Fixed execution for testcase for regressor

rust-ml · May 7, 2024 · ef04738 · ef04738
1 parent fa83100
commit ef04738
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diff --git a/algorithms/linfa-ensemble/Cargo.toml b/algorithms/linfa-ensemble/Cargo.toml
@@ -26,7 +26,7 @@ features = ["std", "derive"]
 linfa = { version = "0.7.0", path = "../.." }
 linfa-trees = { version = "0.7.0", path = "../linfa-trees"}
 serde = { version = "1.0", features = ["derive","std"] }
-linfa-datasets = { version = "0.7.0", path = "../../datasets/", features = ["iris", "mnist", "boston"] }
+linfa-datasets = { version = "0.7.0", path = "../../datasets/", features = ["iris", "mnist", "boston", "diabetes"] }
 ndarray = { version = "0.15" , features = ["rayon", "approx"]}
 ndarray-rand = "0.14"
 rand = { version = "0.8", features = ["small_rng"] }
@@ -39,7 +39,7 @@ csv = "1.1"
 
 [dev-dependencies]
 rand = { version = "0.8", features = ["small_rng"] }
-linfa-datasets = { version = "0.7.0", path = "../../datasets/", features = ["iris", "mnist", "boston"] }
+linfa-datasets = { version = "0.7.0", path = "../../datasets/", features = ["iris", "mnist", "boston", "diabetes"] }
 rayon = {version = "1.10.0"}
 approx = {version = "0.5"}
 

diff --git a/algorithms/linfa-ensemble/examples/adaboost_regressor.rs b/algorithms/linfa-ensemble/examples/adaboost_regressor.rs
@@ -1,74 +1,72 @@
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use ndarray::{Array2, Array1, s};
-    use ndarray_csv::Array2Reader;
-    use std::fs::File;
-    use rand::rngs::StdRng;
-    use rand::SeedableRng;
-    use csv::ReaderBuilder;
-    use linfa_ensemble::AdaBoostRegressor;
-    use linfa_datasets::{boston, diabetes};
-
-    #[test]
-    fn test_adaboost_with_boston_housing() {
-        // Load the dataset
-        let dataset = boston();  // dataset now contains both features and targets
-
-        // Parameters for AdaBoost
-        let n_estimators = 50;
-        let learning_rate = 1.0;
-        let max_depth = 4;
-        let min_samples_split = 10;
-        let random_state = 42; // Random state for reproducibility
-
-        // Create AdaBoostRegressor instance
-        let mut regressor = AdaBoostRegressor::new(n_estimators, learning_rate, random_state, max_depth, min_samples_split);
-
-        // Fit the regressor to the Boston Housing dataset
-        regressor.fit(dataset.records(), dataset.targets());
-
-        // Make predictions
-        let predictions = regressor.predict(dataset.records());
-
-        // Calculate Mean Squared Error
-        let mse = (dataset.targets() - &predictions).mapv(|a| a.powi(2)).mean().unwrap_or(0.0);  // Calculate Mean Squared Error
-        let rmse = mse.sqrt();  // Calculate Root Mean Squared Error
-        println!("Root Mean Squared Error: {}", rmse);
-
-        // Assert to check if RMSE is below a threshold
-        assert!(rmse < 25.0, "The RMSE should be lower than 25.0, but it was {}", rmse);
-    }
-
-    #[test]
-    fn test_adaboost_with_diabetes() {
-        // Load the dataset
-        let dataset = diabetes();
-
-        // Parameters for AdaBoost
-        let n_estimators = 100;  
-        let learning_rate = 0.5;
-        let max_depth = 3;        
-        let min_samples_split = 5; 
-        let random_state = 42;  
-
-        // Create AdaBoostRegressor instance
-        let mut regressor = AdaBoostRegressor::new(n_estimators, learning_rate, random_state, max_depth, min_samples_split);
-
-        // Fit the regressor to the Diabetes dataset
-        regressor.fit(dataset.records(), dataset.targets());
-
-        // Make predictions
-        let predictions = regressor.predict(dataset.records());
-
-        // Calculate Mean Squared Error
-        let mse = (dataset.targets() - &predictions).mapv(|a| a.powi(2)).mean().unwrap_or(0.0);  // Calculate Mean Squared Error
-        let rmse = mse.sqrt();  // Calculate Root Mean Squared Error
-        println!("Root Mean Squared Error: {}", rmse);
-
-        // Assert to check if RMSE is below a threshold
-        assert!(rmse < 200.0, "The RMSE should be lower than 200.0, but it was {}", rmse);
-    }
+use ndarray::{Array2, Array1, s};
+use ndarray_csv::Array2Reader;
+use std::fs::File;
+use rand::rngs::StdRng;
+use rand::SeedableRng;
+use csv::ReaderBuilder;
+use linfa_ensemble::AdaBoostRegressor;
+use linfa_datasets::{boston, diabetes};
+
+pub fn test_adaboost_with_boston_housing() {
+    // Load the dataset
+    let dataset = boston();  // dataset now contains both features and targets
+
+    // Parameters for AdaBoost
+    let n_estimators = 50;
+    let learning_rate = 1.0;
+    let max_depth = 4;
+    let min_samples_split = 10;
+    let random_state = 42; // Random state for reproducibility
+
+    // Create AdaBoostRegressor instance
+    let mut regressor = AdaBoostRegressor::new(n_estimators, learning_rate, random_state, max_depth, min_samples_split);
+
+    // Fit the regressor to the Boston Housing dataset
+    regressor.fit(dataset.records(), dataset.targets());
+
+    // Make predictions
+    let predictions = regressor.predict(dataset.records());
+
+    // Calculate Mean Squared Error
+    let mse = (dataset.targets() - &predictions).mapv(|a| a.powi(2)).mean().unwrap_or(0.0);  // Calculate Mean Squared Error
+    let rmse = mse.sqrt();  // Calculate Root Mean Squared Error
+    println!("Root Mean Squared Error for Boston Housing Dataset: {}", rmse);
+
+    // Assert to check if RMSE is below a threshold
+    assert!(rmse < 25.0, "The RMSE should be lower than 25.0, but it was {}", rmse);
+}
+
+pub fn test_adaboost_with_diabetes() {
+    // Load the dataset
+    let dataset = diabetes();
+
+    // Parameters for AdaBoost
+    let n_estimators = 100;  
+    let learning_rate = 0.5;
+    let max_depth = 3;        
+    let min_samples_split = 5; 
+    let random_state = 42;  
+
+    // Create AdaBoostRegressor instance
+    let mut regressor = AdaBoostRegressor::new(n_estimators, learning_rate, random_state, max_depth, min_samples_split);
 
+    // Fit the regressor to the Diabetes dataset
+    regressor.fit(dataset.records(), dataset.targets());
 
+    // Make predictions
+    let predictions = regressor.predict(dataset.records());
+
+    // Calculate Mean Squared Error
+    let mse = (dataset.targets() - &predictions).mapv(|a| a.powi(2)).mean().unwrap_or(0.0);  // Calculate Mean Squared Error
+    let rmse = mse.sqrt();  // Calculate Root Mean Squared Error
+    println!("Root Mean Squared Error for diabetes: {}", rmse);
+
+    // Assert to check if RMSE is below a threshold
+    assert!(rmse < 200.0, "The RMSE should be lower than 200.0, but it was {}", rmse);
 }
+
+
+fn main(){
+    test_adaboost_with_boston_housing();
+    test_adaboost_with_diabetes();
+}