Add regression task and parent_has_finite_values test

examples/anomaly_detection/credit_card.rs

@@ -1,6 +1,6 @@
 use light_river::anomaly::half_space_tree::HalfSpaceTree;
 use light_river::common::ClassifierOutput;
-use light_river::common::ClassifierTarget;
+use light_river::common::ClfTarget;
 use light_river::datasets::credit_card::CreditCard;
 use light_river::metrics::rocauc::ROCAUC;
 use light_river::metrics::traits::ClassificationMetric;
@@ -16,7 +16,7 @@ fn main() {
     let window_size: u32 = 1000;
     let n_trees: u32 = 50;
     let height: u32 = 6;
-    let pos_val_metric = ClassifierTarget::from("1".to_string());
+    let pos_val_metric = ClfTarget::from("1".to_string());
     let pos_val_tree = pos_val_metric.clone();
     let mut roc_auc: ROCAUC<f32> = ROCAUC::new(Some(10), pos_val_metric.clone());
     // INITIALIZATION

examples/classification/keystroke.rs

@@ -1,4 +1,4 @@
-use light_river::common::ClassifierTarget;
+use light_river::common::ClfTarget;
 use light_river::datasets::keystroke::Keystroke;
 use light_river::mondrian_forest::mondrian_forest::{MondrianForest, MondrianForestClassifier};
 
@@ -24,7 +24,7 @@ fn get_labels(transactions: IterCsv<f32, File>) -> Vec<String> {
     let mut labels = vec![];
     for t in transactions {
         let data = t.unwrap();
-        // TODO: use instead 'to_classifier_target' and a vector of 'ClassifierTarget'
+        // TODO: use instead 'to_classifier_target' and a vector of 'ClfTarget'
         let target = data.get_y().unwrap()["subject"].to_string();
         if !labels.contains(&target) {
             labels.push(target);
@@ -59,10 +59,10 @@ fn main() {
 
         let x = data.get_observation();
         let y = data.to_classifier_target("subject").unwrap();
-        // TODO: generalize to non-classification only by implementing 'ClassifierTarget'
+        // TODO: generalize to non-classification only by implementing 'ClfTarget'
         // instead of taking directly the string.
         let y = match y {
-            ClassifierTarget::String(y) => y,
+            ClfTarget::String(y) => y,
             _ => unimplemented!(),
         };
         let y = labels.clone().iter().position(|l| l == &y).unwrap();

examples/classification/synthetic.rs

@@ -1,6 +1,6 @@
 use light_river::mondrian_forest::mondrian_forest::MondrianForestClassifier;
 
-use light_river::common::{Classifier, ClassifierTarget};
+use light_river::common::{Classifier, ClfTarget};
 use light_river::datasets::synthetic::Synthetic;
 use light_river::stream::iter_csv::IterCsv;
 use ndarray::Array1;
@@ -24,7 +24,7 @@ fn get_labels(transactions: IterCsv<f32, File>) -> Vec<String> {
     let mut labels = vec![];
     for t in transactions {
         let data = t.unwrap();
-        // TODO: use instead 'to_classifier_target' and a vector of 'ClassifierTarget'
+        // TODO: use instead 'to_classifier_target' and a vector of 'ClfTarget'
         let target = data.get_y().unwrap()["label"].to_string();
         if !labels.contains(&target) {
             labels.push(target);
@@ -68,11 +68,11 @@ fn main() {
 
         let y = data.to_classifier_target("label").unwrap();
         let y = match y {
-            ClassifierTarget::String(y) => y,
+            ClfTarget::String(y) => y,
             _ => unimplemented!(),
         };
         let y = labels.clone().iter().position(|l| l == &y).unwrap();
-        let y = ClassifierTarget::from(y);
+        let y = ClfTarget::from(y);
         // println!("=M=1 x:{}, idx: {}", x, idx);
 
         // Skip first sample since tree has still no node
@@ -87,7 +87,7 @@ fn main() {
             );
         }
 
-        // if idx == 527 {
+        // if idx == 4 {
         //     break;
         // }
 

examples/regression/synthetic_regression.rs

@@ -1,6 +1,6 @@
 use light_river::mondrian_forest::mondrian_forest::MondrianForestRegressor;
 
-use light_river::common::{Regressor, RegressorTarget};
+use light_river::common::{RegTarget, Regressor};
 use light_river::datasets::synthetic_regression::SyntheticRegression;
 use light_river::stream::iter_csv::IterCsv;
 use ndarray::Array1;
@@ -52,18 +52,13 @@ fn main() {
 
         let y = data.to_regression_target("label").unwrap();
 
-        // println!("=M=1 x:{}, idx: {}", x, idx);
+        println!("=M=1 idx={idx}, x={x}, y={y}");
 
         // Skip first sample since tree has still no node
         if idx != 0 {
-            let score = mf.predict_one(&x, &y);
-            score_total += score;
-            // println!(
-            //     "Accuracy: {} / {} = {}",
-            //     score_total,
-            //     dataset_size - 1,
-            //     score_total / idx.to_f32().unwrap()
-            // );
+            let pred = mf.predict_one(&x, &y);
+            let err = (pred - y).powi(2);
+            println!("pred: {pred}, y: {y}, err: {err}");
         }
 
         // if idx == 527 {

src/anomaly/half_space_tree.rs

@@ -8,7 +8,7 @@ use std::convert::TryFrom;
 use std::mem;
 use std::ops::{AddAssign, DivAssign, MulAssign, SubAssign};
 
-use crate::common::{ClassifierOutput, ClassifierTarget, Observation};
+use crate::common::{ClassifierOutput, ClfTarget, Observation};
 
 // Return the index of a node's left child node.
 #[inline]
@@ -97,15 +97,15 @@ pub struct HalfSpaceTree<F: Float + FromPrimitive + AddAssign + SubAssign + MulA
     n_nodes: u32,
     trees: Option<Trees<F>>,
     first_learn: bool,
-    pos_val: Option<ClassifierTarget>,
+    pos_val: Option<ClfTarget>,
 }
 impl<F: Float + FromPrimitive + AddAssign + SubAssign + MulAssign + DivAssign> HalfSpaceTree<F> {
     pub fn new(
         window_size: u32,
         n_trees: u32,
         height: u32,
         features: Option<Vec<String>>,
-        pos_val: Option<ClassifierTarget>,
+        pos_val: Option<ClfTarget>,
         // rng: ThreadRng,
     ) -> Self {
         // let mut rng = rand::thread_rng();
@@ -220,9 +220,7 @@ impl<F: Float + FromPrimitive + AddAssign + SubAssign + MulAssign + DivAssign> H
             score = F::one() - (score / self.max_score());
 
             return Some(ClassifierOutput::Probabilities(HashMap::from([(
-                ClassifierTarget::from(
-                    self.pos_val.clone().unwrap_or(ClassifierTarget::from(true)),
-                ),
+                ClfTarget::from(self.pos_val.clone().unwrap_or(ClfTarget::from(true))),
                 score,
             )])));
             // return Some(score);
@@ -262,7 +260,7 @@ mod tests {
             n_trees,
             height,
             None,
-            Some(ClassifierTarget::from("1".to_string())),
+            Some(ClfTarget::from("1".to_string())),
         );
 
         // LOOP