[mob][photos] Fix regression in suggestion calculation
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689833d8aa
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@ -481,46 +481,46 @@ class ClusterFeedbackService {
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late Map<int, Vector> clusterAvgBigClusters;
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late Map<int, Vector> clusterAvgBigClusters;
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final List<(int, double)> suggestionsMean = [];
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final List<(int, double)> suggestionsMean = [];
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for (final minimumSize in checkSizes.toSet()) {
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for (final minimumSize in checkSizes.toSet()) {
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// if (smallestPersonClusterSize >= minimumSize) {
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if (smallestPersonClusterSize >= minimumSize) {
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clusterAvgBigClusters = await _getUpdateClusterAvg(
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clusterAvgBigClusters = await _getUpdateClusterAvg(
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allClusterIdsToCountMap,
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allClusterIdsToCountMap,
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ignoredClusters,
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ignoredClusters,
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minClusterSize: minimumSize,
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minClusterSize: minimumSize,
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);
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);
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w?.log(
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w?.log(
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'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
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'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
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);
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);
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final List<(int, double)> suggestionsMeanBigClusters =
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final List<(int, double)> suggestionsMeanBigClusters =
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_calcSuggestionsMean(
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_calcSuggestionsMean(
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clusterAvgBigClusters,
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clusterAvgBigClusters,
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personClusters,
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personClusters,
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ignoredClusters,
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ignoredClusters,
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goodMeanDistance,
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goodMeanDistance,
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);
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);
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w?.log(
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w?.log(
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'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
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'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
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);
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);
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for (final suggestion in suggestionsMeanBigClusters) {
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for (final suggestion in suggestionsMeanBigClusters) {
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// Skip suggestions that have a high overlap with the person's files
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// Skip suggestions that have a high overlap with the person's files
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final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
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final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
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.map((faceID) => getFileIdFromFaceId(faceID))
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.map((faceID) => getFileIdFromFaceId(faceID))
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.toSet();
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.toSet();
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final overlap = personFileIDs.intersection(suggestionSet);
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final overlap = personFileIDs.intersection(suggestionSet);
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if (overlap.isNotEmpty &&
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if (overlap.isNotEmpty &&
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((overlap.length / suggestionSet.length) > 0.5)) {
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((overlap.length / suggestionSet.length) > 0.5)) {
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await FaceMLDataDB.instance.captureNotPersonFeedback(
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await FaceMLDataDB.instance.captureNotPersonFeedback(
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personID: p.remoteID,
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personID: p.remoteID,
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clusterID: suggestion.$1,
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clusterID: suggestion.$1,
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);
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);
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continue;
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continue;
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}
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suggestionsMean.add(suggestion);
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}
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if (suggestionsMean.isNotEmpty) {
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return suggestionsMean
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.map((e) => (e.$1, e.$2, true))
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.toList(growable: false);
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}
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}
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suggestionsMean.add(suggestion);
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}
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if (suggestionsMean.isNotEmpty) {
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return suggestionsMean
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.map((e) => (e.$1, e.$2, true))
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.toList(growable: false);
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// }
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}
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}
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}
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}
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w?.reset();
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w?.reset();
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