Added TernaryModel.

src/model/binary.rs

@@ -0,0 +1,152 @@
+use std::f64;
+
+use crate::fitter::RecursiveFitter;
+use crate::item::Item;
+use crate::kernel::Kernel;
+use crate::observation::*;
+use crate::storage::Storage;
+
+pub enum BinaryModelObservation {
+    Probit,
+    Logit,
+}
+
+#[derive(Clone, Copy)]
+pub enum BinaryModelFitMethod {
+    Ep,
+    Kl,
+}
+
+pub struct BinaryModel {
+    storage: Storage,
+    last_t: f64,
+    win_obs: BinaryModelObservation,
+    observations: Vec<Box<dyn Observation>>,
+    last_method: Option<BinaryModelFitMethod>,
+}
+
+impl BinaryModel {
+    pub fn new(win_obs: BinaryModelObservation) -> Self {
+        BinaryModel {
+            storage: Storage::new(),
+            last_t: f64::NEG_INFINITY,
+            win_obs,
+            observations: Vec::new(),
+            last_method: None,
+        }
+    }
+
+    pub fn add_item(&mut self, name: &str, kernel: Box<dyn Kernel>) {
+        if self.storage.contains_key(name) {
+            panic!("item '{}' already added", name);
+        }
+
+        self.storage.insert(
+            name.to_string(),
+            Item::new(Box::new(RecursiveFitter::new(kernel))),
+        );
+    }
+
+    pub fn contains_item(&self, name: &str) -> bool {
+        self.storage.contains_key(name)
+    }
+
+    pub fn item_score(&self, name: &str, t: f64) -> (f64, f64) {
+        let id = self.storage.get_id(name);
+        let (ms, vs) = self.storage.item(id).fitter.predict(&[t]);
+
+        (ms[0], vs[0])
+    }
+
+    pub fn observe(&mut self, winners: &[&str], losers: &[&str], t: f64) {
+        if t < self.last_t {
+            panic!("observations must be added in chronological order");
+        }
+
+        let mut elems = self.process_items(winners, 1.0);
+        elems.extend(self.process_items(losers, -1.0));
+
+        let obs: Box<dyn Observation> = match self.win_obs {
+            BinaryModelObservation::Probit => {
+                Box::new(ProbitWinObservation::new(&mut self.storage, &elems, t, 0.0))
+            }
+            BinaryModelObservation::Logit => {
+                Box::new(LogitWinObservation::new(&mut self.storage, &elems, t, 0.0))
+            }
+        };
+
+        self.observations.push(obs);
+
+        /*
+        for (item, _) in elems {
+            item.link_observation(obs)
+        }
+        */
+
+        self.last_t = t;
+    }
+
+    pub fn fit(&mut self) -> bool {
+        // method="ep", lr=1.0, tol=1e-3, max_iter=100, verbose=False):
+
+        let method = BinaryModelFitMethod::Ep;
+        let lr = 1.0;
+        let tol = 1e-3;
+        let max_iter = 100;
+        let verbose = true;
+
+        self.last_method = Some(method);
+
+        for item in self.storage.items_mut() {
+            item.fitter.allocate();
+        }
+
+        for i in 0..max_iter {
+            let mut max_diff = 0.0;
+
+            for obs in &mut self.observations {
+                let diff = match method {
+                    BinaryModelFitMethod::Ep => obs.ep_update(lr, &mut self.storage),
+                    BinaryModelFitMethod::Kl => obs.kl_update(lr, &mut self.storage),
+                };
+
+                if diff > max_diff {
+                    max_diff = diff;
+                }
+            }
+
+            for item in self.storage.items_mut() {
+                item.fitter.fit();
+            }
+
+            if verbose {
+                println!("iteration {}, max diff: {:.5}", i + 1, max_diff);
+            }
+
+            if max_diff < tol {
+                return true;
+            }
+        }
+
+        false
+    }
+
+    pub fn probabilities(&mut self, team_1: &[&str], team_2: &[&str], t: f64) -> (f64, f64) {
+        let mut elems = self.process_items(team_1, 1.0);
+        elems.extend(self.process_items(team_2, -1.0));
+
+        let prob = match self.win_obs {
+            BinaryModelObservation::Probit => probit_win_observation(&elems, t, 0.0, &self.storage),
+            BinaryModelObservation::Logit => todo!(),
+        };
+
+        (prob, 1.0 - prob)
+    }
+
+    fn process_items(&self, items: &[&str], sign: f64) -> Vec<(usize, f64)> {
+        items
+            .iter()
+            .map(|key| (self.storage.get_id(&key), sign))
+            .collect()
+    }
+}