Improved docs [skip ci]

src/bindings.rs

@@ -50,14 +50,23 @@ pub struct MfModel {
 #[repr(C)]
 #[derive(Clone, Copy)]
 pub enum Loss {
+    /// Squared error (L2-norm).
     RealL2 = 0,
+    /// Absolute error (L1-norm).
     RealL1 = 1,
+    /// Generalized KL-divergence.
     RealKL = 2,
+    /// Logarithmic error.
     BinaryLog = 5,
+    /// Squared hinge loss.
     BinaryL2 = 6,
+    /// Hinge loss.
     BinaryL1 = 7,
+    /// Row-oriented pair-wise logarithmic loss.
     OneClassRow = 10,
+    /// Column-oriented pair-wise logarithmic loss.
     OneClassCol = 11,
+    /// Squared error (L2-norm).
     OneClassL2 = 12,
 }
 

src/model.rs

@@ -15,6 +15,7 @@ impl Model {
         Params::new()
     }
 
+    /// Loads a model from a file.
     pub fn load<P: AsRef<Path>>(path: P) -> Result<Self, Error> {
         // TODO better conversion
         let cpath = CString::new(path.as_ref().to_str().unwrap())?;
@@ -25,10 +26,12 @@ impl Model {
         Ok(Model { model })
     }
 
+    /// Returns the predicted value for a specific row and column.
     pub fn predict(&self, row_index: i32, column_index: i32) -> f32 {
         unsafe { mf_predict(self.model, row_index, column_index) }
     }
 
+    /// Saves the model to a file.
     pub fn save<P: AsRef<Path>>(&self, path: P) -> Result<(), Error> {
         // TODO better conversion
         let cpath = CString::new(path.as_ref().to_str().unwrap())?;
@@ -39,18 +42,22 @@ impl Model {
         Ok(())
     }
 
+    /// Returns the number of rows.
     pub fn rows(&self) -> i32 {
         unsafe { (*self.model).m }
     }
 
+    /// Returns the number of columns.
     pub fn columns(&self) -> i32 {
         unsafe { (*self.model).n }
     }
 
+    /// Returns the number of factors.
     pub fn factors(&self) -> i32 {
         unsafe { (*self.model).k }
     }
 
+    /// Returns the bias.
     pub fn bias(&self) -> f32 {
         unsafe { (*self.model).b }
     }
@@ -91,36 +98,43 @@ impl Model {
         self.q_factors().chunks(self.factors() as usize)
     }
 
+    /// Calculates RMSE (for real-valued MF).
     pub fn rmse(&self, data: &Matrix) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_rmse(&prob, self.model) }
     }
 
+    /// Calculates MAE (for real-valued MF).
     pub fn mae(&self, data: &Matrix) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_mae(&prob, self.model) }
     }
 
+    /// Calculates generalized KL-divergence (for non-negative real-valued MF).
     pub fn gkl(&self, data: &Matrix) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_gkl(&prob, self.model) }
     }
 
+    /// Calculates logarithmic loss (for binary MF).
     pub fn logloss(&self, data: &Matrix) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_logloss(&prob, self.model) }
     }
 
+    /// Calculates accuracy (for binary MF).
     pub fn accuracy(&self, data: &Matrix) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_accuracy(&prob, self.model) }
     }
 
+    /// Calculates MPR (for one-class MF).
     pub fn mpr(&self, data: &Matrix, transpose: bool) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_mpr(&prob, self.model, transpose) }
     }
 
+    /// Calculates AUC (for one-class MF).
     pub fn auc(&self, data: &Matrix, transpose: bool) -> f64 {
         let prob = data.to_problem();
         unsafe { calc_auc(&prob, self.model, transpose) }

src/params.rs

@@ -13,76 +13,91 @@ impl Params {
         Self { param }
     }
 
+    /// Sets the loss function.
     pub fn loss(&mut self, value: Loss) -> &mut Self {
         self.param.fun = value;
         self
     }
 
+    /// Sets the number of latent factors.
     pub fn factors(&mut self, value: i32) -> &mut Self {
         self.param.k = value;
         self
     }
 
+    /// Sets the number of threads.
     pub fn threads(&mut self, value: i32) -> &mut Self {
         self.param.nr_threads = value;
         self
     }
 
+    /// Sets the number of bins.
     pub fn bins(&mut self, value: i32) -> &mut Self {
         self.param.nr_bins = value;
         self
     }
 
+    /// Sets the number of iterations.
     pub fn iterations(&mut self, value: i32) -> &mut Self {
         self.param.nr_iters = value;
         self
     }
 
+    /// Sets the coefficient of L1-norm regularization on P.
     pub fn lambda_p1(&mut self, value: f32) -> &mut Self {
         self.param.lambda_p1 = value;
         self
     }
 
+    /// Sets the coefficient of L2-norm regularization on P.
     pub fn lambda_p2(&mut self, value: f32) -> &mut Self {
         self.param.lambda_p2 = value;
         self
     }
 
+    /// Sets the coefficient of L1-norm regularization on Q.
     pub fn lambda_q1(&mut self, value: f32) -> &mut Self {
         self.param.lambda_q1 = value;
         self
     }
 
+    /// Sets the coefficient of L2-norm regularization on Q.
     pub fn lambda_q2(&mut self, value: f32) -> &mut Self {
         self.param.lambda_q2 = value;
         self
     }
 
+    /// Sets the learning rate.
     pub fn learning_rate(&mut self, value: f32) -> &mut Self {
         self.param.eta = value;
         self
     }
 
+    /// Sets the importance of negative entries.
     pub fn alpha(&mut self, value: f32) -> &mut Self {
         self.param.alpha = value;
         self
     }
 
+    /// Sets the desired value of negative entries.
     pub fn c(&mut self, value: f32) -> &mut Self {
         self.param.c = value;
         self
     }
 
+    /// Sets whether to perform non-negative MF (NMF).
     pub fn nmf(&mut self, value: bool) -> &mut Self {
         self.param.do_nmf = value;
         self
     }
 
+    /// Sets whether to output to stdout.
     pub fn quiet(&mut self, value: bool) -> &mut Self {
         self.param.quiet = value;
         self
     }
 
+    /// Fits a model.
     pub fn fit(&mut self, data: &Matrix) -> Result<Model, Error> {
         let prob = data.to_problem();
         let param = self.build_param()?;
@@ -93,6 +108,7 @@ impl Params {
         Ok(Model { model })
     }
 
+    /// Fits a model and performs cross-validation.
     pub fn fit_eval(&mut self, train_set: &Matrix, eval_set: &Matrix) -> Result<Model, Error> {
         let tr = train_set.to_problem();
         let va = eval_set.to_problem();
@@ -104,6 +120,7 @@ impl Params {
         Ok(Model { model })
     }
 
+    /// Performs cross-validation.
     pub fn cv(&mut self, data: &Matrix, folds: i32) -> Result<f64, Error> {
         let prob = data.to_problem();
         let param = self.build_param()?;