Implement predict_proba for DecisionTreeClassifier

src/tree/decision_tree_classifier.rs

@@ -78,6 +78,8 @@ use serde::{Deserialize, Serialize};
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
+use crate::linalg::basic::matrix::DenseMatrix;
+use crate::linalg::basic::arrays::MutArray;
 use crate::numbers::basenum::Number;
 use crate::rand_custom::get_rng_impl;
 
@@ -887,12 +889,79 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         }
         importances
     }
+
+
+    /// Predict class probabilities for the input samples.
+    ///
+    /// # Arguments
+    ///
+    /// * `x` - The input samples as a matrix where each row is a sample and each column is a feature.
+    ///
+    /// # Returns
+    ///
+    /// A `Result` containing a `DenseMatrix<f64>` where each row corresponds to a sample and each column
+    /// corresponds to a class. The values represent the probability of the sample belonging to each class.
+    ///
+    /// # Errors
+    ///
+    /// Returns an error if the 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);
+            for (j, &prob) in probs.iter().enumerate() {
+                result.set((i, j), prob);
+            }
+        }
+
+        Ok(result)
+    }
+
+    /// Predict class probabilities for a single input sample.
+    ///
+    /// # Arguments
+    ///
+    /// * `x` - The input matrix containing all samples.
+    /// * `row` - The index of the row in `x` for which to predict probabilities.
+    ///
+    /// # Returns
+    ///
+    /// 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> {
+        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;
+            }
+            
+            let split_feature = current_node.split_feature;
+            let split_value = current_node.split_value.unwrap_or(f64::NAN);
+            
+            if x.get((row, split_feature)).to_f64().unwrap() <= split_value {
+                node = current_node.true_child.unwrap();
+            } else {
+                node = current_node.false_child.unwrap();
+            }
+        }
+        
+        // This should never happen if the tree is properly constructed
+        vec![0.0; self.classes().len()]
+    }
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
     use crate::linalg::basic::matrix::DenseMatrix;
+    use crate::linalg::basic::arrays::Array;
 
     #[test]
     fn search_parameters() {
@@ -934,6 +1003,47 @@ mod tests {
         );
     }
 
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn test_predict_proba() {
+        let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[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();
+        let y: Vec<usize> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
+    
+        let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
+        let probabilities = tree.predict_proba(&x).unwrap();
+    
+        assert_eq!(probabilities.shape(), (10, 2));
+    
+        for row in 0..10 {
+            let row_sum: f64 = probabilities.get_row(row).sum();
+            assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+        }
+    
+        // Check if the first 5 samples have higher probability for class 0
+        for i in 0..5 {
+            assert!(probabilities.get((i, 0)) > probabilities.get((i, 1)));
+        }
+    
+        // Check if the last 5 samples have higher probability for class 1
+        for i in 5..10 {
+            assert!(probabilities.get((i, 1)) > probabilities.get((i, 0)));
+        }
+    }
+
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test