111 lines
2.8 KiB
Rust
111 lines
2.8 KiB
Rust
extern crate blas_src;
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use std::cmp::Ordering;
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use std::collections::HashSet;
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use std::fs;
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use std::io::{self, BufRead};
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use approx::assert_abs_diff_eq;
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use kickscore as ks;
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use time::{macros::date, Date};
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#[test]
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fn nba_history() {
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let reader = fs::File::open("data/nba.csv")
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.map(io::BufReader::new)
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.unwrap();
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let mut teams = HashSet::new();
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let mut observations = Vec::new();
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let cutoff = date!(2019 - 06 - 01);
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let format = time::format_description::parse("[year]-[month]-[day]").unwrap();
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for line in reader.lines().skip(1) {
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let line = line.unwrap();
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let data = line.split(',').collect::<Vec<_>>();
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assert!(data.len() == 5);
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let t = Date::parse(data[0], &format).unwrap();
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if t > cutoff {
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break;
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}
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teams.insert(data[1].to_string());
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teams.insert(data[2].to_string());
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if data[3].is_empty() || data[4].is_empty() {
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continue;
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}
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let t = t.midnight().assume_utc().unix_timestamp() as f64;
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let score_1: u16 = data[3].parse().unwrap();
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let score_2: u16 = data[4].parse().unwrap();
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match score_1.cmp(&score_2) {
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Ordering::Greater => {
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observations.push((data[1].to_string(), data[2].to_string(), t));
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}
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Ordering::Less => {
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observations.push((data[2].to_string(), data[1].to_string(), t));
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}
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_ => panic!("there shouldn't be any tie games"),
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}
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}
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let seconds_in_year = 365.25 * 24.0 * 60.0 * 60.0;
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let mut model = ks::model::Binary::probit();
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for team in teams {
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let kernel = (
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ks::kernel::Constant::new(0.03),
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ks::kernel::Matern32::new(0.138, 1.753 * seconds_in_year),
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);
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model.add_item(&team, kernel);
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}
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for (winner, loser, t) in observations {
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model.observe(&[&winner], &[&loser], t);
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}
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model.fit();
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(1996 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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assert_abs_diff_eq!(p_win, 0.8987931627209078, epsilon = f64::EPSILON);
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(2001 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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assert_abs_diff_eq!(p_win, 0.22890824995747874, epsilon = f64::EPSILON);
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(2020 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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assert_abs_diff_eq!(p_win, 0.2748029998412422, epsilon = f64::EPSILON);
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}
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