Examples with GLRMParameters hex.glrm.GLRMModel.GLRMParameters used on opensource projects

Example 6 with GLRMParameters

use of hex.glrm.GLRMModel.GLRMParameters in project h2o-3 by h2oai.

the class GLRMTest method testMojo.

@Test
public void testMojo() throws InterruptedException, ExecutionException {
    GLRM glrm;
    GLRMModel model = null;
    Frame train = null;
    try {
        Scope.enter();
        train = parse_test_file(Key.<Frame>make("birds"), "./smalldata/pca_test/AustraliaCoast.csv");
        GLRMParameters parms = new GLRMParameters();
        parms._train = train._key;
        parms._k = 4;
        parms._loss = GlrmLoss.Quadratic;
        parms._init = GlrmInitialization.Random;
        parms._max_iterations = 2000;
        parms._regularization_x = GlrmRegularizer.Quadratic;
        parms._gamma_x = 0;
        parms._gamma_y = 0;
        glrm = new GLRM(parms);
        model = glrm.trainModel().get();
        assert model != null;
        checkLossbyCol(parms, model);
        boolean res = model.testJavaScoring(train, model._output._representation_key.get(), 1e-6, 1);
    // Disable for now
    // Assert.assertTrue(res);
    } finally {
        if (train != null)
            train.delete();
        if (model != null)
            model.delete();
    }
}

Example 7 with GLRMParameters

use of hex.glrm.GLRMModel.GLRMParameters in project h2o-3 by h2oai.

the class GLRMTest method testSetColumnLoss.

@Test
public void testSetColumnLoss() throws InterruptedException, ExecutionException {
    GLRM job = null;
    GLRMModel model = null;
    Frame train = null;
    try {
        train = parse_test_file(Key.make("benign.hex"), "smalldata/logreg/benign.csv");
        GLRMParameters parms = new GLRMParameters();
        parms._train = train._key;
        parms._k = 12;
        parms._loss = GlrmLoss.Quadratic;
        parms._loss_by_col = new GlrmLoss[] { GlrmLoss.Absolute, GlrmLoss.Huber };
        parms._loss_by_col_idx = new int[] { 2, /* AGMT */
        5 };
        parms._transform = DataInfo.TransformType.STANDARDIZE;
        parms._init = GlrmInitialization.PlusPlus;
        parms._min_step_size = 1e-5;
        parms._recover_svd = false;
        parms._max_iterations = 2000;
        job = new GLRM(parms);
        model = job.trainModel().get();
        Log.info("Iteration " + model._output._iterations + ": Objective value = " + model._output._objective);
        checkLossbyCol(parms, model);
        model.score(train).delete();
        ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
        Log.info("Numeric Sum of Squared Error = " + mm._numerr + "\tCategorical Misclassification Error = " + mm._caterr);
    } finally {
        if (train != null)
            train.delete();
        if (model != null)
            model.delete();
    }
}

Example 8 with GLRMParameters

use of hex.glrm.GLRMModel.GLRMParameters in project h2o-3 by h2oai.

the class GLRMCategoricalTest method testSetColumnLossCats.

@Test
public void testSetColumnLossCats() throws InterruptedException, ExecutionException {
    GLRMModel model = null;
    Frame train = null;
    // Categoricals: CAPSULE, RACE, DPROS, DCAPS
    final int[] cats = new int[] { 1, 3, 4, 5 };
    Scope.enter();
    try {
        train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
        for (int i = 0; i < cats.length; i++) Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec()));
        train.remove("ID").remove();
        DKV.put(train._key, train);
        GLRMParameters parms = new GLRMParameters();
        parms._train = train._key;
        parms._k = 12;
        parms._loss = GlrmLoss.Quadratic;
        parms._multi_loss = GlrmLoss.Categorical;
        parms._loss_by_col = new GlrmLoss[] { GlrmLoss.Ordinal, GlrmLoss.Poisson, GlrmLoss.Absolute };
        parms._loss_by_col_idx = new int[] { 3, /* DPROS */
        1, /* AGE */
        6 };
        parms._init = GlrmInitialization.PlusPlus;
        parms._min_step_size = 1e-5;
        parms._recover_svd = false;
        parms._max_iterations = 2000;
        model = new GLRM(parms).trainModel().get();
        Log.info("Iteration " + model._output._iterations + ": Objective value = " + model._output._objective);
        GLRMTest.checkLossbyCol(parms, model);
        model.score(train).delete();
        ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
        Log.info("Numeric Sum of Squared Error = " + mm._numerr + "\tCategorical Misclassification Error = " + mm._caterr);
    } finally {
        if (train != null)
            train.delete();
        if (model != null)
            model.delete();
        Scope.exit();
    }
}

Example 9 with GLRMParameters

use of hex.glrm.GLRMModel.GLRMParameters in project h2o-3 by h2oai.

the class GLRMCategoricalTest method testLosses.

@Test
public void testLosses() throws InterruptedException, ExecutionException {
    long seed = 0xDECAF;
    Random rng = new Random(seed);
    Frame train = null;
    // Categoricals: CAPSULE, RACE, DPROS, DCAPS
    final int[] cats = new int[] { 1, 3, 4, 5 };
    final GlrmRegularizer[] regs = new GlrmRegularizer[] { GlrmRegularizer.Quadratic, GlrmRegularizer.L1, GlrmRegularizer.NonNegative, GlrmRegularizer.OneSparse, GlrmRegularizer.UnitOneSparse, GlrmRegularizer.Simplex };
    Scope.enter();
    try {
        train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
        for (int i = 0; i < cats.length; i++) Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec()));
        train.remove("ID").remove();
        DKV.put(train._key, train);
        for (GlrmLoss loss : new GlrmLoss[] { GlrmLoss.Quadratic, GlrmLoss.Absolute, GlrmLoss.Huber, GlrmLoss.Poisson }) {
            for (GlrmLoss multiloss : new GlrmLoss[] { GlrmLoss.Categorical, GlrmLoss.Ordinal }) {
                GLRMModel model = null;
                try {
                    Scope.enter();
                    long myseed = rng.nextLong();
                    Log.info("GLRM using seed = " + myseed);
                    GLRMParameters parms = new GLRMParameters();
                    parms._train = train._key;
                    parms._transform = DataInfo.TransformType.NONE;
                    parms._k = 5;
                    parms._loss = loss;
                    parms._multi_loss = multiloss;
                    parms._init = GlrmInitialization.SVD;
                    parms._regularization_x = regs[rng.nextInt(regs.length)];
                    parms._regularization_y = regs[rng.nextInt(regs.length)];
                    parms._gamma_x = Math.abs(rng.nextDouble());
                    parms._gamma_y = Math.abs(rng.nextDouble());
                    parms._recover_svd = false;
                    parms._seed = myseed;
                    parms._verbose = false;
                    parms._max_iterations = 500;
                    model = new GLRM(parms).trainModel().get();
                    Log.info("Iteration " + model._output._iterations + ": Objective value = " + model._output._objective);
                    model.score(train).delete();
                    ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
                    Log.info("Numeric Sum of Squared Error = " + mm._numerr + "\tCategorical Misclassification Error = " + mm._caterr);
                } finally {
                    if (model != null)
                        model.delete();
                    Scope.exit();
                }
            }
        }
    } finally {
        if (train != null)
            train.delete();
        Scope.exit();
    }
}

Example 10 with GLRMParameters

use of hex.glrm.GLRMModel.GLRMParameters in project h2o-3 by h2oai.

the class GLRMCategoricalTest method testCategoricalProstate.

@Test
public void testCategoricalProstate() throws InterruptedException, ExecutionException {
    GLRMModel model = null;
    Frame train = null;
    // Categoricals: CAPSULE, RACE, DPROS, DCAPS
    final int[] cats = new int[] { 1, 3, 4, 5 };
    try {
        Scope.enter();
        train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
        for (int i = 0; i < cats.length; i++) Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec()));
        train.remove("ID").remove();
        DKV.put(train._key, train);
        GLRMParameters parms = new GLRMParameters();
        parms._train = train._key;
        parms._k = 8;
        parms._gamma_x = parms._gamma_y = 0.1;
        parms._regularization_x = GlrmRegularizer.Quadratic;
        parms._regularization_y = GlrmRegularizer.Quadratic;
        parms._init = GlrmInitialization.PlusPlus;
        parms._transform = DataInfo.TransformType.STANDARDIZE;
        parms._recover_svd = false;
        parms._max_iterations = 200;
        model = new GLRM(parms).trainModel().get();
        Log.info("Iteration " + model._output._iterations + ": Objective value = " + model._output._objective);
        model.score(train).delete();
        ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
        Log.info("Numeric Sum of Squared Error = " + mm._numerr + "\tCategorical Misclassification Error = " + mm._caterr);
    } finally {
        if (train != null)
            train.delete();
        if (model != null)
            model.delete();
        Scope.exit();
    }
}