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use rayon::prelude::*;
use std::collections::HashMap;
use std::f64;
#[derive(Debug, Clone)]
pub struct BM25Options {
k1: f64,
b: f64,
epsilon: f64,
}
impl Default for BM25Options {
fn default() -> Self {
Self {
k1: 1.5,
b: 0.75,
epsilon: 0.25,
}
}
}
#[derive(Debug, Clone)]
pub struct BM25<T> {
options: BM25Options,
corpus_size: usize,
avgdl: f64,
doc_freqs: Vec<HashMap<String, u32>>,
doc_ids: Vec<T>,
idf: HashMap<String, f64>,
doc_len: Vec<usize>,
}
impl<T: Clone> BM25<T> {
pub fn new(corpus: Vec<(T, String)>, options: BM25Options) -> Self {
let mut doc_ids = vec![];
let mut docs = vec![];
for (id, value) in corpus {
doc_ids.push(id);
docs.push(value);
}
let tokenized_docs = docs.into_par_iter().map(|text| tokenize(&text)).collect();
let mut bm25 = BM25 {
options,
corpus_size: 0,
avgdl: 0.0,
doc_freqs: Vec::new(),
doc_ids,
idf: HashMap::new(),
doc_len: Vec::new(),
};
let map = bm25.initialize(tokenized_docs);
bm25.calc_idf(map);
bm25
}
pub fn search(&self, query: &str, top_k: usize, min_score: Option<f64>) -> Vec<T> {
let scores = self.get_scores(query);
let mut indexed_scores: Vec<(T, f64)> = scores
.into_iter()
.enumerate()
.filter_map(|(i, v)| match min_score {
Some(minimum_score) => {
if v < minimum_score {
None
} else {
Some((self.doc_ids[i].clone(), v))
}
}
None => Some((self.doc_ids[i].clone(), v)),
})
.collect();
indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
indexed_scores
.into_iter()
.take(top_k)
.map(|(id, _)| id)
.collect()
}
pub fn get_scores(&self, query: &str) -> Vec<f64> {
let mut score = vec![0.0; self.corpus_size];
for q in tokenize(query) {
if let Some(idf) = self.idf.get(&q) {
for (i, doc) in self.doc_freqs.iter().enumerate() {
let q_freq = doc.get(&q).unwrap_or(&0);
score[i] += *idf
* (*q_freq as f64 * (self.options.k1 + 1.0)
/ (*q_freq as f64
+ self.options.k1
* (1.0 - self.options.b
+ self.options.b * self.doc_len[i] as f64 / self.avgdl)));
}
}
}
score
}
fn initialize(&mut self, corpus: Vec<Vec<String>>) -> HashMap<String, usize> {
let mut map = HashMap::new();
let mut num_doc = 0;
for document in corpus {
self.doc_len.push(document.len());
num_doc += document.len();
let mut frequencies = HashMap::new();
for word in document {
*frequencies.entry(word).or_insert(0) += 1;
}
self.doc_freqs.push(frequencies);
for word in self.doc_freqs[self.doc_freqs.len() - 1].keys() {
*map.entry(word.clone()).or_insert(0) += 1;
}
self.corpus_size += 1;
}
self.avgdl = num_doc as f64 / self.corpus_size as f64;
map
}
fn calc_idf(&mut self, map: HashMap<String, usize>) {
let mut idf_sum = 0.0;
let mut negative_idfs = Vec::new();
for (word, freq) in map {
let idf = (self.corpus_size as f64 - freq as f64 + 0.5).ln() - (freq as f64 + 0.5).ln();
self.idf.insert(word.clone(), idf);
idf_sum += idf;
if idf < 0.0 {
negative_idfs.push(word);
}
}
let average_idf = idf_sum / self.idf.len() as f64;
for word in negative_idfs {
self.idf.insert(word, self.options.epsilon * average_idf);
}
}
}
fn tokenize(text: &str) -> Vec<String> {
text.split(' ').map(|v| v.to_string()).collect()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_bm25() {
let corpus = vec![
(0, "Hello there good man!".into()),
(1, "It is quite windy in London".into()),
(2, "How is the weather today?".into()),
];
let bm25 = BM25::new(corpus, BM25Options::default());
let scores = bm25.get_scores("windy London");
assert_eq!(scores, [0.0, 0.9372947225064051, 0.0]);
let top_n = bm25.search("windy London", 3, None);
assert_eq!(top_n, vec![1, 0, 2])
}
}
|
