Use ScoreTracker to avoid wasteful searching for very large k (#384)

* clarify

* use scoreTracker to short circuit new edge evaluation once we hit a local maximum

jvector-base/src/main/java/io/github/jbellis/jvector/graph/GraphSearcher.java

@@ -264,11 +264,8 @@ private SearchResult resume(int initialVisited, int topK, int rerankK, float thr
             rerankedResults.setMaxSize(topK);
 
             int numVisited = initialVisited;
-            // A bound that holds the minimum similarity to the query vector that a candidate vector must
-            // have to be considered -- will be set to the lowest score in the results queue once the queue is full.
-            var minAcceptedSimilarity = Float.NEGATIVE_INFINITY;
             // track scores to predict when we are done with threshold queries
-            var scoreTracker = threshold > 0 ? new ScoreTracker.TwoPhaseTracker(threshold) : ScoreTracker.NO_OP;
+            var scoreTracker = threshold > 0 ? new ScoreTracker.TwoPhaseTracker(threshold) : new ScoreTracker.TwoPhaseTracker(1.0);
             VectorFloat<?> similarities = null;
 
             // add evicted results from the last call back to the candidates
@@ -283,37 +280,36 @@ private SearchResult resume(int initialVisited, int topK, int rerankK, float thr
             while (candidates.size() > 0) {
                 // we're done when we have K results and the best candidate is worse than the worst result so far
                 float topCandidateScore = candidates.topScore();
-                if (topCandidateScore < minAcceptedSimilarity) {
+                if (approximateResults.size() >= rerankK && topCandidateScore < approximateResults.topScore()) {
                     break;
                 }
                 // when querying by threshold, also stop when we are probabilistically unlikely to find more qualifying results
-                if (scoreTracker.shouldStop()) {
+                if (threshold > 0 && scoreTracker.shouldStop()) {
                     break;
                 }
 
                 // process the top candidate
                 int topCandidateNode = candidates.pop();
                 if (acceptOrds.get(topCandidateNode) && topCandidateScore >= threshold) {
                     addTopCandidate(topCandidateNode, topCandidateScore, rerankK);
-
-                    // update minAcceptedSimilarity if we've found K results
-                    if (approximateResults.size() >= rerankK) {
-                        minAcceptedSimilarity = approximateResults.topScore();
-                    }
                 }
 
                 // if this candidate came from evictedResults, we don't need to evaluate its neighbors again
                 if (previouslyEvicted.get(topCandidateNode)) {
                     continue;
                 }
 
+                // skip edge loading if we've found a local maximum and we have enough results
+                if (scoreTracker.shouldStop() && candidates.size() >= rerankK - approximateResults.size()) {
+                    continue;
+                }
+
                 // score the neighbors of the top candidate and add them to the queue
                 var scoreFunction = scoreProvider.scoreFunction();
                 var useEdgeLoading = scoreFunction.supportsEdgeLoadingSimilarity();
                 if (useEdgeLoading) {
                     similarities = scoreFunction.edgeLoadingSimilarityTo(topCandidateNode);
                 }
-
                 var it = view.getNeighborsIterator(topCandidateNode);
                 for (int i = 0; i < it.size(); i++) {
                     var friendOrd = it.nextInt();

jvector-base/src/main/java/io/github/jbellis/jvector/graph/ScoreTracker.java

@@ -55,7 +55,7 @@ class TwoPhaseTracker implements ScoreTracker {
         private int recentEntryIndex;
 
         // Heap of the best scores seen so far
-        AbstractLongHeap bestScores;
+        BoundedLongHeap bestScores;
 
         // observation count
         private int observationCount;
@@ -87,8 +87,10 @@ public boolean shouldStop() {
                 return false;
             }
 
-            // we're in phase 2 if the 99th percentile of the recent scores is worse than the best score
-            // (paper suggests median, but experimentally that is too prone to false positives.
+            // We're in phase 2 if the 99th percentile of the recent scores evaluated is lower
+            // than the worst of the best scores seen.
+            //
+            // (paper suggests using the median of recent scores, but experimentally that is too prone to false positives.
             // 90th does seem to be enough, but 99th doesn't result in much extra work, so we'll be conservative)
             double windowMedian = StatUtils.percentile(recentScores, 99);
             double worstBest = sortableIntToFloat((int) bestScores.top());