trueskill-tt/examples/atp.rs

use plotters::prelude::*;
use smallvec::smallvec;
use time::{Date, Month};
use trueskill_tt::{Event, History, Member, Outcome, Team, drift::ConstantDrift};

fn main() {
    let mut csv = csv::Reader::open("examples/atp.csv").unwrap();

    let from = Date::from_calendar_date(1900, Month::January, 1).unwrap();
    let time_format = time::format_description::parse("[year]-[month]-[day]").unwrap();

    let mut events: Vec<Event<i64, String>> = Vec::new();

    for row in csv.records() {
        let date = Date::parse(&row["time_start"], &time_format).unwrap();
        let time = (date - from).whole_days();

        if &row["double"] == "t" {
            events.push(Event {
                time,
                teams: smallvec![
                    Team::with_members([
                        Member::new(row["w1_id"].to_owned()),
                        Member::new(row["w2_id"].to_owned()),
                    ]),
                    Team::with_members([
                        Member::new(row["l1_id"].to_owned()),
                        Member::new(row["l2_id"].to_owned()),
                    ]),
                ],
                outcome: Outcome::winner(0, 2),
            });
        } else {
            events.push(Event {
                time,
                teams: smallvec![
                    Team::with_members([Member::new(row["w1_id"].to_owned())]),
                    Team::with_members([Member::new(row["l1_id"].to_owned())]),
                ],
                outcome: Outcome::winner(0, 2),
            });
        }
    }

    let mut hist: History<i64, _, _, String> = History::builder()
        .key_type::<String>()
        .sigma(1.6)
        .drift(ConstantDrift::new(0.036))
        .convergence(trueskill_tt::ConvergenceOptions {
            // This history needs 30 sweeps to reach the epsilon below. It was
            // capped at 10 until the `#[must_use]` on `ConvergenceReport`
            // surfaced that the example had been shipping a short fit.
            max_iter: 100,
            epsilon: 0.01,
            alpha: 1.0,
        })
        .build();

    hist.add_events(events).unwrap();

    // Read the report rather than discarding it. A fit that hits `max_iter`
    // without reaching `epsilon` is not an error and does not look wrong — every
    // rating comes back finite and sensibly ordered — so this flag is the only
    // thing that says the numbers were still moving when the sweep stopped.
    let report = hist.converge().unwrap();
    eprintln!(
        "converged={} after {} sweeps, final step {:?}",
        report.converged, report.iterations, report.final_step
    );
    if !report.converged {
        eprintln!(
            "warning: stopped after {} sweeps with a final step of {:?}, \
             short of epsilon — raise ConvergenceOptions::max_iter",
            report.iterations, report.final_step
        );
    }

    let players = [
        ("aggasi", "a092", 38800i64),
        ("borg", "b058", 30300),
        ("connors", "c044", 31250),
        ("courier", "c243", 35750),
        ("djokovic", "d643", i64::MAX),
        ("edberg", "e004", 34750),
        ("federer", "f324", i64::MAX),
        ("hewitt", "h432", 40750),
        ("mcenroe", "m047", 33000),
        ("lendl", "l018", 33750),
        ("murray", "mc10", 60750),
        ("nadal", "n409", i64::MAX),
        ("nastase", "n008", 28750),
        ("sampras", "s402", i64::MAX),
        ("wilander", "w023", 32600),
    ];

    let mut x_spec = (f64::MAX, f64::MIN);
    let mut y_spec = (f64::MAX, f64::MIN);

    for &(_, id, cutoff) in &players {
        for (ts, gs) in hist.learning_curve(id) {
            if ts >= cutoff {
                continue;
            }

            let ts = ts as f64;

            if ts < x_spec.0 {
                x_spec.0 = ts;
            }

            if ts > x_spec.1 {
                x_spec.1 = ts;
            }

            let upper = gs.mu() + gs.sigma();
            let lower = gs.mu() - gs.sigma();

            if lower < y_spec.0 {
                y_spec.0 = lower;
            }

            if upper > y_spec.1 {
                y_spec.1 = upper;
            }
        }
    }

    let root = SVGBackend::new("plot.svg", (1280, 640)).into_drawing_area();

    root.fill(&WHITE).unwrap();

    let mut chart = ChartBuilder::on(&root)
        .margin(5)
        .x_label_area_size(30)
        .y_label_area_size(30)
        .build_cartesian_2d(x_spec.0..x_spec.1, y_spec.0..y_spec.1)
        .unwrap();

    chart.configure_mesh().draw().unwrap();

    for (idx, &(player, id, cutoff)) in players.iter().enumerate() {
        let mut data = Vec::new();
        let mut upper = Vec::new();
        let mut lower = Vec::new();

        for (ts, gs) in hist.learning_curve(id) {
            if ts >= cutoff {
                continue;
            }

            data.push((ts as f64, gs.mu()));
            upper.push((ts as f64, gs.mu() + gs.sigma()));
            lower.push((ts as f64, gs.mu() - gs.sigma()));
        }

        let color = Palette99::pick(idx);

        let band = upper
            .into_iter()
            .chain(lower.into_iter().rev())
            .collect::<Vec<_>>();

        chart
            .plotting_area()
            .draw(&Polygon::new(band, color.mix(0.15)))
            .unwrap();

        chart
            .draw_series(LineSeries::new(data, &color))
            .unwrap()
            .label(player)
            .legend(move |(x, y)| PathElement::new(vec![(x, y), (x + 20, y)], &color));
    }

    chart
        .configure_series_labels()
        .background_style(WHITE.mix(0.8))
        .border_style(BLACK)
        .draw()
        .unwrap();
}

mod csv {
    use std::{
        fs::File,
        io::{self, BufRead, BufReader, Lines},
        ops,
        path::Path,
    };

    pub struct Reader {
        header_map: Vec<String>,
        lines: Lines<BufReader<File>>,
    }

    impl Reader {
        pub fn open<P: AsRef<Path>>(path: P) -> Result<Self, io::Error> {
            let mut lines = File::open(path).map(BufReader::new)?.lines();

            let header_map = if let Some(header) = lines.next() {
                let header = header?;

                header.split(',').map(Into::into).collect::<Vec<_>>()
            } else {
                Vec::new()
            };

            Ok(Self { header_map, lines })
        }

        pub fn records(&mut self) -> Records<'_> {
            Records {
                header_map: &self.header_map,
                lines: &mut self.lines,
            }
        }
    }

    pub struct Records<'a> {
        header_map: &'a Vec<String>,
        lines: &'a mut Lines<BufReader<File>>,
    }

    impl<'a> Iterator for Records<'a> {
        type Item = Record<'a>;

        fn next(&mut self) -> Option<Self::Item> {
            let line = self.lines.next()?;

            Some(Record {
                header_map: self.header_map,
                columns: line.unwrap().split(',').map(Into::into).collect::<Vec<_>>(),
            })
        }
    }

    pub struct Record<'a> {
        header_map: &'a Vec<String>,
        columns: Vec<String>,
    }

    impl<'a> ops::Index<&str> for Record<'a> {
        type Output = str;

        fn index(&self, index: &str) -> &Self::Output {
            &self.columns[self
                .header_map
                .iter()
                .position(|header| header == index)
                .unwrap()]
        }
    }
}