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

Example 1 with GBM

use of hex.tree.gbm.GBM 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 2 with GBM

use of hex.tree.gbm.GBM 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 3 with GBM

use of hex.tree.gbm.GBM 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 4 with GBM

use of hex.tree.gbm.GBM 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();
    }
}

Example 5 with GBM

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

the class TestCase method execute.

public TestCaseResult execute() throws Exception, AssertionError {
    loadTestCaseDataSets();
    makeModelParameters();
    double startTime = 0, stopTime = 0;
    if (!grid) {
        Model.Output modelOutput = null;
        DRF drfJob;
        DRFModel drfModel = null;
        GLM glmJob;
        GLMModel glmModel = null;
        GBM gbmJob;
        GBMModel gbmModel = null;
        DeepLearning dlJob;
        DeepLearningModel dlModel = null;
        String bestModelJson = null;
        try {
            switch(algo) {
                case "drf":
                    drfJob = new DRF((DRFModel.DRFParameters) params);
                    AccuracyTestingSuite.summaryLog.println("Training DRF model.");
                    startTime = System.currentTimeMillis();
                    drfModel = drfJob.trainModel().get();
                    stopTime = System.currentTimeMillis();
                    modelOutput = drfModel._output;
                    bestModelJson = drfModel._parms.toJsonString();
                    break;
                case "glm":
                    glmJob = new GLM((GLMModel.GLMParameters) params, Key.<GLMModel>make("GLMModel"));
                    AccuracyTestingSuite.summaryLog.println("Training GLM model.");
                    startTime = System.currentTimeMillis();
                    glmModel = glmJob.trainModel().get();
                    stopTime = System.currentTimeMillis();
                    modelOutput = glmModel._output;
                    bestModelJson = glmModel._parms.toJsonString();
                    break;
                case "gbm":
                    gbmJob = new GBM((GBMModel.GBMParameters) params);
                    AccuracyTestingSuite.summaryLog.println("Training GBM model.");
                    startTime = System.currentTimeMillis();
                    gbmModel = gbmJob.trainModel().get();
                    stopTime = System.currentTimeMillis();
                    modelOutput = gbmModel._output;
                    bestModelJson = gbmModel._parms.toJsonString();
                    break;
                case "dl":
                    dlJob = new DeepLearning((DeepLearningModel.DeepLearningParameters) params);
                    AccuracyTestingSuite.summaryLog.println("Training DL model.");
                    startTime = System.currentTimeMillis();
                    dlModel = dlJob.trainModel().get();
                    stopTime = System.currentTimeMillis();
                    modelOutput = dlModel._output;
                    bestModelJson = dlModel._parms.toJsonString();
                    break;
            }
        } catch (Exception e) {
            throw new Exception(e);
        } finally {
            if (drfModel != null) {
                drfModel.delete();
            }
            if (glmModel != null) {
                glmModel.delete();
            }
            if (gbmModel != null) {
                gbmModel.delete();
            }
            if (dlModel != null) {
                dlModel.delete();
            }
        }
        removeTestCaseDataSetFrames();
        //Add check if cv is used
        if (params._nfolds > 0) {
            return new TestCaseResult(testCaseId, getMetrics(modelOutput._training_metrics), getMetrics(modelOutput._cross_validation_metrics), stopTime - startTime, bestModelJson, this, trainingDataSet, testingDataSet);
        } else {
            return new TestCaseResult(testCaseId, getMetrics(modelOutput._training_metrics), getMetrics(modelOutput._validation_metrics), stopTime - startTime, bestModelJson, this, trainingDataSet, testingDataSet);
        }
    } else {
        assert !modelSelectionCriteria.equals("");
        makeGridParameters();
        makeSearchCriteria();
        Grid grid = null;
        Model bestModel = null;
        String bestModelJson = null;
        try {
            SchemaServer.registerAllSchemasIfNecessary();
            switch(// TODO: Hack for PUBDEV-2812
            algo) {
                case "drf":
                    if (!drfRegistered) {
                        new DRF(true);
                        new DRFParametersV3();
                        drfRegistered = true;
                    }
                    break;
                case "glm":
                    if (!glmRegistered) {
                        new GLM(true);
                        new GLMParametersV3();
                        glmRegistered = true;
                    }
                    break;
                case "gbm":
                    if (!gbmRegistered) {
                        new GBM(true);
                        new GBMParametersV3();
                        gbmRegistered = true;
                    }
                    break;
                case "dl":
                    if (!dlRegistered) {
                        new DeepLearning(true);
                        new DeepLearningParametersV3();
                        dlRegistered = true;
                    }
                    break;
            }
            startTime = System.currentTimeMillis();
            // TODO: ModelParametersBuilderFactory parameter must be instantiated properly
            Job<Grid> gs = GridSearch.startGridSearch(null, params, hyperParms, new GridSearch.SimpleParametersBuilderFactory<>(), searchCriteria);
            grid = gs.get();
            stopTime = System.currentTimeMillis();
            boolean higherIsBetter = higherIsBetter(modelSelectionCriteria);
            double bestScore = higherIsBetter ? -Double.MAX_VALUE : Double.MAX_VALUE;
            for (Model m : grid.getModels()) {
                double validationMetricScore = getMetrics(m._output._validation_metrics).get(modelSelectionCriteria);
                AccuracyTestingSuite.summaryLog.println(modelSelectionCriteria + " for model " + m._key.toString() + " is " + validationMetricScore);
                if (higherIsBetter ? validationMetricScore > bestScore : validationMetricScore < bestScore) {
                    bestScore = validationMetricScore;
                    bestModel = m;
                    bestModelJson = bestModel._parms.toJsonString();
                }
            }
            AccuracyTestingSuite.summaryLog.println("Best model: " + bestModel._key.toString());
            AccuracyTestingSuite.summaryLog.println("Best model parameters: " + bestModelJson);
        } catch (Exception e) {
            throw new Exception(e);
        } finally {
            if (grid != null) {
                grid.delete();
            }
        }
        removeTestCaseDataSetFrames();
        //Add check if cv is used
        if (params._nfolds > 0) {
            return new TestCaseResult(testCaseId, getMetrics(bestModel._output._training_metrics), getMetrics(bestModel._output._cross_validation_metrics), stopTime - startTime, bestModelJson, this, trainingDataSet, testingDataSet);
        } else {
            return new TestCaseResult(testCaseId, getMetrics(bestModel._output._training_metrics), getMetrics(bestModel._output._validation_metrics), stopTime - startTime, bestModelJson, this, trainingDataSet, testingDataSet);
        }
    }
}