Run cargo clippy --fix (#250)

* Run `cargo clippy --fix`
* Run `cargo clippy --all-features --fix`
* Fix other clippy warnings
* cargo fmt

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>

src/preprocessing/categorical.rs

@@ -132,8 +132,7 @@ impl OneHotEncoder {
                     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
+                            "Column {idx} of data matrix containts non categorizable (integer) values"
                         );
                         return Err(Failed::fit(&msg[..]));
                     }
@@ -182,7 +181,7 @@ impl OneHotEncoder {
                 match oh_vec {
                     None => {
                         // Since we support T types, bad value in a series causes in to be invalid
-                        let msg = format!("At least one value in column {} doesn't conform to category definition", old_cidx);
+                        let msg = format!("At least one value in column {old_cidx} doesn't conform to category definition");
                         return Err(Failed::transform(&msg[..]));
                     }
                     Some(v) => {
@@ -338,11 +337,7 @@ mod tests {
         ]);
 
         let params = OneHotEncoderParams::from_cat_idx(&[1]);
-        match OneHotEncoder::fit(&m, params) {
-            Err(_) => {
-                assert!(true);
-            }
-            _ => assert!(false),
-        }
+        let result = OneHotEncoder::fit(&m, params);
+        assert!(result.is_err());
     }
 }

src/preprocessing/numerical.rs

@@ -294,7 +294,7 @@ mod tests {
                     &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
                     &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
                 ]));
-            println!("{}", transformed_values);
+            println!("{transformed_values}");
             assert!(transformed_values.approximate_eq(
                 &DenseMatrix::from_2d_array(&[
                     &[-1.1154020653, -0.4031985330, 0.9284605204, -0.4271473866],

src/preprocessing/series_encoder.rs

@@ -206,7 +206,7 @@ mod tests {
     #[test]
     fn from_categories() {
         let fake_categories: Vec<usize> = vec![1, 2, 3, 4, 5, 3, 5, 3, 1, 2, 4];
-        let it = fake_categories.iter().map(|&a| a);
+        let it = fake_categories.iter().copied();
         let enc = CategoryMapper::<usize>::fit_to_iter(it);
         let oh_vec: Vec<f64> = match enc.get_one_hot(&1) {
             None => panic!("Wrong categories"),
@@ -218,8 +218,8 @@ mod tests {
 
     fn build_fake_str_enc<'a>() -> CategoryMapper<&'a str> {
         let fake_category_pos = vec!["background", "dog", "cat"];
-        let enc = CategoryMapper::<&str>::from_positional_category_vec(fake_category_pos);
-        enc
+
+        CategoryMapper::<&str>::from_positional_category_vec(fake_category_pos)
     }
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -275,7 +275,7 @@ mod tests {
         let lab = enc.invert_one_hot(res).unwrap();
         assert_eq!(lab, "dog");
         if let Err(e) = enc.invert_one_hot(vec![0.0, 0.0, 0.0]) {
-            let pos_entries = format!("Expected a single positive entry, 0 entires found");
+            let pos_entries = "Expected a single positive entry, 0 entires found".to_string();
             assert_eq!(e, Failed::transform(&pos_entries[..]));
         };
     }