implement MLE ranker

planetwars-server/src/modules/ranking.rs

@@ -2,14 +2,14 @@ use crate::{db::bots::Bot, DbPool};
 
 use crate::db;
 use crate::modules::matches::RunMatch;
+use diesel::{PgConnection, QueryResult};
 use rand::seq::SliceRandom;
-use std::time::Duration;
+use std::collections::HashMap;
+use std::mem;
+use std::time::{Duration, Instant};
 use tokio;
 
 const RANKER_INTERVAL: u64 = 60;
-const START_RATING: f64 = 0.0;
-const SCALE: f64 = 100.0;
-const MAX_UPDATE: f64 = 0.1;
 
 pub async fn run_ranker(db_pool: DbPool) {
     // TODO: make this configurable
@@ -31,6 +31,7 @@ pub async fn run_ranker(db_pool: DbPool) {
             bots.choose_multiple(&mut rng, 2).cloned().collect()
         };
         play_ranking_match(selected_bots, db_pool.clone()).await;
+        recalculate_ratings(&db_conn).expect("could not recalculate ratings");
     }
 }
 
@@ -43,45 +44,193 @@ async fn play_ranking_match(selected_bots: Vec<Bot>, db_pool: DbPool) {
         code_bundles.push(code_bundle);
     }
 
-    let code_bundle_refs = code_bundles.iter().map(|b| b).collect::<Vec<_>>();
+    let code_bundle_refs = code_bundles.iter().collect::<Vec<_>>();
 
     let mut run_match = RunMatch::from_players(code_bundle_refs);
     run_match
         .store_in_database(&db_conn)
         .expect("could not store match in db");
-    let outcome = run_match
+    run_match
         .spawn(db_pool.clone())
         .await
         .expect("running match failed");
+}
 
-    let mut ratings = Vec::new();
+fn recalculate_ratings(db_conn: &PgConnection) -> QueryResult<()> {
-    for bot in &selected_bots {
+    let start = Instant::now();
-        let rating = db::ratings::get_rating(bot.id, &db_conn)
+    let match_stats = fetch_match_stats(db_conn)?;
-            .expect("could not get bot rating")
+    let ratings = estimate_ratings_from_stats(match_stats);
-            .unwrap_or(START_RATING);
+
-        ratings.push(rating);
+    for (bot_id, rating) in ratings {
+        db::ratings::set_rating(bot_id, rating, db_conn).expect("could not update bot rating");
     }
+    let elapsed = Instant::now() - start;
+    println!("computed ratings in {} ms", elapsed.subsec_millis());
+    Ok(())
+}
 
-    // simple elo rating
+#[derive(Default)]
+struct MatchStats {
+    total_score: f64,
+    num_matches: usize,
+}
 
-    let scores = match outcome.winner {
+fn fetch_match_stats(db_conn: &PgConnection) -> QueryResult<HashMap<(i32, i32), MatchStats>> {
-        None => vec![0.5; 2],
+    let matches = db::matches::list_matches(db_conn)?;
-        Some(player_num) => {
+
-            // TODO: please get rid of this offset
+    let mut match_stats = HashMap::<(i32, i32), MatchStats>::new();
-            let player_ix = player_num - 1;
+    for m in matches {
-            let mut scores = vec![0.0; 2];
+        if m.match_players.len() != 2 {
-            scores[player_ix] = 1.0;
+            continue;
-            scores
         }
+        let (mut a_id, mut b_id) = match (&m.match_players[0].bot, &m.match_players[1].bot) {
+            (Some(ref a), Some(ref b)) => (a.id, b.id),
+            _ => continue,
+        };
+        // score of player a
+        let mut score = match m.base.winner {
+            None => 0.5,
+            Some(0) => 1.0,
+            Some(1) => 0.0,
+            _ => panic!("invalid winner"),
         };
 
-    for i in 0..2 {
+        // put players in canonical order: smallest id first
-        let j = 1 - i;
+        if b_id < a_id {
+            mem::swap(&mut a_id, &mut b_id);
+            score = 1.0 - score;
+        }
 
-        let scaled_difference = (ratings[j] - ratings[i]) / SCALE;
+        let entry = match_stats.entry((a_id, b_id)).or_default();
-        let expected = 1.0 / (1.0 + 10f64.powf(scaled_difference));
+        entry.num_matches += 1;
-        let new_rating = ratings[i] + MAX_UPDATE * (scores[i] - expected);
+        entry.total_score += score;
-        db::ratings::set_rating(selected_bots[i].id, new_rating, &db_conn)
+    }
-            .expect("could not update bot rating");
+    Ok(match_stats)
+}
+
+/// Tokenizes player ids to a set of consecutive numbers
+struct PlayerTokenizer {
+    id_to_ix: HashMap<i32, usize>,
+    ids: Vec<i32>,
+}
+
+impl PlayerTokenizer {
+    fn new() -> Self {
+        PlayerTokenizer {
+            id_to_ix: HashMap::new(),
+            ids: Vec::new(),
+        }
+    }
+
+    fn tokenize(&mut self, id: i32) -> usize {
+        match self.id_to_ix.get(&id) {
+            Some(&ix) => ix,
+            None => {
+                let ix = self.ids.len();
+                self.ids.push(id);
+                self.id_to_ix.insert(id, ix);
+                ix
+            }
+        }
+    }
+
+    fn detokenize(&self, ix: usize) -> i32 {
+        self.ids[ix]
+    }
+
+    fn player_count(&self) -> usize {
+        self.ids.len()
+    }
+}
+
+fn sigmoid(logit: f64) -> f64 {
+    1.0 / (1.0 + (-logit).exp())
+}
+
+fn estimate_ratings_from_stats(match_stats: HashMap<(i32, i32), MatchStats>) -> Vec<(i32, f64)> {
+    // map player ids to player indexes in the ratings array
+    let mut input_records = Vec::<RatingInputRecord>::with_capacity(match_stats.len());
+    let mut player_tokenizer = PlayerTokenizer::new();
+
+    for ((a_id, b_id), stats) in match_stats.into_iter() {
+        input_records.push(RatingInputRecord {
+            p1_ix: player_tokenizer.tokenize(a_id),
+            p2_ix: player_tokenizer.tokenize(b_id),
+            score: stats.total_score / stats.num_matches as f64,
+        })
+    }
+
+    let mut ratings = vec![0f64; player_tokenizer.player_count()];
+    optimize_ratings(&mut ratings, &input_records);
+
+    ratings
+        .into_iter()
+        .enumerate()
+        .map(|(ix, rating)| {
+            (
+                player_tokenizer.detokenize(ix),
+                rating * 100f64 / 10f64.ln(),
+            )
+        })
+        .collect()
+}
+
+struct RatingInputRecord {
+    /// index of first player
+    p1_ix: usize,
+    /// index of secord player
+    p2_ix: usize,
+    /// score of player 1 (= 1 - score of player 2)
+    score: f64,
+}
+
+fn optimize_ratings(ratings: &mut [f64], input_records: &[RatingInputRecord]) {
+    // TODO: group this in a params struct
+    let tolerance = 10f64.powi(-6);
+    let learning_rate = 0.1;
+    let max_iterations = 10000;
+
+    for _iteration in 0..max_iterations {
+        let mut gradients = vec![0f64; ratings.len()];
+
+        // calculate gradients
+        for record in input_records.iter() {
+            let predicted = sigmoid(ratings[record.p1_ix] - ratings[record.p2_ix]);
+            let gradient = predicted - record.score;
+            gradients[record.p1_ix] += gradient;
+            gradients[record.p2_ix] -= gradient;
+        }
+
+        // apply update step
+        let mut converged = true;
+        for (rating, gradient) in ratings.iter_mut().zip(&gradients) {
+            let update = learning_rate * gradient / input_records.len() as f64;
+            if update > tolerance {
+                converged = false;
+            }
+            *rating -= update;
+        }
+
+        if converged {
+            break;
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_optimize_ratings() {
+        let input_records = vec![RatingInputRecord {
+            p1_ix: 0,
+            p2_ix: 1,
+            score: 0.8,
+        }];
+
+        let mut ratings = vec![0.0; 2];
+        optimize_ratings(&mut ratings, &input_records);
+        assert!(sigmoid(ratings[0] - ratings[1]) - 0.8 < 10f64.powi(-6));
     }
 }