mirror of
https://github.com/zed-industries/zed.git
synced 2024-09-19 18:41:56 +03:00
532 lines
16 KiB
Rust
532 lines
16 KiB
Rust
use anyhow::{anyhow, Result};
|
|
use client::{self, UserStore};
|
|
use gpui::{AsyncAppContext, ModelHandle, Task};
|
|
use language::LanguageRegistry;
|
|
use node_runtime::RealNodeRuntime;
|
|
use project::{Project, RealFs};
|
|
use semantic_index::embedding::OpenAIEmbeddings;
|
|
use semantic_index::semantic_index_settings::SemanticIndexSettings;
|
|
use semantic_index::{SearchResult, SemanticIndex};
|
|
use serde::{Deserialize, Serialize};
|
|
use settings::{default_settings, handle_settings_file_changes, watch_config_file, SettingsStore};
|
|
use std::path::{Path, PathBuf};
|
|
use std::process::Command;
|
|
use std::sync::Arc;
|
|
use std::time::{Duration, Instant};
|
|
use std::{cmp, env, fs};
|
|
use util::channel::{RELEASE_CHANNEL, RELEASE_CHANNEL_NAME};
|
|
use util::http::{self};
|
|
use util::paths::{self, EMBEDDINGS_DIR};
|
|
use zed::languages;
|
|
|
|
#[derive(Deserialize, Clone, Serialize)]
|
|
struct EvaluationQuery {
|
|
query: String,
|
|
matches: Vec<String>,
|
|
}
|
|
|
|
impl EvaluationQuery {
|
|
fn match_pairs(&self) -> Vec<(PathBuf, u32)> {
|
|
let mut pairs = Vec::new();
|
|
for match_identifier in self.matches.iter() {
|
|
let mut match_parts = match_identifier.split(":");
|
|
|
|
if let Some(file_path) = match_parts.next() {
|
|
if let Some(row_number) = match_parts.next() {
|
|
pairs.push((PathBuf::from(file_path), row_number.parse::<u32>().unwrap()));
|
|
}
|
|
}
|
|
}
|
|
pairs
|
|
}
|
|
}
|
|
|
|
#[derive(Deserialize, Clone)]
|
|
struct RepoEval {
|
|
repo: String,
|
|
commit: String,
|
|
assertions: Vec<EvaluationQuery>,
|
|
}
|
|
|
|
const TMP_REPO_PATH: &str = "eval_repos";
|
|
|
|
fn parse_eval() -> anyhow::Result<Vec<RepoEval>> {
|
|
let eval_folder = env::current_dir()?
|
|
.as_path()
|
|
.parent()
|
|
.unwrap()
|
|
.join("crates/semantic_index/eval");
|
|
|
|
let mut repo_evals: Vec<RepoEval> = Vec::new();
|
|
for entry in fs::read_dir(eval_folder)? {
|
|
let file_path = entry.unwrap().path();
|
|
if let Some(extension) = file_path.extension() {
|
|
if extension == "json" {
|
|
if let Ok(file) = fs::read_to_string(file_path) {
|
|
let repo_eval = serde_json::from_str(file.as_str());
|
|
|
|
match repo_eval {
|
|
Ok(repo_eval) => {
|
|
repo_evals.push(repo_eval);
|
|
}
|
|
Err(err) => {
|
|
println!("Err: {:?}", err);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
Ok(repo_evals)
|
|
}
|
|
|
|
fn clone_repo(repo_eval: RepoEval) -> anyhow::Result<(String, PathBuf)> {
|
|
let repo_name = Path::new(repo_eval.repo.as_str())
|
|
.file_name()
|
|
.unwrap()
|
|
.to_str()
|
|
.unwrap()
|
|
.to_owned()
|
|
.replace(".git", "");
|
|
|
|
let clone_path = fs::canonicalize(env::current_dir()?)?
|
|
.parent()
|
|
.ok_or(anyhow!("path canonicalization failed"))?
|
|
.parent()
|
|
.unwrap()
|
|
.join(TMP_REPO_PATH);
|
|
|
|
// Delete Clone Path if already exists
|
|
let _ = fs::remove_dir_all(&clone_path);
|
|
let _ = fs::create_dir(&clone_path);
|
|
|
|
let _ = Command::new("git")
|
|
.args(["clone", repo_eval.repo.as_str()])
|
|
.current_dir(clone_path.clone())
|
|
.output()?;
|
|
// Update clone path to be new directory housing the repo.
|
|
let clone_path = clone_path.join(repo_name.clone());
|
|
let _ = Command::new("git")
|
|
.args(["checkout", repo_eval.commit.as_str()])
|
|
.current_dir(clone_path.clone())
|
|
.output()?;
|
|
|
|
Ok((repo_name, clone_path))
|
|
}
|
|
|
|
fn dcg(hits: Vec<usize>) -> f32 {
|
|
let mut result = 0.0;
|
|
for (idx, hit) in hits.iter().enumerate() {
|
|
result += *hit as f32 / (2.0 + idx as f32).log2();
|
|
}
|
|
|
|
result
|
|
}
|
|
|
|
fn get_hits(
|
|
eval_query: EvaluationQuery,
|
|
search_results: Vec<SearchResult>,
|
|
k: usize,
|
|
cx: &AsyncAppContext,
|
|
) -> (Vec<usize>, Vec<usize>) {
|
|
let ideal = vec![1; cmp::min(eval_query.matches.len(), k)];
|
|
|
|
let mut hits = Vec::new();
|
|
for result in search_results {
|
|
let (path, start_row, end_row) = result.buffer.read_with(cx, |buffer, cx| {
|
|
let path = buffer.file().unwrap().path().to_path_buf();
|
|
let start_row = buffer.offset_to_point(result.range.start.offset).row;
|
|
let end_row = buffer.offset_to_point(result.range.end.offset).row;
|
|
(path, start_row, end_row)
|
|
});
|
|
|
|
let match_pairs = eval_query.match_pairs();
|
|
let mut found = 0;
|
|
for (match_path, match_row) in match_pairs {
|
|
if match_path == path {
|
|
if match_row >= start_row && match_row <= end_row {
|
|
found = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
hits.push(found);
|
|
}
|
|
|
|
// For now, we are calculating ideal_hits a bit different, as technically
|
|
// with overlapping ranges, one match can result in more than result.
|
|
let mut ideal_hits = hits.clone();
|
|
ideal_hits.retain(|x| x == &1);
|
|
|
|
let ideal = if ideal.len() > ideal_hits.len() {
|
|
ideal
|
|
} else {
|
|
ideal_hits
|
|
};
|
|
|
|
// Fill ideal to 10 length
|
|
let mut filled_ideal = [0; 10];
|
|
for (idx, i) in ideal.to_vec().into_iter().enumerate() {
|
|
filled_ideal[idx] = i;
|
|
}
|
|
|
|
(filled_ideal.to_vec(), hits)
|
|
}
|
|
|
|
fn evaluate_ndcg(hits: Vec<usize>, ideal: Vec<usize>) -> Vec<f32> {
|
|
// NDCG or Normalized Discounted Cumulative Gain, is determined by comparing the relevance of
|
|
// items returned by the search engine relative to the hypothetical ideal.
|
|
// Relevance is represented as a series of booleans, in which each search result returned
|
|
// is identified as being inside the test set of matches (1) or not (0).
|
|
|
|
// For example, if result 1, 3 and 5 match the 3 relevant results provided
|
|
// actual dcg is calculated against a vector of [1, 0, 1, 0, 1]
|
|
// whereas ideal dcg is calculated against a vector of [1, 1, 1, 0, 0]
|
|
// as this ideal vector assumes the 3 relevant results provided were returned first
|
|
// normalized dcg is then calculated as actual dcg / ideal dcg.
|
|
|
|
// NDCG ranges from 0 to 1, which higher values indicating better performance
|
|
// Commonly NDCG is expressed as NDCG@k, in which k represents the metric calculated
|
|
// including only the top k values returned.
|
|
// The @k metrics can help you identify, at what point does the relevant results start to fall off.
|
|
// Ie. a NDCG@1 of 0.9 and a NDCG@3 of 0.5 may indicate that the first result returned in usually
|
|
// very high quality, whereas rank results quickly drop off after the first result.
|
|
|
|
let mut ndcg = Vec::new();
|
|
for idx in 1..(hits.len() + 1) {
|
|
let hits_at_k = hits[0..idx].to_vec();
|
|
let ideal_at_k = ideal[0..idx].to_vec();
|
|
|
|
let at_k = dcg(hits_at_k.clone()) / dcg(ideal_at_k.clone());
|
|
|
|
ndcg.push(at_k);
|
|
}
|
|
|
|
ndcg
|
|
}
|
|
|
|
fn evaluate_map(hits: Vec<usize>) -> Vec<f32> {
|
|
let mut map_at_k = Vec::new();
|
|
|
|
let non_zero = hits.iter().sum::<usize>() as f32;
|
|
if non_zero == 0.0 {
|
|
return vec![0.0; hits.len()];
|
|
}
|
|
|
|
let mut rolling_non_zero = 0.0;
|
|
let mut rolling_map = 0.0;
|
|
for (idx, h) in hits.into_iter().enumerate() {
|
|
rolling_non_zero += h as f32;
|
|
if h == 1 {
|
|
rolling_map += rolling_non_zero / (idx + 1) as f32;
|
|
}
|
|
map_at_k.push(rolling_map / non_zero);
|
|
}
|
|
|
|
map_at_k
|
|
}
|
|
|
|
fn evaluate_mrr(hits: Vec<usize>) -> f32 {
|
|
for (idx, h) in hits.into_iter().enumerate() {
|
|
if h == 1 {
|
|
return 1.0 / (idx + 1) as f32;
|
|
}
|
|
}
|
|
|
|
return 0.0;
|
|
}
|
|
|
|
fn init_logger() {
|
|
env_logger::init();
|
|
}
|
|
|
|
#[derive(Serialize)]
|
|
struct QueryMetrics {
|
|
query: EvaluationQuery,
|
|
millis_to_search: Duration,
|
|
ndcg: Vec<f32>,
|
|
map: Vec<f32>,
|
|
mrr: f32,
|
|
hits: Vec<usize>,
|
|
precision: Vec<f32>,
|
|
recall: Vec<f32>,
|
|
}
|
|
|
|
#[derive(Serialize)]
|
|
struct SummaryMetrics {
|
|
millis_to_search: f32,
|
|
ndcg: Vec<f32>,
|
|
map: Vec<f32>,
|
|
mrr: f32,
|
|
precision: Vec<f32>,
|
|
recall: Vec<f32>,
|
|
}
|
|
|
|
#[derive(Serialize)]
|
|
struct RepoEvaluationMetrics {
|
|
millis_to_index: Duration,
|
|
query_metrics: Vec<QueryMetrics>,
|
|
repo_metrics: Option<SummaryMetrics>,
|
|
}
|
|
|
|
impl RepoEvaluationMetrics {
|
|
fn new(millis_to_index: Duration) -> Self {
|
|
RepoEvaluationMetrics {
|
|
millis_to_index,
|
|
query_metrics: Vec::new(),
|
|
repo_metrics: None,
|
|
}
|
|
}
|
|
|
|
fn save(&self, repo_name: String) -> Result<()> {
|
|
let results_string = serde_json::to_string(&self)?;
|
|
fs::write(format!("./{}_evaluation.json", repo_name), results_string)
|
|
.expect("Unable to write file");
|
|
Ok(())
|
|
}
|
|
|
|
fn summarize(&mut self) {
|
|
let l = self.query_metrics.len() as f32;
|
|
let millis_to_search: f32 = self
|
|
.query_metrics
|
|
.iter()
|
|
.map(|metrics| metrics.millis_to_search.as_millis())
|
|
.sum::<u128>() as f32
|
|
/ l;
|
|
|
|
let mut ndcg_sum = vec![0.0; 10];
|
|
let mut map_sum = vec![0.0; 10];
|
|
let mut precision_sum = vec![0.0; 10];
|
|
let mut recall_sum = vec![0.0; 10];
|
|
let mut mmr_sum = 0.0;
|
|
|
|
for query_metric in self.query_metrics.iter() {
|
|
for (ndcg, query_ndcg) in ndcg_sum.iter_mut().zip(query_metric.ndcg.clone()) {
|
|
*ndcg += query_ndcg;
|
|
}
|
|
|
|
for (mapp, query_map) in map_sum.iter_mut().zip(query_metric.map.clone()) {
|
|
*mapp += query_map;
|
|
}
|
|
|
|
for (pre, query_pre) in precision_sum.iter_mut().zip(query_metric.precision.clone()) {
|
|
*pre += query_pre;
|
|
}
|
|
|
|
for (rec, query_rec) in recall_sum.iter_mut().zip(query_metric.recall.clone()) {
|
|
*rec += query_rec;
|
|
}
|
|
|
|
mmr_sum += query_metric.mrr;
|
|
}
|
|
|
|
let ndcg = ndcg_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
|
|
let map = map_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
|
|
let precision = precision_sum
|
|
.iter()
|
|
.map(|val| val / l)
|
|
.collect::<Vec<f32>>();
|
|
let recall = recall_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
|
|
let mrr = mmr_sum / l;
|
|
|
|
self.repo_metrics = Some(SummaryMetrics {
|
|
millis_to_search,
|
|
ndcg,
|
|
map,
|
|
mrr,
|
|
precision,
|
|
recall,
|
|
})
|
|
}
|
|
}
|
|
|
|
fn evaluate_precision(hits: Vec<usize>) -> Vec<f32> {
|
|
let mut rolling_hit: f32 = 0.0;
|
|
let mut precision = Vec::new();
|
|
for (idx, hit) in hits.into_iter().enumerate() {
|
|
rolling_hit += hit as f32;
|
|
precision.push(rolling_hit / ((idx as f32) + 1.0));
|
|
}
|
|
|
|
precision
|
|
}
|
|
|
|
fn evaluate_recall(hits: Vec<usize>, ideal: Vec<usize>) -> Vec<f32> {
|
|
let total_relevant = ideal.iter().sum::<usize>() as f32;
|
|
let mut recall = Vec::new();
|
|
let mut rolling_hit: f32 = 0.0;
|
|
for hit in hits {
|
|
rolling_hit += hit as f32;
|
|
recall.push(rolling_hit / total_relevant);
|
|
}
|
|
|
|
recall
|
|
}
|
|
|
|
async fn evaluate_repo(
|
|
repo_name: String,
|
|
index: ModelHandle<SemanticIndex>,
|
|
project: ModelHandle<Project>,
|
|
query_matches: Vec<EvaluationQuery>,
|
|
cx: &mut AsyncAppContext,
|
|
) -> Result<RepoEvaluationMetrics> {
|
|
// Index Project
|
|
let index_t0 = Instant::now();
|
|
index
|
|
.update(cx, |index, cx| index.index_project(project.clone(), cx))
|
|
.await?;
|
|
let mut repo_metrics = RepoEvaluationMetrics::new(index_t0.elapsed());
|
|
|
|
for query in query_matches {
|
|
// Query each match in order
|
|
let search_t0 = Instant::now();
|
|
let search_results = index
|
|
.update(cx, |index, cx| {
|
|
index.search_project(project.clone(), query.clone().query, 10, vec![], vec![], cx)
|
|
})
|
|
.await?;
|
|
let millis_to_search = search_t0.elapsed();
|
|
|
|
// Get Hits/Ideal
|
|
let k = 10;
|
|
let (ideal, hits) = self::get_hits(query.clone(), search_results, k, cx);
|
|
|
|
// Evaluate ndcg@k, for k = 1, 3, 5, 10
|
|
let ndcg = evaluate_ndcg(hits.clone(), ideal.clone());
|
|
|
|
// Evaluate map@k, for k = 1, 3, 5, 10
|
|
let map = evaluate_map(hits.clone());
|
|
|
|
// Evaluate mrr
|
|
let mrr = evaluate_mrr(hits.clone());
|
|
|
|
// Evaluate precision
|
|
let precision = evaluate_precision(hits.clone());
|
|
|
|
// Evaluate Recall
|
|
let recall = evaluate_recall(hits.clone(), ideal);
|
|
|
|
let query_metrics = QueryMetrics {
|
|
query,
|
|
millis_to_search,
|
|
ndcg,
|
|
map,
|
|
mrr,
|
|
hits,
|
|
precision,
|
|
recall,
|
|
};
|
|
|
|
repo_metrics.query_metrics.push(query_metrics);
|
|
}
|
|
|
|
repo_metrics.summarize();
|
|
repo_metrics.save(repo_name);
|
|
|
|
anyhow::Ok(repo_metrics)
|
|
}
|
|
|
|
fn main() {
|
|
// Launch new repo as a new Zed workspace/project
|
|
let app = gpui::App::new(()).unwrap();
|
|
let fs = Arc::new(RealFs);
|
|
let http = http::client();
|
|
let http_client = http::client();
|
|
init_logger();
|
|
|
|
app.run(move |cx| {
|
|
cx.set_global(*RELEASE_CHANNEL);
|
|
|
|
let client = client::Client::new(http.clone(), cx);
|
|
let user_store = cx.add_model(|cx| UserStore::new(client.clone(), http_client.clone(), cx));
|
|
|
|
// Initialize Settings
|
|
let mut store = SettingsStore::default();
|
|
store
|
|
.set_default_settings(default_settings().as_ref(), cx)
|
|
.unwrap();
|
|
cx.set_global(store);
|
|
|
|
// Initialize Languages
|
|
let login_shell_env_loaded = Task::ready(());
|
|
let mut languages = LanguageRegistry::new(login_shell_env_loaded);
|
|
languages.set_executor(cx.background().clone());
|
|
let languages = Arc::new(languages);
|
|
|
|
let node_runtime = RealNodeRuntime::new(http.clone());
|
|
languages::init(languages.clone(), node_runtime.clone());
|
|
language::init(cx);
|
|
|
|
project::Project::init(&client, cx);
|
|
semantic_index::init(fs.clone(), http.clone(), languages.clone(), cx);
|
|
|
|
settings::register::<SemanticIndexSettings>(cx);
|
|
|
|
let db_file_path = EMBEDDINGS_DIR
|
|
.join(Path::new(RELEASE_CHANNEL_NAME.as_str()))
|
|
.join("embeddings_db");
|
|
|
|
let languages = languages.clone();
|
|
let fs = fs.clone();
|
|
cx.spawn(|mut cx| async move {
|
|
let semantic_index = SemanticIndex::new(
|
|
fs.clone(),
|
|
db_file_path,
|
|
Arc::new(OpenAIEmbeddings::new(http_client, cx.background())),
|
|
languages.clone(),
|
|
cx.clone(),
|
|
)
|
|
.await?;
|
|
|
|
if let Ok(repo_evals) = parse_eval() {
|
|
for repo in repo_evals {
|
|
let cloned = clone_repo(repo.clone());
|
|
match cloned {
|
|
Ok((repo_name, clone_path)) => {
|
|
println!(
|
|
"Cloned {:?} @ {:?} into {:?}",
|
|
repo.repo, repo.commit, &clone_path
|
|
);
|
|
|
|
// Create Project
|
|
let project = cx.update(|cx| {
|
|
Project::local(
|
|
client.clone(),
|
|
user_store.clone(),
|
|
languages.clone(),
|
|
fs.clone(),
|
|
cx,
|
|
)
|
|
});
|
|
|
|
// Register Worktree
|
|
let _ = project
|
|
.update(&mut cx, |project, cx| {
|
|
project.find_or_create_local_worktree(clone_path, true, cx)
|
|
})
|
|
.await;
|
|
|
|
let repo_metrics = evaluate_repo(
|
|
repo_name,
|
|
semantic_index.clone(),
|
|
project,
|
|
repo.assertions,
|
|
&mut cx,
|
|
)
|
|
.await?;
|
|
}
|
|
Err(err) => {
|
|
println!("Error cloning: {:?}", err);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
anyhow::Ok(())
|
|
})
|
|
.detach();
|
|
});
|
|
}
|