format

src/ensemble/random_forest_classifier.rs

@@ -55,11 +55,11 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::{Failed, FailedError};
+use crate::linalg::basic::arrays::MutArray;
 use crate::linalg::basic::arrays::{Array1, Array2};
+use crate::linalg::basic::matrix::DenseMatrix;
 use crate::numbers::basenum::Number;
 use crate::numbers::floatnum::FloatNumber;
-use crate::linalg::basic::matrix::DenseMatrix;
-use crate::linalg::basic::arrays::MutArray;
 
 use crate::rand_custom::get_rng_impl;
 use crate::tree::decision_tree_classifier::{
@@ -667,16 +667,15 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
 
         Ok(probas)
     }
-
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::linalg::basic::matrix::DenseMatrix;
-    use crate::metrics::*;
     use crate::ensemble::random_forest_classifier::RandomForestClassifier;
     use crate::linalg::basic::arrays::Array;
+    use crate::linalg::basic::matrix::DenseMatrix;
+    use crate::metrics::*;
 
     #[test]
     fn search_parameters() {
@@ -846,7 +845,8 @@ mod tests {
             &[6.9, 3.1, 4.9, 1.5],
             &[5.5, 2.3, 4.0, 1.3],
             &[6.5, 2.8, 4.6, 1.5],
-        ]).unwrap();
+        ])
+        .unwrap();
         let y: Vec<u32> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
 
         let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
@@ -858,36 +858,64 @@ mod tests {
         // Test probability sum
         for i in 0..10 {
             let row_sum: f64 = probas.get_row(i).sum();
-            assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+            assert!(
+                (row_sum - 1.0).abs() < 1e-6,
+                "Row probabilities should sum to 1"
+            );
         }
 
         // Test class prediction
         let predictions: Vec<u32> = (0..10)
-            .map(|i| if probas.get((i, 0)) > probas.get((i, 1)) { 0 } else { 1 })
+            .map(|i| {
+                if probas.get((i, 0)) > probas.get((i, 1)) {
+                    0
+                } else {
+                    1
+                }
+            })
             .collect();
         let acc = accuracy(&y, &predictions);
         assert!(acc > 0.8, "Accuracy should be high for the training set");
 
         // Test probability values
         // These values are approximate and based on typical random forest behavior
         for i in 0..5 {
-            assert!(*probas.get((i, 0)) > 0.6, "Class 0 samples should have high probability for class 0");
-            assert!(*probas.get((i, 1)) < 0.4, "Class 0 samples should have low probability for class 1");
+            assert!(
+                *probas.get((i, 0)) > 0.6,
+                "Class 0 samples should have high probability for class 0"
+            );
+            assert!(
+                *probas.get((i, 1)) < 0.4,
+                "Class 0 samples should have low probability for class 1"
+            );
         }
         for i in 5..10 {
-            assert!(*probas.get((i, 1)) > 0.6, "Class 1 samples should have high probability for class 1");
-            assert!(*probas.get((i, 0)) < 0.4, "Class 1 samples should have low probability for class 0");
+            assert!(
+                *probas.get((i, 1)) > 0.6,
+                "Class 1 samples should have high probability for class 1"
+            );
+            assert!(
+                *probas.get((i, 0)) < 0.4,
+                "Class 1 samples should have low probability for class 0"
+            );
         }
 
         // Test with new data
         let x_new = DenseMatrix::from_2d_array(&[
-            &[5.0, 3.4, 1.5, 0.2],  // Should be close to class 0
-            &[6.3, 3.3, 4.7, 1.6],  // Should be close to class 1
-        ]).unwrap();
+            &[5.0, 3.4, 1.5, 0.2], // Should be close to class 0
+            &[6.3, 3.3, 4.7, 1.6], // Should be close to class 1
+        ])
+        .unwrap();
         let probas_new = forest.predict_proba(&x_new).unwrap();
         assert_eq!(probas_new.shape(), (2, 2));
-        assert!(probas_new.get((0, 0)) > probas_new.get((0, 1)), "First sample should be predicted as class 0");
-        assert!(probas_new.get((1, 1)) > probas_new.get((1, 0)), "Second sample should be predicted as class 1");
+        assert!(
+            probas_new.get((0, 0)) > probas_new.get((0, 1)),
+            "First sample should be predicted as class 0"
+        );
+        assert!(
+            probas_new.get((1, 1)) > probas_new.get((1, 0)),
+            "Second sample should be predicted as class 1"
+        );
     }
 
     #[cfg_attr(