Examples with DRFModel hex.tree.drf.DRFModel used on opensource projects

Example 6 with DRFModel

use of hex.tree.drf.DRFModel 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);
        }
    }
}

Example 7 with DRFModel

use of hex.tree.drf.DRFModel in project h2o-3 by h2oai.

the class XValPredictionsCheck method checkModel.

void checkModel(Model m, Vec foldId, int nclass) {
    if (// DRF does out of back instead of true training, nobs might be different
    !(m instanceof DRFModel))
        assertEquals(m._output._training_metrics._nobs, m._output._cross_validation_metrics._nobs);
    m.delete();
    m.deleteCrossValidationModels();
    Key[] xvalKeys = m._output._cross_validation_predictions;
    Key xvalKey = m._output._cross_validation_holdout_predictions_frame_id;
    final int[] id = new int[1];
    for (Key k : xvalKeys) {
        Frame preds = DKV.getGet(k);
        assert preds.numRows() == foldId.length();
        Vec[] vecs = new Vec[nclass + 1];
        vecs[0] = foldId;
        if (nclass == 1)
            vecs[1] = preds.anyVec();
        else
            System.arraycopy(preds.vecs(ArrayUtils.range(1, nclass)), 0, vecs, 1, nclass);
        new MRTask() {

            @Override
            public void map(Chunk[] cs) {
                Chunk foldId = cs[0];
                for (int r = 0; r < cs[0]._len; ++r) if (foldId.at8(r) != id[0])
                    for (int i = 1; i < cs.length; ++i) // no prediction for this row!
                    assert cs[i].atd(r) == 0;
            }
        }.doAll(vecs);
        id[0]++;
        preds.delete();
    }
    xvalKey.remove();
}

Example 8 with DRFModel

use of hex.tree.drf.DRFModel in project h2o-3 by h2oai.

the class XValPredictionsCheck method testXValPredictions.

@Test
public void testXValPredictions() {
    final int nfolds = 3;
    Frame tfr = null;
    try {
        // Load data, hack frames
        tfr = parse_test_file("smalldata/iris/iris_wheader.csv");
        Frame foldId = new Frame(new String[] { "foldId" }, new Vec[] { AstKFold.kfoldColumn(tfr.vec("class").makeZero(), nfolds, 543216789) });
        tfr.add(foldId);
        DKV.put(tfr);
        // GBM
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = tfr._key;
        parms._response_column = "class";
        parms._ntrees = 1;
        parms._max_depth = 1;
        parms._fold_column = "foldId";
        parms._distribution = DistributionFamily.multinomial;
        parms._keep_cross_validation_predictions = true;
        GBM job = new GBM(parms);
        GBMModel gbm = job.trainModel().get();
        checkModel(gbm, foldId.anyVec(), 3);
        // DRF
        DRFModel.DRFParameters parmsDRF = new DRFModel.DRFParameters();
        parmsDRF._train = tfr._key;
        parmsDRF._response_column = "class";
        parmsDRF._ntrees = 1;
        parmsDRF._max_depth = 1;
        parmsDRF._fold_column = "foldId";
        parmsDRF._distribution = DistributionFamily.multinomial;
        parmsDRF._keep_cross_validation_predictions = true;
        DRF drfJob = new DRF(parmsDRF);
        DRFModel drf = drfJob.trainModel().get();
        checkModel(drf, foldId.anyVec(), 3);
        // GLM
        GLMModel.GLMParameters parmsGLM = new GLMModel.GLMParameters();
        parmsGLM._train = tfr._key;
        parmsGLM._response_column = "sepal_len";
        parmsGLM._fold_column = "foldId";
        parmsGLM._keep_cross_validation_predictions = true;
        GLM glmJob = new GLM(parmsGLM);
        GLMModel glm = glmJob.trainModel().get();
        checkModel(glm, foldId.anyVec(), 1);
        // DL
        DeepLearningModel.DeepLearningParameters parmsDL = new DeepLearningModel.DeepLearningParameters();
        parmsDL._train = tfr._key;
        parmsDL._response_column = "class";
        parmsDL._hidden = new int[] { 1 };
        parmsDL._epochs = 1;
        parmsDL._fold_column = "foldId";
        parmsDL._keep_cross_validation_predictions = true;
        DeepLearning dlJob = new DeepLearning(parmsDL);
        DeepLearningModel dl = dlJob.trainModel().get();
        checkModel(dl, foldId.anyVec(), 3);
    } finally {
        if (tfr != null)
            tfr.remove();
    }
}