Make API a bit prettier

examples/abcdef.rs

@@ -3,7 +3,7 @@ extern crate blas_src;
 use kickscore as ks;
 
 fn main() {
-    let mut model = ks::model::Binary::new(ks::model::binary::Observation::Probit);
+    let mut model = ks::model::Binary::probit();
 
     for player in &["A", "B", "C", "D", "E", "F"] {
         let kernel: [Box<dyn ks::Kernel>; 2] = [

examples/kickscore-basics.rs

@@ -1,12 +1,9 @@
 extern crate blas_src;
 
-use kickscore::{
+use kickscore::{kernel, model::Binary};
-    kernel::{self, Kernel},
-    model::{binary, Binary},
-};
 
 fn main() {
-    let mut model = Binary::new(binary::Observation::Probit);
+    let mut model = Binary::probit();
 
     // Spike's skill does not change over time.
     let k_spike = kernel::Constant::new(0.5);
@@ -15,10 +12,7 @@ fn main() {
     let k_tom = kernel::Exponential::new(1.0, 1.0);
 
     // Jerry's skill has a constant offset and smooth dynamics.
-    let k_jerry: [Box<dyn Kernel>; 2] = [
+    let k_jerry = (kernel::Constant::new(1.0), kernel::Matern52::new(0.5, 1.0));
-        Box::new(kernel::Constant::new(1.0)),
-        Box::new(kernel::Matern52::new(0.5, 1.0)),
-    ];
 
     // Now we are ready to add the items in the model.
     model.add_item("Spike", k_spike);

examples/nba-history.rs

@@ -54,7 +54,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     let seconds_in_year = 365.25 * 24.0 * 60.0 * 60.0;
 
-    let mut model = ks::model::Binary::new(ks::model::binary::Observation::Probit);
+    let mut model = ks::model::Binary::probit();
 
     for team in teams {
         let kernel: [Box<dyn ks::Kernel>; 2] = [

src/model/binary.rs

@@ -42,6 +42,14 @@ impl Binary {
         }
     }
 
+    pub fn probit() -> Self {
+        Self::new(Observation::Probit)
+    }
+
+    pub fn logit() -> Self {
+        Self::new(Observation::Logit)
+    }
+
     pub fn add_item<K: Kernel + 'static>(&mut self, name: &str, kernel: K) {
         if self.storage.contains_key(name) {
             panic!("item '{}' already added", name);

src/model/ternary.rs

@@ -7,13 +7,13 @@ use crate::observation::*;
 use crate::storage::Storage;
 
 #[derive(Clone, Copy)]
-pub enum TernaryModelObservation {
+pub enum Observation {
     Probit,
     Logit,
 }
 
 #[derive(Clone, Copy)]
-pub enum TernaryModelFitMethod {
+pub enum FitMethod {
     Ep,
     Kl,
 }
@@ -21,14 +21,14 @@ pub enum TernaryModelFitMethod {
 pub struct TernaryModel {
     storage: Storage,
     last_t: f64,
-    obs: TernaryModelObservation,
+    obs: Observation,
-    observations: Vec<Box<dyn Observation>>,
+    observations: Vec<Box<dyn crate::observation::Observation>>,
-    last_method: Option<TernaryModelFitMethod>,
+    last_method: Option<FitMethod>,
     margin: f64,
 }
 
 impl TernaryModel {
-    pub fn new(obs: TernaryModelObservation, margin: f64) -> Self {
+    pub fn new(obs: Observation, margin: f64) -> Self {
         TernaryModel {
             storage: Storage::default(),
             last_t: f64::NEG_INFINITY,
@@ -39,6 +39,14 @@ impl TernaryModel {
         }
     }
 
+    pub fn probit(margin: f64) -> Self {
+        Self::new(Observation::Probit, margin)
+    }
+
+    pub fn logit(margin: f64) -> Self {
+        Self::new(Observation::Logit, margin)
+    }
+
     pub fn add_item<K: Kernel + 'static>(&mut self, name: &str, kernel: K) {
         if self.storage.contains_key(name) {
             panic!("item '{}' already added", name);
@@ -78,26 +86,26 @@ impl TernaryModel {
         let mut elems = self.process_items(winners, 1.0);
         elems.extend(self.process_items(losers, -1.0));
 
-        let obs: Box<dyn Observation> = match (tie, self.obs) {
+        let obs: Box<dyn crate::observation::Observation> = match (tie, self.obs) {
-            (false, TernaryModelObservation::Probit) => Box::new(ProbitWinObservation::new(
+            (false, Observation::Probit) => Box::new(ProbitWinObservation::new(
                 &mut self.storage,
                 &elems,
                 t,
                 margin,
             )),
-            (false, TernaryModelObservation::Logit) => Box::new(LogitWinObservation::new(
+            (false, Observation::Logit) => Box::new(LogitWinObservation::new(
                 &mut self.storage,
                 &elems,
                 t,
                 margin,
             )),
-            (true, TernaryModelObservation::Probit) => Box::new(ProbitTieObservation::new(
+            (true, Observation::Probit) => Box::new(ProbitTieObservation::new(
                 &mut self.storage,
                 &elems,
                 t,
                 margin,
             )),
-            (true, TernaryModelObservation::Logit) => Box::new(LogitTieObservation::new(
+            (true, Observation::Logit) => Box::new(LogitTieObservation::new(
                 &mut self.storage,
                 &elems,
                 t,
@@ -113,7 +121,7 @@ impl TernaryModel {
     pub fn fit(&mut self) -> bool {
         // method="ep", lr=1.0, tol=1e-3, max_iter=100, verbose=False):
 
-        let method = TernaryModelFitMethod::Ep;
+        let method = FitMethod::Ep;
         let lr = 1.0;
         let tol = 1e-3;
         let max_iter = 100;
@@ -130,8 +138,8 @@ impl TernaryModel {
 
             for obs in &mut self.observations {
                 let diff = match method {
-                    TernaryModelFitMethod::Ep => obs.ep_update(&mut self.storage, lr),
+                    FitMethod::Ep => obs.ep_update(&mut self.storage, lr),
-                    TernaryModelFitMethod::Kl => obs.kl_update(&mut self.storage, lr),
+                    FitMethod::Kl => obs.kl_update(&mut self.storage, lr),
                 };
 
                 if diff > max_diff {
@@ -168,17 +176,17 @@ impl TernaryModel {
         elems.extend(self.process_items(team_2, -1.0));
 
         let prob_1 = match self.obs {
-            TernaryModelObservation::Probit => {
+            Observation::Probit => {
                 ProbitWinObservation::probability(&self.storage, &elems, t, margin)
             }
-            TernaryModelObservation::Logit => unimplemented!(),
+            Observation::Logit => unimplemented!(),
         };
 
         let prob_2 = match self.obs {
-            TernaryModelObservation::Probit => {
+            Observation::Probit => {
                 ProbitTieObservation::probability(&self.storage, &elems, t, margin)
             }
-            TernaryModelObservation::Logit => unimplemented!(),
+            Observation::Logit => unimplemented!(),
         };
 
         (prob_1, prob_2, 1.0 - prob_1 - prob_2)

tests/binary-1.rs

@@ -4,7 +4,7 @@ use kickscore as ks;
 
 #[test]
 fn binary_1() {
-    let mut model = ks::model::Binary::new(ks::model::binary::Observation::Probit);
+    let mut model = ks::model::Binary::probit();
 
     let k_audrey = ks::kernel::Matern52::new(1.0, 2.0);
     let k_benjamin = ks::kernel::Matern52::new(1.0, 2.0);

tests/kickscore-basics.rs

@@ -5,7 +5,7 @@ use kickscore as ks;
 
 #[test]
 fn kickscore_basic() {
-    let mut model = ks::model::Binary::new(ks::model::binary::Observation::Probit);
+    let mut model = ks::model::Binary::probit();
 
     let k_spike = ks::kernel::Constant::new(0.5);
     let k_tom = ks::kernel::Exponential::new(1.0, 1.0);

tests/nba-history.rs

@@ -58,7 +58,7 @@ fn nba_history() {
 
     let seconds_in_year = 365.25 * 24.0 * 60.0 * 60.0;
 
-    let mut model = ks::model::Binary::new(ks::model::binary::Observation::Probit);
+    let mut model = ks::model::Binary::probit();
 
     for team in teams {
         let kernel = (