Fix tests

Cargo.toml

@@ -15,5 +15,6 @@ rand_xoshiro = "0.4"
 statrs = "0.13"
 
 [dev-dependencies]
-openblas-src = { version = "0.10", features = ["system"] }
+approx = "0.4"
+intel-mkl-src = "0.5"
 time = "0.2"

examples/abcdef.rs

@@ -1,4 +1,4 @@
-extern crate openblas_src;
+extern crate intel_mkl_src;
 
 use kickscore as ks;
 

examples/kickscore-basics.rs

@@ -1,4 +1,4 @@
-extern crate openblas_src;
+extern crate intel_mkl_src;
 
 use kickscore as ks;
 

examples/nba-history.rs

@@ -1,4 +1,4 @@
-extern crate openblas_src;
+extern crate intel_mkl_src;
 
 use std::collections::HashSet;
 use std::fs;
@@ -8,14 +8,14 @@ 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 reader = fs::File::open("data/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() {
+    for line in reader.lines().skip(1) {
         let line = line?;
         let data = line.split(',').collect::<Vec<_>>();
 

tests/kickscore-basics.rs

@@ -0,0 +1,45 @@
+extern crate intel_mkl_src;
+
+use approx::assert_abs_diff_eq;
+use kickscore as ks;
+
+#[test]
+fn kickscore_basic() {
+    let mut model = ks::BinaryModel::new(ks::BinaryModelObservation::Probit);
+
+    let k_spike = ks::kernel::Constant::new(0.5);
+
+    let k_tom = ks::kernel::Exponential::new(1.0, 1.0);
+
+    let k_jerry: Vec<Box<dyn ks::Kernel>> = vec![
+        Box::new(ks::kernel::Constant::new(1.0)),
+        Box::new(ks::kernel::Matern52::new(0.5, 1.0)),
+    ];
+
+    model.add_item("Spike", Box::new(k_spike));
+    model.add_item("Tom", Box::new(k_tom));
+    model.add_item("Jerry", Box::new(k_jerry));
+
+    model.observe(&["Jerry"], &["Tom"], 0.0);
+    model.observe(&["Jerry"], &["Tom"], 0.9);
+
+    model.observe(&["Tom"], &["Spike"], 1.7);
+    model.observe(&["Tom"], &["Jerry"], 2.1);
+
+    model.observe(&["Jerry"], &["Tom"], 3.0);
+    model.observe(&["Jerry"], &["Tom", "Spike"], 3.5);
+
+    model.fit();
+
+    let (p_win, _p_los) = model.probabilities(&[&"Jerry"], &[&"Tom"], 4.0);
+
+    assert_abs_diff_eq!(p_win, 0.7299975928462964, epsilon = f64::EPSILON);
+
+    let (p_win, _p_los) = model.probabilities(&[&"Jerry"], &[&"Tom"], 2.0);
+
+    assert_abs_diff_eq!(p_win, 0.4455095363120037, epsilon = f64::EPSILON);
+
+    let (p_win, _p_los) = model.probabilities(&[&"Jerry"], &[&"Tom"], -1.0);
+
+    assert_abs_diff_eq!(p_win, 0.903756079972532, epsilon = f64::EPSILON);
+}

tests/nba-history.rs

@@ -0,0 +1,106 @@
+extern crate intel_mkl_src;
+
+use std::collections::HashSet;
+use std::fs;
+use std::io::{self, BufRead};
+
+use approx::assert_abs_diff_eq;
+use kickscore as ks;
+use time::Date;
+
+#[test]
+fn nba_history() {
+    let reader = fs::File::open("data/nba.csv")
+        .map(io::BufReader::new)
+        .unwrap();
+
+    let mut teams = HashSet::new();
+    let mut observations = Vec::new();
+
+    let cutoff = time::date!(2019 - 06 - 01);
+
+    for line in reader.lines().skip(1) {
+        let line = line.unwrap();
+        let data = line.split(',').collect::<Vec<_>>();
+
+        assert!(data.len() == 5);
+
+        let t = Date::parse(data[0], "%F").unwrap();
+
+        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().assume_utc().timestamp() as f64;
+
+        let score_1: u16 = data[3].parse().unwrap();
+        let score_2: u16 = data[4].parse().unwrap();
+
+        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();
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(1996 - 01 - 01)
+            .midnight()
+            .assume_utc()
+            .timestamp() as f64,
+    );
+
+    assert_abs_diff_eq!(p_win, 0.9002599772490479, epsilon = f64::EPSILON);
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(2001 - 01 - 01)
+            .midnight()
+            .assume_utc()
+            .timestamp() as f64,
+    );
+
+    assert_abs_diff_eq!(p_win, 0.22837870685441986, epsilon = f64::EPSILON);
+
+    let (p_win, _) = model.probabilities(
+        &[&"CHI"],
+        &[&"BOS"],
+        time::date!(2020 - 01 - 01)
+            .midnight()
+            .assume_utc()
+            .timestamp() as f64,
+    );
+
+    assert_abs_diff_eq!(p_win, 0.2748029998412422, epsilon = f64::EPSILON);
+}