feat: refactoring, adds Result to most public API

src/linalg/ndarray_bindings.rs

@@ -36,8 +36,8 @@
 //!             1., 1., 1., 1., 1., 1., 1., 1., 1., 1.
 //!         ]);
 //!
-//! let lr = LogisticRegression::fit(&x, &y);
-//! let y_hat = lr.predict(&x);
+//! let lr = LogisticRegression::fit(&x, &y).unwrap();
+//! let y_hat = lr.predict(&x).unwrap();
 //! ```
 use std::iter::Sum;
 use std::ops::AddAssign;
@@ -395,6 +395,7 @@ mod tests {
     use super::*;
     use crate::ensemble::random_forest_regressor::*;
     use crate::linear::logistic_regression::*;
+    use crate::metrics::mean_absolute_error;
     use ndarray::{arr1, arr2, Array1, Array2};
 
     #[test]
@@ -736,9 +737,9 @@ mod tests {
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
         ]);
 
-        let lr = LogisticRegression::fit(&x, &y);
+        let lr = LogisticRegression::fit(&x, &y).unwrap();
 
-        let y_hat = lr.predict(&x);
+        let y_hat = lr.predict(&x).unwrap();
 
         let error: f64 = y
             .into_iter()
@@ -774,10 +775,6 @@ mod tests {
             114.2, 115.7, 116.9,
         ]);
 
-        let expected_y: Vec<f64> = vec![
-            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
-        ];
-
         let y_hat = RandomForestRegressor::fit(
             &x,
             &y,
@@ -789,10 +786,10 @@ mod tests {
                 m: Option::None,
             },
         )
-        .predict(&x);
+        .unwrap()
+        .predict(&x)
+        .unwrap();
 
-        for i in 0..y_hat.len() {
-            assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
-        }
+        assert!(mean_absolute_error(&y, &y_hat) < 1.0);
     }
 }