Examples with GBMModel hex.tree.gbm.GBMModel used on opensource projects

Example 1 with GBMModel

use of hex.tree.gbm.GBMModel in project h2o-3 by h2oai.

the class ModelSerializationTest method testGBMModelBinomial.

@Test
public void testGBMModelBinomial() throws IOException {
    GBMModel model, loadedModel = null;
    try {
        model = prepareGBMModel("smalldata/logreg/prostate.csv", ar("ID"), "CAPSULE", true, 5);
        CompressedTree[][] trees = getTrees(model);
        loadedModel = saveAndLoad(model);
        // And compare
        assertModelBinaryEquals(model, loadedModel);
        CompressedTree[][] loadedTrees = getTrees(loadedModel);
        assertTreeEquals("Trees have to be binary same", trees, loadedTrees);
    } finally {
        if (loadedModel != null)
            loadedModel.delete();
    }
}

Example 2 with GBMModel

use of hex.tree.gbm.GBMModel in project h2o-3 by h2oai.

the class ModelSerializationTest method prepareGBMModel.

private GBMModel prepareGBMModel(String dataset, String[] ignoredColumns, String response, boolean classification, int ntrees) {
    Frame f = parse_test_file(dataset);
    try {
        if (classification && !f.vec(response).isCategorical()) {
            f.replace(f.find(response), f.vec(response).toCategoricalVec()).remove();
            DKV.put(f._key, f);
        }
        GBMModel.GBMParameters gbmParams = new GBMModel.GBMParameters();
        gbmParams._train = f._key;
        gbmParams._ignored_columns = ignoredColumns;
        gbmParams._response_column = response;
        gbmParams._ntrees = ntrees;
        gbmParams._score_each_iteration = true;
        return new GBM(gbmParams).trainModel().get();
    } finally {
        if (f != null)
            f.delete();
    }
}

Example 3 with GBMModel

use of hex.tree.gbm.GBMModel in project h2o-3 by h2oai.

the class SSLEncryptionTest method testGBMRegressionGaussian.

private static void testGBMRegressionGaussian() {
    GBMModel gbm = null;
    Frame fr = null, fr2 = null;
    try {
        Date start = new Date();
        fr = parse_test_file("./smalldata/gbm_test/Mfgdata_gaussian_GBM_testing.csv");
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = fr._key;
        parms._distribution = gaussian;
        // Row in col 0, dependent in col 1, predictor in col 2
        parms._response_column = fr._names[1];
        parms._ntrees = 1;
        parms._max_depth = 1;
        parms._min_rows = 1;
        parms._nbins = 20;
        // Drop ColV2 0 (row), keep 1 (response), keep col 2 (only predictor), drop remaining cols
        String[] xcols = parms._ignored_columns = new String[fr.numCols() - 2];
        xcols[0] = fr._names[0];
        System.arraycopy(fr._names, 3, xcols, 1, fr.numCols() - 3);
        parms._learn_rate = 1.0f;
        parms._score_each_iteration = true;
        GBM job = new GBM(parms);
        gbm = job.trainModel().get();
        Log.info(">>> GBM parsing and training took: " + (new Date().getTime() - start.getTime()) + " ms.");
        //HEX-1817
        Assert.assertTrue(job.isStopped());
        // Done building model; produce a score column with predictions
        Date scoringStart = new Date();
        fr2 = gbm.score(fr);
        Log.info(">>> GBM scoring took: " + (new Date().getTime() - scoringStart.getTime()) + " ms.");
    } finally {
        if (fr != null)
            fr.remove();
        if (fr2 != null)
            fr2.remove();
        if (gbm != null)
            gbm.remove();
    }
}

Example 4 with GBMModel

use of hex.tree.gbm.GBMModel in project h2o-3 by h2oai.

the class PartialDependenceTest method prostateBinary.

@Test
public void prostateBinary() {
    Frame fr = null;
    GBMModel model = null;
    PartialDependence partialDependence = null;
    try {
        // Frame
        fr = parse_test_file("smalldata/prostate/prostate.csv");
        for (String s : new String[] { "RACE", "GLEASON", "DPROS", "DCAPS", "CAPSULE" }) {
            Vec v = fr.remove(s);
            fr.add(s, v.toCategoricalVec());
            v.remove();
        }
        DKV.put(fr);
        // Model
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = fr._key;
        parms._ignored_columns = new String[] { "ID" };
        parms._response_column = "CAPSULE";
        model = new GBM(parms).trainModel().get();
        // PartialDependence
        partialDependence = new PartialDependence(Key.<PartialDependence>make());
        //      partialDependence._cols = model._output._names;
        partialDependence._nbins = 10;
        partialDependence._model_id = (Key) model._key;
        partialDependence._frame_id = fr._key;
        partialDependence.execImpl().get();
        for (TwoDimTable t : partialDependence._partial_dependence_data) Log.info(t);
    } finally {
        if (fr != null)
            fr.remove();
        if (model != null)
            model.remove();
        if (partialDependence != null)
            partialDependence.remove();
    }
}

Example 5 with GBMModel

use of hex.tree.gbm.GBMModel in project h2o-3 by h2oai.

the class PartialDependenceTest method prostateRegression.

@Test
public void prostateRegression() {
    Frame fr = null;
    GBMModel model = null;
    PartialDependence partialDependence = null;
    try {
        // Frame
        fr = parse_test_file("smalldata/prostate/prostate.csv");
        for (String s : new String[] { "RACE", "GLEASON", "DPROS", "DCAPS", "CAPSULE" }) {
            Vec v = fr.remove(s);
            fr.add(s, v.toCategoricalVec());
            v.remove();
        }
        DKV.put(fr);
        // Model
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = fr._key;
        parms._ignored_columns = new String[] { "ID" };
        parms._response_column = "AGE";
        model = new GBM(parms).trainModel().get();
        // PartialDependence
        partialDependence = new PartialDependence(Key.<PartialDependence>make());
        partialDependence._nbins = 10;
        partialDependence._model_id = (Key) model._key;
        partialDependence._frame_id = fr._key;
        partialDependence.execImpl().get();
        for (TwoDimTable t : partialDependence._partial_dependence_data) Log.info(t);
    } finally {
        if (fr != null)
            fr.remove();
        if (model != null)
            model.remove();
        if (partialDependence != null)
            partialDependence.remove();
    }
}