Fix #245: return error for NaN in naive bayes

src/lib.rs

@@ -6,7 +6,6 @@
     clippy::upper_case_acronyms
 )]
 #![warn(missing_docs)]
-#![warn(rustdoc::missing_doc_code_examples)]
 
 //! # smartcore
 //!

src/naive_bayes/gaussian.rs

@@ -425,6 +425,27 @@ mod tests {
         );
     }
 
+    #[test]
+    fn run_gaussian_naive_bayes_with_few_samples() {
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[-1., -1.],
+            &[-2., -1.],
+            &[-3., -2.],
+            &[1., 1.],
+        ]);
+        let y: Vec<u32> = vec![1, 1, 1, 2];
+
+        let gnb = GaussianNB::fit(&x, &y, Default::default());
+
+        match gnb.unwrap().predict(&x) {
+            Ok(_) => assert!(false, "test should return Failed"),
+            Err(err) => {
+                assert!(err.to_string() == "Can't find solution: log_likelihood for distribution of one of the rows is NaN"); 
+                assert!(true)
+            },
+        }
+    }
+
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test

src/naive_bayes/mod.rs

@@ -35,7 +35,7 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-use crate::error::Failed;
+use crate::error::{Failed, FailedError};
 use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
 use crate::numbers::basenum::Number;
 #[cfg(feature = "serde")]
@@ -93,24 +93,36 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let y_classes = self.distribution.classes();
         let (rows, _) = x.shape();
+        let mut log_likehood_is_nan = false;
         let predictions = (0..rows)
             .map(|row_index| {
                 let row = x.get_row(row_index);
                 let (prediction, _probability) = y_classes
                     .iter()
                     .enumerate()
                     .map(|(class_index, class)| {
+                        let mut log_likelihood = self.distribution.log_likelihood(class_index, &row);
+                        if log_likelihood.is_nan() {
+                            log_likelihood = 0f64;
+                            log_likehood_is_nan = true;
+                        }
                         (
                             class,
-                            self.distribution.log_likelihood(class_index, &row)
+                            log_likelihood
                                 + self.distribution.prior(class_index).ln(),
                         )
-                    })
+                      })
                     .max_by(|(_, p1), (_, p2)| p1.partial_cmp(p2).unwrap())
                     .unwrap();
                 *prediction
             })
             .collect::<Vec<TY>>();
+        if log_likehood_is_nan {
+            return Err(Failed::because(
+                FailedError::SolutionFailed,
+                "log_likelihood for distribution of one of the rows is NaN",
+            ));
+        }
         let y_hat = Y::from_vec_slice(&predictions);
         Ok(y_hat)
     }