public static PredictionStat getPredictionStatVertexSampling()

in src/main/java/com/twitter/sbf/core/MHAlgorithm.java [775:837]
61 lines of code
14 McCabe index (conditional complexity)

  public static PredictionStat getPredictionStatVertexSampling(
      Graph graph,
      SparseBinaryMatrix matrix,
      ExecutorService executor,
      int[] evalVertexIds) {
    Range[] ranges = Util.chunkArray(evalVertexIds.length, 1000);
    ObjectList<Future<PredictionStat>> futures = new ObjectArrayList<>(ranges.length);
    for (Range range : ranges) {
      Future<PredictionStat> future = executor.submit(() -> {
        PredictionStat myStat = new PredictionStat();
        for (int i = range.start; i < range.end; i++) {
          int vertex1 = evalVertexIds[i];
          int truePosForVertex = 0;
          for (int vertex2 = 0; vertex2 < graph.getNumVertices(); vertex2++) {
            if (vertex1 == vertex2) {
              continue;
            }
            float edgeWeight = graph.getWeightOfEdge(vertex1, vertex2);
            boolean isEdge = edgeWeight > 0;
            boolean predictEdge =
                Util.hasCommonElement(matrix.getRow(vertex1), matrix.getRow(vertex2));
            if (isEdge) {
              myStat.incActualPositive();
              truePosForVertex++;
              myStat.incWeightActualPositive(edgeWeight);
            }
            if (predictEdge) {
              myStat.incPredictedPositive();
              // what should this be set to?
              // Let's set this to be the average weight across the two nodes incident on this edge.
              double add =
                  (graph.getWeightedOutDegree(vertex1) + graph.getWeightedOutDegree(vertex2))
                      / (graph.getDegree(vertex1) + graph.getDegree(vertex2));
              myStat.incWeightPredictedPositive(add);
            }
            if (isEdge && predictEdge) {
              myStat.incTruePositive();
              myStat.incWeightTruePositive(edgeWeight);
            }
            if (!isEdge && !predictEdge) {
              myStat.incTrueNegative();
            }
          }
          myStat.incEval(graph.getNumVertices() - 1);
          myStat.incEvalVertices();
          if (truePosForVertex == 0) {
            myStat.incEvalVerticesWithZeroTruePos();
          }
        }
        return myStat;
      });
      futures.add(future);
    }
    PredictionStat stat = new PredictionStat();
    for (Future<PredictionStat> future : futures) {
      try {
        stat.add(future.get());
      } catch (InterruptedException | ExecutionException e) {
        e.printStackTrace();
      }
    }
    return stat;
  }