Merge branch 'development' into predict-probability

src/ensemble/random_forest_classifier.rs

@@ -317,7 +317,8 @@ 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
+    /// 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());
 
@@ -326,8 +327,8 @@ impl<T: RealNumber> RandomForestClassifier<T> {
         for i in 0..n {
             let row_probs = self.predict_probs_for_row(x, i);
 
-            for j in 0..row_probs.len() {
+            for (j, item) in row_probs.iter().enumerate() {
-                result.set(i, j, row_probs[j]);
+                result.set(i, j, *item);
             }
         }
 
@@ -372,7 +373,7 @@ impl<T: RealNumber> RandomForestClassifier<T> {
 }
 
 #[cfg(test)]
-mod tests {
+mod tests_prob {
     use super::*;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
     use crate::metrics::*;
@@ -513,4 +514,71 @@ mod tests {
 
         assert_eq!(forest, deserialized_forest);
     }
+
+    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
+    #[test]
+    fn fit_predict_probabilities() {
+        let x = DenseMatrix::<f64>::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],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[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],
+            &[5.7, 2.8, 4.5, 1.3],
+            &[6.3, 3.3, 4.7, 1.6],
+            &[4.9, 2.4, 3.3, 1.0],
+            &[6.6, 2.9, 4.6, 1.3],
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
+        let y = vec![
+            0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+        ];
+
+        let classifier = RandomForestClassifier::fit(
+            &x,
+            &y,
+            RandomForestClassifierParameters {
+                criterion: SplitCriterion::Gini,
+                max_depth: None,
+                min_samples_leaf: 1,
+                min_samples_split: 2,
+                n_trees: 100,
+                m: Option::None,
+                keep_samples: false,
+                seed: 87,
+            },
+        )
+        .unwrap();
+
+        println!("{:?}", classifier.classes);
+
+        let results = classifier.predict_probs(&x).unwrap();
+        println!("{:?}", x.shape());
+        println!("{:?}", results);
+        println!("{:?}", results.shape());
+
+        assert_eq!(
+            results,
+            DenseMatrix::<f64>::from_array(
+                20,
+                2,
+                &[
+                    1.0, 0.78, 0.95, 0.82, 1.0, 0.92, 0.99, 0.96, 0.36, 0.33, 0.02, 0.02, 0.0, 0.0,
+                    0.0, 0.0, 0.03, 0.05, 0.0, 0.02, 0.0, 0.22, 0.05, 0.18, 0.0, 0.08, 0.01, 0.04,
+                    0.64, 0.67, 0.98, 0.98, 1.0, 1.0, 1.0, 1.0, 0.97, 0.95, 1.0, 0.98
+                ]
+            )
+        );
+        assert!(false);
+    }
 }