add proper error handling

src/tree/decision_tree_classifier.rs

@@ -903,14 +903,14 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
     ///
     /// # Errors
     ///
-    /// Returns an error if the prediction process fails.
+    /// Returns an error if at least one row prediction process fails.
     pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
         let (n_samples, _) = x.shape();
         let n_classes = self.classes().len();
         let mut result = DenseMatrix::<f64>::zeros(n_samples, n_classes);
 
         for i in 0..n_samples {
-            let probs = self.predict_proba_for_row(x, i);
+            let probs = self.predict_proba_for_row(x, i)?;
             for (j, &prob) in probs.iter().enumerate() {
                 result.set((i, j), prob);
             }
@@ -930,15 +930,15 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
     ///
     /// A vector of probabilities, one for each class, representing the probability
     /// of the input sample belonging to each class.
-    fn predict_proba_for_row(&self, x: &X, row: usize) -> Vec<f64> {
+    fn predict_proba_for_row(&self, x: &X, row: usize) -> Result<Vec<f64>, Failed> {
         let mut node = 0;
 
         while let Some(current_node) = self.nodes().get(node) {
             if current_node.true_child.is_none() && current_node.false_child.is_none() {
                 // Leaf node reached
                 let mut probs = vec![0.0; self.classes().len()];
                 probs[current_node.output] = 1.0;
-                return probs;
+                return Ok(probs);
             }
 
             let split_feature = current_node.split_feature;
@@ -952,7 +952,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         }
 
         // This should never happen if the tree is properly constructed
-        vec![0.0; self.classes().len()]
+        Err(Failed::predict("Nodes iteration did not reach leaf"))
     }
 }