two encounters of a bad pattern is_none() + unwrap(). FIXED.

Author: skywardfire1

src/ensemble/base_forest_regressor.rs

@@ -161,25 +161,31 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
     /// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training.
     pub fn predict_oob(&self, x: &X) -> Result<Y, Failed> {
         let (n, _) = x.shape();
-        if self.samples.is_none() {
-            Err(Failed::because(
-                FailedError::PredictFailed,
-                "Need samples=true for OOB predictions.",
-            ))
-        } else if self.samples.as_ref().unwrap()[0].len() != n {
-            Err(Failed::because(
+
+        let samples = match &self.samples {
+            Some(s) => s,
+            None => {
+                return Err(Failed::because(
+                    FailedError::PredictFailed,
+                    "Need samples=true for OOB predictions.",
+                ))
+            }
+        };
+
+        if samples[0].len() != n {
+            return Err(Failed::because(
                 FailedError::PredictFailed,
                 "Prediction matrix must match matrix used in training for OOB predictions.",
-            ))
-        } else {
-            let mut result = Y::zeros(n);
+            ));
+        }
 
-            for i in 0..n {
-                result.set(i, self.predict_for_row_oob(x, i));
-            }
+        let mut result = Y::zeros(n);
 
-            Ok(result)
+        for i in 0..n {
+            result.set(i, self.predict_for_row_oob(x, i));
         }
+
+        Ok(result)
     }
 
     fn predict_for_row_oob(&self, x: &X, row: usize) -> TY {

src/ensemble/random_forest_classifier.rs

@@ -539,27 +539,34 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
     /// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training.
     pub fn predict_oob(&self, x: &X) -> Result<Y, Failed> {
         let (n, _) = x.shape();
-        if self.samples.is_none() {
-            Err(Failed::because(
-                FailedError::PredictFailed,
-                "Need samples=true for OOB predictions.",
-            ))
-        } else if self.samples.as_ref().unwrap()[0].len() != n {
-            Err(Failed::because(
+
+        let samples = match &self.samples {
+            Some(s) => s,
+            None => {
+                return Err(Failed::because(
+                    FailedError::PredictFailed,
+                    "Need samples=true for OOB predictions.",
+                ));
+            }
+        };
+
+        if samples[0].len() != n {
+            return Err(Failed::because(
                 FailedError::PredictFailed,
                 "Prediction matrix must match matrix used in training for OOB predictions.",
-            ))
-        } else {
-            let mut result = Y::zeros(n);
+            ));
+        }
 
-            for i in 0..n {
-                result.set(
-                    i,
-                    self.classes.as_ref().unwrap()[self.predict_for_row_oob(x, i)],
-                );
-            }
-            Ok(result)
+        let mut result = Y::zeros(n);
+
+        for i in 0..n {
+            result.set(
+                i,
+                self.classes.as_ref().unwrap()[self.predict_for_row_oob(x, i)],
+            );
         }
+
+        Ok(result)
     }
 
     fn predict_for_row_oob(&self, x: &X, row: usize) -> usize {