Merge #317

317: Latin camelcase wrong segmentation r=ManyTheFish a=PedroTurik

# Pull Request
All interesting changes are in charabia/src/segmenter/latin/camel_case.rs
other changes are import reordering caused by cargo fmt.

This change definitely looks like a small speed regression, but I will be opening for review nonetheless.

First time contributor to any meilisearch repo.

## Related issue
Fixes [#289](#289)

## What does this PR do?
# This PR changes the Latin camelCase segmentation to address the following cases: 
- openSSL should be segmented as ['open', 'SSL']
- openSSLError should be segmented as ['open', 'SSL', 'Error']

This is done by introducing a helper iteration for the main iterator to "peek" the next char, and this is needed in the currently solution with `StrGroupBy::linear_group_by` afaik since the segmentation sometimes depends on the existence of a lowercase letter after the one currently being analyzed ( like openSSLError )

## Benchmarks

the benchmarks ran once WITH the changes, and once more after commenting the changes out. The output here is from after removing the changes

```bash 
cargo bench
```
And what I believe is the relevant output is the following

```bash
segment/132/Latin/Eng   time:   [2.5651 µs 2.5714 µs 2.5786 µs]
                        change: [+0.6549% +0.8593% +1.0487%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 6 outliers among 100 measurements (6.00%)
  1 (1.00%) low mild
  2 (2.00%) high mild
  3 (3.00%) high severe

segment/132/Latin/Fra   time:   [2.3521 µs 2.3547 µs 2.3572 µs]
                        change: [-0.3826% -0.0443% +0.2662%] (p = 0.79 > 0.05)
                        No change in performance detected.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) low mild

segment/131/Latin/Deu   time:   [2.1346 µs 2.1372 µs 2.1403 µs]
                        change: [+0.4418% +0.5896% +0.7584%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 5 outliers among 100 measurements (5.00%)
  4 (4.00%) high mild
  1 (1.00%) high severe

segment/363/Latin/Eng   time:   [6.6601 µs 6.6749 µs 6.6911 µs]
                        change: [+3.1214% +3.4133% +3.7267%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 18 outliers among 100 measurements (18.00%)
  8 (8.00%) low mild
  3 (3.00%) high mild
  7 (7.00%) high severe

segment/363/Latin/Fra   time:   [6.5405 µs 6.5481 µs 6.5566 µs]
                        change: [-0.4942% -0.2417% +0.0139%] (p = 0.07 > 0.05)
                        No change in performance detected.
Found 6 outliers among 100 measurements (6.00%)
  5 (5.00%) high mild
  1 (1.00%) high severe

segment/365/Latin/Vie   time:   [5.6049 µs 5.6109 µs 5.6170 µs]
                        change: [-4.2406% -3.9811% -3.7043%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
  1 (1.00%) low mild
  4 (4.00%) high mild

tokenize/132/Latin/Eng  time:   [6.1291 µs 6.1349 µs 6.1406 µs]
                        change: [-1.4925% -1.3344% -1.1740%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
  4 (4.00%) high mild
  2 (2.00%) high severe
tokenize/132/Latin/Fra  time:   [6.9543 µs 6.9617 µs 6.9696 µs]
                        change: [-1.5821% -1.3067% -1.0380%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 8 outliers among 100 measurements (8.00%)
  3 (3.00%) low mild
  4 (4.00%) high mild
  1 (1.00%) high severe

tokenize/131/Latin/Deu  time:   [6.4793 µs 6.4916 µs 6.5050 µs]
                        change: [-3.1861% -2.9351% -2.6852%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) low mild

tokenize/363/Latin/Eng  time:   [15.510 µs 15.523 µs 15.535 µs]
                        change: [-8.2776% -7.9652% -7.6477%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
  9 (9.00%) high mild
tokenize/363/Latin/Fra  time:   [18.026 µs 18.044 µs 18.064 µs]
                        change: [-2.4157% -1.5634% -1.0380%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
  2 (2.00%) low mild
  3 (3.00%) high mild

tokenize/365/Latin/Vie  time:   [27.604 µs 27.626 µs 27.651 µs]
                        change: [-2.5536% -1.9732% -1.4007%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe

tokenize/354/Latin/Deu  time:   [17.874 µs 17.904 µs 17.936 µs]
                        change: [+2.2411% +2.4528% +2.6719%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild
```

## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [x] Have you read the contributing guidelines?
- [x] Have you made sure that the title is accurate and descriptive of the changes?


Co-authored-by: Pedro Turik Firmino <[email protected]>

charabia/src/lib.rs

@@ -59,9 +59,8 @@ mod tokenizer;
 pub use detection::{Language, Script, StrDetection};
 pub use normalizer::Normalize;
 pub use segmenter::Segment;
-pub use token::{SeparatorKind, Token, TokenKind};
-
 #[cfg(test)]
 pub use token::StaticToken;
+pub use token::{SeparatorKind, Token, TokenKind};
 
 pub use crate::tokenizer::{ReconstructedTokenIter, Tokenize, Tokenizer, TokenizerBuilder};

charabia/src/normalizer/mod.rs

@@ -2,6 +2,7 @@ use std::borrow::Cow;
 
 use once_cell::sync::Lazy;
 
+pub use self::ae_oe_normalizer::AeOeNormalizer;
 pub use self::arabic::ArabicNormalizer;
 #[cfg(feature = "chinese-normalization")]
 pub use self::chinese::ChineseNormalizer;
@@ -24,8 +25,6 @@ pub use self::vietnamese::VietnameseNormalizer;
 use crate::segmenter::SegmentedTokenIter;
 use crate::Token;
 
-pub use self::ae_oe_normalizer::AeOeNormalizer;
-
 mod arabic;
 #[cfg(feature = "chinese-normalization")]
 mod chinese;

charabia/src/segmenter/latin/camel_case.rs

@@ -5,20 +5,26 @@ use slice_group_by::StrGroupBy;
 /// For instance, "camelCase" is split into ["camel", "Case"].
 /// A camelCase boundary constitutes a lowercase letter directly followed by an uppercase letter
 /// optionally with any number of non-spacing marks in between.
+/// Two consecutive uppercase letters constitute a boundary only if the following letter is lowercase
+/// (eg., "MongoDBError" is split into ["Mongo", "DB", "Error"])  
 pub(crate) fn split_camel_case_bounds(str: &str) -> impl Iterator<Item = &str> {
-    let mut last_char_was_lowercase = str.chars().next().map_or(false, |c| c.is_lowercase());
+    let mut peek_char = str.chars().map(|c| c.is_lowercase());
+    let mut last_char_was_lowercase: bool = peek_char.next().unwrap_or_default();
+
+    peek_char.next();
 
     str.linear_group_by(move |_, char| {
+        let peek_char_is_lowercase: bool = peek_char.next().unwrap_or_default();
+
         if char.is_mark_nonspacing() {
             return true;
         }
 
-        if last_char_was_lowercase && char.is_letter_uppercase() {
-            return false;
-        }
+        let should_group =
+            !((last_char_was_lowercase || peek_char_is_lowercase) && char.is_letter_uppercase());
 
         last_char_was_lowercase = char.is_letter_lowercase();
-        true
+        should_group
     })
 }
 
@@ -48,4 +54,10 @@ mod test {
         non_spacing_marks_are_respected
     );
     test_segmentation!("a\u{0301}B", ["a\u{0301}", "B"], non_spacing_mark_after_first_letter);
+    test_segmentation!("openSSL", ["open", "SSL"], consecutive_uppercase_is_not_split);
+    test_segmentation!(
+        "MongoDBDatabase",
+        ["Mongo", "DB", "Database"],
+        last_uppercase_from_non_final_sequence
+    );
 }

irg-kvariants/build.rs

@@ -1,5 +1,8 @@
+use std::env;
+use std::error::Error;
+use std::path::Path;
+
 use serde::Deserialize;
-use std::{env, error::Error, path::Path};
 
 #[derive(Deserialize)]
 pub struct TsvRow {

irg-kvariants/src/lib.rs

@@ -1,6 +1,7 @@
+use std::collections::HashMap;
+
 use once_cell::sync::Lazy;
 use serde::Deserialize;
-use std::collections::HashMap;
 
 #[derive(Debug, Eq, PartialEq)]
 pub enum KVariantClass {