Пример #1
0
  // Test kaggle/creditsample-test data
  @org.junit.Test
  public void kaggle_credit() {
    Key okey = loadAndParseFile("credit.hex", "smalldata/kaggle/creditsample-training.csv.gz");
    UKV.remove(Key.make("smalldata/kaggle/creditsample-training.csv.gz_UNZIPPED"));
    UKV.remove(Key.make("smalldata\\kaggle\\creditsample-training.csv.gz_UNZIPPED"));
    ValueArray val = DKV.get(okey).get();

    // Check parsed dataset
    final int n = new int[] {4, 2, 1}[ValueArray.LOG_CHK - 20];
    assertEquals("Number of chunks", n, val.chunks());
    assertEquals("Number of rows", 150000, val.numRows());
    assertEquals("Number of cols", 12, val.numCols());

    // setup default values for DRF
    int ntrees = 3;
    int depth = 30;
    int gini = StatType.GINI.ordinal();
    int seed = 42;
    StatType statType = StatType.values()[gini];
    final int cols[] =
        new int[] {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 1}; // ignore column 6, classify column 1

    // Start the distributed Random Forest
    final Key modelKey = Key.make("model");
    DRFJob result =
        hex.rf.DRF.execute(
            modelKey,
            cols,
            val,
            ntrees,
            depth,
            1024,
            statType,
            seed,
            true,
            null,
            -1,
            Sampling.Strategy.RANDOM,
            1.0f,
            null,
            0,
            0,
            false);
    // Wait for completion on all nodes
    RFModel model = result.get();

    assertEquals("Number of classes", 2, model.classes());
    assertEquals("Number of trees", ntrees, model.size());

    model.deleteKeys();
    UKV.remove(modelKey);
    UKV.remove(okey);
  }
Пример #2
0
  /*@org.junit.Test*/ public void covtype() {
    // Key okey = loadAndParseFile("covtype.hex", "smalldata/covtype/covtype.20k.data");
    // Key okey = loadAndParseFile("covtype.hex", "../datasets/UCI/UCI-large/covtype/covtype.data");
    // Key okey = loadAndParseFile("covtype.hex", "/home/0xdiag/datasets/standard/covtype.data");
    Key okey = loadAndParseFile("mnist.hex", "smalldata/mnist/mnist8m.10k.csv.gz");
    // Key okey = loadAndParseFile("mnist.hex", "/home/0xdiag/datasets/mnist/mnist8m.csv");
    ValueArray val = UKV.get(okey);

    // setup default values for DRF
    int ntrees = 8;
    int depth = 999;
    int gini = StatType.ENTROPY.ordinal();
    int seed = 42;
    StatType statType = StatType.values()[gini];
    final int cols[] = new int[val.numCols()];
    for (int i = 1; i < cols.length; i++) cols[i] = i - 1;
    cols[cols.length - 1] = 0; // Class is in column 0 for mnist

    // Start the distributed Random Forest
    final Key modelKey = Key.make("model");
    DRFJob result =
        hex.rf.DRF.execute(
            modelKey,
            cols,
            val,
            ntrees,
            depth,
            1024,
            statType,
            seed,
            true,
            null,
            -1,
            Sampling.Strategy.RANDOM,
            1.0f,
            null,
            0,
            0,
            false);
    // Wait for completion on all nodes
    RFModel model = result.get();

    assertEquals("Number of classes", 10, model.classes());
    assertEquals("Number of trees", ntrees, model.size());

    model.deleteKeys();
    UKV.remove(modelKey);
    UKV.remove(okey);
  }