extern crate intel_mkl_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().assume_utc().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()
            .assume_utc()
            .timestamp() as f64,
    );
    println!("  ... in 1996: {:.2}%", 100.0 * p_win);

    let (p_win, _) = model.probabilities(
        &[&"CHI"],
        &[&"BOS"],
        time::date!(2001 - 01 - 01)
            .midnight()
            .assume_utc()
            .timestamp() as f64,
    );
    println!("  ... in 2001: {:.2}%", 100.0 * p_win);

    let (p_win, _) = model.probabilities(
        &[&"CHI"],
        &[&"BOS"],
        time::date!(2020 - 01 - 01)
            .midnight()
            .assume_utc()
            .timestamp() as f64,
    );
    println!("  ... in 2020: {:.2}%", 100.0 * p_win);

    Ok(())
}