); + let remaining = MAX - for_tier; + let for_rank = 0.125 * remaining * for_rank(rank); + let from_dist = 0.875 * remaining * dist_points; + + for_tier + for_rank + from_dist +} + +/// Calculates the amount of points to award for completing a difficult course. +/// +/// # Panics +/// +/// This function will panic if tier > [Tier::Death]. +pub const fn for_tier(tier: Tier, is_pro_leaderboard: bool) -> f64 { + const POINTS_BY_TIER: [f64; 8] = [ + 0.0, 500.0, 2_000.0, 3_500.0, 5_000.0, 6_500.0, 8_000.0, 9_500.0, + ]; + + const POINTS_BY_TIER_PRO: [f64; 8] = [ + 1_000.0, 1_450.0, 2_800.0, 4_150.0, 5_500.0, 6_850.0, 8_200.0, 9_550.0, + ]; + + [POINTS_BY_TIER, POINTS_BY_TIER_PRO][is_pro_leaderboard as usize][(tier as usize) - 1] +} + +/// Calculates the amount of points to award for achieving a high rank on the leaderboard. +pub fn for_rank(rank: u32) -> f64 { + let mut points = 0.0; + + if rank < 100 { + points += ((100 - rank) as f64) * 0.004; + } + + if rank < 20 { + points += ((20 - rank) as f64) * 0.02; + } + + if let Some(&extra) = [0.2, 0.12, 0.09, 0.06, 0.02].get(rank as usize) { + points += extra; + } + + points +} + +/// Calculates the amount of points to award for completing a course only few others have also +/// completed. +/// +/// The threshold for a "small" leaderboard is [`SMALL_LEADERBOARD_THRESHOLD`]. +pub fn for_small_leaderboard(tier: Tier, top_time: f64, time: f64) -> f64 { + // no idea what any of this means; consult zer0.k + + assert!(top_time <= time); + + let x = 2.1 - 0.25 * (tier as u8 as f64); + let y = 1.0 + (x * -0.5).exp(); + let z = 1.0 + (x * (time / top_time - 1.5)).exp(); + + y / z +} + pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { - if leaderboard.leaderboard_size < points::SMALL_LEADERBOARD_THRESHOLD { - return points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); + if leaderboard.leaderboard_size < SMALL_LEADERBOARD_THRESHOLD { + return for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); } let Some(dist) = leaderboard.dist_params else { - return points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); + return for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); }; let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; @@ -89,7 +164,7 @@ fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> (NigParam top_scale: 0.0, }; - if times.len() < 50 { + if times.len() < SMALL_LEADERBOARD_THRESHOLD as usize { return (zero_params, false); } @@ -156,8 +231,8 @@ mod tests { assert_abs_close( calculate_fraction(8.0, &leaderboard), - points::for_small_leaderboard(leaderboard.tier, leaderboard.top_time, 8.0), + for_small_leaderboard(leaderboard.tier, leaderboard.top_time, 8.0), 1e-12, ); } -} +} \ No newline at end of file diff --git a/crates/cs2kz/src/records.rs b/crates/cs2kz/src/records.rs index bf926223..0262dda0 100644 --- a/crates/cs2kz/src/records.rs +++ b/crates/cs2kz/src/records.rs @@ -14,7 +14,7 @@ use crate::num::AsF64; use crate::pagination::{Limit, Offset, Paginated}; use crate::players::{CalculateRatingError, PlayerId, PlayerInfo}; use crate::plugin::PluginVersionId; -use crate::points::{self, NigParams, calculator}; +use crate::points::{self, NigParams}; use crate::servers::{ServerId, ServerInfo}; use crate::styles::{ClientStyleInfo, Styles}; use crate::time::Seconds; @@ -325,7 +325,7 @@ pub async fn submit( (None, Some(_)) => unreachable!(), }; - let nub_data = points::calculator::LeaderboardData { + let nub_data = points::LeaderboardData { dist_params: nub_dist, tier: nub_tier, leaderboard_size: nub_leaderboard_size, @@ -369,7 +369,7 @@ pub async fn submit( row.map_or((0, None), |row| (row.size as u64, row.top_time)) })?; - Some(points::calculator::LeaderboardData { + Some(points::LeaderboardData { dist_params: pro_dist, tier: pro_tier, leaderboard_size: pro_leaderboard_size + u64::from(pro_pb_time.is_none()), @@ -379,10 +379,10 @@ pub async fn submit( None }; - let nub_fraction = calculator::calculate_fraction(time.as_f64(), &nub_data); + let nub_fraction = points::calculate_fraction(time.as_f64(), &nub_data); let pro_fraction = pro_data .as_ref() - .map(|leaderboard| calculator::calculate_fraction(time.as_f64(), leaderboard)) + .map(|leaderboard| points::calculate_fraction(time.as_f64(), leaderboard)) .map(|fraction| fraction.max(nub_fraction)); let points = CalculatedPoints { nub_fraction, pro_fraction }; From ad495820e7e65f2c1d84230267adbdf166979e5c Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 00:49:21 +0200 Subject: [PATCH 07/24] distribution fitting returns option --- crates/cs2kz/src/points/daemon.rs | 28 ++++++++++++++-------------- crates/cs2kz/src/points/mod.rs | 28 +++++++--------------------- 2 files changed, 21 insertions(+), 35 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index c766a35a..fbe0e0ac 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -267,8 +267,8 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d tracing::debug!( %filter_id, - nub_fitted = nub_result.fitted, - pro_fitted = pro_result.fitted, + nub_fitted = nub_result.params.is_some(), + pro_fitted = pro_result.params.is_some(), "recalculation complete, writing to DB" ); @@ -289,7 +289,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d ) .await?; - if nub_result.fitted { + if let Some(params) = nub_result.params { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale @@ -302,17 +302,17 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d scale = VALUES(scale), top_scale = VALUES(top_scale)", filter_id, - nub_result.params.a, - nub_result.params.b, - nub_result.params.loc, - nub_result.params.scale, - nub_result.params.top_scale, + params.a, + params.b, + params.loc, + params.scale, + params.top_scale, ) .execute(&mut *conn) .await?; } - if pro_result.fitted { + if let Some(params) = pro_result.params { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale @@ -325,11 +325,11 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d scale = VALUES(scale), top_scale = VALUES(top_scale)", filter_id, - pro_result.params.a, - pro_result.params.b, - pro_result.params.loc, - pro_result.params.scale, - pro_result.params.top_scale, + params.a, + params.b, + params.loc, + params.scale, + params.top_scale, ) .execute(&mut *conn) .await?; diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index d9b10995..d09ded8a 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -34,8 +34,7 @@ pub struct RecordPoints { pub struct RecalculatedLeaderboard { pub leaderboard: LeaderboardData, pub records: Vec, - pub params: NigParams, - pub fitted: bool, + pub params: Option, } /// The maximum points for any record. @@ -130,10 +129,10 @@ pub fn recalculate_leaderboard( prev_params: Option<&NigParams>, ) -> RecalculatedLeaderboard { let times: Vec = records.iter().map(|record| record.time).collect(); - let (params, fitted) = fit_distribution(×, prev_params); + let params = fit_distribution(×, prev_params); let leaderboard = LeaderboardData { - dist_params: fitted.then_some(params), + dist_params: params, tier, leaderboard_size: records.len() as u64, top_time: times.first().copied().unwrap_or(0.0), @@ -151,29 +150,16 @@ pub fn recalculate_leaderboard( leaderboard, records: recalculated_records, params, - fitted, } } -fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> (NigParams, bool) { - let zero_params = NigParams { - a: 0.0, - b: 0.0, - loc: 0.0, - scale: 0.0, - top_scale: 0.0, - }; - +fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> Option { if times.len() < SMALL_LEADERBOARD_THRESHOLD as usize { - return (zero_params, false); + return None; } let result = nig::fit_nig(times, prev_params); - if result.valid { - (result.params, true) - } else { - (zero_params, false) - } + if result.valid { Some(result.params) } else { None } } #[cfg(test)] @@ -235,4 +221,4 @@ mod tests { 1e-12, ); } -} \ No newline at end of file +} From 8369069c2a6c7762b7eb495f010325d9791b4e10 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 00:54:45 +0200 Subject: [PATCH 08/24] use tokio's threadpool --- crates/cs2kz/src/points/daemon.rs | 38 +++++++++++-------------------- 1 file changed, 13 insertions(+), 25 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index fbe0e0ac..4780042f 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -1,5 +1,4 @@ use std::sync::Arc; -use std::thread; use std::time::Duration; use futures_util::TryFutureExt as _; @@ -235,34 +234,23 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .fetch_optional(db) .await?; - // heavy calcs on dedicated thread - let (tx, rx) = tokio::sync::oneshot::channel(); + let (nub_result, pro_result) = tokio::task::spawn_blocking(move || { + let nub_result = + points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); - thread::spawn({ - let nub_recs = nub_recs.clone(); - let pro_recs = pro_recs.clone(); - let prev_nub_params = prev_nub_params; - let prev_pro_params = prev_pro_params; + let mut pro_result = + points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); - move || { - let nub_result = - points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); - - let mut pro_result = - points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); - - for (record, recalculated_record) in pro_recs.iter().zip(pro_result.records.iter_mut()) - { - let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); - recalculated_record.points = recalculated_record.points.max(nub_fraction); - } - - let _ = tx.send((nub_result, pro_result)); + for (record, recalculated_record) in pro_recs.iter().zip(pro_result.records.iter_mut()) { + let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); + recalculated_record.points = recalculated_record.points.max(nub_fraction); } - }); - let (nub_result, pro_result) = rx.await.map_err(|_| { - database::Error::decode(std::io::Error::other("points recalculation thread panicked")) + (nub_result, pro_result) + }) + .await + .map_err(|_| { + database::Error::decode(std::io::Error::other("points recalculation task panicked")) })?; tracing::debug!( From ca7c21352f915a96357793f8de86e5b879e5be5a Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 01:18:03 +0200 Subject: [PATCH 09/24] simplify notify --- crates/cs2kz/src/config/mod.rs | 3 --- crates/cs2kz/src/context.rs | 15 +++++++++------ crates/cs2kz/src/points/daemon.rs | 32 +------------------------------ crates/cs2kz/src/records.rs | 2 +- 4 files changed, 11 insertions(+), 41 deletions(-) diff --git a/crates/cs2kz/src/config/mod.rs b/crates/cs2kz/src/config/mod.rs index 3cfed706..a6316585 100644 --- a/crates/cs2kz/src/config/mod.rs +++ b/crates/cs2kz/src/config/mod.rs @@ -1,9 +1,6 @@ mod database; pub use database::DatabaseConfig; -mod points; -pub use points::PointsConfig; - mod replays; pub use replays::ReplayStorageConfig; diff --git a/crates/cs2kz/src/context.rs b/crates/cs2kz/src/context.rs index 0fcb2572..d79671d4 100644 --- a/crates/cs2kz/src/context.rs +++ b/crates/cs2kz/src/context.rs @@ -2,6 +2,7 @@ use std::fmt; use std::sync::Arc; use std::time::Duration; +use tokio::sync::Notify; use tokio::task; use tokio_util::sync::CancellationToken; use tokio_util::task::TaskTracker; @@ -14,7 +15,6 @@ use crate::database::{ DatabaseConnectionOptions, EstablishDatabaseConnectionError, }; -use crate::points; mod inner { use super::*; @@ -25,7 +25,7 @@ mod inner { pub(super) database: Database, pub(super) shutdown_token: CancellationToken, pub(super) tasks: TaskTracker, - pub(super) points_daemon: points::daemon::PointsDaemonHandle, + pub(super) points_recalculation_notify: Notify, pub(super) s3_client: Option, } } @@ -68,7 +68,6 @@ impl Context { database::MIGRATIONS.run(database.as_ref()).await?; let tasks = TaskTracker::new(); - let points_daemon = points::daemon::PointsDaemonHandle::new(); let s3_client = if let Some(ref cfg) = config.replay_storage { let config = aws_config::defaults(aws_config::BehaviorVersion::v2026_01_12()) @@ -94,7 +93,7 @@ impl Context { database, shutdown_token, tasks, - points_daemon, + points_recalculation_notify: Notify::new(), s3_client, }))) } @@ -107,8 +106,12 @@ impl Context { &self.0.database } - pub fn points_daemon(&self) -> &points::daemon::PointsDaemonHandle { - &self.0.points_daemon + pub(crate) fn notify_points_recalculation(&self) { + self.0.points_recalculation_notify.notify_waiters(); + } + + pub(crate) async fn wait_for_points_recalculation(&self) { + self.0.points_recalculation_notify.notified().await; } pub fn s3_client(&self) -> &aws_sdk_s3::Client { diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 4780042f..3351237b 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -1,8 +1,6 @@ -use std::sync::Arc; use std::time::Duration; use futures_util::TryFutureExt as _; -use tokio::sync::Notify; use tokio::time::interval; use tokio_util::sync::CancellationToken; @@ -24,29 +22,6 @@ struct BestRecordRow { time: f64, } -#[derive(Debug, Clone)] -pub struct PointsDaemonHandle { - notifications: Arc, -} - -impl PointsDaemonHandle { - #[expect(clippy::new_without_default)] - pub fn new() -> Self { - Self { - notifications: Arc::new(Notifications { record_submitted: Notify::new() }), - } - } - - pub fn notify_record_submitted(&self) { - self.notifications.record_submitted.notify_waiters(); - } -} - -#[derive(Debug)] -struct Notifications { - record_submitted: Notify, -} - #[derive(Debug, Display, Error, From)] pub enum Error { DetermineFilterToRecalculate(DetermineFilterToRecalculateError), @@ -115,12 +90,7 @@ async fn determine_filter_to_recalculate( break Ok(data); } - () = cx - .points_daemon() - .notifications - .record_submitted - .notified() - .await; + cx.wait_for_points_recalculation().await; tracing::trace!("received notification about submitted record"); } diff --git a/crates/cs2kz/src/records.rs b/crates/cs2kz/src/records.rs index 926754a7..92ca9f37 100644 --- a/crates/cs2kz/src/records.rs +++ b/crates/cs2kz/src/records.rs @@ -500,7 +500,7 @@ pub async fn submit( } } - cx.points_daemon().notify_record_submitted(); + cx.notify_points_recalculation(); events::dispatch(Event::NewRecord { player_id, From b27bc8dd08e126da6199859d7104efe995306072 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 02:51:24 +0200 Subject: [PATCH 10/24] simplify upsert --- crates/cs2kz/src/points/daemon.rs | 26 +++++++++----------------- crates/cs2kz/src/points/mod.rs | 14 ++------------ 2 files changed, 11 insertions(+), 29 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 3351237b..ad27855e 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -211,9 +211,9 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let mut pro_result = points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); - for (record, recalculated_record) in pro_recs.iter().zip(pro_result.records.iter_mut()) { + for (record, recalculated_points) in pro_recs.iter().zip(pro_result.records.iter_mut()) { let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); - recalculated_record.points = recalculated_record.points.max(nub_fraction); + *recalculated_points = (*recalculated_points).max(nub_fraction); } (nub_result, pro_result) @@ -304,35 +304,27 @@ async fn upsert_best_records( conn: &mut database::Connection, insert_prefix: &'static str, rows: &[BestRecordRow], - recalculated_records: &[points::RecordPoints], + recalculated_points: &[f64], ) -> Result<(), database::Error> { - if rows.len() != recalculated_records.len() { + if rows.len() != recalculated_points.len() { return Err(database::Error::decode(std::io::Error::other( "recalculated record count does not match fetched best record rows", ))); } - for (row_chunk, recalculated_chunk) in rows + for (row_chunk, points_chunk) in rows .chunks(UPSERT_CHUNK_SIZE) - .zip(recalculated_records.chunks(UPSERT_CHUNK_SIZE)) + .zip(recalculated_points.chunks(UPSERT_CHUNK_SIZE)) { - for (row, recalculated_record) in row_chunk.iter().zip(recalculated_chunk.iter()) { - if row.record_id != recalculated_record.record_id { - return Err(database::Error::decode(std::io::Error::other( - "recalculated record order no longer matches fetched best record rows", - ))); - } - } - let mut query = database::QueryBuilder::new(insert_prefix); query.push_values( - row_chunk.iter().zip(recalculated_chunk.iter()), - |mut query, (row, recalculated_record)| { + row_chunk.iter().zip(points_chunk.iter()), + |mut query, (row, points)| { query.push_bind(row.filter_id); query.push_bind(row.player_id); query.push_bind(row.record_id); - query.push_bind(recalculated_record.points); + query.push_bind(points); query.push_bind(row.time); }, ); diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index d09ded8a..a02f0905 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -22,18 +22,11 @@ pub struct RecordTime { pub time: f64, } -/// Output record with recalculated distribution points fraction. -#[derive(Debug, Clone)] -pub struct RecordPoints { - pub record_id: RecordId, - pub points: f64, -} - /// Result of a leaderboard recalculation. #[derive(Debug, Clone)] pub struct RecalculatedLeaderboard { pub leaderboard: LeaderboardData, - pub records: Vec, + pub records: Vec, pub params: Option, } @@ -140,10 +133,7 @@ pub fn recalculate_leaderboard( let recalculated_records = records .iter() - .map(|record| RecordPoints { - record_id: record.record_id, - points: calculate_fraction(record.time, &leaderboard), - }) + .map(|record| calculate_fraction(record.time, &leaderboard)) .collect(); RecalculatedLeaderboard { From 9d6eb5da83d9a5aeb192a5b545dd4042cf7a4b00 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 02:54:12 +0200 Subject: [PATCH 11/24] remove duplicate param field --- crates/cs2kz/src/points/daemon.rs | 8 ++++---- crates/cs2kz/src/points/mod.rs | 2 -- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index ad27855e..142f27e4 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -225,8 +225,8 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d tracing::debug!( %filter_id, - nub_fitted = nub_result.params.is_some(), - pro_fitted = pro_result.params.is_some(), + nub_fitted = nub_result.leaderboard.dist_params.is_some(), + pro_fitted = pro_result.leaderboard.dist_params.is_some(), "recalculation complete, writing to DB" ); @@ -247,7 +247,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d ) .await?; - if let Some(params) = nub_result.params { + if let Some(params) = nub_result.leaderboard.dist_params { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale @@ -270,7 +270,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .await?; } - if let Some(params) = pro_result.params { + if let Some(params) = pro_result.leaderboard.dist_params { sqlx::query!( "INSERT INTO PointDistributionData ( filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index a02f0905..c4e7bd08 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -27,7 +27,6 @@ pub struct RecordTime { pub struct RecalculatedLeaderboard { pub leaderboard: LeaderboardData, pub records: Vec, - pub params: Option, } /// The maximum points for any record. @@ -139,7 +138,6 @@ pub fn recalculate_leaderboard( RecalculatedLeaderboard { leaderboard, records: recalculated_records, - params, } } From 104b899d2752438980e8782e434c4c54a7326ba4 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Thu, 14 May 2026 02:55:25 +0200 Subject: [PATCH 12/24] remove legacy python scripts --- scripts/.gitignore | 1 - scripts/calc_filter.py | 410 --------------------------------------- scripts/calc_run.py | 138 ------------- scripts/common.py | 15 -- scripts/requirements.txt | 2 - 5 files changed, 566 deletions(-) delete mode 100644 scripts/.gitignore delete mode 100644 scripts/calc_filter.py delete mode 100644 scripts/calc_run.py delete mode 100644 scripts/common.py delete mode 100644 scripts/requirements.txt diff --git a/scripts/.gitignore b/scripts/.gitignore deleted file mode 100644 index 225fc6f6..00000000 --- a/scripts/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/__pycache__ diff --git a/scripts/calc_filter.py b/scripts/calc_filter.py deleted file mode 100644 index 290b8127..00000000 --- a/scripts/calc_filter.py +++ /dev/null @@ -1,410 +0,0 @@ -# SPDX-License-Identifier: AGPL-3.0-only -# Copyright (C) zer0.k, AlphaKeks - -import common -import json -import mariadb -import numpy as np -import os -import sys -import time -import traceback - -from scipy import stats -from typing import Any, List, Tuple -from urllib.parse import urlparse - -def warn(msg): - sys.stderr.write(json.dumps({'warning': msg}) + '\n') - -def open_database_conn(): - DATABASE_URL = os.getenv('DATABASE_URL', 'mysql://schnose:csgo-kz-is-dead-boys@127.0.0.1:3306/cs2kz') - - database_url = urlparse(DATABASE_URL) - return mariadb.connect( - user = database_url.username, - password = database_url.password, - host = database_url.hostname, - port = database_url.port or 3306, - database = database_url.path.lstrip('/'), - reconnect = True - ) - -def process_input(database_conn, line): - """ - Processes a single line read from stdin. - - The line is expected to contain a JSON object with the following keys: - * `filter_id` - ID of the filter to calculate - - An example object could look like this: - ```json - { - "filter_id": 74 - } - ``` - - The function will write a single line response to stdout. - - That line is a JSON object with the following keys: - * `filter_id` - the ID of the calculated filter - * `timings` - an object containing timings of the individual operations - performed - - The `timings` object will contain the following keys: - * `db_query_ms` - the time it took to query the database for the - required information, in milliseconds, as a floating - point number - * `nub_fit_ms` - the time it took to fit the NUB distribution, in - milliseconds, as a floating point number - * `nub_compute_ms` - the time it took to calculate new points for the - NUB leaderboard, in milliseconds, as a floating - point number - * `pro_fit_ms` - the time it took to fit the PRO distribution, in - milliseconds, as a floating point number - * `pro_compute_ms` - the time it took to calculate new points for the - PRO leaderboard, in milliseconds, as a floating - point number - * `db_write_ms` - the time it took to write everything back to the - database, in milliseconds, as a floating point number - """ - - cursor = database_conn.cursor() - - response = { - 'filter_id': None, - 'timings': { - 'db_query_ms': 0.0, - 'nub_fit_ms': 0.0, - 'nub_compute_ms': 0.0, - 'pro_fit_ms': 0.0, - 'pro_compute_ms': 0.0, - 'db_write_ms': 0.0 - } - } - data = json.loads(line) - filter_id = data['filter_id'] - response['filter_id'] = filter_id - - start = time.time() - cursor.execute(""" - SELECT - bnr.record_id, - bnr.time, - bnr.points - FROM - BestNubRecords AS bnr - WHERE - bnr.filter_id = ? - ORDER BY - bnr.time ASC - """, ( - filter_id, - )) - nub_records: List[Tuple[Any, float, float]] = cursor.fetchall() - cursor.execute(""" - SELECT - bpr.record_id, - bpr.time, - bpr.points - FROM - BestProRecords AS bpr - WHERE - bpr.filter_id = ? - ORDER BY - bpr.time ASC - """, ( - filter_id, - )) - pro_records: List[Tuple[Any, float, float]] = cursor.fetchall() - cursor.execute(""" - SELECT - cf.nub_tier, - cf.pro_tier - FROM - CourseFilters cf - WHERE - cf.id = ? - """, ( - filter_id, - )) - filter_row = cursor.fetchone() - - # Fetch previous distribution parameters for warm start - # (both nub and pro in one query) - cursor.execute(""" - SELECT - is_pro_leaderboard, - a, - b, - loc, - scale, - top_scale - FROM - PointDistributionData - WHERE - filter_id = ? - ORDER BY - is_pro_leaderboard - """, ( - filter_id, - )) - dist_params_rows = cursor.fetchall() - - prev_nub_params = None - prev_pro_params = None - for row in dist_params_rows: - if row[0] == 0: # is_pro_leaderboard = 0 (nub) - prev_nub_params = (row[1], row[2], row[3], row[4], row[5]) - elif row[0] == 1: # is_pro_leaderboard = 1 (pro) - prev_pro_params = (row[1], row[2], row[3], row[4], row[5]) - - response['timings']['db_query_ms'] = (time.time() - start) * 1000 - - if filter_row is None: - warn(f'Filter ID {filter_id} not found in CourseFilters.') - return response - - nub_times = [row[1] for row in nub_records] - pro_times = [row[1] for row in pro_records] - nub_tier = filter_row[0] - pro_tier = filter_row[1] - - ''' - There are 3 possible cases: - 1. Less than 50 nub times (and therefore less than 50 pro times as well) - -> do not fit distribution, use sigmoid function - 2. 50 or more nub times but less than 50 pro times - -> fit nub distribution, use sigmoid for pro - 3. 50 or more nub times and 50 or more pro times - -> fit both distributions - - Overall/nub portion only depends on its own distribution. - Pro portion takes the higher of the two distributions - ((un)fitted nub or (un)fitted pro) to avoid situations where pro portion - is lower than nub portion. - - ''' - if len(nub_times) >= 50: - start = time.time() - nub_dist, nub_params = refit_dist(nub_times, prev_nub_params) - response['timings']['nub_fit_ms'] = (time.time() - start) * 1000 - elif len(nub_times) > 0: - nub_dist, nub_params = None, (0,0,0,0,0) - response['timings']['nub_fit_ms'] = 0 - else: - warn(f'No overall records found for filter ID {filter_id}.') - return response - - # Compute nub fractions - start = time.time() - nub_times_array = np.array(nub_times) - new_fractions = common.get_dist_points_portion( - nub_times_array, - nub_times[0], - nub_dist, - nub_tier, - nub_params[4], - len(nub_times) - ) - nub_records = [ - (record_id, time, fraction) - for (record_id, time, _), fraction - in zip(nub_records, new_fractions) - ] - response['timings']['nub_compute_ms'] = (time.time() - start) * 1000 - - # Fit pro distribution - if len(pro_times) >= 50: - start = time.time() - pro_dist, pro_params = refit_dist(pro_times, prev_pro_params) - response['timings']['pro_fit_ms'] = (time.time() - start) * 1000 - elif len(pro_times) > 0: - pro_dist, pro_params = None, (0,0,0,0,0) - - # Compute pro fractions if there are any pro records - if len(pro_times) > 0: - start = time.time() - pro_times_array = np.array(pro_times) - new_fractions = np.maximum( - common.get_dist_points_portion( - pro_times_array, - pro_times[0], - pro_dist, - pro_tier, - pro_params[4], - len(pro_times) - ), - common.get_dist_points_portion( - pro_times_array, - nub_times[0], - nub_dist, - nub_tier, - nub_params[4], - len(nub_times) - ) - ) - pro_records = [ - (record_id, time, fraction) - for (record_id, time, _), fraction - in zip(pro_records, new_fractions) - ] - response['timings']['pro_compute_ms'] = (time.time() - start) * 1000 - - # Database write timing - start = time.time() - if len(nub_records) > 0: - cursor.executemany(""" - UPDATE BestNubRecords SET - points = ? - WHERE - record_id = ? - """, [ - (points, record_id) - for record_id, time, points - in nub_records - ]) - if len(pro_records) > 0: - cursor.executemany(""" - UPDATE BestProRecords SET - points = ? - WHERE - record_id = ? - """, [ - (points, record_id) - for record_id, time, points - in pro_records - ]) - if len(nub_times) >= 50: - cursor.execute(""" - INSERT INTO PointDistributionData ( - filter_id, - is_pro_leaderboard, - a, - b, - loc, - scale, - top_scale - ) VALUES ( - ?, - 0, - ?, - ?, - ?, - ?, - ? - ) - ON DUPLICATE KEY UPDATE - a = VALUES(a), - b = VALUES(b), - loc = VALUES(loc), - scale = VALUES(scale), - top_scale = VALUES(top_scale) - """, ( - filter_id, - nub_params[0], - nub_params[1], - nub_params[2], - nub_params[3], - nub_params[4] - )) - if len(pro_times) >= 50: - cursor.execute(""" - INSERT INTO PointDistributionData ( - filter_id, - is_pro_leaderboard, - a, - b, - loc, - scale, - top_scale - ) VALUES ( - ?, - 1, - ?, - ?, - ?, - ?, - ? - ) - ON DUPLICATE KEY UPDATE - a = VALUES(a), - b = VALUES(b), - loc = VALUES(loc), - scale = VALUES(scale), - top_scale = VALUES(top_scale) - """, ( - filter_id, - pro_params[0], - pro_params[1], - pro_params[2], - pro_params[3], - pro_params[4] - )) - database_conn.commit() - response['timings']['db_write_ms'] = (time.time() - start) * 1000 - response['timings']['total_ms'] = sum(response['timings'].values()) - return response - -def refit_dist(times, prev_params=None): - if prev_params is not None: - # Use previous parameters as initial guess for faster convergence - a_init, b_init, loc_init, scale_init, _ = prev_params - norminvgauss_params = stats.norminvgauss.fit( - times, - a_init, b_init, - loc=loc_init, - scale=scale_init - ) - else: - # Cold start - no initial parameters - norminvgauss_params = stats.norminvgauss.fit(times) - - norminvgauss_dist = stats.norminvgauss(*norminvgauss_params) - top_scale = norminvgauss_dist.sf(times[0]) - # Sanity safeguard - if top_scale <= 0: - warn('Fitted top_scale <= 0, resetting to 1') - top_scale = 1 - - a, b, loc, scale = norminvgauss_params - return norminvgauss_dist, (a, b, loc, scale, top_scale) - -if __name__ == '__main__': - database_conn = None - - try: - database_conn = open_database_conn() - except mariadb.Error as e: - sys.stderr.write(f'Error connecting to database: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - - for line in sys.stdin: - try: - response = process_input(database_conn, line) - sys.stderr.flush() - sys.stdout.write(json.dumps(response) + '\n') - sys.stdout.flush() - except KeyError as e: - sys.stderr.write(f'Missing key in input data: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - except json.JSONDecodeError as e: - sys.stderr.write(f'JSON decode error: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - except mariadb.Error as e: - sys.stderr.write(f'Database error: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - except Exception as e: - sys.stderr.write(f'An unexpected error occurred: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - - try: - database_conn.close() - except mariadb.Error as e: - sys.stderr.write(f'Failed to close database connection: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') diff --git a/scripts/calc_run.py b/scripts/calc_run.py deleted file mode 100644 index ebf967d4..00000000 --- a/scripts/calc_run.py +++ /dev/null @@ -1,138 +0,0 @@ -# SPDX-License-Identifier: AGPL-3.0-only -# Copyright (C) zer0.k, AlphaKeks - -import common -import json -import scipy.stats as stats -import sys -import sys -import traceback - -def process_input(line): - """ - Processes a single line read from stdin. - - The line is expected to contain a JSON object with the following keys: - * `time` - the time of the run, in seconds, as a floating point number - * `nub_data` - an object containing information about the nub - leaderboard the run belongs to - * `pro_data` - an object containing information about the pro - leaderboard the run belongs to - - Both `nub_data` and `pro_data` should contain the following keys: - * `tier` - the filter tier - * `wr` - the time of the world record run, in seconds, as a floating - point number - * `leaderboard_size` - the number of runs on the leaderboard - * `dist_params` - distribution parameters calculated by `calc_filter.py` - - An example object could look like this: - ```json - { - "time": 8.609375, - "nub_data": { - "tier": 1, - "wr": 7.6484375, - "leaderboard_size": 224, - "dist_params": { - "a": 33.53900289787477, - "b": 33.52140111667502, - "loc": 6.3663207368487065, - "scale": 0.4480388195262859, - "top_scale": 0.9979285278452101 - } - }, - "pro_data": { - "tier": 1, - "wr": 7.6484375, - "leaderboard_size": 165, - "dist_params": { - "a": 2.6294814553333743, - "b": 2.511121972118702, - "loc": 8.713014153227697, - "scale": 2.2226724397990805, - "top_scale": 0.9952929135343108 - } - } - } - ``` - - The function will write a single line response to stdout. - - That line is a JSON object with the following keys: - * `nub_fraction` - a floating point number - * `pro_fraction` - a floating point number - - An example object could look like this: - ```json - { - "nub_fraction": 0.9745534941686896, - "pro_fraction": 0.9760910013054752 - } - ``` - """ - - data = json.loads(line) - - nub_data = data['nub_data'] - nub_dist = stats.norminvgauss( - a = nub_data['dist_params']['a'], - b = nub_data['dist_params']['b'], - loc = nub_data['dist_params']['loc'], - scale = nub_data['dist_params']['scale'] - ) - nub_fraction = common.get_dist_points_portion(data['time'], - nub_data['wr'], - nub_dist, - nub_data['tier'], - nub_data['dist_params']['top_scale'], - nub_data['leaderboard_size']) - if 'pro_data' in data and data['pro_data'] is not None: - pro_data = data['pro_data'] - pro_dist = stats.norminvgauss( - a = pro_data['dist_params']['a'], - b = pro_data['dist_params']['b'], - loc = pro_data['dist_params']['loc'], - scale = pro_data['dist_params']['scale'] - ) - pro_fraction = common.get_dist_points_portion(data['time'], - pro_data['wr'], - pro_dist, - pro_data['tier'], - pro_data['dist_params']['top_scale'], - pro_data['leaderboard_size']) - response = { - 'nub_fraction': nub_fraction, - # Pro run in the pro leaderboard should never be worth less than - # the same run in the nub leaderboard. - 'pro_fraction': max(nub_fraction, pro_fraction) - } - return response - response = { - 'nub_fraction': nub_fraction, - 'pro_fraction': None - } - return response - -def main(): - for line in sys.stdin: - try: - response = process_input(line) - sys.stderr.flush() - sys.stdout.write(json.dumps(response) + '\n') - sys.stdout.flush() - except KeyError as e: - sys.stderr.write(f'Missing key in input data: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - except json.JSONDecodeError as e: - sys.stderr.write(f'JSON decode error: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - except Exception as e: - sys.stderr.write(f'An unexpected error occurred: {e}\n') - sys.stderr.write(traceback.format_exc() + '\n') - sys.exit(1) - -if __name__ == '__main__': - main() diff --git a/scripts/common.py b/scripts/common.py deleted file mode 100644 index 63284150..00000000 --- a/scripts/common.py +++ /dev/null @@ -1,15 +0,0 @@ -# SPDX-License-Identifier: AGPL-3.0-only -# Copyright (C) zer0.k, AlphaKeks - -import numpy as np - -from scipy import stats - -def get_distribution_points_portion_under_50(time, wr_time, tier): - return ((1+np.exp((2.1 - 0.25 * tier) * -0.5))/(1+np.exp((2.1 - 0.25 * tier) * (time/wr_time-1.5)))) - -def get_dist_points_portion(time, wr_time, dist: stats.rv_continuous, tier, top_scale, total): - if total < 50: - return get_distribution_points_portion_under_50(time, wr_time, tier) - else: - return np.clip(dist.sf(time) / top_scale, 0, 1) diff --git a/scripts/requirements.txt b/scripts/requirements.txt deleted file mode 100644 index 58ce7356..00000000 --- a/scripts/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -scipy -mariadb From eea7f759b20903fc1c07edca775e428e54ac7b60 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Fri, 15 May 2026 00:03:02 +0200 Subject: [PATCH 13/24] regenerate sqlx offline queries --- ...0f981a505fdb2593449bbe901b7e6c1fa847d.json | 54 ++++++++++++ ...acd971aad0f636644a6388169df5b37fa3234.json | 64 ++++++++++++++ ...b67103102461886e58387deef918fb076e4a3.json | 64 ++++++++++++++ ...f2a9cfb5e537551932bb2f133f6030fa6ddb3.json | 84 ------------------- ...f7ec190f757fcb1e97a954b1a83d8fe970a75.json | 12 +++ ...f4084fc4334a0a404cd8c416865049daccf2f.json | 12 +++ ...c6bfbce0f6dc2a14aa104e269530eb266ceee.json | 34 ++++++++ ...e4eed58f18536c81e4f7cc10906aa86faff2f.json | 54 ++++++++++++ ...2e30379638b9f72a15be83220d1900a6104f1.json | 84 ------------------- crates/cs2kz-api/src/bin/generator/main.rs | 1 - crates/cs2kz/src/points/daemon.rs | 2 +- 11 files changed, 295 insertions(+), 170 deletions(-) create mode 100644 .sqlx/query-40dc82fa83d150c24dab57f4a610f981a505fdb2593449bbe901b7e6c1fa847d.json create mode 100644 .sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json create mode 100644 .sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json delete mode 100644 .sqlx/query-5fc61264428ce56fd2b3b4a6d7bf2a9cfb5e537551932bb2f133f6030fa6ddb3.json create mode 100644 .sqlx/query-8d0c9e3b783635e444300881be5f7ec190f757fcb1e97a954b1a83d8fe970a75.json create mode 100644 .sqlx/query-90ad65e337a74790d72d43e4881f4084fc4334a0a404cd8c416865049daccf2f.json create mode 100644 .sqlx/query-91f17d399f1dc6daf4ca3363cb1c6bfbce0f6dc2a14aa104e269530eb266ceee.json create mode 100644 .sqlx/query-efd574ea4128095b642003df53ce4eed58f18536c81e4f7cc10906aa86faff2f.json delete mode 100644 .sqlx/query-faf48343593496abef8a35d44cc2e30379638b9f72a15be83220d1900a6104f1.json diff --git a/.sqlx/query-40dc82fa83d150c24dab57f4a610f981a505fdb2593449bbe901b7e6c1fa847d.json b/.sqlx/query-40dc82fa83d150c24dab57f4a610f981a505fdb2593449bbe901b7e6c1fa847d.json new file mode 100644 index 00000000..5adf54b9 --- /dev/null +++ b/.sqlx/query-40dc82fa83d150c24dab57f4a610f981a505fdb2593449bbe901b7e6c1fa847d.json @@ -0,0 +1,54 @@ +{ + "db_name": "MySQL", + "query": "SELECT\n filter_id AS `filter_id: CourseFilterId`,\n player_id AS `player_id: PlayerId`,\n record_id AS `record_id: RecordId`,\n time\n FROM BestNubRecords\n WHERE filter_id = ?\n ORDER BY time ASC", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "filter_id: CourseFilterId", + "type_info": { + "type": "Short", + "flags": "NOT_NULL | PRIMARY_KEY | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 5 + } + }, + { + "ordinal": 1, + "name": "player_id: PlayerId", + "type_info": { + "type": "LongLong", + "flags": "NOT_NULL | PRIMARY_KEY | MULTIPLE_KEY | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 20 + } + }, + { + "ordinal": 2, + "name": "record_id: RecordId", + "type_info": { + "type": "String", + "flags": "NOT_NULL | MULTIPLE_KEY | BINARY | NO_DEFAULT_VALUE", + "max_size": 16 + } + }, + { + "ordinal": 3, + "name": "time", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | MULTIPLE_KEY | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false + ] + }, + "hash": "40dc82fa83d150c24dab57f4a610f981a505fdb2593449bbe901b7e6c1fa847d" +} diff --git a/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json b/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json new file mode 100644 index 00000000..ba9731b6 --- /dev/null +++ b/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json @@ -0,0 +1,64 @@ +{ + "db_name": "MySQL", + "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ? AND (NOT is_pro_leaderboard)", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "a", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 1, + "name": "b", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 2, + "name": "loc", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 3, + "name": "scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 4, + "name": "top_scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false, + false + ] + }, + "hash": "50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234" +} diff --git a/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json b/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json new file mode 100644 index 00000000..f9b724f8 --- /dev/null +++ b/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json @@ -0,0 +1,64 @@ +{ + "db_name": "MySQL", + "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ? AND is_pro_leaderboard", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "a", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 1, + "name": "b", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 2, + "name": "loc", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 3, + "name": "scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 4, + "name": "top_scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false, + false + ] + }, + "hash": "5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3" +} diff --git a/.sqlx/query-5fc61264428ce56fd2b3b4a6d7bf2a9cfb5e537551932bb2f133f6030fa6ddb3.json b/.sqlx/query-5fc61264428ce56fd2b3b4a6d7bf2a9cfb5e537551932bb2f133f6030fa6ddb3.json deleted file mode 100644 index c8055805..00000000 --- a/.sqlx/query-5fc61264428ce56fd2b3b4a6d7bf2a9cfb5e537551932bb2f133f6030fa6ddb3.json +++ /dev/null @@ -1,84 +0,0 @@ -{ - "db_name": "MySQL", - "query": "WITH BanCounts AS (\n SELECT b.player_id, COUNT(*) AS count\n FROM Bans AS b\n RIGHT JOIN Unbans AS ub ON ub.ban_id = b.id\n WHERE (b.id IS NULL OR b.expires_at > NOW())\n )\n SELECT\n p.id AS `id: PlayerId`,\n p.name,\n p.vnl_rating,\n p.ckz_rating,\n (COALESCE(BanCounts.count, 0) > 0) AS `is_banned!: bool`,\n p.first_joined_at,\n p.last_joined_at\n FROM Players AS p\n LEFT JOIN BanCounts ON BanCounts.player_id = p.id WHERE p.id = ?", - "describe": { - "columns": [ - { - "ordinal": 0, - "name": "id: PlayerId", - "type_info": { - "type": "LongLong", - "flags": "NOT_NULL | PRIMARY_KEY | UNSIGNED | NO_DEFAULT_VALUE", - "max_size": 20 - } - }, - { - "ordinal": 1, - "name": "name", - "type_info": { - "type": "VarString", - "flags": "NOT_NULL | MULTIPLE_KEY | NO_DEFAULT_VALUE", - "max_size": 1020 - } - }, - { - "ordinal": 2, - "name": "vnl_rating", - "type_info": { - "type": "Double", - "flags": "NOT_NULL", - "max_size": 22 - } - }, - { - "ordinal": 3, - "name": "ckz_rating", - "type_info": { - "type": "Double", - "flags": "NOT_NULL", - "max_size": 22 - } - }, - { - "ordinal": 4, - "name": "is_banned!: bool", - "type_info": { - "type": "Long", - "flags": "BINARY", - "max_size": 1 - } - }, - { - "ordinal": 5, - "name": "first_joined_at", - "type_info": { - "type": "Timestamp", - "flags": "NOT_NULL | UNSIGNED | BINARY | TIMESTAMP", - "max_size": 19 - } - }, - { - "ordinal": 6, - "name": "last_joined_at", - "type_info": { - "type": "Timestamp", - "flags": "NOT_NULL | UNSIGNED | BINARY | TIMESTAMP", - "max_size": 19 - } - } - ], - "parameters": { - "Right": 1 - }, - "nullable": [ - false, - false, - false, - false, - true, - false, - false - ] - }, - "hash": "5fc61264428ce56fd2b3b4a6d7bf2a9cfb5e537551932bb2f133f6030fa6ddb3" -} diff --git a/.sqlx/query-8d0c9e3b783635e444300881be5f7ec190f757fcb1e97a954b1a83d8fe970a75.json b/.sqlx/query-8d0c9e3b783635e444300881be5f7ec190f757fcb1e97a954b1a83d8fe970a75.json new file mode 100644 index 00000000..bd604b7b --- /dev/null +++ b/.sqlx/query-8d0c9e3b783635e444300881be5f7ec190f757fcb1e97a954b1a83d8fe970a75.json @@ -0,0 +1,12 @@ +{ + "db_name": "MySQL", + "query": "INSERT INTO PointDistributionData (\n filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale\n )\n VALUES (?, TRUE, ?, ?, ?, ?, ?)\n ON DUPLICATE KEY UPDATE\n a = VALUES(a),\n b = VALUES(b),\n loc = VALUES(loc),\n scale = VALUES(scale),\n top_scale = VALUES(top_scale)", + "describe": { + "columns": [], + "parameters": { + "Right": 6 + }, + "nullable": [] + }, + "hash": "8d0c9e3b783635e444300881be5f7ec190f757fcb1e97a954b1a83d8fe970a75" +} diff --git a/.sqlx/query-90ad65e337a74790d72d43e4881f4084fc4334a0a404cd8c416865049daccf2f.json b/.sqlx/query-90ad65e337a74790d72d43e4881f4084fc4334a0a404cd8c416865049daccf2f.json new file mode 100644 index 00000000..42ee3584 --- /dev/null +++ b/.sqlx/query-90ad65e337a74790d72d43e4881f4084fc4334a0a404cd8c416865049daccf2f.json @@ -0,0 +1,12 @@ +{ + "db_name": "MySQL", + "query": "INSERT INTO PointDistributionData (\n filter_id, is_pro_leaderboard, a, b, loc, scale, top_scale\n )\n VALUES (?, FALSE, ?, ?, ?, ?, ?)\n ON DUPLICATE KEY UPDATE\n a = VALUES(a),\n b = VALUES(b),\n loc = VALUES(loc),\n scale = VALUES(scale),\n top_scale = VALUES(top_scale)", + "describe": { + "columns": [], + "parameters": { + "Right": 6 + }, + "nullable": [] + }, + "hash": "90ad65e337a74790d72d43e4881f4084fc4334a0a404cd8c416865049daccf2f" +} diff --git a/.sqlx/query-91f17d399f1dc6daf4ca3363cb1c6bfbce0f6dc2a14aa104e269530eb266ceee.json b/.sqlx/query-91f17d399f1dc6daf4ca3363cb1c6bfbce0f6dc2a14aa104e269530eb266ceee.json new file mode 100644 index 00000000..01c78a73 --- /dev/null +++ b/.sqlx/query-91f17d399f1dc6daf4ca3363cb1c6bfbce0f6dc2a14aa104e269530eb266ceee.json @@ -0,0 +1,34 @@ +{ + "db_name": "MySQL", + "query": "SELECT\n nub_tier AS `nub_tier: Tier`,\n pro_tier AS `pro_tier: Tier`\n FROM CourseFilters\n WHERE id = ?", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "nub_tier: Tier", + "type_info": { + "type": "Tiny", + "flags": "NOT_NULL | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 3 + } + }, + { + "ordinal": 1, + "name": "pro_tier: Tier", + "type_info": { + "type": "Tiny", + "flags": "NOT_NULL | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 3 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false + ] + }, + "hash": "91f17d399f1dc6daf4ca3363cb1c6bfbce0f6dc2a14aa104e269530eb266ceee" +} diff --git a/.sqlx/query-efd574ea4128095b642003df53ce4eed58f18536c81e4f7cc10906aa86faff2f.json b/.sqlx/query-efd574ea4128095b642003df53ce4eed58f18536c81e4f7cc10906aa86faff2f.json new file mode 100644 index 00000000..4ae40411 --- /dev/null +++ b/.sqlx/query-efd574ea4128095b642003df53ce4eed58f18536c81e4f7cc10906aa86faff2f.json @@ -0,0 +1,54 @@ +{ + "db_name": "MySQL", + "query": "SELECT\n filter_id AS `filter_id: CourseFilterId`,\n player_id AS `player_id: PlayerId`,\n record_id AS `record_id: RecordId`,\n time\n FROM BestProRecords\n WHERE filter_id = ?\n ORDER BY time ASC", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "filter_id: CourseFilterId", + "type_info": { + "type": "Short", + "flags": "NOT_NULL | PRIMARY_KEY | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 5 + } + }, + { + "ordinal": 1, + "name": "player_id: PlayerId", + "type_info": { + "type": "LongLong", + "flags": "NOT_NULL | PRIMARY_KEY | MULTIPLE_KEY | UNSIGNED | NO_DEFAULT_VALUE", + "max_size": 20 + } + }, + { + "ordinal": 2, + "name": "record_id: RecordId", + "type_info": { + "type": "String", + "flags": "NOT_NULL | MULTIPLE_KEY | BINARY | NO_DEFAULT_VALUE", + "max_size": 16 + } + }, + { + "ordinal": 3, + "name": "time", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | MULTIPLE_KEY | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false + ] + }, + "hash": "efd574ea4128095b642003df53ce4eed58f18536c81e4f7cc10906aa86faff2f" +} diff --git a/.sqlx/query-faf48343593496abef8a35d44cc2e30379638b9f72a15be83220d1900a6104f1.json b/.sqlx/query-faf48343593496abef8a35d44cc2e30379638b9f72a15be83220d1900a6104f1.json deleted file mode 100644 index 45bf854a..00000000 --- a/.sqlx/query-faf48343593496abef8a35d44cc2e30379638b9f72a15be83220d1900a6104f1.json +++ /dev/null @@ -1,84 +0,0 @@ -{ - "db_name": "MySQL", - "query": "WITH BanCounts AS (\n SELECT b.player_id, COUNT(*) AS count\n FROM Bans AS b\n RIGHT JOIN Unbans AS ub ON ub.ban_id = b.id\n WHERE (b.id IS NULL OR b.expires_at > NOW())\n )\n SELECT\n p.id AS `id: PlayerId`,\n p.name,\n p.vnl_rating,\n p.ckz_rating,\n (COALESCE(BanCounts.count, 0) > 0) AS `is_banned!: bool`,\n p.first_joined_at,\n p.last_joined_at\n FROM Players AS p\n LEFT JOIN BanCounts ON BanCounts.player_id = p.id WHERE p.name LIKE ?", - "describe": { - "columns": [ - { - "ordinal": 0, - "name": "id: PlayerId", - "type_info": { - "type": "LongLong", - "flags": "NOT_NULL | PRIMARY_KEY | UNSIGNED | NO_DEFAULT_VALUE", - "max_size": 20 - } - }, - { - "ordinal": 1, - "name": "name", - "type_info": { - "type": "VarString", - "flags": "NOT_NULL | MULTIPLE_KEY | NO_DEFAULT_VALUE", - "max_size": 1020 - } - }, - { - "ordinal": 2, - "name": "vnl_rating", - "type_info": { - "type": "Double", - "flags": "NOT_NULL", - "max_size": 22 - } - }, - { - "ordinal": 3, - "name": "ckz_rating", - "type_info": { - "type": "Double", - "flags": "NOT_NULL", - "max_size": 22 - } - }, - { - "ordinal": 4, - "name": "is_banned!: bool", - "type_info": { - "type": "Long", - "flags": "BINARY", - "max_size": 1 - } - }, - { - "ordinal": 5, - "name": "first_joined_at", - "type_info": { - "type": "Timestamp", - "flags": "NOT_NULL | UNSIGNED | BINARY | TIMESTAMP", - "max_size": 19 - } - }, - { - "ordinal": 6, - "name": "last_joined_at", - "type_info": { - "type": "Timestamp", - "flags": "NOT_NULL | UNSIGNED | BINARY | TIMESTAMP", - "max_size": 19 - } - } - ], - "parameters": { - "Right": 1 - }, - "nullable": [ - false, - false, - false, - false, - true, - false, - false - ] - }, - "hash": "faf48343593496abef8a35d44cc2e30379638b9f72a15be83220d1900a6104f1" -} diff --git a/crates/cs2kz-api/src/bin/generator/main.rs b/crates/cs2kz-api/src/bin/generator/main.rs index 63b66322..0e0d99d5 100644 --- a/crates/cs2kz-api/src/bin/generator/main.rs +++ b/crates/cs2kz-api/src/bin/generator/main.rs @@ -380,7 +380,6 @@ async fn create_records( match records::submit(cx, record).await { Ok(SubmittedRecord { record_id: id, .. }) => info!(%id, "created record"), - Err(SubmitRecordError::CalculatePoints(error)) => return Err(error.into()), Err(SubmitRecordError::CalculateRating(error)) => return Err(error.into()), Err(SubmitRecordError::Database(error)) => return Err(error.into()), } diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 142f27e4..1707145a 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -330,7 +330,7 @@ async fn upsert_best_records( ); query.push(" ON DUPLICATE KEY UPDATE points = VALUES(points)"); - query.build().execute(&mut *conn).await?; + query.build().persistent(false).execute(&mut *conn).await?; } Ok(()) From 4137e9ff18c35db4bcfa0f8d56fa50b06918e790 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Fri, 15 May 2026 01:35:26 +0200 Subject: [PATCH 14/24] nig cleanups --- Cargo.lock | 1 + crates/cs2kz/src/points/mod.rs | 3 +- crates/nig/Cargo.toml | 1 + crates/nig/src/bessel.rs | 47 ++++--------- crates/nig/src/distribution.rs | 4 +- crates/nig/src/fitting.rs | 116 +++++++++++++++++++++++---------- crates/nig/src/lib.rs | 2 +- crates/nig/src/params.rs | 6 -- 8 files changed, 101 insertions(+), 79 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index fc04732b..54b42901 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -2535,6 +2535,7 @@ name = "nig" version = "0.1.0" dependencies = [ "serde", + "tracing", ] [[package]] diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index c4e7bd08..5b911b95 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -146,8 +146,7 @@ fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> Option f64 { } fn bessel_k1_large(x: f64) -> f64 { + let scaled = bessel_k1e_large(x); + if x < 709.0 { + return scaled * (-x).exp(); + } + + let exp_half = (-x / 2.0).exp(); + scaled * exp_half * exp_half +} + +fn bessel_k1e_large(x: f64) -> f64 { const Y: f64 = 1.45034217834472656e+00; const P: [f64; 9] = [ @@ -93,16 +103,10 @@ fn bessel_k1_large(x: f64) -> f64 { 2.50358186953478678e-02, ]; - let rat = evaluate_rational(&P, &Q, 1.0 / x) + Y; - if x < 709.0 { - return rat * (-x).exp() / x.sqrt(); - } - - let exp_half = (-x / 2.0).exp(); - rat * exp_half / x.sqrt() * exp_half + (evaluate_rational(&P, &Q, 1.0 / x) + Y) / x.sqrt() } -pub(crate) fn bessel_k1_scaled(x: f64) -> f64 { +pub(crate) fn bessel_k1e(x: f64) -> f64 { if x <= 0.0 { return f64::INFINITY; } @@ -111,32 +115,7 @@ pub(crate) fn bessel_k1_scaled(x: f64) -> f64 { return bessel_k1(x) * x.exp(); } - const Y: f64 = 1.45034217834472656e+00; - - const P: [f64; 9] = [ - -1.97028041029226295e-01, - -2.32408961548087617e+00, - -7.98269784507699938e+00, - -2.39968410774221632e+00, - 3.28314043780858713e+01, - 5.67713761158496058e+01, - 3.30907788466509823e+01, - 6.62582288933739787e+00, - 3.08851840645286691e-01, - ]; - const Q: [f64; 9] = [ - 1.00000000000000000e+00, - 1.41811409298826118e+01, - 7.35979466317556420e+01, - 1.77821793937080859e+02, - 2.11014501598705982e+02, - 1.19425262951064454e+02, - 2.88448064302447607e+01, - 2.27912927104139732e+00, - 2.50358186953478678e-02, - ]; - - (evaluate_rational(&P, &Q, 1.0 / x) + Y) / x.sqrt() + bessel_k1e_large(x) } #[cfg(test)] diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs index f6e86cbe..9e7f6132 100644 --- a/crates/nig/src/distribution.rs +++ b/crates/nig/src/distribution.rs @@ -1,4 +1,4 @@ -use crate::bessel::bessel_k1_scaled; +use crate::bessel::bessel_k1e; pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { if a <= 0.0 || delta <= 0.0 || b.abs() >= a { @@ -9,7 +9,7 @@ pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { let z = (x - mu) / delta; let sqrt_z2p1 = (z * z + 1.0).sqrt(); let y = a * sqrt_z2p1; - let scaled_bessel = bessel_k1_scaled(y); + let scaled_bessel = bessel_k1e(y); if scaled_bessel <= 0.0 { return 0.0; diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs index 6c6f5e98..5a25b209 100644 --- a/crates/nig/src/fitting.rs +++ b/crates/nig/src/fitting.rs @@ -1,8 +1,14 @@ -use crate::bessel::bessel_k1; +use crate::bessel::bessel_k1e; use crate::distribution::nig_survival; -use crate::params::{FitResult, NigParams}; +use crate::params::NigParams; -pub(crate) fn central_diff(mut f: impl FnMut(f64) -> f64, x0: f64) -> f64 { +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum OptimizeNigError { + MaxIterationsReached, + StepSizeExhausted, +} + +fn central_diff(mut f: impl FnMut(f64) -> f64, x0: f64) -> f64 { let eps = f64::EPSILON; let h = eps.cbrt() * x0.abs().max(1.0); // https://docs.sciml.ai/FiniteDiff/dev/epsilons/ let x_plus = x0 + h; @@ -11,7 +17,7 @@ pub(crate) fn central_diff(mut f: impl FnMut(f64) -> f64, x0: f64) -> f64 { (f(x_plus) - f(x_minus)) / denom } -pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> FitResult { +pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> Option { let (a, b, loc, scale); if let Some(prev) = prev_params.filter(|p| p.a > 0.0) { @@ -26,21 +32,24 @@ pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> FitResult { b = estimate_beta(times, loc, scale, a); } - let (a, b, loc, scale) = optimize_nig(times, a, b, loc, scale); + let (a, b, loc, scale) = match optimize_nig(times, a, b, loc, scale) { + Ok(params) => params, + Err(err) => { + tracing::warn!(?err, samples = times.len(), "NIG optimization failed; using initial estimates"); + (a, b, loc, scale) + } + }; let top_scale = { let sf = nig_survival(a, b, loc, scale, times[0]); if sf <= 0.0 { 1.0 } else { sf } }; - FitResult { - params: NigParams { a, b, loc, scale, top_scale }, - valid: true, - } + Some(NigParams { a, b, loc, scale, top_scale }) } /// Median-based location estimate. -pub(crate) fn estimate_location(times: &[f64]) -> f64 { +fn estimate_location(times: &[f64]) -> f64 { let n = times.len(); if n % 2 == 0 { (times[n / 2 - 1] + times[n / 2]) / 2.0 @@ -50,14 +59,14 @@ pub(crate) fn estimate_location(times: &[f64]) -> f64 { } /// Standard-deviation-based scale estimate. -pub(crate) fn estimate_scale(times: &[f64], loc: f64) -> f64 { +fn estimate_scale(times: &[f64], loc: f64) -> f64 { let n = times.len() as f64; let variance = times.iter().map(|&t| (t - loc).powi(2)).sum::() / n; variance.max(1e-10).sqrt() } /// Kurtosis-based estimate -pub(crate) fn estimate_alpha(times: &[f64], loc: f64, _scale: f64) -> f64 { +fn estimate_alpha(times: &[f64], loc: f64, _scale: f64) -> f64 { let n = times.len() as f64; let (mut m2, mut m4) = (0.0, 0.0); for &t in times { @@ -77,7 +86,7 @@ pub(crate) fn estimate_alpha(times: &[f64], loc: f64, _scale: f64) -> f64 { } /// Skewness-based estimate -pub(crate) fn estimate_beta(times: &[f64], loc: f64, _scale: f64, alpha: f64) -> f64 { +fn estimate_beta(times: &[f64], loc: f64, _scale: f64, alpha: f64) -> f64 { let n = times.len() as f64; let (mut m2, mut m3) = (0.0, 0.0); for &t in times { @@ -100,7 +109,7 @@ pub(crate) fn estimate_beta(times: &[f64], loc: f64, _scale: f64, alpha: f64) -> beta } -pub(crate) fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: f64) -> f64 { +fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: f64) -> f64 { if a <= 0.0 || scale <= 0.0 || b.abs() >= a { return f64::INFINITY; } @@ -113,25 +122,29 @@ pub(crate) fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: for &x in times { let z = (x - mu) / delta; let sqrt_z2p1 = (z * z + 1.0).sqrt(); - let bessel = bessel_k1(a * sqrt_z2p1); - if bessel <= 0.0 { + let y = a * sqrt_z2p1; + let scaled_bessel = bessel_k1e(y); + if scaled_bessel <= 0.0 { return f64::INFINITY; } - let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() + gamma + b * z + bessel.ln() - - sqrt_z2p1.ln(); + let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() - sqrt_z2p1.ln() + + gamma + + b * z + - y + + scaled_bessel.ln(); nll -= log_pdf; } nll } -pub(crate) fn optimize_nig( +fn optimize_nig( times: &[f64], mut a: f64, mut b: f64, mut loc: f64, mut scale: f64, -) -> (f64, f64, f64, f64) { +) -> Result<(f64, f64, f64, f64), OptimizeNigError> { const MAX_ITER: usize = 200; const TOL: f64 = 1e-8; const INIT_LR: f64 = 0.01; @@ -169,7 +182,7 @@ pub(crate) fn optimize_nig( b = new_b; loc = new_loc; scale = new_scale; - break; + return Ok((a, b, loc, scale)); } a = new_a; b = new_b; @@ -186,12 +199,12 @@ pub(crate) fn optimize_nig( } else { lr *= 0.5; if lr < 1e-10 { - break; + return Err(OptimizeNigError::StepSizeExhausted); } } } - (a, b, loc, scale) + Err(OptimizeNigError::MaxIterationsReached) } #[cfg(test)] @@ -223,6 +236,22 @@ mod tests { assert!(nll > 0.0); } + #[test] + fn neg_log_likelihood_matches_scipy_reference_value() { + let times: Vec = (0..200) + .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) + .collect(); + let nll = neg_log_likelihood( + ×, + 86.72396846356486, + 86.68319372270773, + 4.487105095426718, + 0.24914444085073106, + ); + + assert_abs_close(nll, 559.6276051205211, 1e-6); + } + #[test] fn estimate_location_is_median() { let mut times = vec![1.0, 2.0, 3.0, 4.0, 5.0]; @@ -265,13 +294,12 @@ mod tests { let times: Vec = (0..200) .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) .collect(); - let result = fit_nig(×, None); - assert!(result.valid); - assert!(result.params.a > 0.01); - assert!(result.params.b.abs() < result.params.a); - assert!(result.params.scale > 1e-6); - assert!(result.params.top_scale > 0.0); - assert!(result.params.top_scale <= 1.01); + let result = fit_nig(×, None).expect("expected fit to converge"); + assert!(result.a > 0.01); + assert!(result.b.abs() < result.a); + assert!(result.scale > 1e-6); + assert!(result.top_scale > 0.0); + assert!(result.top_scale <= 1.01); } #[test] @@ -279,9 +307,29 @@ mod tests { let times: Vec = (0..200) .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) .collect(); - let cold_result = fit_nig(×, None); - let warm_result = fit_nig(×, Some(&cold_result.params)); - assert!(warm_result.valid); - assert_abs_close(warm_result.params.a, cold_result.params.a, 1.0); + let cold_result = fit_nig(×, None).expect("expected cold fit to converge"); + let warm_result = fit_nig(×, Some(&cold_result)).expect("expected warm fit to converge"); + assert_abs_close(warm_result.a, cold_result.a, 1.0); + } + + #[test] + fn fit_nig_improves_initial_estimates_on_synthetic_data() { + let times: Vec = (0..200) + .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) + .collect(); + let loc = estimate_location(×); + let scale = estimate_scale(×, loc); + let a = estimate_alpha(×, loc, scale); + let b = estimate_beta(×, loc, scale, a); + let initial_nll = neg_log_likelihood(×, a, b, loc, scale); + + let fitted = fit_nig(×, None).expect("expected fit to converge"); + let fitted_nll = neg_log_likelihood(×, fitted.a, fitted.b, fitted.loc, fitted.scale); + + assert!(fitted_nll.is_finite()); + assert!( + fitted_nll <= initial_nll, + "expected fitted params to improve NLL; initial={initial_nll:.6}, fitted={fitted_nll:.6}", + ); } } diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs index fc8fdec9..eb2e33ed 100644 --- a/crates/nig/src/lib.rs +++ b/crates/nig/src/lib.rs @@ -5,4 +5,4 @@ mod params; pub use distribution::nig_survival; pub use fitting::fit_nig; -pub use params::{FitResult, NigParams}; +pub use params::NigParams; diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs index e74cce7b..4d14a01b 100644 --- a/crates/nig/src/params.rs +++ b/crates/nig/src/params.rs @@ -8,9 +8,3 @@ pub struct NigParams { pub scale: f64, // delta pub top_scale: f64, // used to normalize the pdf } - -#[derive(Debug, Clone)] -pub struct FitResult { - pub params: NigParams, - pub valid: bool, -} From 487c1d1b4cc66473f3866c25305e8d1bccefd46e Mon Sep 17 00:00:00 2001 From: samayala22 Date: Sat, 16 May 2026 00:18:13 +0200 Subject: [PATCH 15/24] apply suggestions --- crates/cs2kz/src/points/mod.rs | 2 +- crates/nig/src/fitting.rs | 23 ++++++++++------------- 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index 5b911b95..20d20f29 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -146,7 +146,7 @@ fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> Option f64, x0: f64) -> f64 { (f(x_plus) - f(x_minus)) / denom } -pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> Option { +pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> NigParams { let (a, b, loc, scale); if let Some(prev) = prev_params.filter(|p| p.a > 0.0) { @@ -32,20 +32,17 @@ pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> Option params, - Err(err) => { - tracing::warn!(?err, samples = times.len(), "NIG optimization failed; using initial estimates"); - (a, b, loc, scale) - } - }; + let (a, b, loc, scale) = optimize_nig(times, a, b, loc, scale).unwrap_or_else(|err| { + tracing::warn!(?err, samples = times.len(), "NIG optimization failed; using initial estimates"); + (a, b, loc, scale) + }); let top_scale = { let sf = nig_survival(a, b, loc, scale, times[0]); if sf <= 0.0 { 1.0 } else { sf } }; - Some(NigParams { a, b, loc, scale, top_scale }) + NigParams { a, b, loc, scale, top_scale } } /// Median-based location estimate. @@ -294,7 +291,7 @@ mod tests { let times: Vec = (0..200) .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) .collect(); - let result = fit_nig(×, None).expect("expected fit to converge"); + let result = fit_nig(×, None); assert!(result.a > 0.01); assert!(result.b.abs() < result.a); assert!(result.scale > 1e-6); @@ -307,8 +304,8 @@ mod tests { let times: Vec = (0..200) .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) .collect(); - let cold_result = fit_nig(×, None).expect("expected cold fit to converge"); - let warm_result = fit_nig(×, Some(&cold_result)).expect("expected warm fit to converge"); + let cold_result = fit_nig(×, None); + let warm_result = fit_nig(×, Some(&cold_result)); assert_abs_close(warm_result.a, cold_result.a, 1.0); } @@ -323,7 +320,7 @@ mod tests { let b = estimate_beta(×, loc, scale, a); let initial_nll = neg_log_likelihood(×, a, b, loc, scale); - let fitted = fit_nig(×, None).expect("expected fit to converge"); + let fitted = fit_nig(×, None); let fitted_nll = neg_log_likelihood(×, fitted.a, fitted.b, fitted.loc, fitted.scale); assert!(fitted_nll.is_finite()); From c918325061dee6209235d35e177273efa41515a9 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Sat, 16 May 2026 20:10:29 -0400 Subject: [PATCH 16/24] rework nig crate for better perf & accuracy --- ...b10d600f0ec9f1ab78fa81f1629f476ae3edb.json | 54 ++ ...3d1b5eddbbe16122998033aa8f4c9991f3b36.json | 54 ++ crates/cs2kz/src/points/daemon.rs | 8 +- crates/cs2kz/src/points/mod.rs | 14 +- crates/cs2kz/src/records.rs | 6 +- crates/nig/src/bessel.rs | 2 +- crates/nig/src/distribution.rs | 52 +- crates/nig/src/fitting.rs | 602 +++++++++++------- crates/nig/src/lib.rs | 2 +- crates/nig/src/params.rs | 33 +- 10 files changed, 552 insertions(+), 275 deletions(-) create mode 100644 .sqlx/query-b340e131ba26df0a381b1be9e26b10d600f0ec9f1ab78fa81f1629f476ae3edb.json create mode 100644 .sqlx/query-fc902d6636c7b4df7fa8fbaf1fe3d1b5eddbbe16122998033aa8f4c9991f3b36.json diff --git a/.sqlx/query-b340e131ba26df0a381b1be9e26b10d600f0ec9f1ab78fa81f1629f476ae3edb.json b/.sqlx/query-b340e131ba26df0a381b1be9e26b10d600f0ec9f1ab78fa81f1629f476ae3edb.json new file mode 100644 index 00000000..c4292bda --- /dev/null +++ b/.sqlx/query-b340e131ba26df0a381b1be9e26b10d600f0ec9f1ab78fa81f1629f476ae3edb.json @@ -0,0 +1,54 @@ +{ + "db_name": "MySQL", + "query": "SELECT a, b, loc, scale\n FROM PointDistributionData\n WHERE filter_id = ? AND (NOT is_pro_leaderboard)", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "a", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 1, + "name": "b", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 2, + "name": "loc", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 3, + "name": "scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false + ] + }, + "hash": "b340e131ba26df0a381b1be9e26b10d600f0ec9f1ab78fa81f1629f476ae3edb" +} diff --git a/.sqlx/query-fc902d6636c7b4df7fa8fbaf1fe3d1b5eddbbe16122998033aa8f4c9991f3b36.json b/.sqlx/query-fc902d6636c7b4df7fa8fbaf1fe3d1b5eddbbe16122998033aa8f4c9991f3b36.json new file mode 100644 index 00000000..d264dcf6 --- /dev/null +++ b/.sqlx/query-fc902d6636c7b4df7fa8fbaf1fe3d1b5eddbbe16122998033aa8f4c9991f3b36.json @@ -0,0 +1,54 @@ +{ + "db_name": "MySQL", + "query": "SELECT a, b, loc, scale\n FROM PointDistributionData\n WHERE filter_id = ? AND is_pro_leaderboard", + "describe": { + "columns": [ + { + "ordinal": 0, + "name": "a", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 1, + "name": "b", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 2, + "name": "loc", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + }, + { + "ordinal": 3, + "name": "scale", + "type_info": { + "type": "Double", + "flags": "NOT_NULL | NO_DEFAULT_VALUE", + "max_size": 22 + } + } + ], + "parameters": { + "Right": 1 + }, + "nullable": [ + false, + false, + false, + false + ] + }, + "hash": "fc902d6636c7b4df7fa8fbaf1fe3d1b5eddbbe16122998033aa8f4c9991f3b36" +} diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 1707145a..ba5c11de 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -186,7 +186,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d // Previous distribution parameters for warm start let prev_nub_params = sqlx::query_as!( NigParams, - "SELECT a, b, loc, scale, top_scale + "SELECT a, b, loc, scale FROM PointDistributionData WHERE filter_id = ? AND (NOT is_pro_leaderboard)", filter_id, @@ -196,7 +196,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let prev_pro_params = sqlx::query_as!( NigParams, - "SELECT a, b, loc, scale, top_scale + "SELECT a, b, loc, scale FROM PointDistributionData WHERE filter_id = ? AND is_pro_leaderboard", filter_id, @@ -206,10 +206,10 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d let (nub_result, pro_result) = tokio::task::spawn_blocking(move || { let nub_result = - points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params.as_ref()); + points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params); let mut pro_result = - points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params.as_ref()); + points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params); for (record, recalculated_points) in pro_recs.iter().zip(pro_result.records.iter_mut()) { let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index 20d20f29..d23aa841 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -3,11 +3,11 @@ use crate::records::RecordId; pub mod daemon; -pub use nig::NigParams; +pub use nig::{NigData, NigParams}; #[derive(Debug, Clone, serde::Serialize)] pub struct LeaderboardData { - pub dist_params: Option, + pub dist_params: Option, #[serde(serialize_with = "Tier::serialize_as_integer")] pub tier: Tier, pub leaderboard_size: u64, @@ -111,14 +111,14 @@ pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { }; let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; - (nig::nig_survival(dist.a, dist.b, dist.loc, dist.scale, time) / top_scale).clamp(0.0, 1.0) + (nig::nig_survival(&dist.params(), time) / top_scale).clamp(0.0, 1.0) } /// Recompute point fractions for a single leaderboard. pub fn recalculate_leaderboard( records: &[RecordTime], tier: Tier, - prev_params: Option<&NigParams>, + prev_params: Option, ) -> RecalculatedLeaderboard { let times: Vec = records.iter().map(|record| record.time).collect(); let params = fit_distribution(×, prev_params); @@ -141,7 +141,7 @@ pub fn recalculate_leaderboard( } } -fn fit_distribution(times: &[f64], prev_params: Option<&NigParams>) -> Option { +fn fit_distribution(times: &[f64], prev_params: Option) -> Option { if times.len() < SMALL_LEADERBOARD_THRESHOLD as usize { return None; } @@ -165,7 +165,7 @@ mod tests { fn calculate_fraction_matches_python_example() { let time = 8.609375; let nub_data = LeaderboardData { - dist_params: Some(NigParams { + dist_params: Some(NigData { a: 33.53900289787477, b: 33.52140111667502, loc: 6.3663207368487065, @@ -177,7 +177,7 @@ mod tests { top_time: 7.6484375, }; let pro_data = LeaderboardData { - dist_params: Some(NigParams { + dist_params: Some(NigData { a: 2.6294814553333743, b: 2.511121972118702, loc: 8.713014153227697, diff --git a/crates/cs2kz/src/records.rs b/crates/cs2kz/src/records.rs index 92ca9f37..2410dabc 100644 --- a/crates/cs2kz/src/records.rs +++ b/crates/cs2kz/src/records.rs @@ -14,7 +14,7 @@ use crate::num::AsF64; use crate::pagination::{Limit, Offset, Paginated}; use crate::players::{CalculateRatingError, PlayerId, PlayerInfo}; use crate::plugin::PluginVersionId; -use crate::points::{self, NigParams}; +use crate::points::{self, NigData}; use crate::servers::{ServerId, ServerInfo}; use crate::styles::{ClientStyleInfo, Styles}; use crate::time::Seconds; @@ -285,7 +285,7 @@ pub async fn submit( .await?; let nub_dist = sqlx::query_as!( - NigParams, + NigData, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? @@ -338,7 +338,7 @@ pub async fn submit( let pro_data = if teleports == 0 { let pro_dist = sqlx::query_as!( - NigParams, + NigData, "SELECT a, b, loc, scale, top_scale FROM PointDistributionData WHERE filter_id = ? diff --git a/crates/nig/src/bessel.rs b/crates/nig/src/bessel.rs index 87b4e1e3..3ab15fb0 100644 --- a/crates/nig/src/bessel.rs +++ b/crates/nig/src/bessel.rs @@ -1,5 +1,5 @@ /// Evaluates the rational function P(z)/Q(z) using Horner's method. -/// +/// https://github.com/boostorg/math/blob/develop/include/boost/math/tools/rational.hpp pub(crate) fn evaluate_rational(p: &[f64], q: &[f64], z: f64) -> f64 { if p.is_empty() || q.is_empty() { return 0.0; diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs index 9e7f6132..3db368c9 100644 --- a/crates/nig/src/distribution.rs +++ b/crates/nig/src/distribution.rs @@ -1,22 +1,23 @@ use crate::bessel::bessel_k1e; +use crate::params::NigParams; -pub(crate) fn nig_pdf(a: f64, b: f64, mu: f64, delta: f64, x: f64) -> f64 { - if a <= 0.0 || delta <= 0.0 || b.abs() >= a { +pub(crate) fn nig_pdf(p: &NigParams, x: f64) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { return 0.0; } - let gamma = (a * a - b * b).sqrt(); - let z = (x - mu) / delta; + let gamma = (p.a * p.a - p.b * p.b).sqrt(); + let z = (x - p.loc) / p.scale; let sqrt_z2p1 = (z * z + 1.0).sqrt(); - let y = a * sqrt_z2p1; + let y = p.a * sqrt_z2p1; let scaled_bessel = bessel_k1e(y); if scaled_bessel <= 0.0 { return 0.0; } - let net_exp = gamma + b * z - y; - let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() - sqrt_z2p1.ln() + let net_exp = gamma + p.b * z - y; + let log_pdf = p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() - sqrt_z2p1.ln() + net_exp + scaled_bessel.ln(); @@ -85,21 +86,21 @@ fn adaptive_simpson_rec( ) } -pub fn nig_survival(a: f64, b: f64, loc: f64, scale: f64, x: f64) -> f64 { - if a <= 0.0 || scale <= 0.0 || b.abs() >= a { +pub fn nig_survival(p: &NigParams, x: f64) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { return 0.0; } - let gamma = (a * a - b * b).sqrt().max(1e-10); - let mean = loc + scale * b / gamma; - let stddev = (scale * a * a / (gamma * gamma * gamma)).sqrt(); + let gamma = (p.a * p.a - p.b * p.b).sqrt().max(1e-10); + let mean = p.loc + p.scale * p.b / gamma; + let stddev = (p.scale * p.a * p.a / (gamma * gamma * gamma)).sqrt(); let mut upper = mean + 20.0 * stddev; - if upper < x + scale { - upper = x + 10.0 * scale; + if upper < x + p.scale { + upper = x + 10.0 * p.scale; } - adaptive_simpson(|t| nig_pdf(a, b, loc, scale, t), x, upper, 1e-12, 64) + adaptive_simpson(|t| nig_pdf(p, t), x, upper, 1e-12, 64) .unwrap_or(0.0) .clamp(0.0, 1.0) } @@ -107,6 +108,7 @@ pub fn nig_survival(a: f64, b: f64, loc: f64, scale: f64, x: f64) -> f64 { #[cfg(test)] mod tests { use super::*; + use crate::params::NigParams; fn assert_rel_close(actual: f64, expected: f64, tolerance: f64) { let rel_error = if expected == 0.0 { @@ -130,8 +132,12 @@ mod tests { #[test] fn nig_pdf_matches_reference_values() { - let (a, b, loc, scale) = - (33.53900289787477, 33.52140111667502, 6.3663207368487065, 0.4480388195262859); + let p = NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + }; for (x, expected) in [ (7.648, 9.314339782198335e-03), @@ -139,14 +145,18 @@ mod tests { (10.0, 7.240000069597700e-02), (20.0, 3.070336727949191e-02), ] { - assert_rel_close(nig_pdf(a, b, loc, scale, x), expected, 1e-10); + assert_rel_close(nig_pdf(&p, x), expected, 1e-10); } } #[test] fn nig_survival_matches_reference_values() { - let (a, b, loc, scale) = - (33.53900289787477, 33.52140111667502, 6.3663207368487065, 0.4480388195262859); + let p = NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + }; for (x, expected) in [ (7.0, 9.999892785756547e-01), @@ -154,7 +164,7 @@ mod tests { (10.0, 8.873317615160712e-01), (20.0, 3.429376452167427e-01), ] { - assert_abs_close(nig_survival(a, b, loc, scale, x), expected, 1e-4); + assert_abs_close(nig_survival(&p, x), expected, 1e-4); } } } diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs index b3c0199d..9fd1bf23 100644 --- a/crates/nig/src/fitting.rs +++ b/crates/nig/src/fitting.rs @@ -1,212 +1,315 @@ use crate::bessel::bessel_k1e; use crate::distribution::nig_survival; -use crate::params::NigParams; +use crate::params::{NigData, NigParams, NigParamsReparametrized}; -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -enum OptimizeNigError { - MaxIterationsReached, - StepSizeExhausted, -} - -fn central_diff(mut f: impl FnMut(f64) -> f64, x0: f64) -> f64 { - let eps = f64::EPSILON; - let h = eps.cbrt() * x0.abs().max(1.0); // https://docs.sciml.ai/FiniteDiff/dev/epsilons/ - let x_plus = x0 + h; - let x_minus = x0 - h; - let denom = x_plus - x_minus; // representable floating point nb (not same as 2h) - (f(x_plus) - f(x_minus)) / denom -} - -pub fn fit_nig(times: &[f64], prev_params: Option<&NigParams>) -> NigParams { - let (a, b, loc, scale); - - if let Some(prev) = prev_params.filter(|p| p.a > 0.0) { - a = prev.a; - b = prev.b; - loc = prev.loc; - scale = prev.scale; - } else { - loc = estimate_location(times); - scale = estimate_scale(times, loc); - a = estimate_alpha(times, loc, scale); - b = estimate_beta(times, loc, scale, a); - } +/// ln(f64::MAX): upper bound to avoid exp() overflow during optimization. +const MAX_LOG_VALUE: f64 = 709.782_712_893_384; - let (a, b, loc, scale) = optimize_nig(times, a, b, loc, scale).unwrap_or_else(|err| { - tracing::warn!(?err, samples = times.len(), "NIG optimization failed; using initial estimates"); - (a, b, loc, scale) - }); - - let top_scale = { - let sf = nig_survival(a, b, loc, scale, times[0]); - if sf <= 0.0 { 1.0 } else { sf } - }; - - NigParams { a, b, loc, scale, top_scale } +/// P1 Error: a combination of absolute and relative errors. +/// +fn p1_err(x: f64, y: f64) -> f64 { + (x - y).abs() / (1.0 + y.abs()) } -/// Median-based location estimate. -fn estimate_location(times: &[f64]) -> f64 { - let n = times.len(); - if n % 2 == 0 { - (times[n / 2 - 1] + times[n / 2]) / 2.0 - } else { - times[n / 2] +fn encode_nig_params(p: &NigParams) -> NigParamsReparametrized { + let safe_a = p.a.max(1e-6); + let safe_scale = p.scale.max(1e-6); + let beta_ratio = (p.b / safe_a).clamp(-1.0 + 1e-12, 1.0 - 1e-12); + NigParamsReparametrized { + log_a: safe_a.ln(), + skew_raw: beta_ratio.atanh(), + loc: p.loc, + log_scale: safe_scale.ln(), } } -/// Standard-deviation-based scale estimate. -fn estimate_scale(times: &[f64], loc: f64) -> f64 { - let n = times.len() as f64; - let variance = times.iter().map(|&t| (t - loc).powi(2)).sum::() / n; - variance.max(1e-10).sqrt() +fn decode_nig_params(pr: &NigParamsReparametrized) -> NigParams { + let a = pr.log_a.exp(); + let b = a * pr.skew_raw.tanh(); + let scale = pr.log_scale.exp(); + NigParams { a, b, loc: pr.loc, scale } } -/// Kurtosis-based estimate -fn estimate_alpha(times: &[f64], loc: f64, _scale: f64) -> f64 { +/// Moment-based initial parameter estimate for the NIG distribution. +fn estimate_nig_start(times: &[f64]) -> NigParams { let n = times.len() as f64; - let (mut m2, mut m4) = (0.0, 0.0); - for &t in times { - let d = t - loc; - m2 += d * d; - m4 += d * d * d * d; - } - m2 /= n; - m4 /= n; + let mean = times.iter().sum::() / n; + let m2 = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; - if m2 < 1e-10 { - return 1.0; + if m2 < 1e-12 { + return NigParams { a: 1.0, b: 0.0, loc: mean, scale: 1.0 }; } - let kurtosis = m4 / (m2 * m2); - 1.0_f64.max(3.0 / (kurtosis - 3.0).max(0.1)) -} + let m3 = times.iter().map(|&t| (t - mean).powi(3)).sum::() / n; + let m4 = times.iter().map(|&t| (t - mean).powi(4)).sum::() / n; + let skewness = m3 / m2.powf(1.5); + let excess_kurtosis = m4 / (m2 * m2) - 3.0; + let denominator = excess_kurtosis - (4.0 * skewness * skewness) / 3.0; -/// Skewness-based estimate -fn estimate_beta(times: &[f64], loc: f64, _scale: f64, alpha: f64) -> f64 { - let n = times.len() as f64; - let (mut m2, mut m3) = (0.0, 0.0); - for &t in times { - let d = t - loc; - m2 += d * d; - m3 += d * d * d; + if denominator <= 1e-8 { + let scale = m2.sqrt(); + return NigParams { a: (1.0 / scale).max(1e-3), b: 0.0, loc: mean, scale }; } - m2 /= n; - m3 /= n; - if m2 < 1e-10 { - return 0.0; - } + let delta_gamma = 3.0 / denominator; + let beta_ratio = + (skewness * delta_gamma.sqrt() / 3.0).clamp(-1.0 + 1e-8, 1.0 - 1e-8); + let cos_theta = (1.0 - beta_ratio * beta_ratio).max(1e-12).sqrt(); + let alpha = (delta_gamma / (m2 * cos_theta.powi(4))).max(1e-12).sqrt(); + let beta = alpha * beta_ratio; + let scale = (delta_gamma / (alpha * cos_theta)).max(1e-12); + let loc = mean - scale * beta_ratio / cos_theta; - let skewness = m3 / m2.powf(1.5); - let mut beta = skewness * alpha * 0.5; - if beta.abs() >= alpha { - beta = 0.9 * alpha * beta.signum(); - } - beta + NigParams { a: alpha, b: beta, loc, scale } } -fn neg_log_likelihood(times: &[f64], a: f64, b: f64, loc: f64, scale: f64) -> f64 { - if a <= 0.0 || scale <= 0.0 || b.abs() >= a { +fn neg_log_likelihood(times: &[f64], p: &NigParams) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { return f64::INFINITY; } - let delta = scale; - let mu = loc; - let gamma = (a * a - b * b).sqrt(); - + let gamma = (p.a * p.a - p.b * p.b).sqrt(); let mut nll = 0.0; + for &x in times { - let z = (x - mu) / delta; + let z = (x - p.loc) / p.scale; let sqrt_z2p1 = (z * z + 1.0).sqrt(); - let y = a * sqrt_z2p1; + let y = p.a * sqrt_z2p1; let scaled_bessel = bessel_k1e(y); if scaled_bessel <= 0.0 { return f64::INFINITY; } - - let log_pdf = a.ln() - std::f64::consts::PI.ln() - delta.ln() - sqrt_z2p1.ln() + let log_pdf = p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() + - sqrt_z2p1.ln() + gamma - + b * z + + p.b * z - y + scaled_bessel.ln(); nll -= log_pdf; } + nll } -fn optimize_nig( - times: &[f64], - mut a: f64, - mut b: f64, - mut loc: f64, - mut scale: f64, -) -> Result<(f64, f64, f64, f64), OptimizeNigError> { - const MAX_ITER: usize = 200; - const TOL: f64 = 1e-8; - const INIT_LR: f64 = 0.01; - - let mut lr = INIT_LR; - let mut best_ll = neg_log_likelihood(times, a, b, loc, scale); - - for _iter in 0..MAX_ITER { - // Compute gradients via central differences on each parameter - let da = central_diff(|x| neg_log_likelihood(times, x, b, loc, scale), a); - let db = central_diff(|x| neg_log_likelihood(times, a, x, loc, scale), b); - let dloc = central_diff(|x| neg_log_likelihood(times, a, b, x, scale), loc); - let dscale = central_diff(|x| neg_log_likelihood(times, a, b, loc, x), scale); - - let mut new_a = a - lr * da; - let mut new_b = b - lr * db; - let new_loc = loc - lr * dloc; - let mut new_scale = scale - lr * dscale; - - // Enforce constraints - if new_a < 0.01 { - new_a = 0.01; +// Vector arithmetic helpers for [f64; 4] used in the Nelder-Mead simplex. +fn vsub(a: [f64; 4], b: [f64; 4]) -> [f64; 4] { + [a[0] - b[0], a[1] - b[1], a[2] - b[2], a[3] - b[3]] +} + +fn vmadd(a: [f64; 4], s: f64, b: [f64; 4]) -> [f64; 4] { + [a[0] + s * b[0], a[1] + s * b[1], a[2] + s * b[2], a[3] + s * b[3]] +} + +/// Adaptive Nelder-Mead simplex optimizer. +/// +/// Gao, F. and Han, L. (2012). Implementing the Nelder-Mead simplex algorithm +/// with adaptive parameters. *Computational Optimization and Applications*, 51:1, pp. 259–277. +fn nelder_mead( + mut objective: impl FnMut(&[f64; 4]) -> f64, // hardcoded to 4 params + initial: [f64; 4], + tol: f64, + max_iter: usize, + bounds: &[(f64, f64); 4], +) -> ([f64; 4], f64, usize) { + const N: usize = 4; + let alpha = 1.0_f64; + let beta = 1.0 + 2.0 / N as f64; + let gamma = 0.75 - 1.0 / (2.0 * N as f64); + let delta = 1.0 - 1.0 / N as f64; + + let clip = |x: [f64; N]| -> [f64; N] { + let mut out = x; + for (i, &(lo, hi)) in bounds.iter().enumerate() { + out[i] = out[i].clamp(lo, hi); } - if new_b.abs() >= new_a { - new_b = 0.99 * new_a * new_b.signum(); + out + }; + + let mut simplex = [[0.0_f64; N]; N + 1]; + let mut values = [0.0_f64; N + 1]; + let mut nfev: usize = 0; + + simplex[0] = initial; + values[0] = { nfev += 1; objective(&simplex[0]) }; + + for i in 0..N { + let mut vertex = simplex[0]; + vertex[i] += if vertex[i] == 0.0 { 1.0 } else { vertex[i] * 0.05 }; + simplex[i + 1] = clip(vertex); + values[i + 1] = { nfev += 1; objective(&simplex[i + 1]) }; + } + + for _ in 0..max_iter { + // Sort simplex by ascending function value. + let mut order = [0usize, 1, 2, 3, 4]; + order.sort_by(|&a, &b| { + values[a] + .partial_cmp(&values[b]) + .unwrap_or(std::cmp::Ordering::Equal) + }); + let sorted_s: [[f64; N]; N + 1] = std::array::from_fn(|i| simplex[order[i]]); + let sorted_v: [f64; N + 1] = std::array::from_fn(|i| values[order[i]]); + simplex = sorted_s; + values = sorted_v; + + let best = simplex[0]; + let worst = simplex[N]; + + // Centroid of all vertices except the worst. + let mut centroid = [0.0_f64; N]; + for i in 0..N { + for j in 0..N { + centroid[j] += simplex[i][j]; + } } - if new_scale < 1e-6 { - new_scale = 1e-6; + for j in 0..N { + centroid[j] /= N as f64; } - let new_ll = neg_log_likelihood(times, new_a, new_b, new_loc, new_scale); - if new_ll < best_ll { - if best_ll - new_ll < TOL { - a = new_a; - b = new_b; - loc = new_loc; - scale = new_scale; - return Ok((a, b, loc, scale)); + let reflected = clip(vmadd(centroid, alpha, vsub(centroid, worst))); + let f_reflect = { nfev += 1; objective(&reflected) }; + + if f_reflect < values[0] { + let expanded = clip(vmadd(centroid, beta, vsub(centroid, worst))); + let f_expand = { nfev += 1; objective(&expanded) }; + if f_expand < f_reflect { + simplex[N] = expanded; + values[N] = f_expand; + } else { + simplex[N] = reflected; + values[N] = f_reflect; } - a = new_a; - b = new_b; - loc = new_loc; - scale = new_scale; - best_ll = new_ll; - // Gradually restore learning rate on success - if lr < INIT_LR { - lr *= 1.2; - if lr > INIT_LR { - lr = INIT_LR; + } else if f_reflect < values[N - 1] { + simplex[N] = reflected; + values[N] = f_reflect; + } else if f_reflect < values[N] { + // Outside contraction. + let xc = clip(vmadd(centroid, gamma, vsub(centroid, worst))); + let f_contract = { nfev += 1; objective(&xc) }; + if f_contract <= f_reflect { + simplex[N] = xc; + values[N] = f_contract; + } else { + for i in 1..=N { + simplex[i] = clip(vmadd(best, delta, vsub(simplex[i], best))); + values[i] = { nfev += 1; objective(&simplex[i]) }; } } } else { - lr *= 0.5; - if lr < 1e-10 { - return Err(OptimizeNigError::StepSizeExhausted); + // Inside contraction. + let xc = clip(vmadd(centroid, -gamma, vsub(centroid, worst))); + let f_contract = { nfev += 1; objective(&xc) }; + if f_contract < values[N] { + simplex[N] = xc; + values[N] = f_contract; + } else { + for i in 1..=N { + simplex[i] = clip(vmadd(best, delta, vsub(simplex[i], best))); + values[i] = { nfev += 1; objective(&simplex[i]) }; + } } } + + // Convergence: max P1 error across all non-best vertices vs best. + let max_simplex_err = (1..=N) + .flat_map(|i| (0..N).map(move |j| p1_err(simplex[i][j], simplex[0][j]))) + .fold(f64::NEG_INFINITY, f64::max); + let max_value_err = (1..=N) + .map(|i| p1_err(values[i], values[0])) + .fold(f64::NEG_INFINITY, f64::max); + + if max_simplex_err <= tol && max_value_err <= tol { + break; + } + } + + let best_idx = values + .iter() + .enumerate() + .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) + .map(|(i, _)| i) + .unwrap_or(0); + + (simplex[best_idx], values[best_idx], nfev) +} + +fn optimize_nig(times: &[f64], p: &NigParams) -> Result<(NigParams, usize), ()> { + const MAX_ITER: usize = 1000; + const TOL: f64 = 1e-4; + + let n = times.len() as f64; + let mean = times.iter().sum::() / n; + let variance = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; + let loc_step = variance.sqrt().max(1.0); + + let initial = encode_nig_params(p); + let initial_arr = [initial.log_a, initial.skew_raw, initial.loc, initial.log_scale]; + + let bounds = [ + (initial.log_a - 4.0, initial.log_a + 6.0), + (initial.skew_raw - 4.0, initial.skew_raw + 4.0), + (initial.loc - 10.0 * loc_step, initial.loc + 10.0 * loc_step), + (initial.log_scale - 5.0, initial.log_scale + 5.0), + ]; + + let objective = |values: &[f64; 4]| -> f64 { + if !values.iter().all(|v| v.is_finite()) { + return f64::INFINITY; + } + if values[0] >= MAX_LOG_VALUE || values[3] >= MAX_LOG_VALUE { + return f64::INFINITY; + } + let pr = NigParamsReparametrized { + log_a: values[0], + skew_raw: values[1], + loc: values[2], + log_scale: values[3], + }; + neg_log_likelihood(times, &decode_nig_params(&pr)) + }; + + let (optimum, best_ll, nfev) = nelder_mead(objective, initial_arr, TOL, MAX_ITER, &bounds); + + if !best_ll.is_finite() { + return Err(()); + } + + let pr = NigParamsReparametrized { + log_a: optimum[0], + skew_raw: optimum[1], + loc: optimum[2], + log_scale: optimum[3], + }; + + Ok((decode_nig_params(&pr), nfev)) +} + +pub fn fit_nig(times: &[f64], params: Option) -> NigData { + let mut p = match params { + Some(p) if p.a > 0.0 => p, + _ => estimate_nig_start(times), + }; + + match optimize_nig(times, &p) { + Ok((optimized, _nfev)) => p = optimized, + Err(()) => { + tracing::warn!( + samples = times.len(), + "NIG optimization failed; using initial estimates", + ); + } } - Err(OptimizeNigError::MaxIterationsReached) + let sf = nig_survival(&p, times[0]); + let top_scale = if sf <= 0.0 { 1.0 } else { sf }; + + NigData { a: p.a, b: p.b, loc: p.loc, scale: p.scale, top_scale } } #[cfg(test)] mod tests { use super::*; + use crate::params::{NigParams}; fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { let abs_error = (actual - expected).abs(); @@ -216,117 +319,150 @@ mod tests { ); } + /// Generates the 5 test datasets from the Python benchmark. + fn test_datasets() -> Vec> { + let linspace = |start: f64, end: f64, n: usize| -> Vec { + (0..n).map(|i| start + (end - start) / (n - 1) as f64 * i as f64).collect() + }; + vec![ + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(), + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.05).collect(), + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.5).collect(), + (0..50).map(|i: i32| 3.0 + (i as f64).powi(2) * 0.02).collect(), + linspace(5.0, 35.0, 80), + ] + } + #[test] fn neg_log_likelihood_returns_inf_for_invalid_params() { let times = [7.0, 8.0, 9.0, 10.0]; - assert!(neg_log_likelihood(×, 0.0, 1.0, 5.0, 1.0).is_infinite()); - assert!(neg_log_likelihood(×, 2.0, 0.0, 5.0, 0.0).is_infinite()); - assert!(neg_log_likelihood(×, 1.0, 1.0, 5.0, 1.0).is_infinite()); - assert!(neg_log_likelihood(×, 1.0, -1.1, 5.0, 1.0).is_infinite()); + assert!(neg_log_likelihood( + ×, + &NigParams { a: 0.0, b: 1.0, loc: 5.0, scale: 1.0 } + ) + .is_infinite()); + assert!(neg_log_likelihood( + ×, + &NigParams { a: 2.0, b: 0.0, loc: 5.0, scale: 0.0 } + ) + .is_infinite()); + assert!(neg_log_likelihood( + ×, + &NigParams { a: 1.0, b: 1.0, loc: 5.0, scale: 1.0 } + ) + .is_infinite()); + assert!(neg_log_likelihood( + ×, + &NigParams { a: 1.0, b: -1.1, loc: 5.0, scale: 1.0 } + ) + .is_infinite()); } #[test] fn neg_log_likelihood_returns_finite_for_valid_params() { let times = [7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0]; - let nll = neg_log_likelihood(×, 5.0, 2.0, 8.0, 1.0); + let nll = neg_log_likelihood( + ×, + &NigParams { a: 5.0, b: 2.0, loc: 8.0, scale: 1.0 }, + ); assert!(nll.is_finite()); assert!(nll > 0.0); } #[test] fn neg_log_likelihood_matches_scipy_reference_value() { - let times: Vec = (0..200) - .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) - .collect(); + let times: Vec = + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); let nll = neg_log_likelihood( ×, - 86.72396846356486, - 86.68319372270773, - 4.487105095426718, - 0.24914444085073106, + &NigParams { + a: 86.72396846356486, + b: 86.68319372270773, + loc: 4.487105095426718, + scale: 0.24914444085073106, + }, ); - assert_abs_close(nll, 559.6276051205211, 1e-6); } #[test] - fn estimate_location_is_median() { - let mut times = vec![1.0, 2.0, 3.0, 4.0, 5.0]; - times.sort_by(f64::total_cmp); - assert_abs_close(estimate_location(×), 3.0, 1e-12); - } - - #[test] - fn estimate_scale_is_positive() { - let mut times = vec![8.0, 8.5, 9.0, 9.5, 10.0]; - times.sort_by(f64::total_cmp); - let loc = estimate_location(×); - let scale = estimate_scale(×, loc); - assert!(scale > 0.0); - } - - #[test] - fn estimate_alpha_is_at_least_one() { - let mut times: Vec = (0..100).map(|i| 7.0 + i as f64 * 0.01).collect(); - times.sort_by(f64::total_cmp); - let loc = estimate_location(×); - let scale = estimate_scale(×, loc); - let alpha = estimate_alpha(×, loc, scale); - assert!(alpha >= 1.0); + fn estimate_nig_start_produces_valid_params() { + let times: Vec = + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); + let p = estimate_nig_start(×); + assert!(p.a > 0.0, "a must be positive"); + assert!(p.scale > 0.0, "scale must be positive"); + assert!(p.b.abs() < p.a, "|b| must be < a"); } #[test] - fn estimate_beta_constrained_by_alpha() { - let mut times: Vec = (0..100).map(|i| 7.0 + i as f64 * 0.01).collect(); - times.sort_by(f64::total_cmp); - let loc = estimate_location(×); - let scale = estimate_scale(×, loc); - let alpha = estimate_alpha(×, loc, scale); - let beta = estimate_beta(×, loc, scale, alpha); - assert!(beta.abs() < alpha); + fn encode_decode_roundtrips() { + let p = NigParams { a: 5.0, b: 2.0, loc: 8.0, scale: 1.5 }; + let decoded = decode_nig_params(&encode_nig_params(&p)); + assert_abs_close(decoded.a, p.a, 1e-12); + assert_abs_close(decoded.b, p.b, 1e-12); + assert_abs_close(decoded.loc, p.loc, 1e-12); + assert_abs_close(decoded.scale, p.scale, 1e-12); } #[test] - fn fit_nig_converges_on_synthetic_data() { - let times: Vec = (0..200) - .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) - .collect(); - let result = fit_nig(×, None); - assert!(result.a > 0.01); - assert!(result.b.abs() < result.a); - assert!(result.scale > 1e-6); - assert!(result.top_scale > 0.0); - assert!(result.top_scale <= 1.01); - } - - #[test] - fn fit_nig_warm_start_works() { - let times: Vec = (0..200) - .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) - .collect(); - let cold_result = fit_nig(×, None); - let warm_result = fit_nig(×, Some(&cold_result)); - assert_abs_close(warm_result.a, cold_result.a, 1.0); + fn fit_nig_matches_python_reference_all_datasets() { + struct Ref { + a: f64, + b: f64, + loc: f64, + scale: f64, + nll: f64, + nfev: usize, + } + let refs = [ + Ref { a: 72.467, b: 72.418, loc: 4.491, scale: 0.298, nll: 559.637, nfev: 354 }, + Ref { a: 9.536, b: 9.276, loc: -11.183, scale: 17.732, nll: 1021.770, nfev: 685 }, + Ref { a: 0.954, b: 0.650, loc: 232.337, scale: 361.840, nll: 1502.561, nfev: 488 }, + Ref { a: 21.769, b: 21.754, loc: 0.365, scale: 0.687, nll: 195.001, nfev: 468 }, + Ref { a: 46.005, b: -0.001, loc: 20.001, scale: 59.861, nll: 287.470, nfev: 831 }, + ]; + + for (idx, (times, r)) in test_datasets().iter().zip(&refs).enumerate() { + let ds = idx + 1; + let initial = estimate_nig_start(times); + let (p, nfev) = optimize_nig(times, &initial) + .unwrap_or_else(|_| panic!("ds{ds}: optimization failed")); + let nll = neg_log_likelihood(times, &p); + let tol = 1e-3; + assert!((p.a - r.a).abs() <= tol, "ds{ds} a: got {:.3}, expected {:.3}", p.a, r.a); + assert!((p.b - r.b).abs() <= tol, "ds{ds} b: got {:.3}, expected {:.3}", p.b, r.b); + assert!( + (p.loc - r.loc).abs() <= tol, + "ds{ds} loc: got {:.3}, expected {:.3}", + p.loc, r.loc, + ); + assert!( + (p.scale - r.scale).abs() <= tol, + "ds{ds} scale: got {:.3}, expected {:.3}", + p.scale, r.scale, + ); + assert!( + (nll - r.nll).abs() <= tol, + "ds{ds} nll: got {:.3}, expected {:.3}", + nll, r.nll, + ); + assert_eq!(nfev, r.nfev, "ds{ds}: nfev"); + } } #[test] - fn fit_nig_improves_initial_estimates_on_synthetic_data() { - let times: Vec = (0..200) - .map(|i| 7.0 + (i as f64).powf(1.5) * 0.005) - .collect(); - let loc = estimate_location(×); - let scale = estimate_scale(×, loc); - let a = estimate_alpha(×, loc, scale); - let b = estimate_beta(×, loc, scale, a); - let initial_nll = neg_log_likelihood(×, a, b, loc, scale); - - let fitted = fit_nig(×, None); - let fitted_nll = neg_log_likelihood(×, fitted.a, fitted.b, fitted.loc, fitted.scale); - - assert!(fitted_nll.is_finite()); + fn fit_nig_warm_start_from_previous_result() { + let times: Vec = + (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); + let cold = fit_nig(×, None); + let warm = fit_nig(×, Some(cold.params())); + let cold_nll = neg_log_likelihood(×, &cold.params()); + let warm_nll = neg_log_likelihood(×, &warm.params()); + // Warm start should reach same or better NLL. assert!( - fitted_nll <= initial_nll, - "expected fitted params to improve NLL; initial={initial_nll:.6}, fitted={fitted_nll:.6}", + warm_nll <= cold_nll + 1e-4, + "warm_nll={warm_nll:.6}, cold_nll={cold_nll:.6}", ); } } diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs index eb2e33ed..7712effb 100644 --- a/crates/nig/src/lib.rs +++ b/crates/nig/src/lib.rs @@ -5,4 +5,4 @@ mod params; pub use distribution::nig_survival; pub use fitting::fit_nig; -pub use params::NigParams; +pub use params::{NigData, NigParams}; diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs index 4d14a01b..2e2d7479 100644 --- a/crates/nig/src/params.rs +++ b/crates/nig/src/params.rs @@ -1,10 +1,33 @@ use serde::Serialize; +/// NIG distribution parameters (scipy loc-scale parameterization). #[derive(Debug, Clone, Copy, Serialize)] pub struct NigParams { - pub a: f64, // alpha - pub b: f64, // beta - pub loc: f64, // mu - pub scale: f64, // delta - pub top_scale: f64, // used to normalize the pdf + pub a: f64, + pub b: f64, + pub loc: f64, + pub scale: f64, +} + +#[derive(Debug, Clone, Copy)] +pub(crate) struct NigParamsReparametrized { + pub log_a: f64, + pub skew_raw: f64, + pub loc: f64, + pub log_scale: f64, +} + +#[derive(Debug, Clone, Copy, Serialize)] +pub struct NigData { + pub a: f64, + pub b: f64, + pub loc: f64, + pub scale: f64, + pub top_scale: f64, +} + +impl NigData { + pub fn params(&self) -> NigParams { + NigParams { a: self.a, b: self.b, loc: self.loc, scale: self.scale } + } } From a4a6b2c5cb1454cf5a6f9c286005aca4ef10e241 Mon Sep 17 00:00:00 2001 From: samayala22 Date: Sun, 24 May 2026 19:25:00 +0200 Subject: [PATCH 17/24] move topscale stuff outside of the nig crate --- crates/cs2kz/src/points/mod.rs | 26 ++++++++++++++++++++++---- crates/nig/src/fitting.rs | 16 ++++++---------- crates/nig/src/lib.rs | 2 +- crates/nig/src/params.rs | 15 --------------- 4 files changed, 29 insertions(+), 30 deletions(-) diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index d23aa841..7ce0191e 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -3,7 +3,22 @@ use crate::records::RecordId; pub mod daemon; -pub use nig::{NigData, NigParams}; +pub use nig::NigParams; + +#[derive(Debug, Clone, Copy, serde::Serialize)] +pub struct NigData { + pub a: f64, + pub b: f64, + pub loc: f64, + pub scale: f64, + pub top_scale: f64, +} + +impl NigData { + pub fn params(&self) -> NigParams { + NigParams { a: self.a, b: self.b, loc: self.loc, scale: self.scale } + } +} #[derive(Debug, Clone, serde::Serialize)] pub struct LeaderboardData { @@ -110,8 +125,7 @@ pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { return for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); }; - let top_scale = if dist.top_scale > 0.0 { dist.top_scale } else { 1.0 }; - (nig::nig_survival(&dist.params(), time) / top_scale).clamp(0.0, 1.0) + nig::nig_survival(&dist.params(), time) / dist.top_scale } /// Recompute point fractions for a single leaderboard. @@ -146,7 +160,11 @@ fn fit_distribution(times: &[f64], prev_params: Option) -> Option Result<(NigParams, usize), ()> Ok((decode_nig_params(&pr), nfev)) } -pub fn fit_nig(times: &[f64], params: Option) -> NigData { +pub fn fit_nig(times: &[f64], params: Option) -> NigParams { let mut p = match params { Some(p) if p.a > 0.0 => p, _ => estimate_nig_start(times), @@ -300,10 +299,7 @@ pub fn fit_nig(times: &[f64], params: Option) -> NigData { } } - let sf = nig_survival(&p, times[0]); - let top_scale = if sf <= 0.0 { 1.0 } else { sf }; - - NigData { a: p.a, b: p.b, loc: p.loc, scale: p.scale, top_scale } + p } #[cfg(test)] @@ -456,9 +452,9 @@ mod tests { let times: Vec = (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); let cold = fit_nig(×, None); - let warm = fit_nig(×, Some(cold.params())); - let cold_nll = neg_log_likelihood(×, &cold.params()); - let warm_nll = neg_log_likelihood(×, &warm.params()); + let warm = fit_nig(×, Some(cold)); + let cold_nll = neg_log_likelihood(×, &cold); + let warm_nll = neg_log_likelihood(×, &warm); // Warm start should reach same or better NLL. assert!( warm_nll <= cold_nll + 1e-4, diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs index 7712effb..eb2e33ed 100644 --- a/crates/nig/src/lib.rs +++ b/crates/nig/src/lib.rs @@ -5,4 +5,4 @@ mod params; pub use distribution::nig_survival; pub use fitting::fit_nig; -pub use params::{NigData, NigParams}; +pub use params::NigParams; diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs index 2e2d7479..76ae1286 100644 --- a/crates/nig/src/params.rs +++ b/crates/nig/src/params.rs @@ -16,18 +16,3 @@ pub(crate) struct NigParamsReparametrized { pub loc: f64, pub log_scale: f64, } - -#[derive(Debug, Clone, Copy, Serialize)] -pub struct NigData { - pub a: f64, - pub b: f64, - pub loc: f64, - pub scale: f64, - pub top_scale: f64, -} - -impl NigData { - pub fn params(&self) -> NigParams { - NigParams { a: self.a, b: self.b, loc: self.loc, scale: self.scale } - } -} From 09bd378c47ff54986741d73e9b825a1c76b8f2df Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Sun, 31 May 2026 16:42:17 -0400 Subject: [PATCH 18/24] replace simpson with exp-sinh quadrature --- crates/nig/src/distribution.rs | 151 ++++++++++++++++++++------------- 1 file changed, 92 insertions(+), 59 deletions(-) diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs index 3db368c9..9004098b 100644 --- a/crates/nig/src/distribution.rs +++ b/crates/nig/src/distribution.rs @@ -29,61 +29,105 @@ pub(crate) fn nig_pdf(p: &NigParams, x: f64) -> f64 { log_pdf.exp() } -// Adapted from C code from https://en.wikipedia.org/wiki/Adaptive_Simpson%27s_method -pub(crate) fn adaptive_simpson( - mut f: impl FnMut(f64) -> f64, +fn exp_sinh_g( + m: i64, + h_fine: f64, a: f64, - b: f64, - eps: f64, - max_depth: u32, -) -> Option { - if a == b { - return Some(0.0); + half_pi: f64, + cache: &mut std::collections::HashMap, + f: &mut impl FnMut(f64) -> f64, +) -> f64 { + if let Some(&v) = cache.get(&m) { + return v; } - - let h = b - a; - let fa = f(a); - let fb = f(b); - let fm = f((a + b) / 2.0); - let whole = (h / 6.0) * (fa + 4.0 * fm + fb); - adaptive_simpson_rec(&mut f, a, b, eps, whole, fa, fb, fm, max_depth) + let t = (m as f64) * h_fine; + let arg = half_pi * t.sinh(); + let v = if arg > 690.0 { + 0.0 + } else { + let e = arg.exp(); // = x - a + let x = a + e; + let w = half_pi * t.cosh() * e; // Jacobian dx/dt + let fv = f(x); + if fv.is_finite() { w * fv } else { 0.0 } + }; + cache.insert(m, v); + v } -fn adaptive_simpson_rec( +// Integrate f over [a, +∞) using exp-sinh (double-exponential) quadrature +// with global step-halving (level doubling) and node reuse. +pub(crate) fn exp_sinh( f: &mut impl FnMut(f64) -> f64, a: f64, - b: f64, - eps: f64, - whole: f64, - fa: f64, - fb: f64, - fm: f64, - depth: u32, -) -> Option { - let m = (a + b) / 2.0; - let lm = (a + m) / 2.0; - let rm = (m + b) / 2.0; - - // Numerical trouble: epsilon underflow or interval collapsed - if eps / 2.0 == eps || a == lm { - return None; - } - - let flm = f(lm); - let frm = f(rm); - let h = (b - a) / 2.0; - let left = (h / 6.0) * (fa + 4.0 * flm + fm); - let right = (h / 6.0) * (fm + 4.0 * frm + fb); - let delta = left + right - whole; + tol: f64, + h0: f64, + max_level: u32, +) -> f64 { + let half_pi = std::f64::consts::PI / 2.0; + let stride0 = 1i64 << max_level; + let h_fine = h0 / (stride0 as f64); + let cut = 1e-17_f64; + let mut cache = std::collections::HashMap::::new(); + + let fmax0 = exp_sinh_g(0, h_fine, a, half_pi, &mut cache, f).abs(); + + let ml = { + let mut k = 0i64; + loop { + k -= 1; + let m = k * stride0; + let v = exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f).abs(); + if k.abs() >= 2 && v <= cut * fmax0.max(v).max(1e-300) { + break m; + } + if k.abs() > 64 { + break m; + } + } + }; + + let mr = { + let mut k = 0i64; + loop { + k += 1; + let m = k * stride0; + let v = exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f).abs(); + if k.abs() >= 2 && v <= cut * fmax0.max(v).max(1e-300) { + break m; + } + if k.abs() > 64 { + break m; + } + } + }; - if depth == 0 || delta.abs() <= 15.0 * eps { - return Some(left + right + delta / 15.0); + let mut s = 0.0_f64; + let mut m = ml; + while m <= mr { + s += exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f); + m += stride0; + } + let mut i_prev = h0 * s; + let mut i = i_prev; + + for level in 1..=max_level { + let stride = 1i64 << (max_level - level); + let h = h0 / ((1u64 << level) as f64); + let mut m = ml + stride; + while m < mr { + s += exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f); + m += 2 * stride; + } + i = h * s; + let err = (i - i_prev).abs(); + if level >= 2 && err <= tol + tol * i.abs() { + break; + } + i_prev = i; } - Some( - adaptive_simpson_rec(&mut *f, a, m, eps / 2.0, left, fa, fm, flm, depth - 1)? - + adaptive_simpson_rec(&mut *f, m, b, eps / 2.0, right, fm, fb, frm, depth - 1)?, - ) + i } pub fn nig_survival(p: &NigParams, x: f64) -> f64 { @@ -91,18 +135,7 @@ pub fn nig_survival(p: &NigParams, x: f64) -> f64 { return 0.0; } - let gamma = (p.a * p.a - p.b * p.b).sqrt().max(1e-10); - let mean = p.loc + p.scale * p.b / gamma; - let stddev = (p.scale * p.a * p.a / (gamma * gamma * gamma)).sqrt(); - - let mut upper = mean + 20.0 * stddev; - if upper < x + p.scale { - upper = x + 10.0 * p.scale; - } - - adaptive_simpson(|t| nig_pdf(p, t), x, upper, 1e-12, 64) - .unwrap_or(0.0) - .clamp(0.0, 1.0) + exp_sinh(&mut |t| nig_pdf(p, t), x, 1e-10, 0.5, 12).clamp(0.0, 1.0) } #[cfg(test)] @@ -164,7 +197,7 @@ mod tests { (10.0, 8.873317615160712e-01), (20.0, 3.429376452167427e-01), ] { - assert_abs_close(nig_survival(&p, x), expected, 1e-4); + assert_abs_close(nig_survival(&p, x), expected, 1e-5); } } } From 12d436768caea67017004e23e34c3e70fd9a753d Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Tue, 9 Jun 2026 21:38:57 -0400 Subject: [PATCH 19/24] refactor (untested) --- Cargo.lock | 1 + crates/cs2kz/src/points/daemon.rs | 19 +- crates/cs2kz/src/points/mod.rs | 31 +- crates/nig/Cargo.toml | 1 + crates/nig/src/bessel.rs | 26 +- crates/nig/src/differential_evo.rs | 104 + crates/nig/src/distribution.rs | 203 - crates/nig/src/fitting.rs | 464 - crates/nig/src/lib.rs | 10 +- crates/nig/src/nig.rs | 276 + crates/nig/src/params.rs | 18 - crates/nig/src/quad.rs | 269 + testdata/nub_records.csv | 74628 +++++++++++++++++++++++++++ testdata/point_distribution.csv | 260 + testdata/pro_records.csv | 16732 ++++++ 15 files changed, 92326 insertions(+), 716 deletions(-) create mode 100644 crates/nig/src/differential_evo.rs delete mode 100644 crates/nig/src/distribution.rs delete mode 100644 crates/nig/src/fitting.rs create mode 100644 crates/nig/src/nig.rs delete mode 100644 crates/nig/src/params.rs create mode 100644 crates/nig/src/quad.rs create mode 100644 testdata/nub_records.csv create mode 100644 testdata/point_distribution.csv create mode 100644 testdata/pro_records.csv diff --git a/Cargo.lock b/Cargo.lock index 54b42901..124733cf 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -2534,6 +2534,7 @@ dependencies = [ name = "nig" version = "0.1.0" dependencies = [ + "rand 0.8.5", "serde", "tracing", ] diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index ba5c11de..3598d7fe 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -8,7 +8,7 @@ use crate::maps::CourseFilterId; use crate::maps::courses::Tier; use crate::mode::Mode; use crate::players::PlayerId; -use crate::points::{self, NigParams}; +use crate::points::{self}; use crate::records::RecordId; use crate::{Context, database, players}; @@ -318,16 +318,13 @@ async fn upsert_best_records( { let mut query = database::QueryBuilder::new(insert_prefix); - query.push_values( - row_chunk.iter().zip(points_chunk.iter()), - |mut query, (row, points)| { - query.push_bind(row.filter_id); - query.push_bind(row.player_id); - query.push_bind(row.record_id); - query.push_bind(points); - query.push_bind(row.time); - }, - ); + query.push_values(row_chunk.iter().zip(points_chunk.iter()), |mut query, (row, points)| { + query.push_bind(row.filter_id); + query.push_bind(row.player_id); + query.push_bind(row.record_id); + query.push_bind(points); + query.push_bind(row.time); + }); query.push(" ON DUPLICATE KEY UPDATE points = VALUES(points)"); query.build().persistent(false).execute(&mut *conn).await?; diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index 7ce0191e..a1f355f7 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -1,10 +1,9 @@ use crate::maps::courses::Tier; use crate::records::RecordId; +use crate::nig; pub mod daemon; -pub use nig::NigParams; - #[derive(Debug, Clone, Copy, serde::Serialize)] pub struct NigData { pub a: f64, @@ -15,8 +14,13 @@ pub struct NigData { } impl NigData { - pub fn params(&self) -> NigParams { - NigParams { a: self.a, b: self.b, loc: self.loc, scale: self.scale } + pub fn params(&self) -> nig::NigParams { + nig::NigParams { + a: self.a, + b: self.b, + loc: self.loc, + scale: self.scale, + } } } @@ -125,7 +129,7 @@ pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { return for_small_leaderboard(leaderboard.tier, leaderboard.top_time, time); }; - nig::nig_survival(&dist.params(), time) / dist.top_scale + nig::sf(&dist.params(), time) / dist.top_scale } /// Recompute point fractions for a single leaderboard. @@ -149,10 +153,7 @@ pub fn recalculate_leaderboard( .map(|record| calculate_fraction(record.time, &leaderboard)) .collect(); - RecalculatedLeaderboard { - leaderboard, - records: recalculated_records, - } + RecalculatedLeaderboard { leaderboard, records: recalculated_records } } fn fit_distribution(times: &[f64], prev_params: Option) -> Option { @@ -160,11 +161,17 @@ fn fit_distribution(times: &[f64], prev_params: Option) -> Option f64 { @@ -106,7 +130,7 @@ fn bessel_k1e_large(x: f64) -> f64 { (evaluate_rational(&P, &Q, 1.0 / x) + Y) / x.sqrt() } -pub(crate) fn bessel_k1e(x: f64) -> f64 { +pub fn bessel_k1e(x: f64) -> f64 { if x <= 0.0 { return f64::INFINITY; } diff --git a/crates/nig/src/differential_evo.rs b/crates/nig/src/differential_evo.rs new file mode 100644 index 00000000..abbbc506 --- /dev/null +++ b/crates/nig/src/differential_evo.rs @@ -0,0 +1,104 @@ +use rand::rngs::SmallRng; +use rand::{Rng, SeedableRng}; + +/// Differential Evolution optimizer (DE/rand/1/bin with dithered mutation scaling). +/// +/// Storn, R. and Price, K. Differential Evolution - A Simple and Efficient +/// Heuristic for Global Optimization over Continuous Spaces. Journal of +/// Global Optimization 11, 341-359 (1997). +pub fn differential_evolution( + objective: impl Fn(&[f64; N]) -> f64, + bounds: &[(f64, f64); N], + tol: f64, + max_iter: usize, + pop_factor: usize, + mutation: (f64, f64), + crossover: f64, + seed: u64, +) -> ([f64; N], f64, usize) { + let (f_lo, f_hi) = mutation; + + let mut rng = SmallRng::seed_from_u64(seed); + let n = N; + let np_pop = pop_factor * n; + + let lo: [f64; N] = core::array::from_fn(|i| bounds[i].0); + let hi: [f64; N] = core::array::from_fn(|i| bounds[i].1); + let span: [f64; N] = core::array::from_fn(|i| hi[i] - lo[i]); + + // Initialize population + let mut pop: Vec<[f64; N]> = (0..np_pop) + .map(|_| core::array::from_fn(|i| lo[i] + rng.r#gen::() * span[i])) + .collect(); + let mut fitness: Vec = pop.iter().map(|p| objective(p)).collect(); + let mut nfev = np_pop; + + for _ in 0..max_iter { + let mut improved = false; + + for i in 0..np_pop { + // Pick three distinct random candidates (not i) + let mut candidates: Vec = (0..np_pop).filter(|&j| j != i).collect(); + // Partial shuffle to pick 3 without replacement + for k in 0..3 { + let idx = rng.gen_range(k..candidates.len()); + candidates.swap(k, idx); + } + let a = candidates[0]; + let b = candidates[1]; + let c = candidates[2]; + + // Dithered mutation factor + let f = f_lo + rng.r#gen::() * (f_hi - f_lo); + + // Mutation: v = x_a + F * (x_b - x_c) + let mutant: [f64; N] = core::array::from_fn(|j| { + (pop[a][j] + f * (pop[b][j] - pop[c][j])).clamp(lo[j], hi[j]) + }); + + // Binomial crossover + let mut cross_mask: [bool; N] = + core::array::from_fn(|_| rng.r#gen::() < crossover); + // Ensure at least one dimension is taken from mutant + cross_mask[rng.gen_range(0..n)] = true; + + let trial: [f64; N] = + core::array::from_fn(|j| if cross_mask[j] { mutant[j] } else { pop[i][j] }); + + let f_trial = objective(&trial); + nfev += 1; + + if f_trial <= fitness[i] { + pop[i] = trial; + fitness[i] = f_trial; + improved = true; + } + } + + let best_idx = argmin(&fitness); + if !improved { + break; + } + + // Population spread check + let spread = (fitness + .iter() + .fold(0.0_f64, |acc, &fv| (fv - fitness[best_idx]).abs().max(acc))) + / (1.0 + fitness[best_idx].abs()); + if spread <= tol { + break; + } + } + + let best_idx = argmin(&fitness); + (pop[best_idx], fitness[best_idx], nfev) +} + +fn argmin(slice: &[f64]) -> usize { + slice + .iter() + .enumerate() + .min_by(|(_, a), (_, b)| a.total_cmp(b)) + .map(|(i, _)| i) + .unwrap_or(0) +} diff --git a/crates/nig/src/distribution.rs b/crates/nig/src/distribution.rs deleted file mode 100644 index 9004098b..00000000 --- a/crates/nig/src/distribution.rs +++ /dev/null @@ -1,203 +0,0 @@ -use crate::bessel::bessel_k1e; -use crate::params::NigParams; - -pub(crate) fn nig_pdf(p: &NigParams, x: f64) -> f64 { - if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { - return 0.0; - } - - let gamma = (p.a * p.a - p.b * p.b).sqrt(); - let z = (x - p.loc) / p.scale; - let sqrt_z2p1 = (z * z + 1.0).sqrt(); - let y = p.a * sqrt_z2p1; - let scaled_bessel = bessel_k1e(y); - - if scaled_bessel <= 0.0 { - return 0.0; - } - - let net_exp = gamma + p.b * z - y; - let log_pdf = p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() - sqrt_z2p1.ln() - + net_exp - + scaled_bessel.ln(); - - if log_pdf < -745.0 { - // exp(-745) underflows to 0 in f64 - return 0.0; - } - - log_pdf.exp() -} - -fn exp_sinh_g( - m: i64, - h_fine: f64, - a: f64, - half_pi: f64, - cache: &mut std::collections::HashMap, - f: &mut impl FnMut(f64) -> f64, -) -> f64 { - if let Some(&v) = cache.get(&m) { - return v; - } - let t = (m as f64) * h_fine; - let arg = half_pi * t.sinh(); - let v = if arg > 690.0 { - 0.0 - } else { - let e = arg.exp(); // = x - a - let x = a + e; - let w = half_pi * t.cosh() * e; // Jacobian dx/dt - let fv = f(x); - if fv.is_finite() { w * fv } else { 0.0 } - }; - cache.insert(m, v); - v -} - -// Integrate f over [a, +∞) using exp-sinh (double-exponential) quadrature -// with global step-halving (level doubling) and node reuse. -pub(crate) fn exp_sinh( - f: &mut impl FnMut(f64) -> f64, - a: f64, - tol: f64, - h0: f64, - max_level: u32, -) -> f64 { - let half_pi = std::f64::consts::PI / 2.0; - let stride0 = 1i64 << max_level; - let h_fine = h0 / (stride0 as f64); - let cut = 1e-17_f64; - let mut cache = std::collections::HashMap::::new(); - - let fmax0 = exp_sinh_g(0, h_fine, a, half_pi, &mut cache, f).abs(); - - let ml = { - let mut k = 0i64; - loop { - k -= 1; - let m = k * stride0; - let v = exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f).abs(); - if k.abs() >= 2 && v <= cut * fmax0.max(v).max(1e-300) { - break m; - } - if k.abs() > 64 { - break m; - } - } - }; - - let mr = { - let mut k = 0i64; - loop { - k += 1; - let m = k * stride0; - let v = exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f).abs(); - if k.abs() >= 2 && v <= cut * fmax0.max(v).max(1e-300) { - break m; - } - if k.abs() > 64 { - break m; - } - } - }; - - let mut s = 0.0_f64; - let mut m = ml; - while m <= mr { - s += exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f); - m += stride0; - } - let mut i_prev = h0 * s; - let mut i = i_prev; - - for level in 1..=max_level { - let stride = 1i64 << (max_level - level); - let h = h0 / ((1u64 << level) as f64); - let mut m = ml + stride; - while m < mr { - s += exp_sinh_g(m, h_fine, a, half_pi, &mut cache, f); - m += 2 * stride; - } - i = h * s; - let err = (i - i_prev).abs(); - if level >= 2 && err <= tol + tol * i.abs() { - break; - } - i_prev = i; - } - - i -} - -pub fn nig_survival(p: &NigParams, x: f64) -> f64 { - if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { - return 0.0; - } - - exp_sinh(&mut |t| nig_pdf(p, t), x, 1e-10, 0.5, 12).clamp(0.0, 1.0) -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::params::NigParams; - - fn assert_rel_close(actual: f64, expected: f64, tolerance: f64) { - let rel_error = if expected == 0.0 { - actual.abs() - } else { - (actual - expected).abs() / expected.abs() - }; - assert!( - rel_error <= tolerance, - "expected {expected:.15e}, got {actual:.15e}, rel error {rel_error:.2e}", - ); - } - - fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { - let abs_error = (actual - expected).abs(); - assert!( - abs_error <= tolerance, - "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", - ); - } - - #[test] - fn nig_pdf_matches_reference_values() { - let p = NigParams { - a: 33.53900289787477, - b: 33.52140111667502, - loc: 6.3663207368487065, - scale: 0.4480388195262859, - }; - - for (x, expected) in [ - (7.648, 9.314339782198335e-03), - (8.0, 2.138356268395934e-02), - (10.0, 7.240000069597700e-02), - (20.0, 3.070336727949191e-02), - ] { - assert_rel_close(nig_pdf(&p, x), expected, 1e-10); - } - } - - #[test] - fn nig_survival_matches_reference_values() { - let p = NigParams { - a: 33.53900289787477, - b: 33.52140111667502, - loc: 6.3663207368487065, - scale: 0.4480388195262859, - }; - - for (x, expected) in [ - (7.0, 9.999892785756547e-01), - (7.648, 9.979326056403205e-01), - (10.0, 8.873317615160712e-01), - (20.0, 3.429376452167427e-01), - ] { - assert_abs_close(nig_survival(&p, x), expected, 1e-5); - } - } -} diff --git a/crates/nig/src/fitting.rs b/crates/nig/src/fitting.rs deleted file mode 100644 index 30355aa9..00000000 --- a/crates/nig/src/fitting.rs +++ /dev/null @@ -1,464 +0,0 @@ -use crate::bessel::bessel_k1e; -use crate::params::{NigParams, NigParamsReparametrized}; - -/// ln(f64::MAX): upper bound to avoid exp() overflow during optimization. -const MAX_LOG_VALUE: f64 = 709.782_712_893_384; - -/// P1 Error: a combination of absolute and relative errors. -/// -fn p1_err(x: f64, y: f64) -> f64 { - (x - y).abs() / (1.0 + y.abs()) -} - -fn encode_nig_params(p: &NigParams) -> NigParamsReparametrized { - let safe_a = p.a.max(1e-6); - let safe_scale = p.scale.max(1e-6); - let beta_ratio = (p.b / safe_a).clamp(-1.0 + 1e-12, 1.0 - 1e-12); - NigParamsReparametrized { - log_a: safe_a.ln(), - skew_raw: beta_ratio.atanh(), - loc: p.loc, - log_scale: safe_scale.ln(), - } -} - -fn decode_nig_params(pr: &NigParamsReparametrized) -> NigParams { - let a = pr.log_a.exp(); - let b = a * pr.skew_raw.tanh(); - let scale = pr.log_scale.exp(); - NigParams { a, b, loc: pr.loc, scale } -} - -/// Moment-based initial parameter estimate for the NIG distribution. -fn estimate_nig_start(times: &[f64]) -> NigParams { - let n = times.len() as f64; - let mean = times.iter().sum::() / n; - let m2 = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; - - if m2 < 1e-12 { - return NigParams { a: 1.0, b: 0.0, loc: mean, scale: 1.0 }; - } - - let m3 = times.iter().map(|&t| (t - mean).powi(3)).sum::() / n; - let m4 = times.iter().map(|&t| (t - mean).powi(4)).sum::() / n; - let skewness = m3 / m2.powf(1.5); - let excess_kurtosis = m4 / (m2 * m2) - 3.0; - let denominator = excess_kurtosis - (4.0 * skewness * skewness) / 3.0; - - if denominator <= 1e-8 { - let scale = m2.sqrt(); - return NigParams { a: (1.0 / scale).max(1e-3), b: 0.0, loc: mean, scale }; - } - - let delta_gamma = 3.0 / denominator; - let beta_ratio = - (skewness * delta_gamma.sqrt() / 3.0).clamp(-1.0 + 1e-8, 1.0 - 1e-8); - let cos_theta = (1.0 - beta_ratio * beta_ratio).max(1e-12).sqrt(); - let alpha = (delta_gamma / (m2 * cos_theta.powi(4))).max(1e-12).sqrt(); - let beta = alpha * beta_ratio; - let scale = (delta_gamma / (alpha * cos_theta)).max(1e-12); - let loc = mean - scale * beta_ratio / cos_theta; - - NigParams { a: alpha, b: beta, loc, scale } -} - -fn neg_log_likelihood(times: &[f64], p: &NigParams) -> f64 { - if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { - return f64::INFINITY; - } - - let gamma = (p.a * p.a - p.b * p.b).sqrt(); - let mut nll = 0.0; - - for &x in times { - let z = (x - p.loc) / p.scale; - let sqrt_z2p1 = (z * z + 1.0).sqrt(); - let y = p.a * sqrt_z2p1; - let scaled_bessel = bessel_k1e(y); - if scaled_bessel <= 0.0 { - return f64::INFINITY; - } - let log_pdf = p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() - - sqrt_z2p1.ln() - + gamma - + p.b * z - - y - + scaled_bessel.ln(); - nll -= log_pdf; - } - - nll -} - -// Vector arithmetic helpers for [f64; 4] used in the Nelder-Mead simplex. -fn vsub(a: [f64; 4], b: [f64; 4]) -> [f64; 4] { - [a[0] - b[0], a[1] - b[1], a[2] - b[2], a[3] - b[3]] -} - -fn vmadd(a: [f64; 4], s: f64, b: [f64; 4]) -> [f64; 4] { - [a[0] + s * b[0], a[1] + s * b[1], a[2] + s * b[2], a[3] + s * b[3]] -} - -/// Adaptive Nelder-Mead simplex optimizer. -/// -/// Gao, F. and Han, L. (2012). Implementing the Nelder-Mead simplex algorithm -/// with adaptive parameters. *Computational Optimization and Applications*, 51:1, pp. 259–277. -fn nelder_mead( - mut objective: impl FnMut(&[f64; 4]) -> f64, // hardcoded to 4 params - initial: [f64; 4], - tol: f64, - max_iter: usize, - bounds: &[(f64, f64); 4], -) -> ([f64; 4], f64, usize) { - const N: usize = 4; - let alpha = 1.0_f64; - let beta = 1.0 + 2.0 / N as f64; - let gamma = 0.75 - 1.0 / (2.0 * N as f64); - let delta = 1.0 - 1.0 / N as f64; - - let clip = |x: [f64; N]| -> [f64; N] { - let mut out = x; - for (i, &(lo, hi)) in bounds.iter().enumerate() { - out[i] = out[i].clamp(lo, hi); - } - out - }; - - let mut simplex = [[0.0_f64; N]; N + 1]; - let mut values = [0.0_f64; N + 1]; - let mut nfev: usize = 0; - - simplex[0] = initial; - values[0] = { nfev += 1; objective(&simplex[0]) }; - - for i in 0..N { - let mut vertex = simplex[0]; - vertex[i] += if vertex[i] == 0.0 { 1.0 } else { vertex[i] * 0.05 }; - simplex[i + 1] = clip(vertex); - values[i + 1] = { nfev += 1; objective(&simplex[i + 1]) }; - } - - for _ in 0..max_iter { - // Sort simplex by ascending function value. - let mut order = [0usize, 1, 2, 3, 4]; - order.sort_by(|&a, &b| { - values[a] - .partial_cmp(&values[b]) - .unwrap_or(std::cmp::Ordering::Equal) - }); - let sorted_s: [[f64; N]; N + 1] = std::array::from_fn(|i| simplex[order[i]]); - let sorted_v: [f64; N + 1] = std::array::from_fn(|i| values[order[i]]); - simplex = sorted_s; - values = sorted_v; - - let best = simplex[0]; - let worst = simplex[N]; - - // Centroid of all vertices except the worst. - let mut centroid = [0.0_f64; N]; - for i in 0..N { - for j in 0..N { - centroid[j] += simplex[i][j]; - } - } - for j in 0..N { - centroid[j] /= N as f64; - } - - let reflected = clip(vmadd(centroid, alpha, vsub(centroid, worst))); - let f_reflect = { nfev += 1; objective(&reflected) }; - - if f_reflect < values[0] { - let expanded = clip(vmadd(centroid, beta, vsub(centroid, worst))); - let f_expand = { nfev += 1; objective(&expanded) }; - if f_expand < f_reflect { - simplex[N] = expanded; - values[N] = f_expand; - } else { - simplex[N] = reflected; - values[N] = f_reflect; - } - } else if f_reflect < values[N - 1] { - simplex[N] = reflected; - values[N] = f_reflect; - } else if f_reflect < values[N] { - // Outside contraction. - let xc = clip(vmadd(centroid, gamma, vsub(centroid, worst))); - let f_contract = { nfev += 1; objective(&xc) }; - if f_contract <= f_reflect { - simplex[N] = xc; - values[N] = f_contract; - } else { - for i in 1..=N { - simplex[i] = clip(vmadd(best, delta, vsub(simplex[i], best))); - values[i] = { nfev += 1; objective(&simplex[i]) }; - } - } - } else { - // Inside contraction. - let xc = clip(vmadd(centroid, -gamma, vsub(centroid, worst))); - let f_contract = { nfev += 1; objective(&xc) }; - if f_contract < values[N] { - simplex[N] = xc; - values[N] = f_contract; - } else { - for i in 1..=N { - simplex[i] = clip(vmadd(best, delta, vsub(simplex[i], best))); - values[i] = { nfev += 1; objective(&simplex[i]) }; - } - } - } - - // Convergence: max P1 error across all non-best vertices vs best. - let max_simplex_err = (1..=N) - .flat_map(|i| (0..N).map(move |j| p1_err(simplex[i][j], simplex[0][j]))) - .fold(f64::NEG_INFINITY, f64::max); - let max_value_err = (1..=N) - .map(|i| p1_err(values[i], values[0])) - .fold(f64::NEG_INFINITY, f64::max); - - if max_simplex_err <= tol && max_value_err <= tol { - break; - } - } - - let best_idx = values - .iter() - .enumerate() - .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) - .map(|(i, _)| i) - .unwrap_or(0); - - (simplex[best_idx], values[best_idx], nfev) -} - -fn optimize_nig(times: &[f64], p: &NigParams) -> Result<(NigParams, usize), ()> { - const MAX_ITER: usize = 1000; - const TOL: f64 = 1e-4; - - let n = times.len() as f64; - let mean = times.iter().sum::() / n; - let variance = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; - let loc_step = variance.sqrt().max(1.0); - - let initial = encode_nig_params(p); - let initial_arr = [initial.log_a, initial.skew_raw, initial.loc, initial.log_scale]; - - let bounds = [ - (initial.log_a - 4.0, initial.log_a + 6.0), - (initial.skew_raw - 4.0, initial.skew_raw + 4.0), - (initial.loc - 10.0 * loc_step, initial.loc + 10.0 * loc_step), - (initial.log_scale - 5.0, initial.log_scale + 5.0), - ]; - - let objective = |values: &[f64; 4]| -> f64 { - if !values.iter().all(|v| v.is_finite()) { - return f64::INFINITY; - } - if values[0] >= MAX_LOG_VALUE || values[3] >= MAX_LOG_VALUE { - return f64::INFINITY; - } - let pr = NigParamsReparametrized { - log_a: values[0], - skew_raw: values[1], - loc: values[2], - log_scale: values[3], - }; - neg_log_likelihood(times, &decode_nig_params(&pr)) - }; - - let (optimum, best_ll, nfev) = nelder_mead(objective, initial_arr, TOL, MAX_ITER, &bounds); - - if !best_ll.is_finite() { - return Err(()); - } - - let pr = NigParamsReparametrized { - log_a: optimum[0], - skew_raw: optimum[1], - loc: optimum[2], - log_scale: optimum[3], - }; - - Ok((decode_nig_params(&pr), nfev)) -} - -pub fn fit_nig(times: &[f64], params: Option) -> NigParams { - let mut p = match params { - Some(p) if p.a > 0.0 => p, - _ => estimate_nig_start(times), - }; - - match optimize_nig(times, &p) { - Ok((optimized, _nfev)) => p = optimized, - Err(()) => { - tracing::warn!( - samples = times.len(), - "NIG optimization failed; using initial estimates", - ); - } - } - - p -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::params::{NigParams}; - - fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { - let abs_error = (actual - expected).abs(); - assert!( - abs_error <= tolerance, - "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", - ); - } - - /// Generates the 5 test datasets from the Python benchmark. - fn test_datasets() -> Vec> { - let linspace = |start: f64, end: f64, n: usize| -> Vec { - (0..n).map(|i| start + (end - start) / (n - 1) as f64 * i as f64).collect() - }; - vec![ - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(), - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.05).collect(), - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.5).collect(), - (0..50).map(|i: i32| 3.0 + (i as f64).powi(2) * 0.02).collect(), - linspace(5.0, 35.0, 80), - ] - } - - #[test] - fn neg_log_likelihood_returns_inf_for_invalid_params() { - let times = [7.0, 8.0, 9.0, 10.0]; - assert!(neg_log_likelihood( - ×, - &NigParams { a: 0.0, b: 1.0, loc: 5.0, scale: 1.0 } - ) - .is_infinite()); - assert!(neg_log_likelihood( - ×, - &NigParams { a: 2.0, b: 0.0, loc: 5.0, scale: 0.0 } - ) - .is_infinite()); - assert!(neg_log_likelihood( - ×, - &NigParams { a: 1.0, b: 1.0, loc: 5.0, scale: 1.0 } - ) - .is_infinite()); - assert!(neg_log_likelihood( - ×, - &NigParams { a: 1.0, b: -1.1, loc: 5.0, scale: 1.0 } - ) - .is_infinite()); - } - - #[test] - fn neg_log_likelihood_returns_finite_for_valid_params() { - let times = [7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0]; - let nll = neg_log_likelihood( - ×, - &NigParams { a: 5.0, b: 2.0, loc: 8.0, scale: 1.0 }, - ); - assert!(nll.is_finite()); - assert!(nll > 0.0); - } - - #[test] - fn neg_log_likelihood_matches_scipy_reference_value() { - let times: Vec = - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); - let nll = neg_log_likelihood( - ×, - &NigParams { - a: 86.72396846356486, - b: 86.68319372270773, - loc: 4.487105095426718, - scale: 0.24914444085073106, - }, - ); - assert_abs_close(nll, 559.6276051205211, 1e-6); - } - - #[test] - fn estimate_nig_start_produces_valid_params() { - let times: Vec = - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); - let p = estimate_nig_start(×); - assert!(p.a > 0.0, "a must be positive"); - assert!(p.scale > 0.0, "scale must be positive"); - assert!(p.b.abs() < p.a, "|b| must be < a"); - } - - #[test] - fn encode_decode_roundtrips() { - let p = NigParams { a: 5.0, b: 2.0, loc: 8.0, scale: 1.5 }; - let decoded = decode_nig_params(&encode_nig_params(&p)); - assert_abs_close(decoded.a, p.a, 1e-12); - assert_abs_close(decoded.b, p.b, 1e-12); - assert_abs_close(decoded.loc, p.loc, 1e-12); - assert_abs_close(decoded.scale, p.scale, 1e-12); - } - - #[test] - fn fit_nig_matches_python_reference_all_datasets() { - struct Ref { - a: f64, - b: f64, - loc: f64, - scale: f64, - nll: f64, - nfev: usize, - } - let refs = [ - Ref { a: 72.467, b: 72.418, loc: 4.491, scale: 0.298, nll: 559.637, nfev: 354 }, - Ref { a: 9.536, b: 9.276, loc: -11.183, scale: 17.732, nll: 1021.770, nfev: 685 }, - Ref { a: 0.954, b: 0.650, loc: 232.337, scale: 361.840, nll: 1502.561, nfev: 488 }, - Ref { a: 21.769, b: 21.754, loc: 0.365, scale: 0.687, nll: 195.001, nfev: 468 }, - Ref { a: 46.005, b: -0.001, loc: 20.001, scale: 59.861, nll: 287.470, nfev: 831 }, - ]; - - for (idx, (times, r)) in test_datasets().iter().zip(&refs).enumerate() { - let ds = idx + 1; - let initial = estimate_nig_start(times); - let (p, nfev) = optimize_nig(times, &initial) - .unwrap_or_else(|_| panic!("ds{ds}: optimization failed")); - let nll = neg_log_likelihood(times, &p); - let tol = 1e-3; - assert!((p.a - r.a).abs() <= tol, "ds{ds} a: got {:.3}, expected {:.3}", p.a, r.a); - assert!((p.b - r.b).abs() <= tol, "ds{ds} b: got {:.3}, expected {:.3}", p.b, r.b); - assert!( - (p.loc - r.loc).abs() <= tol, - "ds{ds} loc: got {:.3}, expected {:.3}", - p.loc, r.loc, - ); - assert!( - (p.scale - r.scale).abs() <= tol, - "ds{ds} scale: got {:.3}, expected {:.3}", - p.scale, r.scale, - ); - assert!( - (nll - r.nll).abs() <= tol, - "ds{ds} nll: got {:.3}, expected {:.3}", - nll, r.nll, - ); - assert_eq!(nfev, r.nfev, "ds{ds}: nfev"); - } - } - - #[test] - fn fit_nig_warm_start_from_previous_result() { - let times: Vec = - (0..200).map(|i: i32| 7.0 + (i as f64).powf(1.5) * 0.005).collect(); - let cold = fit_nig(×, None); - let warm = fit_nig(×, Some(cold)); - let cold_nll = neg_log_likelihood(×, &cold); - let warm_nll = neg_log_likelihood(×, &warm); - // Warm start should reach same or better NLL. - assert!( - warm_nll <= cold_nll + 1e-4, - "warm_nll={warm_nll:.6}, cold_nll={cold_nll:.6}", - ); - } -} diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs index eb2e33ed..560172ad 100644 --- a/crates/nig/src/lib.rs +++ b/crates/nig/src/lib.rs @@ -1,8 +1,4 @@ mod bessel; -mod distribution; -mod fitting; -mod params; - -pub use distribution::nig_survival; -pub use fitting::fit_nig; -pub use params::NigParams; +mod differential_evo; +mod quad; +pub mod nig; diff --git a/crates/nig/src/nig.rs b/crates/nig/src/nig.rs new file mode 100644 index 00000000..1915cfae --- /dev/null +++ b/crates/nig/src/nig.rs @@ -0,0 +1,276 @@ +use serde::Serialize; + +use crate::bessel::bessel_k1e; +use crate::differential_evo::differential_evolution; +use crate::quad; + +/// NIG distribution parameters (scipy loc-scale parameterization). +#[derive(Debug, Clone, Copy, Serialize)] +pub struct NigParams { + pub a: f64, + pub b: f64, + pub loc: f64, + pub scale: f64, +} + +#[derive(Debug, Clone, Copy)] +pub(crate) struct NigParamsReparametrized { + pub log_a: f64, + pub skew_raw: f64, + pub loc: f64, + pub log_scale: f64, +} + +pub fn pdf(p: &NigParams, x: f64) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { + return 0.0; + } + + let gamma = (p.a * p.a - p.b * p.b).sqrt(); + let z = (x - p.loc) / p.scale; + let sqrt_z2p1 = (z * z + 1.0).sqrt(); + let y = p.a * sqrt_z2p1; + let scaled_bessel = bessel_k1e(y); + + if scaled_bessel <= 0.0 { + return 0.0; + } + + let net_exp = gamma + p.b * z - y; + let log_pdf = p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() - sqrt_z2p1.ln() + + net_exp + + scaled_bessel.ln(); + + if log_pdf < -745.0 { + // exp(-745) underflows to 0 in f64 + return 0.0; + } + + log_pdf.exp() +} + +pub fn cdf(p: &NigParams, x: f64) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { + return 0.0; + } + + quad::quad(&mut |t| pdf(p, t), f64::NEG_INFINITY, x, 6, 1e-10, None).clamp(0.0, 1.0) +} + +pub fn sf(p: &NigParams, x: f64) -> f64 { + 1.0 - cdf(p, x) +} + +fn encode_nig_params(p: &NigParams) -> NigParamsReparametrized { + let safe_a = p.a.max(1e-6); + let safe_scale = p.scale.max(1e-6); + let beta_ratio = (p.b / safe_a).clamp(-1.0 + 1e-12, 1.0 - 1e-12); + NigParamsReparametrized { + log_a: safe_a.ln(), + skew_raw: beta_ratio.atanh(), + loc: p.loc, + log_scale: safe_scale.ln(), + } +} + +fn decode_nig_params(pr: &NigParamsReparametrized) -> NigParams { + let a = pr.log_a.exp(); + let b = a * pr.skew_raw.tanh(); + let scale = pr.log_scale.exp(); + NigParams { a, b, loc: pr.loc, scale } +} + +/// Moment-based initial parameter estimate for the NIG distribution. +fn estimate_nig_start(times: &[f64]) -> NigParams { + let n = times.len() as f64; + let mean = times.iter().sum::() / n; + let m2 = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; + + if m2 < 1e-12 { + return NigParams { a: 1.0, b: 0.0, loc: mean, scale: 1.0 }; + } + + let m3 = times.iter().map(|&t| (t - mean).powi(3)).sum::() / n; + let m4 = times.iter().map(|&t| (t - mean).powi(4)).sum::() / n; + let skewness = m3 / m2.powf(1.5); + let excess_kurtosis = m4 / (m2 * m2) - 3.0; + let denominator = excess_kurtosis - (4.0 * skewness * skewness) / 3.0; + + if denominator <= 1e-8 { + let scale = m2.sqrt(); + return NigParams { + a: (1.0 / scale).max(1e-3), + b: 0.0, + loc: mean, + scale, + }; + } + + let delta_gamma = 3.0 / denominator; + let beta_ratio = (skewness * delta_gamma.sqrt() / 3.0).clamp(-1.0 + 1e-8, 1.0 - 1e-8); + let cos_theta = (1.0 - beta_ratio * beta_ratio).max(1e-12).sqrt(); + let alpha = (delta_gamma / (m2 * cos_theta.powi(4))).max(1e-12).sqrt(); + let beta = alpha * beta_ratio; + let scale = (delta_gamma / (alpha * cos_theta)).max(1e-12); + let loc = mean - scale * beta_ratio / cos_theta; + + NigParams { a: alpha, b: beta, loc, scale } +} + +fn neg_log_likelihood(times: &[f64], p: &NigParams) -> f64 { + if p.a <= 0.0 || p.scale <= 0.0 || p.b.abs() >= p.a { + return f64::INFINITY; + } + + let gamma = (p.a * p.a - p.b * p.b).sqrt(); + let mut nll = 0.0; + + for &x in times { + let z = (x - p.loc) / p.scale; + let sqrt_z2p1 = (z * z + 1.0).sqrt(); + let y = p.a * sqrt_z2p1; + let scaled_bessel = bessel_k1e(y); + if scaled_bessel <= 0.0 { + return f64::INFINITY; + } + let log_pdf = + p.a.ln() - std::f64::consts::PI.ln() - p.scale.ln() - sqrt_z2p1.ln() + gamma + p.b * z + - y + + scaled_bessel.ln(); + nll -= log_pdf; + } + + nll +} + +fn de_bounds(times: &[f64]) -> [(f64, f64); 4] { + let n = times.len() as f64; + let mean = times.iter().sum::() / n; + let variance = times.iter().map(|&t| (t - mean).powi(2)).sum::() / n; + let std = variance.sqrt().max(1e-6); + let data_range = (times.iter().fold(f64::INFINITY, |a, &b| a.min(b)) + - times.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b))) + .abs() + .max(1e-6); + [ + (-2.0, 12.0), + (-8.0, 8.0), + (mean - 5.0 * std, mean + 5.0 * std), + ((data_range / 100.0).ln(), (data_range * 100.0).ln()), + ] +} + +fn optimize(times: &[f64]) -> Result<(NigParams, usize), ()> { + const MAX_ITER: usize = 1000; + const TOL: f64 = 1e-4; + + let bounds = de_bounds(times); + + let objective = |values: &[f64; 4]| -> f64 { + let pr = NigParamsReparametrized { + log_a: values[0], + skew_raw: values[1], + loc: values[2], + log_scale: values[3], + }; + neg_log_likelihood(times, &decode_nig_params(&pr)) + }; + + let (optimum, best_ll, nfev) = + differential_evolution(objective, &bounds, TOL, MAX_ITER, 15, (0.5, 1.0), 0.7, 0); + + if !best_ll.is_finite() { + return Err(()); + } + + let pr = NigParamsReparametrized { + log_a: optimum[0], + skew_raw: optimum[1], + loc: optimum[2], + log_scale: optimum[3], + }; + + Ok((decode_nig_params(&pr), nfev)) +} + +pub fn fit(times: &[f64], params: Option) -> NigParams { + let mut p = match params { + Some(p) if p.a > 0.0 => p, + _ => estimate_nig_start(times), + }; + + match optimize(times) { + Ok((optimized, _nfev)) => p = optimized, + Err(()) => { + tracing::warn!( + samples = times.len(), + "NIG optimization failed; using initial estimates", + ); + }, + } + + p +} + +#[cfg(test)] +mod tests { + use super::*; + + fn assert_rel_close(actual: f64, expected: f64, tolerance: f64) { + let rel_error = if expected == 0.0 { + actual.abs() + } else { + (actual - expected).abs() / expected.abs() + }; + assert!( + rel_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, rel error {rel_error:.2e}", + ); + } + + fn assert_abs_close(actual: f64, expected: f64, tolerance: f64) { + let abs_error = (actual - expected).abs(); + assert!( + abs_error <= tolerance, + "expected {expected:.15e}, got {actual:.15e}, abs error {abs_error:.2e}", + ); + } + + #[test] + fn pdf_matches_reference_values() { + let p = NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + }; + + for (x, expected) in [ + (7.648, 9.314339782198335e-03), + (8.0, 2.138356268395934e-02), + (10.0, 7.240000069597700e-02), + (20.0, 3.070336727949191e-02), + ] { + assert_rel_close(pdf(&p, x), expected, 1e-10); + } + } + + #[test] + fn sf_matches_reference_values() { + let p = NigParams { + a: 33.53900289787477, + b: 33.52140111667502, + loc: 6.3663207368487065, + scale: 0.4480388195262859, + }; + + for (x, expected) in [ + (7.0, 9.999892785756547e-01), + (7.648, 9.979326056403205e-01), + (10.0, 8.873317615160712e-01), + (20.0, 3.429376452167427e-01), + ] { + assert_abs_close(sf(&p, x), expected, 1e-5); + } + } +} diff --git a/crates/nig/src/params.rs b/crates/nig/src/params.rs deleted file mode 100644 index 76ae1286..00000000 --- a/crates/nig/src/params.rs +++ /dev/null @@ -1,18 +0,0 @@ -use serde::Serialize; - -/// NIG distribution parameters (scipy loc-scale parameterization). -#[derive(Debug, Clone, Copy, Serialize)] -pub struct NigParams { - pub a: f64, - pub b: f64, - pub loc: f64, - pub scale: f64, -} - -#[derive(Debug, Clone, Copy)] -pub(crate) struct NigParamsReparametrized { - pub log_a: f64, - pub skew_raw: f64, - pub loc: f64, - pub log_scale: f64, -} diff --git a/crates/nig/src/quad.rs b/crates/nig/src/quad.rs new file mode 100644 index 00000000..7970c4a8 --- /dev/null +++ b/crates/nig/src/quad.rs @@ -0,0 +1,269 @@ +// BSD 3-Clause License + +// Copyright (c) 2022, Robert A. van Engelen +// All rights reserved. + +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: + +// 1. Redistributions of source code must retain the above copyright notice, this +// list of conditions and the following disclaimer. + +// 2. Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. + +// 3. Neither the name of the copyright holder nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. + +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// article: https://www.genivia.com/qthsh.html +// original code: https://github.com/Robert-van-Engelen/Tanh-Sinh + +const FUDGE1: f64 = 10.0; +const FUDGE2: f64 = 1.0; + +fn exp_sinh_opt_d(f: &mut impl FnMut(f64) -> f64, a: f64, eps: f64, mut d: f64) -> f64 { + let mut _ev = 2; + // const base = 2; // 2 or 3 or exp(1) for example + let h2 = f(a + d / 2.0) - f(a + d * 2.0) * 4.0; + let mut i = 1; + let mut j = 32; // j=32 is optimal to search for r + + if h2.is_finite() && h2.abs() > 1e-5 { + // if |h2| > 2^-16 + let mut fl: f64; + let mut fr: f64; + let mut h: f64; + let mut s = 0.0; + let mut lfl: f64; + let mut lfr: f64; + let mut lr = 2.0; + + // find max j such that fl and fr are finite + loop { + j /= 2; + let r = (1 << (i + j)) as f64; + fl = f(a + d / r); + fr = f(a + d * r) * r * r; + _ev += 2; + h = fl - fr; + if j <= 1 || h.is_finite() { + break; + } + } + + if j > 1 && h.is_finite() && h.signum() != h2.signum() { + lfl = fl; // last fl=f(a+d/r) + lfr = fr; // last fr=f(a+d*r)*r*r + + // bisect in 4 iterations + loop { + j /= 2; + let r = (1 << (i + j)) as f64; + fl = f(a + d / r); + fr = f(a + d * r) * r * r; + _ev += 2; + h = fl - fr; + if h.is_finite() { + s += h.abs(); // sum |h| to remove noisy cases + if h.signum() == h2.signum() { + i += j; // search right half + } else { + // search left half + lfl = fl; // record last fl=f(a+d/r) + lfr = fr; // record last fr=f(a+d*r)*r*r + lr = r; // record last r + } + } + if j <= 1 { + break; + } + } + + if s > eps { + // if sum of |h| > eps + h = lfl - lfr; // use last fl and fr before the sign change + let mut r = lr; // use last r before the sign change + if h != 0.0 { + // if last difference was nonzero, back up r by one step + r /= 2.0; + } + if lfl.abs() < lfr.abs() { + d /= r; // move d closer to the finite endpoint + } else { + d *= r; // move d closer to the infinite endpoint + } + } + } + } + + d +} + +/// Integrate function `f` over the range `a..b`. +/// +/// `n` is the max number of levels (2 to 7, 6 is recommended). +/// `eps` is the relative error tolerance. +/// If `err` is `Some`, the estimated relative error is written to it. +pub fn quad(f: &mut impl FnMut(f64) -> f64, a: f64, b: f64, n: i32, eps: f64, err: Option<&mut f64>) -> f64 { + let tol = FUDGE1 * eps; + let mut c = 0.0; + let mut d = 1.0; + let mut sign = 1.0; + let mut h: f64 = 2.0; + let mut k = 0; + let mut mode = 0; // Tanh-Sinh = 0, Exp-Sinh = 1, Sinh-Sinh = 2 + + let (mut a, mut b) = (a, b); + if b < a { + // swap bounds + let v = b; + b = a; + a = v; + sign = -1.0; + } + + let mut v: f64; + if a.is_finite() && b.is_finite() { + c = (a + b) / 2.0; + d = (b - a) / 2.0; + v = c; + } else if a.is_finite() { + mode = 1; // Exp-Sinh + d = exp_sinh_opt_d(f, a, eps, d); + c = a; + v = a + d; + } else if b.is_finite() { + mode = 1; // Exp-Sinh + d = exp_sinh_opt_d(f, b, eps, -d); + sign = -sign; + c = b; + v = b + d; + } else { + mode = 2; // Sinh-Sinh + v = 0.0; + } + + let mut s = f(v); + + loop { + let mut p = 0.0; + let mut q: f64; + let mut fp = 0.0; + let mut fm = 0.0; + let mut t: f64; + let mut eh: f64; + + h /= 2.0; + t = h.exp(); + eh = t; + if k > 0 { + eh *= eh; + } + + if mode == 0 { + // Tanh-Sinh + loop { + let u = (1.0 / t - t).exp(); // = exp(-2*sinh(j*h)) = 1/exp(sinh(j*h))^2 + let r = 2.0 * u / (1.0 + u); // = 1 - tanh(sinh(j*h)) + let w = (t + 1.0 / t) * r / (1.0 + u); // = cosh(j*h)/cosh(sinh(j*h))^2 + let x = d * r; + + if a + x > a { + // if too close to a then reuse previous fp + let y = f(a + x); + if y.is_finite() { + fp = y; // if f(x) is finite, add to local sum + } + } + if b - x < b { + // if too close to b then reuse previous fm + let y = f(b - x); + if y.is_finite() { + fm = y; // if f(x) is finite, add to local sum + } + } + + q = w * (fp + fm); + p += q; + t *= eh; + if q.abs() <= eps * p.abs() { + break; + } + } + } else { + t /= 2.0; + loop { + let mut r = (t - 0.25 / t).exp(); // = exp(sinh(j*h)) + let mut x: f64; + let mut y: f64; + let mut w = r; + q = 0.0; + + if mode == 1 { + // Exp-Sinh + x = c + d / r; + if x == c { + // if x hit the finite endpoint then break + break; + } + y = f(x); + if y.is_finite() { + // if f(x) is finite, add to local sum + q += y / w; + } + } else { + // Sinh-Sinh + r = (r - 1.0 / r) / 2.0; // = sinh(sinh(j*h)) + w = (w + 1.0 / w) / 2.0; // = cosh(sinh(j*h)) + x = c - d * r; + y = f(x); + if y.is_finite() { + // if f(x) is finite, add to local sum + q += y * w; + } + } + + x = c + d * r; + y = f(x); + if y.is_finite() { + // if f(x) is finite, add to local sum + q += y * w; + } + q *= t + 0.25 / t; // q *= cosh(j*h) + p += q; + t *= eh; + if q.abs() <= eps * p.abs() { + break; + } + } + } + + v = s - p; + s += p; + k += 1; + if v.abs() <= tol * s.abs() || k > n { + break; + } + } + + // if the estimated relative error is desired, then return it + if let Some(err) = err { + *err = v.abs() / (FUDGE2 * s.abs() + eps); + } + + // result with estimated relative error err + sign * d * s * h +} diff --git a/testdata/nub_records.csv b/testdata/nub_records.csv new file mode 100644 index 00000000..9f31c341 --- /dev/null +++ b/testdata/nub_records.csv @@ -0,0 +1,74628 @@ +filter_id,player_id,time,points,course_id,mode,nub_tier,pro_tier +1,76561198298554432,354.375,1.0,1,1,3,4 +1,76561199586734632,361.234375,0.9967583250137777,1,1,3,4 +1,76561198417871586,367.265625,0.9933678797362329,1,1,3,4 +1,76561198193522606,369.40625,0.9920444258888274,1,1,3,4 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+180,76561198853455429,496.7578125,0.23677256304023483,90,2,3,4 +180,76561198828145929,500.015625,0.23489684736406355,90,2,3,4 From a498f859598e302cc7bb9f6488e549630768f550 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Thu, 11 Jun 2026 23:22:38 -0400 Subject: [PATCH 20/24] cleanups + fixes + more testing --- .gitignore | 3 + ...b67103102461886e58387deef918fb076e4a3.json | 64 ----------- crates/cs2kz/src/points/daemon.rs | 7 +- crates/cs2kz/src/points/mod.rs | 5 +- crates/cs2kz/src/records.rs | 2 +- crates/nig/src/differential_evo.rs | 27 +++-- crates/nig/src/lib.rs | 1 + crates/nig/src/nelder_mead.rs | 105 ++++++++++++++++++ crates/nig/src/nig.rs | 73 ++++++++++-- 9 files changed, 195 insertions(+), 92 deletions(-) delete mode 100644 .sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json create mode 100644 crates/nig/src/nelder_mead.rs diff --git a/.gitignore b/.gitignore index 31caeb05..a88be23d 100644 --- a/.gitignore +++ b/.gitignore @@ -16,3 +16,6 @@ # config /cs2kz-api.toml + +testdata/ +examples/ \ No newline at end of file diff --git a/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json b/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json deleted file mode 100644 index f9b724f8..00000000 --- a/.sqlx/query-5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "db_name": "MySQL", - "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ? AND is_pro_leaderboard", - "describe": { - "columns": [ - { - "ordinal": 0, - "name": "a", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 1, - "name": "b", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 2, - "name": "loc", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 3, - "name": "scale", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 4, - "name": "top_scale", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - } - ], - "parameters": { - "Right": 1 - }, - "nullable": [ - false, - false, - false, - false, - false - ] - }, - "hash": "5839ff47ed01c835b6c11941830b67103102461886e58387deef918fb076e4a3" -} diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index 3598d7fe..f48244db 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -1,6 +1,7 @@ use std::time::Duration; use futures_util::TryFutureExt as _; +use nig::nig::NigParams; use tokio::time::interval; use tokio_util::sync::CancellationToken; @@ -205,11 +206,9 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .await?; let (nub_result, pro_result) = tokio::task::spawn_blocking(move || { - let nub_result = - points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params); + let nub_result = points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params); - let mut pro_result = - points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params); + let mut pro_result = points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params); for (record, recalculated_points) in pro_recs.iter().zip(pro_result.records.iter_mut()) { let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index a1f355f7..983b1511 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -1,6 +1,7 @@ +use ::nig::nig::{self, NigParams}; + use crate::maps::courses::Tier; use crate::records::RecordId; -use crate::nig; pub mod daemon; @@ -161,7 +162,7 @@ fn fit_distribution(times: &[f64], prev_params: Option) -> Option( mutation: (f64, f64), crossover: f64, seed: u64, + inits: &[[f64; N]], ) -> ([f64; N], f64, usize) { + const MAX_STAGNANT_GENERATIONS: usize = 25; + let (f_lo, f_hi) = mutation; let mut rng = SmallRng::seed_from_u64(seed); let n = N; - let np_pop = pop_factor * n; + let np_pop = (pop_factor * n).max(inits.len() + 4); let lo: [f64; N] = core::array::from_fn(|i| bounds[i].0); let hi: [f64; N] = core::array::from_fn(|i| bounds[i].1); let span: [f64; N] = core::array::from_fn(|i| hi[i] - lo[i]); - // Initialize population + // Initialize population, seeding the first members with the provided + // initial guesses (clamped to bounds). let mut pop: Vec<[f64; N]> = (0..np_pop) .map(|_| core::array::from_fn(|i| lo[i] + rng.r#gen::() * span[i])) .collect(); + for (member, init) in pop.iter_mut().zip(inits) { + *member = core::array::from_fn(|i| init[i].clamp(lo[i], hi[i])); + } let mut fitness: Vec = pop.iter().map(|p| objective(p)).collect(); let mut nfev = np_pop; + let mut stagnant_generations = 0usize; for _ in 0..max_iter { let mut improved = false; @@ -39,7 +47,6 @@ pub fn differential_evolution( for i in 0..np_pop { // Pick three distinct random candidates (not i) let mut candidates: Vec = (0..np_pop).filter(|&j| j != i).collect(); - // Partial shuffle to pick 3 without replacement for k in 0..3 { let idx = rng.gen_range(k..candidates.len()); candidates.swap(k, idx); @@ -48,18 +55,15 @@ pub fn differential_evolution( let b = candidates[1]; let c = candidates[2]; - // Dithered mutation factor let f = f_lo + rng.r#gen::() * (f_hi - f_lo); - // Mutation: v = x_a + F * (x_b - x_c) let mutant: [f64; N] = core::array::from_fn(|j| { (pop[a][j] + f * (pop[b][j] - pop[c][j])).clamp(lo[j], hi[j]) }); - // Binomial crossover let mut cross_mask: [bool; N] = core::array::from_fn(|_| rng.r#gen::() < crossover); - // Ensure at least one dimension is taken from mutant + // ensure at least one dimension is taken from mutant cross_mask[rng.gen_range(0..n)] = true; let trial: [f64; N] = @@ -76,8 +80,13 @@ pub fn differential_evolution( } let best_idx = argmin(&fitness); - if !improved { - break; + if improved { + stagnant_generations = 0; + } else { + stagnant_generations += 1; + if stagnant_generations >= MAX_STAGNANT_GENERATIONS { + break; + } } // Population spread check diff --git a/crates/nig/src/lib.rs b/crates/nig/src/lib.rs index 560172ad..6694b0b0 100644 --- a/crates/nig/src/lib.rs +++ b/crates/nig/src/lib.rs @@ -1,4 +1,5 @@ mod bessel; mod differential_evo; +mod nelder_mead; mod quad; pub mod nig; diff --git a/crates/nig/src/nelder_mead.rs b/crates/nig/src/nelder_mead.rs new file mode 100644 index 00000000..d71700cd --- /dev/null +++ b/crates/nig/src/nelder_mead.rs @@ -0,0 +1,105 @@ +/// Nelder-Mead downhill simplex minimizer, used to polish the result of the +/// differential evolution global search. +/// +/// Nelder, J. A. and Mead, R. A Simplex Method for Function Minimization. +/// The Computer Journal 7, 308-313 (1965). +pub fn nelder_mead( + objective: impl Fn(&[f64; N]) -> f64, + start: &[f64; N], + max_iter: usize, + tol: f64, +) -> ([f64; N], f64, usize) { + const ALPHA: f64 = 1.0; // reflection + const GAMMA: f64 = 2.0; // expansion + const RHO: f64 = 0.5; // contraction + const SIGMA: f64 = 0.5; // shrink + + // Initial simplex: perturb each coordinate (scipy-style). + let mut simplex: Vec<[f64; N]> = Vec::with_capacity(N + 1); + simplex.push(*start); + for i in 0..N { + let mut vertex = *start; + if vertex[i] != 0.0 { + vertex[i] *= 1.05; + } else { + vertex[i] = 0.00025; + } + simplex.push(vertex); + } + + let mut values: Vec = simplex.iter().map(&objective).collect(); + let mut nfev = N + 1; + + for _ in 0..max_iter { + // Sort vertices by objective value. + let mut order: Vec = (0..=N).collect(); + order.sort_by(|&a, &b| values[a].total_cmp(&values[b])); + let simplex_sorted: Vec<[f64; N]> = order.iter().map(|&i| simplex[i]).collect(); + let values_sorted: Vec = order.iter().map(|&i| values[i]).collect(); + simplex = simplex_sorted; + values = values_sorted; + + let best = values[0]; + let worst = values[N]; + if (worst - best).abs() <= tol * (1.0 + best.abs()) { + break; + } + + // Centroid of all vertices except the worst. + let centroid: [f64; N] = + core::array::from_fn(|j| simplex[..N].iter().map(|v| v[j]).sum::() / N as f64); + + let reflected: [f64; N] = + core::array::from_fn(|j| centroid[j] + ALPHA * (centroid[j] - simplex[N][j])); + let f_reflected = objective(&reflected); + nfev += 1; + + if f_reflected < values[0] { + // Try expanding further in the same direction. + let expanded: [f64; N] = + core::array::from_fn(|j| centroid[j] + GAMMA * (reflected[j] - centroid[j])); + let f_expanded = objective(&expanded); + nfev += 1; + + if f_expanded < f_reflected { + simplex[N] = expanded; + values[N] = f_expanded; + } else { + simplex[N] = reflected; + values[N] = f_reflected; + } + } else if f_reflected < values[N - 1] { + simplex[N] = reflected; + values[N] = f_reflected; + } else { + // Contract towards the centroid. + let contracted: [f64; N] = + core::array::from_fn(|j| centroid[j] + RHO * (simplex[N][j] - centroid[j])); + let f_contracted = objective(&contracted); + nfev += 1; + + if f_contracted < values[N] { + simplex[N] = contracted; + values[N] = f_contracted; + } else { + // Shrink the whole simplex towards the best vertex. + for i in 1..=N { + simplex[i] = core::array::from_fn(|j| { + simplex[0][j] + SIGMA * (simplex[i][j] - simplex[0][j]) + }); + values[i] = objective(&simplex[i]); + } + nfev += N; + } + } + } + + let mut best_idx = 0; + for i in 1..=N { + if values[i] < values[best_idx] { + best_idx = i; + } + } + + (simplex[best_idx], values[best_idx], nfev) +} diff --git a/crates/nig/src/nig.rs b/crates/nig/src/nig.rs index 1915cfae..00331a75 100644 --- a/crates/nig/src/nig.rs +++ b/crates/nig/src/nig.rs @@ -2,6 +2,7 @@ use serde::Serialize; use crate::bessel::bessel_k1e; use crate::differential_evo::differential_evolution; +use crate::nelder_mead::nelder_mead; use crate::quad; /// NIG distribution parameters (scipy loc-scale parameterization). @@ -54,7 +55,19 @@ pub fn cdf(p: &NigParams, x: f64) -> f64 { return 0.0; } - quad::quad(&mut |t| pdf(p, t), f64::NEG_INFINITY, x, 6, 1e-10, None).clamp(0.0, 1.0) + // The exp-sinh quadrature clusters its nodes near the finite endpoint, so + // always integrate the side of the distribution whose mass lies closest to + // `x`. + // `loc + scale * b / a` is a cheap proxy for the mode: exact for b = 0 and + // bounded by `loc ± scale`, unlike the mean which diverges as |b| -> a. + let peak = p.loc + p.scale * p.b / p.a; + + if x <= peak { + quad::quad(&mut |t| pdf(p, t), f64::NEG_INFINITY, x, 7, 1e-10, None).clamp(0.0, 1.0) + } else { + let tail = quad::quad(&mut |t| pdf(p, t), x, f64::INFINITY, 7, 1e-10, None); + (1.0 - tail.clamp(0.0, 1.0)).clamp(0.0, 1.0) + } } pub fn sf(p: &NigParams, x: f64) -> f64 { @@ -160,9 +173,11 @@ fn de_bounds(times: &[f64]) -> [(f64, f64); 4] { ] } -fn optimize(times: &[f64]) -> Result<(NigParams, usize), ()> { +fn optimize(times: &[f64], inits: &[NigParams]) -> Result<(NigParams, usize), ()> { const MAX_ITER: usize = 1000; const TOL: f64 = 1e-4; + const POLISH_MAX_ITER: usize = 2000; + const POLISH_TOL: f64 = 1e-10; let bounds = de_bounds(times); @@ -176,13 +191,38 @@ fn optimize(times: &[f64]) -> Result<(NigParams, usize), ()> { neg_log_likelihood(times, &decode_nig_params(&pr)) }; - let (optimum, best_ll, nfev) = - differential_evolution(objective, &bounds, TOL, MAX_ITER, 15, (0.5, 1.0), 0.7, 0); + let init_points: Vec<[f64; 4]> = inits + .iter() + .map(|p| { + let pr = encode_nig_params(p); + [pr.log_a, pr.skew_raw, pr.loc, pr.log_scale] + }) + .collect(); + + let (mut optimum, best_ll, mut nfev) = differential_evolution( + objective, + &bounds, + TOL, + MAX_ITER, + 15, + (0.5, 1.0), + 0.7, + 0, + &init_points, + ); if !best_ll.is_finite() { return Err(()); } + let (polished, polished_ll, polish_nfev) = + nelder_mead(objective, &optimum, POLISH_MAX_ITER, POLISH_TOL); + nfev += polish_nfev; + + if polished_ll.is_finite() && polished_ll < best_ll { + optimum = polished; + } + let pr = NigParamsReparametrized { log_a: optimum[0], skew_raw: optimum[1], @@ -194,22 +234,31 @@ fn optimize(times: &[f64]) -> Result<(NigParams, usize), ()> { } pub fn fit(times: &[f64], params: Option) -> NigParams { - let mut p = match params { - Some(p) if p.a > 0.0 => p, - _ => estimate_nig_start(times), - }; + fit_with_stats(times, params).0 +} + +pub fn fit_with_stats(times: &[f64], params: Option) -> (NigParams, usize) { + let moment_estimate = estimate_nig_start(times); - match optimize(times) { - Ok((optimized, _nfev)) => p = optimized, + let mut inits = vec![moment_estimate]; + if let Some(prev) = params + && prev.a > 0.0 + && prev.scale > 0.0 + && prev.b.abs() < prev.a + { + inits.push(prev); + } + + match optimize(times, &inits) { + Ok((optimized, nfev)) => (optimized, nfev), Err(()) => { tracing::warn!( samples = times.len(), "NIG optimization failed; using initial estimates", ); + (moment_estimate, 0) }, } - - p } #[cfg(test)] From 5bad30e0b40ba091d96d256d24f8cb92f12eb519 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Thu, 11 Jun 2026 23:24:53 -0400 Subject: [PATCH 21/24] remove testdata --- .gitignore | 1 - testdata/nub_records.csv | 74628 ------------------------------ testdata/point_distribution.csv | 260 - testdata/pro_records.csv | 16732 ------- 4 files changed, 91621 deletions(-) delete mode 100644 testdata/nub_records.csv delete mode 100644 testdata/point_distribution.csv delete mode 100644 testdata/pro_records.csv diff --git a/.gitignore b/.gitignore index a88be23d..cdfe79ba 100644 --- a/.gitignore +++ b/.gitignore @@ -17,5 +17,4 @@ # config /cs2kz-api.toml -testdata/ examples/ \ No newline at end of file diff --git a/testdata/nub_records.csv b/testdata/nub_records.csv 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-180,76561198229676444,255.75,0.49353532341773043,90,2,3,4 -180,76561198830511118,264.375,0.4771921416902854,90,2,3,4 -180,76561198061308200,269.71875,0.4675275625412439,90,2,3,4 -180,76561197971258317,305.6796875,0.41036177498775045,90,2,3,4 -180,76561198980495203,353.0,0.3513242054001436,90,2,3,4 -180,76561199192072931,411.9453125,0.2953603617408327,90,2,3,4 -180,76561199234574288,455.3046875,0.26284744010285693,90,2,3,4 -180,76561198853455429,496.7578125,0.23677256304023483,90,2,3,4 -180,76561198828145929,500.015625,0.23489684736406355,90,2,3,4 From 58f4a7f5514c80ae38ba95118595f55ae0d19839 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Thu, 11 Jun 2026 23:28:38 -0400 Subject: [PATCH 22/24] Update .gitignore --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index cdfe79ba..1b275edb 100644 --- a/.gitignore +++ b/.gitignore @@ -17,4 +17,5 @@ # config /cs2kz-api.toml -examples/ \ No newline at end of file +examples/ +testdata/ \ No newline at end of file From 24422222d1d72bed0eae78cb73f19111d88b6d84 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Fri, 12 Jun 2026 19:48:22 -0400 Subject: [PATCH 23/24] update sqlx queries --- ...acd971aad0f636644a6388169df5b37fa3234.json | 64 ------------------- ...7de555e0bff68270ed4594620de6c07228ab.json} | 4 +- crates/nig/src/differential_evo.rs | 2 +- crates/nig/src/nelder_mead.rs | 3 - 4 files changed, 3 insertions(+), 70 deletions(-) delete mode 100644 .sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json rename .sqlx/{query-f1c0f7b12cf83a9d41773011224756e681d4a715a28c6fa57fd76473af674c58.json => query-a9027da8009faf6e3ef578e5994e7de555e0bff68270ed4594620de6c07228ab.json} (90%) diff --git a/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json b/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json deleted file mode 100644 index ba9731b6..00000000 --- a/.sqlx/query-50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "db_name": "MySQL", - "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ? AND (NOT is_pro_leaderboard)", - "describe": { - "columns": [ - { - "ordinal": 0, - "name": "a", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 1, - "name": "b", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 2, - "name": "loc", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 3, - "name": "scale", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - }, - { - "ordinal": 4, - "name": "top_scale", - "type_info": { - "type": "Double", - "flags": "NOT_NULL | NO_DEFAULT_VALUE", - "max_size": 22 - } - } - ], - "parameters": { - "Right": 1 - }, - "nullable": [ - false, - false, - false, - false, - false - ] - }, - "hash": "50db60fdf8fda7c03038ec6adddacd971aad0f636644a6388169df5b37fa3234" -} diff --git a/.sqlx/query-f1c0f7b12cf83a9d41773011224756e681d4a715a28c6fa57fd76473af674c58.json b/.sqlx/query-a9027da8009faf6e3ef578e5994e7de555e0bff68270ed4594620de6c07228ab.json similarity index 90% rename from .sqlx/query-f1c0f7b12cf83a9d41773011224756e681d4a715a28c6fa57fd76473af674c58.json rename to .sqlx/query-a9027da8009faf6e3ef578e5994e7de555e0bff68270ed4594620de6c07228ab.json index e385779e..5dd6467f 100644 --- a/.sqlx/query-f1c0f7b12cf83a9d41773011224756e681d4a715a28c6fa57fd76473af674c58.json +++ b/.sqlx/query-a9027da8009faf6e3ef578e5994e7de555e0bff68270ed4594620de6c07228ab.json @@ -1,6 +1,6 @@ { "db_name": "MySQL", - "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ?\n AND (NOT is_pro_leaderboard)", + "query": "SELECT a, b, loc, scale, top_scale\n FROM PointDistributionData\n WHERE filter_id = ?\n AND is_pro_leaderboard", "describe": { "columns": [ { @@ -60,5 +60,5 @@ false ] }, - "hash": "f1c0f7b12cf83a9d41773011224756e681d4a715a28c6fa57fd76473af674c58" + "hash": "a9027da8009faf6e3ef578e5994e7de555e0bff68270ed4594620de6c07228ab" } diff --git a/crates/nig/src/differential_evo.rs b/crates/nig/src/differential_evo.rs index 4cac8e0c..2af0b4aa 100644 --- a/crates/nig/src/differential_evo.rs +++ b/crates/nig/src/differential_evo.rs @@ -1,7 +1,7 @@ use rand::rngs::SmallRng; use rand::{Rng, SeedableRng}; -/// Differential Evolution optimizer (DE/rand/1/bin with dithered mutation scaling). +/// Differential Evolution optimizer /// /// Storn, R. and Price, K. Differential Evolution - A Simple and Efficient /// Heuristic for Global Optimization over Continuous Spaces. Journal of diff --git a/crates/nig/src/nelder_mead.rs b/crates/nig/src/nelder_mead.rs index d71700cd..64e29f96 100644 --- a/crates/nig/src/nelder_mead.rs +++ b/crates/nig/src/nelder_mead.rs @@ -1,6 +1,3 @@ -/// Nelder-Mead downhill simplex minimizer, used to polish the result of the -/// differential evolution global search. -/// /// Nelder, J. A. and Mead, R. A Simplex Method for Function Minimization. /// The Computer Journal 7, 308-313 (1965). pub fn nelder_mead( From 77a2f01c79a16907d00b6015f5515c0fe212ae34 Mon Sep 17 00:00:00 2001 From: Samuel Ayala Date: Fri, 12 Jun 2026 20:20:00 -0400 Subject: [PATCH 24/24] minor cleanups --- crates/cs2kz/src/points/daemon.rs | 18 +++---- crates/cs2kz/src/points/mod.rs | 84 +++---------------------------- crates/nig/src/nig.rs | 3 +- 3 files changed, 15 insertions(+), 90 deletions(-) diff --git a/crates/cs2kz/src/points/daemon.rs b/crates/cs2kz/src/points/daemon.rs index f48244db..c4097e32 100644 --- a/crates/cs2kz/src/points/daemon.rs +++ b/crates/cs2kz/src/points/daemon.rs @@ -138,10 +138,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .fetch_all(db) .await?; - let nub_recs = nub_rows - .iter() - .map(|row| points::RecordTime { record_id: row.record_id, time: row.time }) - .collect::>(); + let nub_times = nub_rows.iter().map(|row| row.time).collect::>(); // Pro records (sorted by time ASC) let pro_rows = sqlx::query_as!( @@ -159,10 +156,7 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .fetch_all(db) .await?; - let pro_recs = pro_rows - .iter() - .map(|row| points::RecordTime { record_id: row.record_id, time: row.time }) - .collect::>(); + let pro_times = pro_rows.iter().map(|row| row.time).collect::>(); // Filter tiers let tiers_row = sqlx::query!( @@ -206,12 +200,12 @@ async fn process_filter(cx: &Context, filter_id: CourseFilterId) -> Result<(), d .await?; let (nub_result, pro_result) = tokio::task::spawn_blocking(move || { - let nub_result = points::recalculate_leaderboard(&nub_recs, nub_tier, prev_nub_params); + let nub_result = points::recalculate_leaderboard(&nub_times, nub_tier, prev_nub_params); - let mut pro_result = points::recalculate_leaderboard(&pro_recs, pro_tier, prev_pro_params); + let mut pro_result = points::recalculate_leaderboard(&pro_times, pro_tier, prev_pro_params); - for (record, recalculated_points) in pro_recs.iter().zip(pro_result.records.iter_mut()) { - let nub_fraction = points::calculate_fraction(record.time, &nub_result.leaderboard); + for (time, recalculated_points) in pro_times.iter().zip(pro_result.records.iter_mut()) { + let nub_fraction = points::calculate_fraction(*time, &nub_result.leaderboard); *recalculated_points = (*recalculated_points).max(nub_fraction); } diff --git a/crates/cs2kz/src/points/mod.rs b/crates/cs2kz/src/points/mod.rs index 983b1511..4c04b71f 100644 --- a/crates/cs2kz/src/points/mod.rs +++ b/crates/cs2kz/src/points/mod.rs @@ -1,7 +1,6 @@ use ::nig::nig::{self, NigParams}; use crate::maps::courses::Tier; -use crate::records::RecordId; pub mod daemon; @@ -35,13 +34,6 @@ pub struct LeaderboardData { pub top_time: f64, } -/// Input record data for batch recalculation. -#[derive(Debug, Clone)] -pub struct RecordTime { - pub record_id: RecordId, - pub time: f64, -} - /// Result of a leaderboard recalculation. #[derive(Debug, Clone)] pub struct RecalculatedLeaderboard { @@ -134,27 +126,28 @@ pub fn calculate_fraction(time: f64, leaderboard: &LeaderboardData) -> f64 { } /// Recompute point fractions for a single leaderboard. +/// +/// `times` must be sorted ascending (fastest first). pub fn recalculate_leaderboard( - records: &[RecordTime], + times: &[f64], tier: Tier, prev_params: Option, ) -> RecalculatedLeaderboard { - let times: Vec = records.iter().map(|record| record.time).collect(); - let params = fit_distribution(×, prev_params); + let params = fit_distribution(times, prev_params); let leaderboard = LeaderboardData { dist_params: params, tier, - leaderboard_size: records.len() as u64, + leaderboard_size: times.len() as u64, top_time: times.first().copied().unwrap_or(0.0), }; - let recalculated_records = records + let records = times .iter() - .map(|record| calculate_fraction(record.time, &leaderboard)) + .map(|&time| calculate_fraction(time, &leaderboard)) .collect(); - RecalculatedLeaderboard { leaderboard, records: recalculated_records } + RecalculatedLeaderboard { leaderboard, records } } fn fit_distribution(times: &[f64], prev_params: Option) -> Option { @@ -174,64 +167,3 @@ fn fit_distribution(times: &[f64], prev_params: Option) -> Option f64 { if x <= peak { quad::quad(&mut |t| pdf(p, t), f64::NEG_INFINITY, x, 7, 1e-10, None).clamp(0.0, 1.0) } else { - let tail = quad::quad(&mut |t| pdf(p, t), x, f64::INFINITY, 7, 1e-10, None); - (1.0 - tail.clamp(0.0, 1.0)).clamp(0.0, 1.0) + 1.0 - quad::quad(&mut |t| pdf(p, t), x, f64::INFINITY, 7, 1e-10, None).clamp(0.0, 1.0) } }