Esempio n. 1
0
  // Adapt a trained model to a test dataset with different enums
  /*@Test*/ public void testModelAdapt() {
    File file1 = TestUtil.find_test_file("./smalldata/kaggle/KDDTrain.arff.gz");
    Key fkey1 = NFSFileVec.make(file1);
    Key dest1 = Key.make("KDDTrain.hex");
    File file2 = TestUtil.find_test_file("./smalldata/kaggle/KDDTest.arff.gz");
    Key fkey2 = NFSFileVec.make(file2);
    Key dest2 = Key.make("KDDTest.hex");
    GBM gbm = null;
    Frame fr = null;
    try {
      gbm = new GBM();
      gbm.source = ParseDataset2.parse(dest1, new Key[] {fkey1});
      UKV.remove(fkey1);
      gbm.response = gbm.source.remove(41); // Response is col 41
      gbm.ntrees = 2;
      gbm.max_depth = 8;
      gbm.learn_rate = 0.2f;
      gbm.min_rows = 10;
      gbm.nbins = 50;
      gbm.invoke();

      // The test data set has a few more enums than the train
      Frame ftest = ParseDataset2.parse(dest2, new Key[] {fkey2});
      Frame preds = gbm.score(ftest);

    } finally {
      UKV.remove(dest1); // Remove original hex frame key
      if (gbm != null) {
        UKV.remove(gbm.dest()); // Remove the model
        UKV.remove(gbm.response._key);
        gbm.remove(); // Remove GBM Job
        if (fr != null) fr.remove();
      }
    }
  }
Esempio n. 2
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  // ==========================================================================
  public void basicGBM(String fname, String hexname, PrepData prep) {
    File file = TestUtil.find_test_file(fname);
    if (file == null) return; // Silently abort test if the file is missing
    Key fkey = NFSFileVec.make(file);
    Key dest = Key.make(hexname);
    GBM gbm = null;
    Frame fr = null;
    try {
      gbm = new GBM();
      gbm.source = fr = ParseDataset2.parse(dest, new Key[] {fkey});
      UKV.remove(fkey);
      int idx = prep.prep(fr);
      if (idx < 0) {
        gbm.classification = false;
        idx = ~idx;
      }
      String rname = fr._names[idx];
      gbm.response = fr.vecs()[idx];
      fr.remove(idx); // Move response to the end
      fr.add(rname, gbm.response);
      gbm.ntrees = 4;
      gbm.max_depth = 4;
      gbm.min_rows = 1;
      gbm.nbins = 50;
      gbm.cols = new int[fr.numCols()];
      for (int i = 0; i < gbm.cols.length; i++) gbm.cols[i] = i;
      gbm.learn_rate = .2f;
      gbm.invoke();

      fr = gbm.score(gbm.source);

      GBM.GBMModel gbmmodel = UKV.get(gbm.dest());
      // System.out.println(gbmmodel.toJava());

    } finally {
      UKV.remove(dest); // Remove original hex frame key
      if (gbm != null) {
        UKV.remove(gbm.dest()); // Remove the model
        UKV.remove(gbm.response._key);
        gbm.remove(); // Remove GBM Job
        if (fr != null) fr.remove();
      }
    }
  }
Esempio n. 3
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  // Test-on-Train.  Slow test, needed to build a good model.
  @Test
  public void testGBMTrainTest() {
    File file1 = TestUtil.find_test_file("..//classifcation1Train.txt");
    if (file1 == null) return; // Silently ignore if file not found
    Key fkey1 = NFSFileVec.make(file1);
    Key dest1 = Key.make("train.hex");
    File file2 = TestUtil.find_test_file("..//classification1Test.txt");
    Key fkey2 = NFSFileVec.make(file2);
    Key dest2 = Key.make("test.hex");
    GBM gbm = null;
    Frame fr = null, fpreds = null;
    try {
      gbm = new GBM();
      fr = ParseDataset2.parse(dest1, new Key[] {fkey1});
      UKV.remove(fkey1);
      UKV.remove(fr.remove("agentId")._key); // Remove unique ID; too predictive
      gbm.response = fr.remove("outcome"); // Train on the outcome
      gbm.source = fr;
      gbm.ntrees = 5;
      gbm.max_depth = 10;
      gbm.learn_rate = 0.2f;
      gbm.min_rows = 10;
      gbm.nbins = 100;
      gbm.invoke();

      // Test on the train data
      Frame ftest = ParseDataset2.parse(dest2, new Key[] {fkey2});
      UKV.remove(fkey2);
      fpreds = gbm.score(ftest);

      // Build a confusion matrix
      ConfusionMatrix CM = new ConfusionMatrix();
      CM.actual = ftest;
      CM.vactual = ftest.vecs()[ftest.find("outcome")];
      CM.predict = fpreds;
      CM.vpredict = fpreds.vecs()[fpreds.find("predict")];
      CM.serve(); // Start it, do it

      // Really crappy cut-n-paste of what should be in the ConfusionMatrix class itself
      long cm[][] = CM.cm;
      long acts[] = new long[cm.length];
      long preds[] = new long[cm[0].length];
      for (int a = 0; a < cm.length; a++) {
        long sum = 0;
        for (int p = 0; p < cm[a].length; p++) {
          sum += cm[a][p];
          preds[p] += cm[a][p];
        }
        acts[a] = sum;
      }
      String adomain[] = ConfusionMatrix.show(acts, CM.vactual.domain());
      String pdomain[] = ConfusionMatrix.show(preds, CM.vpredict.domain());

      StringBuilder sb = new StringBuilder();
      sb.append("Act/Prd\t");
      for (String s : pdomain) if (s != null) sb.append(s).append('\t');
      sb.append("Error\n");

      long terr = 0;
      for (int a = 0; a < cm.length; a++) {
        if (adomain[a] == null) continue;
        sb.append(adomain[a]).append('\t');
        long correct = 0;
        for (int p = 0; p < pdomain.length; p++) {
          if (pdomain[p] == null) continue;
          if (adomain[a].equals(pdomain[p])) correct = cm[a][p];
          sb.append(cm[a][p]).append('\t');
        }
        long err = acts[a] - correct;
        terr += err; // Bump totals
        sb.append(String.format("%5.3f = %d / %d\n", (double) err / acts[a], err, acts[a]));
      }
      sb.append("Totals\t");
      for (int p = 0; p < pdomain.length; p++)
        if (pdomain[p] != null) sb.append(preds[p]).append("\t");
      sb.append(
          String.format(
              "%5.3f = %d / %d\n", (double) terr / CM.vactual.length(), terr, CM.vactual.length()));

      System.out.println(sb);

    } finally {
      UKV.remove(dest1); // Remove original hex frame key
      UKV.remove(fkey2);
      UKV.remove(dest2);
      if (gbm != null) {
        UKV.remove(gbm.dest()); // Remove the model
        UKV.remove(gbm.response._key);
        gbm.remove(); // Remove GBM Job
      }
      if (fr != null) fr.remove();
      if (fpreds != null) fpreds.remove();
    }
  }