From fd6b2e801479f709870921f192153c6abeeab53d Mon Sep 17 00:00:00 2001
From: gaxler <g.axler@gmail.com>
Date: Sat, 30 Jan 2021 19:29:58 -0800
Subject: [PATCH] Transform matrix

---
 src/preprocessing/categorical_encoders.rs | 42 +++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/src/preprocessing/categorical_encoders.rs b/src/preprocessing/categorical_encoders.rs
index 794c1d6..585f13a 100644
--- a/src/preprocessing/categorical_encoders.rs
+++ b/src/preprocessing/categorical_encoders.rs
@@ -135,9 +135,51 @@ 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> {
+        let (nrows, p) = x.shape();
+        let additional_params: Vec<usize> = self
+            .series_encoders
+            .iter()
+            .map(|enc| enc.num_categories)
+            .collect();
+
+        let new_param_num: usize = p + additional_params.iter().fold(0, |cs, &v| cs + v - 1);
+        let new_col_idx = find_new_idxs(p, &additional_params[..], &self.col_idx_categorical[..]);
+        let mut res = M::zeros(nrows, new_param_num);
+        // copy old data in x to their new location
+        for (old_p, &new_p) in new_col_idx.iter().enumerate() {
+            for r in 0..nrows {
+                let val = x.get(r, old_p);
+                res.set(r, new_p, val);
             }
         }
+        for (pidx, &old_cidx) in self.col_idx_categorical.iter().enumerate() {
+            let cidx = new_col_idx[old_cidx];
+            let col_iter = (0..nrows).map(|r| res.get(r, cidx).to_category());
+            let sencoder = &self.series_encoders[pidx];
+            let oh_series: Vec<Option<Vec<T>>> = sencoder.transform_iter(col_iter);
+
+            for (row, oh_vec) in oh_series.iter().enumerate() {
+                match oh_vec {
+                    None => {
+                        // Bad value in a series causes in to be invalid
+                        // todo: proper error handling, so user can know where the bad value is
+                        return None;
+            }
+                    Some(v) => {
+                        // copy one hot vectors to their place in the data matrix;
+                        for (col_ofst, &val) in v.iter().enumerate() {
+                            res.set(row, cidx + col_ofst, val);
+        }
     }
+                }
+            }
+        }
+        Some(res)
+    }
+}
 
     fn build_series_encoders(data: &M, idxs: &[usize]) -> Vec<SeriesOneHotEncoder<HashableReal>> {
         let (nrows, _) = data.shape();