Added per-class probability prediction for random forests

src/ensemble/random_forest_classifier.rs

@@ -55,7 +55,8 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::{Failed, FailedError};
-use crate::linalg::Matrix;
+use crate::linalg::naive::dense_matrix::DenseMatrix;
+use crate::linalg::{BaseMatrix, Matrix};
 use crate::math::num::RealNumber;
 use crate::tree::decision_tree_classifier::{
     which_max, DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion,
@@ -316,6 +317,36 @@ impl<T: RealNumber> RandomForestClassifier<T> {
         which_max(&result)
     }
 
+    /// Predict the per-class probabilties for each observation. The probability is calculated as the fraction of trees that predicted a given class
+    pub fn predict_probs<M: Matrix<T>>(&self, x: &M) -> Result<DenseMatrix<f64>, Failed> {
+        let mut result = DenseMatrix::<f64>::zeros(x.shape().0, self.classes.len());
+
+        let (n, _) = x.shape();
+
+        for i in 0..n {
+            let row_probs = self.predict_probs_for_row(x, i);
+
+            for j in 0..row_probs.len() {
+                result.set(i, j, row_probs[j]);
+            }
+        }
+
+        Ok(result)
+    }
+
+    fn predict_probs_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> Vec<f64> {
+        let mut result = vec![0; self.classes.len()];
+
+        for tree in self.trees.iter() {
+            result[tree.predict_for_row(x, row)] += 1;
+        }
+
+        result
+            .iter()
+            .map(|n| *n as f64 / self.trees.len() as f64)
+            .collect()
+    }
+
     fn sample_with_replacement(y: &[usize], num_classes: usize, rng: &mut impl Rng) -> Vec<usize> {
         let class_weight = vec![1.; num_classes];
         let nrows = y.len();