feat: refactoring, adds Result to most public API

src/linear/logistic_regression.rs

@@ -40,9 +40,9 @@
 //!           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();
 //! ```
 //!
 //! ## References:
@@ -57,6 +57,7 @@ use std::marker::PhantomData;
 
 use serde::{Deserialize, Serialize};
 
+use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::optimization::first_order::lbfgs::LBFGS;
@@ -208,13 +209,15 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
     /// Fits Logistic Regression to your data.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `y` - target class values
-    pub fn fit(x: &M, y: &M::RowVector) -> LogisticRegression<T, M> {
+    pub fn fit(x: &M, y: &M::RowVector) -> Result<LogisticRegression<T, M>, Failed> {
         let y_m = M::from_row_vector(y.clone());
         let (x_nrows, num_attributes) = x.shape();
         let (_, y_nrows) = y_m.shape();
 
         if x_nrows != y_nrows {
-            panic!("Number of rows of X doesn't match number of rows of Y");
+            return Err(Failed::fit(&format!(
+                "Number of rows of X doesn't match number of rows of Y"
+            )));
         }
 
         let classes = y_m.unique();
@@ -229,7 +232,10 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
         }
 
         if k < 2 {
-            panic!("Incorrect number of classes: {}", k);
+            Err(Failed::fit(&format!(
+                "incorrect number of classes: {}. Should be >= 2.",
+                k
+            )))
         } else if k == 2 {
             let x0 = M::zeros(1, num_attributes + 1);
 
@@ -241,12 +247,12 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
 
             let result = LogisticRegression::minimize(x0, objective);
 
-            LogisticRegression {
+            Ok(LogisticRegression {
                 weights: result.x,
                 classes: classes,
                 num_attributes: num_attributes,
                 num_classes: k,
-            }
+            })
         } else {
             let x0 = M::zeros(1, (num_attributes + 1) * k);
 
@@ -261,18 +267,18 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
 
             let weights = result.x.reshape(k, num_attributes + 1);
 
-            LogisticRegression {
+            Ok(LogisticRegression {
                 weights: weights,
                 classes: classes,
                 num_attributes: num_attributes,
                 num_classes: k,
-            }
+            })
         }
     }
 
     /// Predict class labels for samples in `x`.
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
-    pub fn predict(&self, x: &M) -> M::RowVector {
+    pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         let n = x.shape().0;
         let mut result = M::zeros(1, n);
         if self.num_classes == 2 {
@@ -297,7 +303,7 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
                 result.set(0, i, self.classes[class_idxs[i]]);
             }
         }
-        result.to_row_vector()
+        Ok(result.to_row_vector())
     }
 
     /// Get estimates regression coefficients
@@ -444,7 +450,7 @@ mod tests {
         ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
-        let lr = LogisticRegression::fit(&x, &y);
+        let lr = LogisticRegression::fit(&x, &y).unwrap();
 
         assert_eq!(lr.coefficients().shape(), (3, 2));
         assert_eq!(lr.intercept().shape(), (3, 1));
@@ -452,7 +458,7 @@ mod tests {
         assert!((lr.coefficients().get(0, 0) - 0.0435).abs() < 1e-4);
         assert!((lr.intercept().get(0, 0) - 0.1250).abs() < 1e-4);
 
-        let y_hat = lr.predict(&x);
+        let y_hat = lr.predict(&x).unwrap();
 
         assert_eq!(
             y_hat,
@@ -481,7 +487,7 @@ mod tests {
         ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
-        let lr = LogisticRegression::fit(&x, &y);
+        let lr = LogisticRegression::fit(&x, &y).unwrap();
 
         let deserialized_lr: LogisticRegression<f64, DenseMatrix<f64>> =
             serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
@@ -517,9 +523,9 @@ 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()