textdistance.rs

[ [github.com](https://github.com/life4/textdistance.rs) ] [ [docs.rs](https://docs.rs/textdistance/) ] [ [crates.io](https://crates.io/crates/textdistance) ]

Rust library with lots of different algorithms to compare how similar two sequences are.

Features:

• 💪 Based on popular and battle-tested textdistance Python library (and written by the same author).
• 📚 Contains 20+ algorithms for all purposes.
• 🔬 Includes state-of-the-art algorithms like EntropyNCD and Sift4.
• 🪶 Zero-dependency.
• 🔨 Works with any iterators, including bytes, code points, Unicode grapheme clusters, words, and numbers.
• ❤️ Friendly and consistent API for all algorithms.
• 📏 Optional normalization of the result on the 0.0-1.0 interval.
• 🛡 No unsafe code.
• 🦀 Pure Rust.

Available algorithms

Edit-based:

1. DamerauLevenshtein, both optimal string alignment and restricted.
2. Hamming
3. Jaro
4. JaroWinkler
5. Levenshtein
6. Sift4Common
7. Sift4Simple
8. SmithWaterman

Token-based:

1. Bag
2. Cosine (aka Orchini, Tucker, Otsuka–Ochiai)
3. EntropyNCD (Entropy-based Normalized Compression Distance)
4. Jaccard (aka Tanimoto, Critical Success Index)
5. Overlap (aka Szymkiewicz–Simpson)
6. Roberts
7. SorensenDice (aka F1, Czekanowski, Zijdenbos)
8. Tversky

Sequence-based:

1. LCSSeq (Longest Common SubSequence)
2. LCSStr (Longest Common SubString)
3. RatcliffObershelp (aka Gestalt pattern matching)

Naive:

1. Prefix
2. Suffix
3. Length

Normalization for other metrics:

1. LIG3 normalization for Hamming by Levenshtein
2. MLIPNS normalization for Hamming
3. YujianBo normalization for Levenshtein

Installation

shell cargo add textdistance

Usage

The textdistance::str module provides shortcut functions for each algorithm for calculating the distance/similarity between two strings:

rust use textdistance::str::damerau_levenshtein; assert!(damerau_levenshtein("abc", "acbd") == 2);

The textdistance::nstr module is the same but all algorithms return a normalized value (between 0.0 and 1.0):

rust use textdistance::nstr::damerau_levenshtein; assert!(damerau_levenshtein("abc", "acbd") == 2./4.);

For more advanced usage, each algorithm is provided as a struct implementing the Algorithm trait:

```rust use textdistance::{Algorithm, DamerauLevenshtein};

let a = DamerauLevenshtein::default(); let r = a.for_str("abc", "acbd"); assert!(r.val() == 2); assert!(r.nval() == 2./4.); ```

1. The Algorithm trait provides for_str, for_vec, and for_iter to calculate the result for two strings, vectors (slices), or iterators respectively. In addition, there are for_words and for_bigrams methods that split the text into words or bigrams respectively before calculating the distance.
2. Each method returns a textdistance::Result that provides methods to get absolute (val) or normalized (nval) value of the metric, distance (dist and ndist), or similarity (sim and nsim).

Unicode support

The for_str method (and so all functions in the str and nstr modules) uses String.chars to split the string and then runs it through the for_iter method. So, é will be considered two distinct characters ("latin small letter e" and "combining acute accent"). Usually, that's ok and this is how Python works. You can read more in the official Rust documentation.

If you want é to be considered as a single symbol, use the unicode-segmentation crate:

```rust use textdistance::{Algorithm, DamerauLevenshtein}; use unicode_segmentation::UnicodeSegmentation;

let s1 = "a̐éö̲\r\n"; let s2 = "éa̐ö̲\r\n"; let g1 = s1.graphemes(true); let g2 = s2.graphemes(true); let a = DamerauLevenshtein::default(); let r = a.for_iter(g1, g2); assert!(r.val() == 1); ```

Choosing the algorithm

The algorithm to use depends on your use case. First, you need to decide on the algorithm category:

1. Edit-based algorithms work well on short sequences for detecting typos and minor changes.
2. Token-based algorithms work well on longer sequences for comparing long texts with noticeable modifications.
3. Sequence-based algorithms work well for calculating diff size between the original and the changed version of the sequence.

If you go with edit-based, the next thing is to decide what kind of changes you need to detect:

• ✏️ Substitution. One character is replaced by another.
• ➕ Addition. A new character is added.
• 🗑 Deletion. A character is removed.
• 🔄 Transposition. Two sequential characters are swapped.

| alg | sub | add | del | trans | | --------------------- | --- | --- | --- | ----- | | Hamming | ✅ | ❌ | ❌ | ❌ | | Jaro | ❌ | ❌ | ❌ | ✅ | | JaroWinkler | ❌ | ❌ | ❌ | ✅ | | Sift4 | ❌ | ❌ | ❌ | ✅ | | Levenshtein | ✅ | ✅ | ✅ | ❌ | | DamerauLevenshtein | ✅ | ✅ | ✅ | ✅ |

• Hamming is the fastest one but detects only substitutions.
• Sift4 is very fast but not as well-known and battle-tested as other algorithms.
• Jaro is slower than Sift4 but well-known and battle-tested.
• JaroWinkler is like Jaro but gives more weight to strings with a matching prefix.
• Levenshtein detects everything but transpositions and faster than DamerauLevenshtein (but slower than other algorithms).
• DamerauLevenshtein ticks all the boxes but very slow.

There are some use cases:

• DamerauLevenshtein with some optimizations is used in cargo to correct typos in command names.
• Jaro is included in the Elixir standard library (String.jaro_distance). It is used by the compiler and by mix (cargo for Elixir) to provide the "did you mean?" functionality for typos in module or command names.
• RatcliffObershelp variation is included in the Python standard library (difflib.SequenceMatcher).

Benchmarks

Legend:

• 🐌 is very slow (> 5 ms)
• 🐢 is slow (> 1 ms)
• 🐇 is fast (> 500 µs)
• 🐎 is very fast (< 500 µs)

| algorithm | time | | ------------------ | --------- | | bag | 🐇 523.06 µs | | cosine | 🐇 508.59 µs | | dameraulevenshtein | 🐌 41.938 ms | | dameraulevenshteinrestricted | 🐌 10.301 ms | | entropyncd | 🐇 731.68 µs | | hamming | 🐎 19.203 µs | | jaccard | 🐇 580.79 µs | | jarowinkler | 🐢 1.7174 ms | | jaro | 🐢 1.7148 ms | | lcsseq | 🐌 7.4349 ms | | lcsstr | 🐢 3.2658 ms | | length | 🐎 2.5300 µs | | levenshtein | 🐢 4.5999 ms | | lig3 | 🐌 6.5563 ms | | mlipns | 🐎 20.625 µs | | overlap | 🐇 513.76 µs | | prefix | 🐎 22.473 µs | | ratcliffobershelp | 🐌 36.308 ms | | roberts | 🐇 714.79 µs | | sift4common | 🐎 238.86 µs | | sift4simple | 🐎 143.69 µs | | smithwaterman | 🐌 9.5146 ms | | sorensendice | 🐇 510.75 µs | | suffix | 🐎 38.821 µs | | tversky | 🐇 512.41 µs | | yujian_bo | 🐢 4.6044 ms |

The benchmarks are powered by criterion and live in the benches directory. They are quite simple: grab 10 open-source licenses, take a 200 chars prefix from each, and cross-compare these prefixes. The numbers might be very different for a different kind of input, length of the input, when comparing words rather than characters, or running the benchmarks on a different machine. The goal of these benchmarks is to provide basic guidance rather than give a definitive answer. If performance is critical for your application, I encourage you to make your benchmarks on the real data you have.

Versioning

We stick to SemVer:

1. The patch number is for bug fixes. The results of an algorithm may change in some corner cases if we found that the previous implementation doesn't match the algorithm described in the original paper.
2. The minor number is for new algorithms and features.
3. The major number is for big changes in the API. We try to avoid breaking stuff but we prefer to provide a friendly and convenient API over keeping a backward compatibility.

Limitations

• In the original textdisance, most of the algorithms are adjusted to work on any number of the input sequences. However, Rust doesn't support variadic arguments, so all algorithms currently are implemented only for exactly two inputs.
• All algorithms in the crate implement the same Algorithm trait. Hence metrics that have additional limitations on the input sequence elements beyond Eq (like Editex and MRA that work only with ASCII letters) currently cannot be implemented.
• Most of the implemented algorithms have certain properties (like commutative property) that make their behavior more like what you would expect and make normalization simple. So, I haven't implemented yet Needleman-Wunsch and Gotoh, mostly because they are tricky to normalize and I'm still not 100% sure that I did it correctly in the original textdistance.

Acknowledgments

There are the libraries that I used as a reference implementation and the source of test cases:

• 🐍 Python: textdistance, abydos, jellyfish.
• ☕️ JS: talisman.
• 🦀 Rust: strsim, distance, levenshtein-rs.

Specials thanks to Trevor Gross for transferring to me the ownership of the textdistance name on crates.io.

Testing locally

To run everything locally, all you need is Rust, Python, and task. Execute task all to run all code formatters, linters, and tests.

Thank you ❤️