In Naive Bayes, avoid using Option::unwrap and so avoid panicking from NaN values (#274)

src/model_selection/hyper_tuning/grid_search.rs

@@ -3,9 +3,9 @@
 use crate::{
     api::{Predictor, SupervisedEstimator},
     error::{Failed, FailedError},
-    linalg::basic::arrays::{Array2, Array1},
-    numbers::realnum::RealNumber,
+    linalg::basic::arrays::{Array1, Array2},
     numbers::basenum::Number,
+    numbers::realnum::RealNumber,
 };
 
 use crate::model_selection::{cross_validate, BaseKFold, CrossValidationResult};

src/naive_bayes/mod.rs

@@ -40,7 +40,7 @@ use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
 use crate::numbers::basenum::Number;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
-use std::marker::PhantomData;
+use std::{cmp::Ordering, marker::PhantomData};
 
 /// Distribution used in the Naive Bayes classifier.
 pub(crate) trait NBDistribution<X: Number, Y: Number>: Clone {
@@ -92,11 +92,10 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let y_classes = self.distribution.classes();
-        let (rows, _) = x.shape();
-        let predictions = (0..rows)
-            .map(|row_index| {
-                let row = x.get_row(row_index);
-                let (prediction, _probability) = y_classes
+        let predictions = x
+            .row_iter()
+            .map(|row| {
+                y_classes
                     .iter()
                     .enumerate()
                     .map(|(class_index, class)| {
@@ -106,11 +105,26 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
                                 + self.distribution.prior(class_index).ln(),
                         )
                     })
-                    .max_by(|(_, p1), (_, p2)| p1.partial_cmp(p2).unwrap())
-                    .unwrap();
-                *prediction
+                    // For some reason, the max_by method cannot use NaNs for finding the maximum value, it panics.
+                    // NaN must be considered as minimum values,
+                    // therefore it's like NaNs would not be considered for choosing the maximum value.
+                    // So we need to handle this case for avoiding panicking by using `Option::unwrap`.
+                    .max_by(|(_, p1), (_, p2)| match p1.partial_cmp(p2) {
+                        Some(ordering) => ordering,
+                        None => {
+                            if p1.is_nan() {
+                                Ordering::Less
+                            } else if p2.is_nan() {
+                                Ordering::Greater
+                            } else {
+                                Ordering::Equal
+                            }
+                        }
                     })
-            .collect::<Vec<TY>>();
+                    .map(|(prediction, _probability)| *prediction)
+                    .ok_or_else(|| Failed::predict("Failed to predict, there is no result"))
+            })
+            .collect::<Result<Vec<TY>, Failed>>()?;
         let y_hat = Y::from_vec_slice(&predictions);
         Ok(y_hat)
     }
@@ -119,3 +133,63 @@ pub mod bernoulli;
 pub mod categorical;
 pub mod gaussian;
 pub mod multinomial;
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::linalg::basic::arrays::Array;
+    use crate::linalg::basic::matrix::DenseMatrix;
+    use num_traits::float::Float;
+
+    type Model<'d> = BaseNaiveBayes<i32, i32, DenseMatrix<i32>, Vec<i32>, TestDistribution<'d>>;
+
+    #[derive(Debug, PartialEq, Clone)]
+    struct TestDistribution<'d>(&'d Vec<i32>);
+
+    impl<'d> NBDistribution<i32, i32> for TestDistribution<'d> {
+        fn prior(&self, _class_index: usize) -> f64 {
+            1.
+        }
+
+        fn log_likelihood<'a>(
+            &'a self,
+            class_index: usize,
+            _j: &'a Box<dyn ArrayView1<i32> + 'a>,
+        ) -> f64 {
+            match self.0.get(class_index) {
+                &v @ 2 | &v @ 10 | &v @ 20 => v as f64,
+                _ => f64::nan(),
+            }
+        }
+
+        fn classes(&self) -> &Vec<i32> {
+            &self.0
+        }
+    }
+
+    #[test]
+    fn test_predict() {
+        let matrix = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]);
+
+        let val = vec![];
+        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
+            Ok(_) => panic!("Should return error in case of empty classes"),
+            Err(err) => assert_eq!(
+                err.to_string(),
+                "Predict failed: Failed to predict, there is no result"
+            ),
+        }
+
+        let val = vec![1, 2, 3];
+        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
+            Ok(r) => assert_eq!(r, vec![2, 2, 2]),
+            Err(_) => panic!("Should success in normal case with NaNs"),
+        }
+
+        let val = vec![20, 2, 10];
+        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
+            Ok(r) => assert_eq!(r, vec![20, 20, 20]),
+            Err(_) => panic!("Should success in normal case without NaNs"),
+        }
+    }
+}