feat: expose hyper tuning module in model_selection

src/model_selection/hyper_tuning.rs → src/model_selection/hyper_tuning/grid_search.rs

@@ -1,3 +1,12 @@
+use crate::{
+    api::Predictor,
+    error::{Failed, FailedError},
+    linalg::Matrix,
+    math::num::RealNumber,
+};
+
+use crate::model_selection::{cross_validate, BaseKFold, CrossValidationResult};
+
 /// grid search results.
 #[derive(Clone, Debug)]
 pub struct GridSearchResult<T: RealNumber, I: Clone> {
@@ -60,58 +69,61 @@ where
 
 #[cfg(test)]
 mod tests {
-  use crate::linear::logistic_regression::{
-    LogisticRegression, LogisticRegressionSearchParameters,
-};
+    use crate::{
+        linalg::naive::dense_matrix::DenseMatrix,
+        linear::logistic_regression::{LogisticRegression, LogisticRegressionSearchParameters},
+        metrics::accuracy,
+        model_selection::{hyper_tuning::grid_search, KFold},
+    };
 
-  #[test]
-  fn test_grid_search() {
-      let x = DenseMatrix::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.,
-      ];
+    #[test]
+    fn test_grid_search() {
+        let x = DenseMatrix::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 cv = KFold {
-          n_splits: 5,
-          ..KFold::default()
-      };
+        let cv = KFold {
+            n_splits: 5,
+            ..KFold::default()
+        };
 
-      let parameters = LogisticRegressionSearchParameters {
-          alpha: vec![0., 1.],
-          ..Default::default()
-      };
+        let parameters = LogisticRegressionSearchParameters {
+            alpha: vec![0., 1.],
+            ..Default::default()
+        };
 
-      let results = grid_search(
-          LogisticRegression::fit,
-          &x,
-          &y,
-          parameters.into_iter(),
-          cv,
-          &accuracy,
-      )
-      .unwrap();
+        let results = grid_search(
+            LogisticRegression::fit,
+            &x,
+            &y,
+            parameters.into_iter(),
+            cv,
+            &accuracy,
+        )
+        .unwrap();
 
-      assert!([0., 1.].contains(&results.parameters.alpha));
-  }
+        assert!([0., 1.].contains(&results.parameters.alpha));
+    }
 }

src/model_selection/hyper_tuning/mod.rs

@@ -0,0 +1,2 @@
+mod grid_search;
+pub use grid_search::{grid_search, GridSearchResult};

src/model_selection/mod.rs

@@ -110,8 +110,10 @@ use crate::math::num::RealNumber;
 use crate::rand::get_rng_impl;
 use rand::seq::SliceRandom;
 
+pub(crate) mod hyper_tuning;
 pub(crate) mod kfold;
 
+pub use hyper_tuning::{grid_search, GridSearchResult};
 pub use kfold::{KFold, KFoldIter};
 
 /// An interface for the K-Folds cross-validator