Changed fit and fit_eval to return Result

CHANGELOG.md

@@ -1,7 +1,7 @@
 ## 0.2.0 (unreleased)
 
 - Changed pattern for fitting models - use `Model::params()` instead of `Model::new()`
-- Changed `Model::load` to return `Result`
+- Changed `fit`, `fit_eval`, and `load` to return `Result`
 
 ## 0.1.1 (2021-07-27)
 

README.md

@@ -26,7 +26,7 @@ data.push(1, 1, 4.0);
 Fit a model
 
 ```rust
-let model = libmf::Model::params().fit(&data);
+let model = libmf::Model::params().fit(&data).unwrap();
 ```
 
 Make predictions
@@ -63,7 +63,7 @@ let model = libmf::Model::load("model.txt").unwrap();
 Pass a validation set
 
 ```rust
-let model = libmf::Model::params().fit_eval(&train_set, &eval_set);
+let model = libmf::Model::params().fit_eval(&train_set, &eval_set).unwrap();
 ```
 
 ## Cross-Validation
@@ -179,7 +179,7 @@ let mut data = libmf::Matrix::with_capacity(3);
 Use
 
 ```rust
-let model = libmf::Model::params().factors(20).fit(&data);
+let model = libmf::Model::params().factors(20).fit(&data).unwrap();
 ```
 
 instead of

src/error.rs

@@ -0,0 +1,2 @@
+#[derive(Debug)]
+pub struct Error(pub(crate) String);

src/lib.rs

@@ -3,10 +3,12 @@
 //! [View the docs](https://github.com/ankane/libmf-rust)
 
 mod bindings;
+mod error;
 mod matrix;
 mod model;
 mod params;
 
+pub use error::Error;
 pub use matrix::Matrix;
 pub use model::Model;
 pub use params::Params;

src/model.rs

@@ -1,10 +1,7 @@
 use crate::bindings::*;
-use crate::{Matrix, Params};
+use crate::{Error, Matrix, Params};
 use std::ffi::CString;
 
-#[derive(Debug)]
-pub struct Error(String);
-
 pub struct Model {
     pub(crate) model: *mut MfModel,
 }
@@ -20,9 +17,7 @@ impl Model {
         if model.is_null() {
             Err(Error("Cannot open model".to_string()))
         } else {
-            Ok(Model {
-                model: model
-            })
+            Ok(Model { model })
         }
     }
 
@@ -117,7 +112,7 @@ mod tests {
     #[test]
     fn test_fit() {
         let data = generate_data();
-        let model = Model::params().quiet(true).fit(&data);
+        let model = Model::params().quiet(true).fit(&data).unwrap();
         model.predict(0, 1);
 
         model.p_factors();
@@ -128,7 +123,7 @@ mod tests {
     #[test]
     fn test_fit_eval() {
         let data = generate_data();
-        Model::params().quiet(true).fit_eval(&data, &data);
+        Model::params().quiet(true).fit_eval(&data, &data).unwrap();
     }
 
     #[test]
@@ -140,7 +135,7 @@ mod tests {
     #[test]
     fn test_save_load() {
         let data = generate_data();
-        let model = Model::params().quiet(true).fit(&data);
+        let model = Model::params().quiet(true).fit(&data).unwrap();
 
         model.save("/tmp/model.txt");
         let model = Model::load("/tmp/model.txt").unwrap();
@@ -159,7 +154,7 @@ mod tests {
     #[test]
     fn test_metrics() {
         let data = generate_data();
-        let model = Model::params().quiet(true).fit(&data);
+        let model = Model::params().quiet(true).fit(&data).unwrap();
 
         assert!(model.rmse(&data) < 0.15);
         assert!(model.mae(&data) < 0.15);
@@ -173,14 +168,14 @@ mod tests {
     #[test]
     fn test_predict_out_of_range() {
         let data = generate_data();
-        let model = Model::params().quiet(true).fit(&data);
+        let model = Model::params().quiet(true).fit(&data).unwrap();
         assert_eq!(model.bias(), model.predict(1000, 1000));
     }
 
     #[test]
     fn test_fit_empty() {
         let data = Matrix::new();
-        let model = Model::params().quiet(true).fit(&data);
+        let model = Model::params().quiet(true).fit(&data).unwrap();
         assert!(model.p_factors().is_empty());
         assert!(model.q_factors().is_empty());
         assert!(model.bias().is_nan());
@@ -189,9 +184,16 @@ mod tests {
     #[test]
     fn test_fit_eval_empty() {
         let data = Matrix::new();
-        let model = Model::params().quiet(true).fit_eval(&data, &data);
+        let model = Model::params().quiet(true).fit_eval(&data, &data).unwrap();
         assert!(model.p_factors().is_empty());
         assert!(model.q_factors().is_empty());
         assert!(model.bias().is_nan());
     }
+
+    #[test]
+    fn test_bad_loss() {
+        let data = generate_data();
+        let result = Model::params().loss(13).fit(&data);
+        assert!(result.is_err());
+    }
 }

src/params.rs

@@ -1,5 +1,5 @@
 use crate::bindings::*;
-use crate::{Matrix, Model};
+use crate::{Error, Matrix, Model};
 
 pub struct Params {
     param: MfParameter
@@ -85,18 +85,24 @@ impl Params {
         self
     }
 
-    pub fn fit(&mut self, data: &Matrix) -> Model {
+    pub fn fit(&mut self, data: &Matrix) -> Result<Model, Error> {
         let prob = data.to_problem();
-        Model {
-            model: unsafe { mf_train(&prob, self.param) }
+        let model = unsafe { mf_train(&prob, self.param) };
+        if model.is_null() {
+            Err(Error("Bad parameters".to_string()))
+        } else {
+            Ok(Model { model })
         }
     }
 
-    pub fn fit_eval(&mut self, train_set: &Matrix, eval_set: &Matrix) -> Model {
+    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();
-        Model {
-            model: unsafe { mf_train_with_validation(&tr, &va, self.param) }
+        let model = unsafe { mf_train_with_validation(&tr, &va, self.param) };
+        if model.is_null() {
+            Err(Error("Bad parameters".to_string()))
+        } else {
+            Ok(Model { model })
         }
     }