docs/Sampling/VarOptSamplingJava.md

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# VarOpt Sampling Java Example

This example was run using word counts from Shakespeare plays, namely
Romeo and Juliet and Hamlet. The scripts, available from various
sources including http://shakespeare.mit.edu/, were converted to
(word, count) files with the following Perl command:

    perl -lane 's/^\s+//; s/[;\.,!?:\x27\[\]&]//g; s/--//g; s/\s+/\n/g; print lc if length > 0' input.txt | sort | uniq -c | awk '{print $1 "\t" $2}' > output.txt

These were then used in the following example, slightly modified to remove error handling for clarity. Serialization and deserialization are completely parallel to the Reservoir Sampling sketch, and example code for that may be found in those Java examples.


    import java.io.BufferedReader;
    import java.io.File;
    import java.io.FileInputStream;
    import java.io.FileOutputStream;
    import java.io.FileReader;

    import org.apache.datasketches.memory.Memory;
    import org.apache.datasketches.ArrayOfLongsSerDe;
    import org.apache.datasketches.sampling.SampleSubsetSummary;
    import org.apache.datasketches.sampling.VarOptItemsSamples;
    import org.apache.datasketches.sampling.VarOptItemsSketch;
    import org.apache.datasketches.sampling.VarOptItemsUnion;

    // load (token, count) data from file, build sketch of size k
    private static VarOptItemsSketch<String> loadFile(final String filename,
                                                      final int k) {
      try (BufferedReader br = new BufferedReader(new FileReader(filename))) {
        VarOptItemsSketch<String> vis = VarOptItemsSketch.newInstance(k);
        String line;
        while ((line = br.readLine()) != null) {
          String[] tokens = line.split("\\s+");
          if (tokens.length == 2) {
            vis.update(tokens[1], Double.parseDouble(tokens[0]));
          }
        }
        return vis;
      }
    }

    // this section loads two sketches from prepared text files, unions them
    // and demonstrates how to estimate subset sums for VarOpt sketches
    {
      final int k = 100;
      VarOptItemsSketch<String> sketch1 = loadFile("/path/to/romeo_juliet.tsv", k);
      VarOptItemsSketch<String> sketch2 = loadFile("/path/to/hamlet.tsv", k);
      VarOptItemsUnion<String> union = VarOptItemsUnion.newInstance(k);
      union.update(sketch1);
      union.update(sketch2);

      // get and iterate over samples
      VarOptItemsSamples<String> samples = union.getResult().getSketchSamples();
      for (VarOptItemsSamples<String>.WeightedSample ws : samples) {
        System.out.println(ws.getItem() + "\t" + ws.getWeight());
      }

      // apply predicate to estimate subset sums, here words of > 7 chars
      SampleSubsetSummary summary = union.getResult().estimateSubsetSum(s -> s.length() > 7);
      System.out.printf("[%f, %f, %f]\n",
            summary.getLowerBound(), summary.getEstimate(), summary.getUpperBound());
    }

Sample Output:

    i	567.0
    i	580.0
    to	737.0
    of	667.0
    and	716.0
    and	964.0
    the	1141.0
    the	681.0
    you	560.5978260869568
    message	560.5978260869568
    yon	560.5978260869568
    thy	560.5978260869568
    a	560.5978260869568
    ...
    [truncated]

Lower bound, estimate, upper bound:

    [594.395954, 2242.391304, 5611.681344]