feat: + cross_validate, trait Predictor, refactoring

src/linear/logistic_regression.rs

@@ -40,7 +40,7 @@
 //!           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
 //! ];
 //!
-//! let lr = LogisticRegression::fit(&x, &y).unwrap();
+//! let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 //!
 //! let y_hat = lr.predict(&x).unwrap();
 //! ```
@@ -58,6 +58,7 @@ use std::marker::PhantomData;
 
 use serde::{Deserialize, Serialize};
 
+use crate::base::Predictor;
 use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
@@ -66,6 +67,11 @@ use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::Backtracking;
 use crate::optimization::FunctionOrder;
 
+/// Logistic Regression parameters
+#[derive(Serialize, Deserialize, Debug, Clone)]
+pub struct LogisticRegressionParameters {
+}
+
 /// Logistic Regression
 #[derive(Serialize, Deserialize, Debug)]
 pub struct LogisticRegression<T: RealNumber, M: Matrix<T>> {
@@ -97,6 +103,13 @@ struct BinaryObjectiveFunction<'a, T: RealNumber, M: Matrix<T>> {
     phantom: PhantomData<&'a T>,
 }
 
+impl Default for LogisticRegressionParameters {
+    fn default() -> Self {
+        LogisticRegressionParameters {            
+        }
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
     fn eq(&self, other: &Self) -> bool {
         if self.num_classes != other.num_classes
@@ -207,11 +220,18 @@ impl<'a, T: RealNumber, M: Matrix<T>> ObjectiveFunction<T, M>
     }
 }
 
+impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for LogisticRegression<T, M> {
+    fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
+        self.predict(x)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
     /// Fits Logistic Regression to your data.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `y` - target class values
-    pub fn fit(x: &M, y: &M::RowVector) -> Result<LogisticRegression<T, M>, Failed> {
+    /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.    
+    pub fn fit(x: &M, y: &M::RowVector, _parameters: LogisticRegressionParameters) -> Result<LogisticRegression<T, M>, Failed> {
         let y_m = M::from_row_vector(y.clone());
         let (x_nrows, num_attributes) = x.shape();
         let (_, y_nrows) = y_m.shape();
@@ -461,7 +481,7 @@ mod tests {
         ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         assert_eq!(lr.coefficients().shape(), (3, 2));
         assert_eq!(lr.intercept().shape(), (3, 1));
@@ -484,7 +504,7 @@ mod tests {
         let x = DenseMatrix::from_vec(15, 4, &blobs.data);
         let y = blobs.target;
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         let y_hat = lr.predict(&x).unwrap();
 
@@ -498,7 +518,7 @@ mod tests {
         let x = DenseMatrix::from_vec(20, 4, &blobs.data);
         let y = blobs.target;
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         let y_hat = lr.predict(&x).unwrap();
 
@@ -526,7 +546,7 @@ mod tests {
         ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         let deserialized_lr: LogisticRegression<f64, DenseMatrix<f64>> =
             serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
@@ -562,7 +582,7 @@ mod tests {
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
         ];
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         let y_hat = lr.predict(&x).unwrap();