fix: fixes a bug in decision_tree_regressor

2020-03-26 16:21:20 -07:00
parent 02b85415d9
commit 4d967858a5
3 changed files with 80 additions and 5 deletions
@@ -104,6 +104,7 @@ impl<T: FloatExt + Debug> RandomForestRegressor<T> {
 mod tests {
    use super::*; 
    use crate::linalg::naive::dense_matrix::DenseMatrix;
+    use ndarray::{arr1, arr2};    

    #[test]
    fn fit_longley() {             
@@ -142,4 +143,45 @@ mod tests {
            
    }

+    #[test]
+    fn my_fit_longley1() {             
+
+        let x = arr2(&[
+            [ 234.289,  235.6,  159.,  107.608, 1947.,   60.323],
+            [ 259.426,  232.5,  145.6,  108.632, 1948.,   61.122],
+            [ 258.054,  368.2,  161.6,  109.773, 1949.,   60.171],
+            [ 284.599,  335.1,  165.,  110.929, 1950.,   61.187],
+            [ 328.975,  209.9,  309.9,  112.075, 1951.,   63.221],
+            [ 346.999,  193.2,  359.4,  113.27 , 1952.,   63.639],
+            [ 365.385,  187.,  354.7,  115.094, 1953.,   64.989],
+            [ 363.112,  357.8,  335.,  116.219, 1954.,   63.761],
+            [ 397.469,  290.4,  304.8,  117.388, 1955.,   66.019],
+            [ 419.18 ,  282.2,  285.7,  118.734, 1956.,   67.857],
+            [ 442.769,  293.6,  279.8,  120.445, 1957.,   68.169],
+            [ 444.546,  468.1,  263.7,  121.95 , 1958.,   66.513],
+            [ 482.704,  381.3,  255.2,  123.366, 1959.,   68.655],
+            [ 502.601,  393.1,  251.4,  125.368, 1960.,   69.564],
+            [ 518.173,  480.6,  257.2,  127.852, 1961.,   69.331],
+            [ 554.894,  400.7,  282.7,  130.081, 1962.,   70.551]]);
+        let y = arr1(&[83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]); 
+        
+        println!("{:?}", y.shape());
+
+        let expected_y: Vec<f64> = vec![85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.];
+
+        let y_hat = RandomForestRegressor::fit(&x, &y, 
+            RandomForestRegressorParameters{max_depth: None,
+                min_samples_leaf: 1,
+                min_samples_split: 2,
+                n_trees: 1000,
+                mtry: Option::None}).predict(&x);    
+                
+        println!("{:?}", y_hat);
+
+        for i in 0..y_hat.len() {
+            assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
+        }
+            
+    }
+
 }