Examples with GLMModel hex.glm.GLMModel used on opensource projects

Example 6 with GLMModel

use of hex.glm.GLMModel in project h2o-2 by h2oai.

the class GLMMakeModel method serve.

@Override
protected Response serve() {
    try {
        double[] b;
        String[] ns = names.split(",");
        if (beta.length == model.coefficients_names.length && Arrays.equals(ns, model.coefficients_names))
            b = beta;
        else {
            b = MemoryManager.malloc8d(model.coefficients_names.length);
            HashMap<String, Integer> map = new HashMap<String, Integer>();
            for (int i = 0; i < model.coefficients_names.length; ++i) map.put(model.coefficients_names[i], i);
            for (int i = 0; i < ns.length; ++i) {
                String s = ns[i];
                if (!map.containsKey(s))
                    throw new IllegalArgumentException("Unknown coefficient " + s);
                b[map.get(s)] = beta[i];
            }
        }
        //Key.make((byte) 1, /*Key.HIDDEN_USER_KEY*/Key.USER_KEY, H2O.SELF);
        destination_key = Key.make();
        GLMModel m = new GLMModel(model.get_params(), destination_key, model._dataKey, model.getParams(), model.coefficients_names, b, model.dinfo(), threshold);
        m.delete_and_lock(null).unlock(null);
        return Response.done(this);
    } catch (Throwable t) {
        return Response.error(t);
    }
}

Example 7 with GLMModel

use of hex.glm.GLMModel 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 8 with GLMModel

use of hex.glm.GLMModel in project h2o-2 by h2oai.

the class GLMPredict method serve.

@Override
protected Response serve() {
    try {
        if (model == null)
            throw new IllegalArgumentException("Model is required to perform validation!");
        final Key predictionKey = (prediction == null) ? Key.make("__Prediction_" + Key.make()) : prediction;
        GLMModel m = new GLMModel.GetScoringModelTask(null, model, lambda).invokeTask()._res;
        // Create a new random key
        if (prediction == null)
            prediction = Key.make("__Prediction_" + Key.make());
        Frame fr = new Frame(prediction, new String[0], new Vec[0]).delete_and_lock(null);
        fr = m.score(data);
        // Jam in the frame key
        fr = new Frame(prediction, fr._names, fr.vecs());
        fr.unlock(null);
        return Inspect2.redirect(this, prediction.toString());
    } catch (Throwable t) {
        return Response.error(t);
    }
}

Example 9 with GLMModel

use of hex.glm.GLMModel 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();
    }
}

Example 10 with GLMModel

use of hex.glm.GLMModel in project h2o-3 by h2oai.

the class MakeGLMModelHandler method make_model.

public GLMModelV3 make_model(int version, MakeGLMModelV3 args) {
    GLMModel model = DKV.getGet(args.model.key());
    if (model == null)
        throw new IllegalArgumentException("missing source model " + args.model);
    String[] names = model._output.coefficientNames();
    Map<String, Double> coefs = model.coefficients();
    for (int i = 0; i < args.names.length; ++i) coefs.put(args.names[i], args.beta[i]);
    double[] beta = model.beta().clone();
    for (int i = 0; i < beta.length; ++i) beta[i] = coefs.get(names[i]);
    GLMModel m = new GLMModel(args.dest != null ? args.dest.key() : Key.make(), model._parms, null, model._ymu, Double.NaN, Double.NaN, -1);
    DataInfo dinfo = model.dinfo();
    dinfo.setPredictorTransform(TransformType.NONE);
    // GLMOutput(DataInfo dinfo, String[] column_names, String[][] domains, String[] coefficient_names, boolean binomial) {
    m._output = new GLMOutput(model.dinfo(), model._output._names, model._output._domains, model._output.coefficientNames(), model._output._binomial, beta);
    DKV.put(m._key, m);
    GLMModelV3 res = new GLMModelV3();
    res.fillFromImpl(m);
    return res;
}