Examples with ModelMetricsRegressionGLM hex.ModelMetricsRegressionGLM used on opensource projects

Example 1 with ModelMetricsRegressionGLM

use of hex.ModelMetricsRegressionGLM in project h2o-3 by h2oai.

the class GLMBasicTestRegression method testPoissonWithOffset.

@Test
public void testPoissonWithOffset() {
    GLMModel model = null;
    Frame scoreTrain = null;
    //    Call:  glm(formula = formula, family = poisson, data = D)
    //
    //    Coefficients:
    //    (Intercept)       Merit1       Merit2       Merit3       Class2       Class3       Class4       Class5
    //    -2.0357          -0.1378      -0.2207      -0.4930       0.2998       0.4691       0.5259       0.2156
    //
    //    Degrees of Freedom: 19 Total (i.e. Null);  12 Residual
    //    Null Deviance:	    33850
    //    Residual Deviance: 579.5 	AIC: 805.9
    String[] cfs1 = new String[] { "Intercept", "Merit.1", "Merit.2", "Merit.3", "Class.2", "Class.3", "Class.4", "Class.5" };
    double[] vals = new double[] { -2.0357, -0.1378, -0.2207, -0.4930, 0.2998, 0.4691, 0.5259, 0.2156 };
    GLMParameters parms = new GLMParameters(Family.poisson);
    parms._train = _canCarTrain._key;
    parms._ignored_columns = new String[] { "Insured", "Premium", "Cost" };
    // "response_column":"Claims","offset_column":"logInsured"
    parms._response_column = "Claims";
    parms._offset_column = "logInsured";
    parms._standardize = false;
    parms._lambda = new double[] { 0 };
    parms._alpha = new double[] { 0 };
    parms._objective_epsilon = 0;
    parms._beta_epsilon = 1e-6;
    parms._gradient_epsilon = 1e-10;
    parms._max_iterations = 1000;
    for (Solver s : GLMParameters.Solver.values()) {
        // skip for now, does not handle zero columns (introduced by extra missing bucket with no missing in the dataset)
        if (s == Solver.COORDINATE_DESCENT_NAIVE)
            continue;
        try {
            parms._solver = s;
            model = new GLM(parms).trainModel().get();
            HashMap<String, Double> coefs = model.coefficients();
            System.out.println("coefs = " + coefs);
            for (int i = 0; i < cfs1.length; ++i) assertEquals(vals[i], coefs.get(cfs1[i]), 1e-4);
            assertEquals(33850, GLMTest.nullDeviance(model), 5);
            assertEquals(579.5, GLMTest.residualDeviance(model), 1e-4 * 579.5);
            assertEquals(19, GLMTest.nullDOF(model), 0);
            assertEquals(12, GLMTest.resDOF(model), 0);
            assertEquals(805.9, GLMTest.aic(model), 1e-4 * 805.9);
            // test scoring
            try {
                Frame fr = new Frame(_canCarTrain.names(), _canCarTrain.vecs());
                fr.remove(parms._offset_column);
                scoreTrain = model.score(fr);
                assertTrue("shoul've thrown IAE", false);
            } catch (IllegalArgumentException iae) {
                assertTrue(iae.getMessage().contains("Test/Validation dataset is missing offset column"));
            }
            scoreTrain = model.score(_canCarTrain);
            hex.ModelMetricsRegressionGLM mmTrain = (ModelMetricsRegressionGLM) hex.ModelMetricsRegression.getFromDKV(model, _canCarTrain);
            assertEquals(model._output._training_metrics._MSE, mmTrain._MSE, 1e-8);
            assertEquals(GLMTest.residualDeviance(model), mmTrain._resDev, 1e-8);
            assertEquals(GLMTest.nullDeviance(model), mmTrain._nullDev, 1e-8);
        } finally {
            if (model != null)
                model.delete();
            if (scoreTrain != null)
                scoreTrain.delete();
        }
    }
}

Example 2 with ModelMetricsRegressionGLM

use of hex.ModelMetricsRegressionGLM in project h2o-3 by h2oai.

the class GLMBasicTestRegression method testTweedie.

@Test
public void testTweedie() {
    GLMModel model = null;
    Frame scoreTrain = null;
    // --------------------------------------  R examples output ----------------------------------------------------------------
    //    Call:  glm(formula = Infections ~ ., family = tweedie(0), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29          SexMale
    //    0.8910            0.8221                 0.7266           -0.5033           -0.2679         -0.1056
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    1564
    //    Residual Deviance: 1469 	AIC: NA
    //    Call:  glm(formula = Infections ~ ., family = tweedie(1), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29           SexMale
    //      -0.12261           0.61149           0.53454          -0.37442          -0.18973          -0.08985
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    824.5
    //    Residual Deviance: 755.4 	AIC: NA
    //    Call:  glm(formula = Infections ~ ., family = tweedie(1.25), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29           SexMale
    //    1.02964          -0.14079          -0.12200           0.08502           0.04269           0.02105
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    834.2
    //    Residual Deviance: 770.8 	AIC: NA
    //    Call:  glm(formula = Infections ~ ., family = tweedie(1.5), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29           SexMale
    //    1.05665          -0.25891          -0.22185           0.15325           0.07624           0.03908
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    967
    //    Residual Deviance: 908.9 	AIC: NA
    //    Call:  glm(formula = Infections ~ ., family = tweedie(1.75), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29           SexMale
    //    1.08076          -0.35690          -0.30154           0.20556           0.10122           0.05375
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    1518
    //    Residual Deviance: 1465 	AIC: NA
    //    Call:  glm(formula = Infections ~ ., family = tweedie(2), data = D)
    //
    //    Coefficients:
    //    (Intercept)      SwimmerOccas  LocationNonBeach          Age20-24          Age25-29           SexMale
    //    1.10230          -0.43751          -0.36337           0.24318           0.11830           0.06467
    //
    //    Degrees of Freedom: 286 Total (i.e. Null);  281 Residual
    //    Null Deviance:	    964.4
    //    Residual Deviance: 915.7 	AIC: NA
    // ---------------------------------------------------------------------------------------------------------------------------
    String[] cfs1 = new String[] { "Intercept", "Swimmer.Occas", "Location.NonBeach", "Age.20-24", "Age.25-29", "Sex.Male" };
    double[][] vals = new double[][] { { 0.89100, 0.82210, 0.72660, -0.50330, -0.26790, -0.10560 }, { -0.12261, 0.61149, 0.53454, -0.37442, -0.18973, -0.08985 }, { 1.02964, -0.14079, -0.12200, 0.08502, 0.04269, 0.02105 }, { 1.05665, -0.25891, -0.22185, 0.15325, 0.07624, 0.03908 }, { 1.08076, -0.35690, -0.30154, 0.20556, 0.10122, 0.05375 }, { 1.10230, -0.43751, -0.36337, 0.24318, 0.11830, 0.06467 } };
    int dof = 286, res_dof = 281;
    double[] nullDev = new double[] { 1564, 824.5, 834.2, 967.0, 1518, 964.4 };
    double[] resDev = new double[] { 1469, 755.4, 770.8, 908.9, 1465, 915.7 };
    double[] varPow = new double[] { 0, 1.0, 1.25, 1.5, 1.75, 2.0 };
    GLMParameters parms = new GLMParameters(Family.tweedie);
    parms._train = _earinf._key;
    parms._ignored_columns = new String[] {};
    // "response_column":"Claims","offset_column":"logInsured"
    parms._response_column = "Infections";
    parms._standardize = false;
    parms._lambda = new double[] { 0 };
    parms._alpha = new double[] { 0 };
    parms._gradient_epsilon = 1e-10;
    parms._max_iterations = 1000;
    parms._objective_epsilon = 0;
    parms._beta_epsilon = 1e-6;
    for (int x = 0; x < varPow.length; ++x) {
        double p = varPow[x];
        parms._tweedie_variance_power = p;
        parms._tweedie_link_power = 1 - p;
        for (Solver s : /*new Solver[]{Solver.IRLSM}*/
        GLMParameters.Solver.values()) {
            // ignore for now, has trouble with zero columns
            if (s == Solver.COORDINATE_DESCENT_NAIVE)
                continue;
            try {
                parms._solver = s;
                model = new GLM(parms).trainModel().get();
                HashMap<String, Double> coefs = model.coefficients();
                System.out.println("coefs = " + coefs);
                for (int i = 0; i < cfs1.length; ++i) assertEquals(vals[x][i], coefs.get(cfs1[i]), 1e-4);
                assertEquals(nullDev[x], (GLMTest.nullDeviance(model)), 5e-4 * nullDev[x]);
                assertEquals(resDev[x], (GLMTest.residualDeviance(model)), 5e-4 * resDev[x]);
                assertEquals(dof, GLMTest.nullDOF(model), 0);
                assertEquals(res_dof, GLMTest.resDOF(model), 0);
                // test scoring
                scoreTrain = model.score(_earinf);
                assertTrue(model.testJavaScoring(_earinf, scoreTrain, 1e-8));
                hex.ModelMetricsRegressionGLM mmTrain = (ModelMetricsRegressionGLM) hex.ModelMetricsRegression.getFromDKV(model, _earinf);
                assertEquals(model._output._training_metrics._MSE, mmTrain._MSE, 1e-8);
                assertEquals(GLMTest.residualDeviance(model), mmTrain._resDev, 1e-8);
                assertEquals(GLMTest.nullDeviance(model), mmTrain._nullDev, 1e-8);
            } finally {
                if (model != null)
                    model.delete();
                if (scoreTrain != null)
                    scoreTrain.delete();
            }
        }
    }
}