From 2f03c1d6d74834d5bad990a5fd9c7cd7962fa351 Mon Sep 17 00:00:00 2001
From: gaxler <g.axler@gmail.com>
Date: Sat, 30 Jan 2021 19:54:42 -0800
Subject: [PATCH] module name change

---
 ...cal_encoders.rs => categorical_encoder.rs} | 49 ++++++++++++++-----
 1 file changed, 37 insertions(+), 12 deletions(-)
 rename src/preprocessing/{categorical_encoders.rs => categorical_encoder.rs} (89%)

diff --git a/src/preprocessing/categorical_encoders.rs b/src/preprocessing/categorical_encoder.rs
similarity index 89%
rename from src/preprocessing/categorical_encoders.rs
rename to src/preprocessing/categorical_encoder.rs
index 063aa5c..22cd052 100644
--- a/src/preprocessing/categorical_encoders.rs
+++ b/src/preprocessing/categorical_encoder.rs
@@ -38,7 +38,7 @@ pub struct OneHotEncoderParams {
     /// Column number that contain categorical variable
     pub col_idx_categorical: Option<Vec<usize>>,
     /// (Currently not implemented) Try and infer which of the matrix columns are categorical variables
-    pub infer_categorical: bool,
+    infer_categorical: bool,
 }
 
 impl OneHotEncoderParams {
@@ -86,14 +86,17 @@ fn find_new_idxs(num_params: usize, cat_sizes: &[usize], encoded_idxs: &[usize])
     new_param_idxs
 }
 
-fn validate_col_is_categorical<T:  Categorizable>(data: &Vec<T>) -> bool {
+fn validate_col_is_categorical<T: Categorizable>(data: &[T]) -> bool {
     for v in data {
-        if !v.is_valid() { return false}
+        if !v.is_valid() {
+            return false;
+        }
     }
     true
 }
 
 /// Encode Categorical variavbles of data matrix to one-hot
+#[derive(Debug, Clone)]
 pub struct OneHotEncoder {
     series_encoders: Vec<SeriesOneHotEncoder<CategoricalFloat>>,
     col_idx_categorical: Vec<usize>,
@@ -102,7 +105,7 @@ pub struct OneHotEncoder {
 impl OneHotEncoder {
     /// PlaceHolder
 
-    pub fn fit<T:  Categorizable, M: Matrix<T>>(
+    pub fn fit<T: Categorizable, M: Matrix<T>>(
         data: &M,
         params: OneHotEncoderParams,
     ) -> Result<OneHotEncoder, Failed> {
@@ -117,20 +120,24 @@ impl OneHotEncoder {
 
             (Some(mut idxs), false) => {
                 // make sure categories have same order as data columns
-                idxs.sort();
+                idxs.sort_unstable();
 
                 let (nrows, _) = data.shape();
 
                 // col buffer to avoid allocations
                 let mut col_buf: Vec<T> = iter::repeat(T::zero()).take(nrows).collect();
-        
-                let mut res: Vec<SeriesOneHotEncoder<CategoricalFloat>> = Vec::with_capacity(idxs.len());
-                
+
+                let mut res: Vec<SeriesOneHotEncoder<CategoricalFloat>> =
+                    Vec::with_capacity(idxs.len());
+
                 for &idx in &idxs {
                     data.copy_col_as_vec(idx, &mut col_buf);
                     if !validate_col_is_categorical(&col_buf) {
-                        let msg = format!("Column {} of data matrix containts non categorizable (integer) values", idx);
-                        return Err(Failed::fit(&msg[..]))
+                        let msg = format!(
+                            "Column {} of data matrix containts non categorizable (integer) values",
+                            idx
+                        );
+                        return Err(Failed::fit(&msg[..]));
                     }
                     let hashable_col = col_buf.iter().map(|v| v.to_category());
                     res.push(SeriesOneHotEncoder::fit_to_iter(hashable_col));
@@ -149,7 +156,7 @@ impl OneHotEncoder {
     }
 
     /// Transform categorical variables to one-hot encoded and return a new matrix
-    pub fn transform<T:  Categorizable, M: Matrix<T>>(&self, x: &M) -> Option<M> {
+    pub fn transform<T: Categorizable, M: Matrix<T>>(&self, x: &M) -> Option<M> {
         let (nrows, p) = x.shape();
         let additional_params: Vec<usize> = self
             .series_encoders
@@ -201,7 +208,7 @@ mod tests {
 
     #[test]
     fn adjust_idxs() {
-        assert_eq!(find_new_idxs(0, &[], &[]), Vec::new());
+        assert_eq!(find_new_idxs(0, &[], &[]), Vec::<usize>::new());
         // [0,1,2] -> [0, 1, 1, 1, 2]
         assert_eq!(find_new_idxs(3, &[3], &[1]), vec![0, 1, 4]);
     }
@@ -282,4 +289,22 @@ mod tests {
         let nm = oh_enc.transform(&X).unwrap();
         assert_eq!(nm, expectedX);
     }
+
+    #[test]
+    fn fail_on_bad_category() {
+        let m = DenseMatrix::from_2d_array(&[
+            &[1.0, 1.5, 3.0],
+            &[2.0, 1.5, 4.0],
+            &[1.0, 1.5, 5.0],
+            &[2.0, 1.5, 6.0],
+        ]);
+
+        let params = OneHotEncoderParams::from_cat_idx(&[1]);
+        match OneHotEncoder::fit(&m, params) {
+            Err(_) => {
+                assert!(true);
+            }
+            _ => assert!(false),
+        }
+    }
 }