Added nba history example, and implemented kernel matern32.

examples/nba-history.rs

@@ -0,0 +1,94 @@
+extern crate openblas_src;
+
+use std::collections::HashSet;
+use std::fs;
+use std::io::{self, BufRead};
+
+use kickscore as ks;
+use time::Date;
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let reader = fs::File::open("examples/nba.csv").map(io::BufReader::new)?;
+
+    let mut teams = HashSet::new();
+    let mut observations = Vec::new();
+
+    let cutoff = time::date!(2019 - 06 - 01);
+
+    for line in reader.lines() {
+        let line = line?;
+        let data = line.split(',').collect::<Vec<_>>();
+
+        assert!(data.len() == 5);
+
+        let t = Date::parse(data[0], "%F")?;
+
+        if t > cutoff {
+            break;
+        }
+
+        teams.insert(data[1].to_string());
+        teams.insert(data[2].to_string());
+
+        if data[3].is_empty() || data[4].is_empty() {
+            continue;
+        }
+
+        let t = t.midnight().timestamp() as f64;
+
+        let score_1: u16 = data[3].parse()?;
+        let score_2: u16 = data[4].parse()?;
+
+        if score_1 > score_2 {
+            observations.push((data[1].to_string(), data[2].to_string(), t));
+        } else if score_1 < score_2 {
+            observations.push((data[2].to_string(), data[1].to_string(), t));
+        } else {
+            panic!("there shouldn't be any tie games");
+        }
+    }
+
+    let seconds_in_year = 365.25 * 24.0 * 60.0 * 60.0;
+
+    let mut model = ks::BinaryModel::new(ks::BinaryModelObservation::Probit);
+
+    for team in teams {
+        let kernel: Vec<Box<dyn ks::Kernel>> = vec![
+            Box::new(ks::kernel::Constant::new(0.03)),
+            Box::new(ks::kernel::Matern32::new(0.138, 1.753 * seconds_in_year)),
+        ];
+
+        model.add_item(&team, Box::new(kernel));
+    }
+
+    for (winner, loser, t) in observations {
+        model.observe(&[&winner], &[&loser], t);
+    }
+
+    model.fit();
+
+    println!("Probability that CHI beats BOS...");
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(1996 - 01 - 01).midnight().timestamp() as f64,
+    );
+    println!("  ... in 1996: {:.2}%", 100.0 * p_win);
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(2001 - 01 - 01).midnight().timestamp() as f64,
+    );
+    println!("  ... in 2001: {:.2}%", 100.0 * p_win);
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(2020 - 01 - 01).midnight().timestamp() as f64,
+    );
+    println!("  ... in 2020: {:.2}%", 100.0 * p_win);
+
+    Ok(())
+}