From faa07a0704ac38056bb008c08109fec4db111593 Mon Sep 17 00:00:00 2001
From: Neeraj Gupta <254676+ua741@users.noreply.github.com>
Date: Tue, 2 Apr 2024 16:56:55 +0530
Subject: [PATCH] [mob] compute suggestion in small batches

---
 .../face_ml/feedback/cluster_feedback.dart    | 84 ++++++++++++-------
 1 file changed, 53 insertions(+), 31 deletions(-)

diff --git a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
index a0ede5809..745c73245 100644
--- a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
+++ b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
@@ -367,11 +367,13 @@ class ClusterFeedbackService {
 
   Future<Map<int, List<double>>> _getUpdateClusterAvg(
     Map<int, int> allClusterIdsToCountMap,
-    Set<int> ignoredClusters,
-  ) async {
+    Set<int> ignoredClusters, {
+    int minClusterSize = 1,
+    int maxClusterInCurrentRun = 500,
+  }) async {
     final faceMlDb = FaceMLDataDB.instance;
     _logger.info(
-      'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters',
+      'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters, minClusterSize $minClusterSize, maxClusterInCurrentRun $maxClusterInCurrentRun',
     );
 
     final Map<int, (Uint8List, int)> clusterToSummary =
@@ -380,42 +382,61 @@ class ClusterFeedbackService {
 
     final Map<int, List<double>> clusterAvg = {};
 
-    final allClusterIds = allClusterIdsToCountMap.keys;
-    for (final clusterID in allClusterIds) {
-      if (ignoredClusters.contains(clusterID)) {
-        continue;
+    final allClusterIds = allClusterIdsToCountMap.keys.toSet();
+    int ignoredClustersCnt = 0, alreadyUpdatedClustersCnt = 0;
+    int smallerClustersCnt = 0;
+    for (final id in allClusterIdsToCountMap.keys) {
+      if (ignoredClusters.contains(id)) {
+        allClusterIds.remove(id);
+        ignoredClustersCnt++;
       }
-      if (allClusterIdsToCountMap[clusterID]! < 2) {
-        continue;
+      if (clusterToSummary[id]?.$2 == allClusterIdsToCountMap[id]) {
+        allClusterIds.remove(id);
+        clusterAvg[id] = EVector.fromBuffer(clusterToSummary[id]!.$1).values;
+        alreadyUpdatedClustersCnt++;
       }
+      if (allClusterIdsToCountMap[id]! < minClusterSize) {
+        allClusterIds.remove(id);
+        smallerClustersCnt++;
+      }
+    }
+    _logger.info(
+      'Ignored $ignoredClustersCnt clusters, already updated $alreadyUpdatedClustersCnt clusters, $smallerClustersCnt clusters are smaller than $minClusterSize',
+    );
+    // get clusterIDs sorted by count in descending order
+    final sortedClusterIDs = allClusterIds.toList();
+    sortedClusterIDs.sort(
+      (a, b) =>
+          allClusterIdsToCountMap[b]!.compareTo(allClusterIdsToCountMap[a]!),
+    );
+    int indexedInCurrentRun = 0;
 
-      late List<double> avg;
-      if (clusterToSummary[clusterID]?.$2 ==
-          allClusterIdsToCountMap[clusterID]) {
-        avg = EVector.fromBuffer(clusterToSummary[clusterID]!.$1).values;
-      } else {
-        final Iterable<Uint8List> embedings =
-            await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
-        final List<double> sum = List.filled(192, 0);
-        for (final embedding in embedings) {
-          final data = EVector.fromBuffer(embedding).values;
-          for (int i = 0; i < sum.length; i++) {
-            sum[i] += data[i];
-          }
-        }
-        avg = sum.map((e) => e / embedings.length).toList();
-        final avgEmbeedingBuffer = EVector(values: avg).writeToBuffer();
-        updatesForClusterSummary[clusterID] =
-            (avgEmbeedingBuffer, embedings.length);
+    for (final clusterID in sortedClusterIDs) {
+      if (maxClusterInCurrentRun-- <= 0) {
+        break;
       }
+      indexedInCurrentRun++;
+      late List<double> avg;
+      final Iterable<Uint8List> embedings =
+          await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
+      final List<double> sum = List.filled(192, 0);
+      for (final embedding in embedings) {
+        final data = EVector.fromBuffer(embedding).values;
+        for (int i = 0; i < sum.length; i++) {
+          sum[i] += data[i];
+        }
+      }
+      avg = sum.map((e) => e / embedings.length).toList();
+      final avgEmbeedingBuffer = EVector(values: avg).writeToBuffer();
+      updatesForClusterSummary[clusterID] =
+          (avgEmbeedingBuffer, embedings.length);
       // store the intermediate updates
       if (updatesForClusterSummary.length > 100) {
         await faceMlDb.clusterSummaryUpdate(updatesForClusterSummary);
         updatesForClusterSummary.clear();
         if (kDebugMode) {
           _logger.info(
-            'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters',
-          );
+              'getUpdateClusterAvg $indexedInCurrentRun clusters in current one');
         }
       }
       clusterAvg[clusterID] = avg;
@@ -549,8 +570,9 @@ class ClusterFeedbackService {
         );
       }
       suggestion.$4.sort((b, a) {
-        final double distanceA = fileIdToDistanceMap[a.uploadedFileID!];
-        final double distanceB = fileIdToDistanceMap[b.uploadedFileID!];
+        //todo: review with @laurens, added this to avoid null safety issue
+        final double distanceA = fileIdToDistanceMap[a.uploadedFileID!] ?? -1;
+        final double distanceB = fileIdToDistanceMap[b.uploadedFileID!] ?? -1;
         return distanceA.compareTo(distanceB);
       });