diff options
Diffstat (limited to 'planetwars-server')
| -rw-r--r-- | planetwars-server/src/modules/ranking.rs | 114 |
1 files changed, 102 insertions, 12 deletions
diff --git a/planetwars-server/src/modules/ranking.rs b/planetwars-server/src/modules/ranking.rs index df8e2d2..5d496d7 100644 --- a/planetwars-server/src/modules/ranking.rs +++ b/planetwars-server/src/modules/ranking.rs @@ -65,6 +65,7 @@ fn recalculate_ratings(db_conn: &PgConnection) -> QueryResult<()> { db::ratings::set_rating(bot_id, rating, db_conn).expect("could not update bot rating"); } let elapsed = Instant::now() - start; + // TODO: set up proper logging infrastructure println!("computed ratings in {} ms", elapsed.subsec_millis()); Ok(()) } @@ -157,11 +158,14 @@ fn estimate_ratings_from_stats(match_stats: HashMap<(i32, i32), MatchStats>) -> p1_ix: player_tokenizer.tokenize(a_id), p2_ix: player_tokenizer.tokenize(b_id), score: stats.total_score / stats.num_matches as f64, + weight: stats.num_matches as f64, }) } let mut ratings = vec![0f64; player_tokenizer.player_count()]; - optimize_ratings(&mut ratings, &input_records); + // TODO: fetch these from config + let params = OptimizeRatingsParams::default(); + optimize_ratings(&mut ratings, &input_records, ¶ms); ratings .into_iter() @@ -182,21 +186,43 @@ struct RatingInputRecord { p2_ix: usize, /// score of player 1 (= 1 - score of player 2) score: f64, + /// weight of this record + weight: 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; +struct OptimizeRatingsParams { + tolerance: f64, + learning_rate: f64, + max_iterations: usize, + regularization_weight: f64, +} + +impl Default for OptimizeRatingsParams { + fn default() -> Self { + OptimizeRatingsParams { + tolerance: 10f64.powi(-8), + learning_rate: 0.1, + max_iterations: 10_000, + regularization_weight: 10.0, + } + } +} + +fn optimize_ratings( + ratings: &mut [f64], + input_records: &[RatingInputRecord], + params: &OptimizeRatingsParams, +) { + let total_weight = + params.regularization_weight + input_records.iter().map(|r| r.weight).sum::<f64>(); - for _iteration in 0..max_iterations { + for _iteration in 0..params.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; + let gradient = record.weight * (predicted - record.score); gradients[record.p1_ix] += gradient; gradients[record.p2_ix] -= gradient; } @@ -204,8 +230,9 @@ fn optimize_ratings(ratings: &mut [f64], input_records: &[RatingInputRecord]) { // 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 { + let update = params.learning_rate * (gradient + params.regularization_weight * *rating) + / total_weight; + if update > params.tolerance { converged = false; } *rating -= update; @@ -221,16 +248,79 @@ fn optimize_ratings(ratings: &mut [f64], input_records: &[RatingInputRecord]) { mod tests { use super::*; + fn is_close(a: f64, b: f64) -> bool { + (a - b).abs() < 10f64.powi(-6) + } + #[test] fn test_optimize_ratings() { let input_records = vec![RatingInputRecord { p1_ix: 0, p2_ix: 1, score: 0.8, + weight: 1.0, + }]; + + let mut ratings = vec![0.0; 2]; + optimize_ratings( + &mut ratings, + &input_records, + &OptimizeRatingsParams { + regularization_weight: 0.0, + ..Default::default() + }, + ); + assert!(is_close(sigmoid(ratings[0] - ratings[1]), 0.8)); + } + + #[test] + fn test_optimize_ratings_weight() { + let input_records = vec![ + RatingInputRecord { + p1_ix: 0, + p2_ix: 1, + score: 1.0, + weight: 1.0, + }, + RatingInputRecord { + p1_ix: 1, + p2_ix: 0, + score: 1.0, + weight: 3.0, + }, + ]; + + let mut ratings = vec![0.0; 2]; + optimize_ratings( + &mut ratings, + &input_records, + &OptimizeRatingsParams { + regularization_weight: 0.0, + ..Default::default() + }, + ); + assert!(is_close(sigmoid(ratings[0] - ratings[1]), 0.25)); + } + + #[test] + fn test_optimize_ratings_regularization() { + let input_records = vec![RatingInputRecord { + p1_ix: 0, + p2_ix: 1, + score: 0.8, + weight: 100.0, }]; let mut ratings = vec![0.0; 2]; - optimize_ratings(&mut ratings, &input_records); - assert!(sigmoid(ratings[0] - ratings[1]) - 0.8 < 10f64.powi(-6)); + optimize_ratings( + &mut ratings, + &input_records, + &OptimizeRatingsParams { + regularization_weight: 1.0, + ..Default::default() + }, + ); + let predicted = sigmoid(ratings[0] - ratings[1]); + assert!(0.5 < predicted && predicted < 0.8); } } |