e2dfa23eac
Attempts to improve method name suggestions when a matching method name is not found. The approach taken is use the Levenshtein distance and account for substrings having a high distance but can sometimes be very close to the intended method (eg. empty vs is_empty).
178 lines
6.1 KiB
Rust
178 lines
6.1 KiB
Rust
//! Levenshtein distances.
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//!
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//! The [Levenshtein distance] is a metric for measuring the difference between two strings.
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//!
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//! [Levenshtein distance]: https://en.wikipedia.org/wiki/Levenshtein_distance
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use crate::symbol::Symbol;
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use std::cmp;
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#[cfg(test)]
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mod tests;
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/// Finds the Levenshtein distance between two strings.
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///
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/// Returns None if the distance exceeds the limit.
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pub fn lev_distance(a: &str, b: &str, limit: usize) -> Option<usize> {
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let n = a.chars().count();
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let m = b.chars().count();
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let min_dist = if n < m { m - n } else { n - m };
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if min_dist > limit {
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return None;
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}
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if n == 0 || m == 0 {
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return (min_dist <= limit).then_some(min_dist);
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}
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let mut dcol: Vec<_> = (0..=m).collect();
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for (i, sc) in a.chars().enumerate() {
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let mut current = i;
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dcol[0] = current + 1;
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for (j, tc) in b.chars().enumerate() {
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let next = dcol[j + 1];
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if sc == tc {
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dcol[j + 1] = current;
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} else {
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dcol[j + 1] = cmp::min(current, next);
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dcol[j + 1] = cmp::min(dcol[j + 1], dcol[j]) + 1;
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}
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current = next;
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}
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}
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(dcol[m] <= limit).then_some(dcol[m])
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}
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/// Provides a word similarity score between two words that accounts for substrings being more
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/// meaningful than a typical Levenshtein distance. The lower the score, the closer the match.
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/// 0 is an identical match.
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///
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/// Uses the Levenshtein distance between the two strings and removes the cost of the length
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/// difference. If this is 0 then it is either a substring match or a full word match, in the
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/// substring match case we detect this and return `1`. To prevent finding meaningless substrings,
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/// eg. "in" in "shrink", we only perform this subtraction of length difference if one of the words
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/// is not greater than twice the length of the other. For cases where the words are close in size
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/// but not an exact substring then the cost of the length difference is discounted by half.
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///
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/// Returns `None` if the distance exceeds the limit.
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pub fn lev_distance_with_substrings(a: &str, b: &str, limit: usize) -> Option<usize> {
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let n = a.chars().count();
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let m = b.chars().count();
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// Check one isn't less than half the length of the other. If this is true then there is a
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// big difference in length.
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let big_len_diff = (n * 2) < m || (m * 2) < n;
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let len_diff = if n < m { m - n } else { n - m };
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let lev = lev_distance(a, b, limit + len_diff)?;
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// This is the crux, subtracting length difference means exact substring matches will now be 0
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let score = lev - len_diff;
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// If the score is 0 but the words have different lengths then it's a substring match not a full
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// word match
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let score = if score == 0 && len_diff > 0 && !big_len_diff {
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1 // Exact substring match, but not a total word match so return non-zero
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} else if !big_len_diff {
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// Not a big difference in length, discount cost of length difference
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score + (len_diff + 1) / 2
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} else {
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// A big difference in length, add back the difference in length to the score
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score + len_diff
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};
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(score <= limit).then_some(score)
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}
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/// Finds the best match for given word in the given iterator where substrings are meaningful.
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///
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/// A version of [`find_best_match_for_name`] that uses [`lev_distance_with_substrings`] as the score
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/// for word similarity. This takes an optional distance limit which defaults to one-third of the
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/// given word.
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///
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/// Besides the modified Levenshtein, we use case insensitive comparison to improve accuracy
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/// on an edge case with a lower(upper)case letters mismatch.
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pub fn find_best_match_for_name_with_substrings(
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candidates: &[Symbol],
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lookup: Symbol,
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dist: Option<usize>,
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) -> Option<Symbol> {
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find_best_match_for_name_impl(true, candidates, lookup, dist)
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}
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/// Finds the best match for a given word in the given iterator.
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///
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/// As a loose rule to avoid the obviously incorrect suggestions, it takes
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/// an optional limit for the maximum allowable edit distance, which defaults
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/// to one-third of the given word.
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///
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/// Besides Levenshtein, we use case insensitive comparison to improve accuracy
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/// on an edge case with a lower(upper)case letters mismatch.
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pub fn find_best_match_for_name(
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candidates: &[Symbol],
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lookup: Symbol,
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dist: Option<usize>,
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) -> Option<Symbol> {
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find_best_match_for_name_impl(false, candidates, lookup, dist)
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}
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#[cold]
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fn find_best_match_for_name_impl(
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use_substring_score: bool,
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candidates: &[Symbol],
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lookup: Symbol,
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dist: Option<usize>,
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) -> Option<Symbol> {
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let lookup = lookup.as_str();
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let lookup_uppercase = lookup.to_uppercase();
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// Priority of matches:
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// 1. Exact case insensitive match
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// 2. Levenshtein distance match
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// 3. Sorted word match
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if let Some(c) = candidates.iter().find(|c| c.as_str().to_uppercase() == lookup_uppercase) {
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return Some(*c);
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}
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let mut dist = dist.unwrap_or_else(|| cmp::max(lookup.len(), 3) / 3);
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let mut best = None;
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for c in candidates {
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match if use_substring_score {
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lev_distance_with_substrings(lookup, c.as_str(), dist)
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} else {
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lev_distance(lookup, c.as_str(), dist)
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} {
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Some(0) => return Some(*c),
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Some(d) => {
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dist = d - 1;
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best = Some(*c);
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}
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None => {}
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}
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}
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if best.is_some() {
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return best;
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}
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find_match_by_sorted_words(candidates, lookup)
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}
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fn find_match_by_sorted_words(iter_names: &[Symbol], lookup: &str) -> Option<Symbol> {
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iter_names.iter().fold(None, |result, candidate| {
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if sort_by_words(candidate.as_str()) == sort_by_words(lookup) {
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Some(*candidate)
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} else {
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result
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}
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})
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}
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fn sort_by_words(name: &str) -> String {
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let mut split_words: Vec<&str> = name.split('_').collect();
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// We are sorting primitive &strs and can use unstable sort here.
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split_words.sort_unstable();
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split_words.join("_")
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}
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