Esempio n. 1
0
 /**
  * Sample rows from a frame. Can be unlucky for small sampling fractions - will continue calling
  * itself until at least 1 row is returned.
  *
  * @param fr Input frame
  * @param rows Approximate number of rows to sample (across all chunks)
  * @param seed Seed for RNG
  * @return Sampled frame
  */
 public static Frame sampleFrame(Frame fr, final long rows, final long seed) {
   if (fr == null) return null;
   final float fraction = rows > 0 ? (float) rows / fr.numRows() : 1.f;
   if (fraction >= 1.f) return fr;
   Frame r =
       new MRTask2() {
         @Override
         public void map(Chunk[] cs, NewChunk[] ncs) {
           final Random rng = getDeterRNG(seed + cs[0].cidx());
           int count = 0;
           for (int r = 0; r < cs[0]._len; r++)
             if (rng.nextFloat() < fraction || (count == 0 && r == cs[0]._len - 1)) {
               count++;
               for (int i = 0; i < ncs.length; i++) {
                 ncs[i].addNum(cs[i].at0(r));
               }
             }
         }
       }.doAll(fr.numCols(), fr).outputFrame(fr.names(), fr.domains());
   if (r.numRows() == 0) {
     Log.warn(
         "You asked for "
             + rows
             + " rows (out of "
             + fr.numRows()
             + "), but you got none (seed="
             + seed
             + ").");
     Log.warn("Let's try again. You've gotta ask yourself a question: \"Do I feel lucky?\"");
     return sampleFrame(fr, rows, seed + 1);
   }
   return r;
 }
Esempio n. 2
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 /**
  * Global redistribution of a Frame (balancing of chunks), done by calling process (all-to-one +
  * one-to-all)
  *
  * @param fr Input frame
  * @param seed RNG seed
  * @param shuffle whether to shuffle the data globally
  * @return Shuffled frame
  */
 public static Frame shuffleAndBalance(
     final Frame fr, int splits, long seed, final boolean local, final boolean shuffle) {
   if ((fr.vecs()[0].nChunks() < splits || shuffle) && fr.numRows() > splits) {
     Vec[] vecs = fr.vecs().clone();
     Log.info("Load balancing dataset, splitting it into up to " + splits + " chunks.");
     long[] idx = null;
     if (shuffle) {
       idx = new long[splits];
       for (int r = 0; r < idx.length; ++r) idx[r] = r;
       Utils.shuffleArray(idx, seed);
     }
     Key keys[] = new Vec.VectorGroup().addVecs(vecs.length);
     final long rows_per_new_chunk = (long) (Math.ceil((double) fr.numRows() / splits));
     // loop over cols (same indexing for each column)
     Futures fs = new Futures();
     for (int col = 0; col < vecs.length; col++) {
       AppendableVec vec = new AppendableVec(keys[col]);
       // create outgoing chunks for this col
       NewChunk[] outCkg = new NewChunk[splits];
       for (int i = 0; i < splits; ++i) outCkg[i] = new NewChunk(vec, i);
       // loop over all incoming chunks
       for (int ckg = 0; ckg < vecs[col].nChunks(); ckg++) {
         final Chunk inCkg = vecs[col].chunkForChunkIdx(ckg);
         // loop over local rows of incoming chunks (fast path)
         for (int row = 0; row < inCkg._len; ++row) {
           int outCkgIdx =
               (int) ((inCkg._start + row) / rows_per_new_chunk); // destination chunk idx
           if (shuffle)
             outCkgIdx = (int) (idx[outCkgIdx]); // shuffle: choose a different output chunk
           assert (outCkgIdx >= 0 && outCkgIdx < splits);
           outCkg[outCkgIdx].addNum(inCkg.at0(row));
         }
       }
       for (int i = 0; i < outCkg.length; ++i) outCkg[i].close(i, fs);
       Vec t = vec.close(fs);
       t._domain = vecs[col]._domain;
       vecs[col] = t;
     }
     fs.blockForPending();
     Log.info("Load balancing done.");
     return new Frame(fr.names(), vecs);
   }
   return fr;
 }
Esempio n. 3
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  // internal version with repeat counter
  // currently hardcoded to do up to 10 tries to get a row from each class, which can be impossible
  // for certain wrong sampling ratios
  private static Frame sampleFrameStratified(
      final Frame fr,
      Vec label,
      final float[] sampling_ratios,
      final long seed,
      final boolean debug,
      int count) {
    if (fr == null) return null;
    assert (label.isEnum());
    assert (sampling_ratios != null && sampling_ratios.length == label.domain().length);
    final int labelidx = fr.find(label); // which column is the label?
    assert (labelidx >= 0);

    final boolean poisson = false; // beta feature

    Frame r =
        new MRTask2() {
          @Override
          public void map(Chunk[] cs, NewChunk[] ncs) {
            final Random rng = getDeterRNG(seed + cs[0].cidx());
            for (int r = 0; r < cs[0]._len; r++) {
              if (cs[labelidx].isNA0(r)) continue; // skip missing labels
              final int label = (int) cs[labelidx].at80(r);
              assert (sampling_ratios.length > label && label >= 0);
              int sampling_reps;
              if (poisson) {
                sampling_reps = Utils.getPoisson(sampling_ratios[label], rng);
              } else {
                final float remainder = sampling_ratios[label] - (int) sampling_ratios[label];
                sampling_reps =
                    (int) sampling_ratios[label] + (rng.nextFloat() < remainder ? 1 : 0);
              }
              for (int i = 0; i < ncs.length; i++) {
                for (int j = 0; j < sampling_reps; ++j) {
                  ncs[i].addNum(cs[i].at0(r));
                }
              }
            }
          }
        }.doAll(fr.numCols(), fr).outputFrame(fr.names(), fr.domains());

    // Confirm the validity of the distribution
    long[] dist = new ClassDist(r.vecs()[labelidx]).doAll(r.vecs()[labelidx]).dist();

    // if there are no training labels in the test set, then there is no point in sampling the test
    // set
    if (dist == null) return fr;

    if (debug) {
      long sumdist = Utils.sum(dist);
      Log.info("After stratified sampling: " + sumdist + " rows.");
      for (int i = 0; i < dist.length; ++i) {
        Log.info(
            "Class "
                + r.vecs()[labelidx].domain(i)
                + ": count: "
                + dist[i]
                + " sampling ratio: "
                + sampling_ratios[i]
                + " actual relative frequency: "
                + (float) dist[i] / sumdist * dist.length);
      }
    }

    // Re-try if we didn't get at least one example from each class
    if (Utils.minValue(dist) == 0 && count < 10) {
      Log.info(
          "Re-doing stratified sampling because not all classes were represented (unlucky draw).");
      r.delete();
      return sampleFrameStratified(fr, label, sampling_ratios, seed + 1, debug, ++count);
    }

    // shuffle intra-chunk
    Frame shuffled = shuffleFramePerChunk(r, seed + 0x580FF13);
    r.delete();

    return shuffled;
  }
Esempio n. 4
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  /**
   * Stratified sampling for classifiers
   *
   * @param fr Input frame
   * @param label Label vector (must be enum)
   * @param sampling_ratios Optional: array containing the requested sampling ratios per class (in
   *     order of domains), will be overwritten if it contains all 0s
   * @param maxrows Maximum number of rows in the returned frame
   * @param seed RNG seed for sampling
   * @param allowOversampling Allow oversampling of minority classes
   * @param verbose Whether to print verbose info
   * @return Sampled frame, with approximately the same number of samples from each class (or given
   *     by the requested sampling ratios)
   */
  public static Frame sampleFrameStratified(
      final Frame fr,
      Vec label,
      float[] sampling_ratios,
      long maxrows,
      final long seed,
      final boolean allowOversampling,
      final boolean verbose) {
    if (fr == null) return null;
    assert (label.isEnum());
    assert (maxrows >= label.domain().length);

    long[] dist = new ClassDist(label).doAll(label).dist();
    assert (dist.length > 0);
    Log.info(
        "Doing stratified sampling for data set containing "
            + fr.numRows()
            + " rows from "
            + dist.length
            + " classes. Oversampling: "
            + (allowOversampling ? "on" : "off"));
    if (verbose) {
      for (int i = 0; i < dist.length; ++i) {
        Log.info(
            "Class "
                + label.domain(i)
                + ": count: "
                + dist[i]
                + " prior: "
                + (float) dist[i] / fr.numRows());
      }
    }

    // create sampling_ratios for class balance with max. maxrows rows (fill existing array if not
    // null)
    if (sampling_ratios == null
        || (Utils.minValue(sampling_ratios) == 0 && Utils.maxValue(sampling_ratios) == 0)) {
      // compute sampling ratios to achieve class balance
      if (sampling_ratios == null) {
        sampling_ratios = new float[dist.length];
      }
      assert (sampling_ratios.length == dist.length);
      for (int i = 0; i < dist.length; ++i) {
        sampling_ratios[i] =
            ((float) fr.numRows() / label.domain().length) / dist[i]; // prior^-1 / num_classes
      }
      final float inv_scale =
          Utils.minValue(
              sampling_ratios); // majority class has lowest required oversampling factor to achieve
                                // balance
      if (!Float.isNaN(inv_scale) && !Float.isInfinite(inv_scale))
        Utils.div(
            sampling_ratios,
            inv_scale); // want sampling_ratio 1.0 for majority class (no downsampling)
    }

    if (!allowOversampling) {
      for (int i = 0; i < sampling_ratios.length; ++i) {
        sampling_ratios[i] = Math.min(1.0f, sampling_ratios[i]);
      }
    }

    // given these sampling ratios, and the original class distribution, this is the expected number
    // of resulting rows
    float numrows = 0;
    for (int i = 0; i < sampling_ratios.length; ++i) {
      numrows += sampling_ratios[i] * dist[i];
    }
    final long actualnumrows = Math.min(maxrows, Math.round(numrows)); // cap #rows at maxrows
    assert (actualnumrows
        >= 0); // can have no matching rows in case of sparse data where we had to fill in a
               // makeZero() vector
    Log.info("Stratified sampling to a total of " + String.format("%,d", actualnumrows) + " rows.");

    if (actualnumrows != numrows) {
      Utils.mult(
          sampling_ratios,
          (float) actualnumrows
              / numrows); // adjust the sampling_ratios by the global rescaling factor
      if (verbose)
        Log.info(
            "Downsampling majority class by "
                + (float) actualnumrows / numrows
                + " to limit number of rows to "
                + String.format("%,d", maxrows));
    }
    Log.info(
        "Majority class ("
            + label.domain()[Utils.minIndex(sampling_ratios)].toString()
            + ") sampling ratio: "
            + Utils.minValue(sampling_ratios));
    Log.info(
        "Minority class ("
            + label.domain()[Utils.maxIndex(sampling_ratios)].toString()
            + ") sampling ratio: "
            + Utils.maxValue(sampling_ratios));

    return sampleFrameStratified(fr, label, sampling_ratios, seed, verbose);
  }