Examples with SplitFrame hex.SplitFrame used on opensource projects

Example 6 with SplitFrame

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

the class DRFTest method testColSamplingPerTree.

@Test
public void testColSamplingPerTree() {
    Frame tfr = null;
    Key[] ksplits = new Key[0];
    try {
        tfr = parse_test_file("./smalldata/gbm_test/ecology_model.csv");
        SplitFrame sf = new SplitFrame(tfr, new double[] { 0.5, 0.5 }, new Key[] { Key.make("train.hex"), Key.make("test.hex") });
        // Invoke the job
        sf.exec().get();
        ksplits = sf._destination_frames;
        DRFModel drf = null;
        float[] sample_rates = new float[] { 0.2f, 0.4f, 0.6f, 0.8f, 1.0f };
        float[] col_sample_rates = new float[] { 0.4f, 0.6f, 0.8f, 1.0f };
        float[] col_sample_rates_per_tree = new float[] { 0.4f, 0.6f, 0.8f, 1.0f };
        Map<Double, Triple<Float>> hm = new TreeMap<>();
        for (float sample_rate : sample_rates) {
            for (float col_sample_rate : col_sample_rates) {
                for (float col_sample_rate_per_tree : col_sample_rates_per_tree) {
                    Scope.enter();
                    try {
                        DRFModel.DRFParameters parms = new DRFModel.DRFParameters();
                        parms._train = ksplits[0];
                        parms._valid = ksplits[1];
                        //regression
                        parms._response_column = "Angaus";
                        parms._seed = 12345;
                        parms._min_rows = 1;
                        parms._max_depth = 15;
                        parms._ntrees = 2;
                        parms._mtries = Math.max(1, (int) (col_sample_rate * (tfr.numCols() - 1)));
                        parms._col_sample_rate_per_tree = col_sample_rate_per_tree;
                        parms._sample_rate = sample_rate;
                        // Build a first model; all remaining models should be equal
                        DRF job = new DRF(parms);
                        drf = job.trainModel().get();
                        // too slow, but passes (now)
                        //            // Build a POJO, validate same results
                        //            Frame pred = drf.score(tfr);
                        //            Assert.assertTrue(drf.testJavaScoring(tfr,pred,1e-15));
                        //            pred.remove();
                        ModelMetricsRegression mm = (ModelMetricsRegression) drf._output._validation_metrics;
                        hm.put(mm.mse(), new Triple<>(sample_rate, col_sample_rate, col_sample_rate_per_tree));
                    } finally {
                        if (drf != null)
                            drf.delete();
                        Scope.exit();
                    }
                }
            }
        }
        Iterator<Map.Entry<Double, Triple<Float>>> it;
        Triple<Float> last = null;
        // iterator over results (min to max MSE) - best to worst
        for (it = hm.entrySet().iterator(); it.hasNext(); ) {
            Map.Entry<Double, Triple<Float>> n = it.next();
            Log.info("MSE: " + n.getKey() + ", row sample: " + n.getValue().v1 + ", col sample: " + n.getValue().v2 + ", col sample per tree: " + n.getValue().v3);
            last = n.getValue();
        }
    // worst validation MSE should belong to the most overfit case (1.0, 1.0, 1.0)
    //      Assert.assertTrue(last.v1==sample_rates[sample_rates.length-1]);
    //      Assert.assertTrue(last.v2==col_sample_rates[col_sample_rates.length-1]);
    //      Assert.assertTrue(last.v3==col_sample_rates_per_tree[col_sample_rates_per_tree.length-1]);
    } finally {
        if (tfr != null)
            tfr.remove();
        for (Key k : ksplits) if (k != null)
            k.remove();
    }
}

Example 7 with SplitFrame

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

the class PCATest method testIrisSplitScoring.

@Test
public void testIrisSplitScoring() throws InterruptedException, ExecutionException {
    PCAModel model = null;
    Frame fr = null, fr2 = null;
    Frame tr = null, te = null;
    try {
        fr = parse_test_file("smalldata/iris/iris_wheader.csv");
        SplitFrame sf = new SplitFrame(fr, new double[] { 0.5, 0.5 }, new Key[] { Key.make("train.hex"), Key.make("test.hex") });
        // Invoke the job
        sf.exec().get();
        Key[] ksplits = sf._destination_frames;
        tr = DKV.get(ksplits[0]).get();
        te = DKV.get(ksplits[1]).get();
        PCAModel.PCAParameters parms = new PCAModel.PCAParameters();
        parms._train = ksplits[0];
        parms._valid = ksplits[1];
        parms._k = 4;
        parms._max_iterations = 1000;
        parms._pca_method = PCAParameters.Method.GramSVD;
        model = new PCA(parms).trainModel().get();
        // Done building model; produce a score column with cluster choices
        fr2 = model.score(te);
        Assert.assertTrue(model.testJavaScoring(te, fr2, 1e-5));
    } finally {
        if (fr != null)
            fr.delete();
        if (fr2 != null)
            fr2.delete();
        if (tr != null)
            tr.delete();
        if (te != null)
            te.delete();
        if (model != null)
            model.delete();
    }
}

Example 8 with SplitFrame

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

the class KMeansTest method testValidation.

@Test
public void testValidation() {
    KMeansModel kmm = null;
    for (boolean standardize : new boolean[] { true, false }) {
        Frame fr = null, fr2 = null;
        Frame tr = null, te = null;
        try {
            fr = parse_test_file("smalldata/iris/iris_wheader.csv");
            SplitFrame sf = new SplitFrame(fr, new double[] { 0.5, 0.5 }, new Key[] { Key.make("train.hex"), Key.make("test.hex") });
            // Invoke the job
            sf.exec().get();
            Key<Frame>[] ksplits = sf._destination_frames;
            tr = DKV.get(ksplits[0]).get();
            te = DKV.get(ksplits[1]).get();
            KMeansModel.KMeansParameters parms = new KMeansModel.KMeansParameters();
            parms._train = ksplits[0];
            parms._valid = ksplits[1];
            parms._k = 3;
            parms._standardize = standardize;
            parms._max_iterations = 10;
            parms._init = KMeans.Initialization.Random;
            kmm = doSeed(parms, 0);
            // Iris last column is categorical; make sure centers are ordered in the
            // same order as the iris columns.
            double[][] /*features*/
            centers = kmm._output._centers_raw;
            for (int k = 0; k < parms._k; k++) {
                double flower = centers[k][4];
                Assert.assertTrue("categorical column expected", flower == (int) flower);
            }
            // Done building model; produce a score column with cluster choices
            fr2 = kmm.score(te);
            Assert.assertTrue(kmm.testJavaScoring(te, fr2, 1e-15));
        } finally {
            if (tr != null)
                tr.delete();
            if (te != null)
                te.delete();
            if (fr2 != null)
                fr2.delete();
            if (fr != null)
                fr.delete();
            if (kmm != null)
                kmm.delete();
        }
    }
}

Example 9 with SplitFrame

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

the class SVDTest method testIrisSplitScoring.

@Test
public void testIrisSplitScoring() throws InterruptedException, ExecutionException {
    SVDModel model = null;
    Frame fr = null, fr2 = null;
    Frame tr = null, te = null;
    try {
        fr = parse_test_file("smalldata/iris/iris_wheader.csv");
        SplitFrame sf = new SplitFrame(fr, new double[] { 0.5, 0.5 }, new Key[] { Key.make("train.hex"), Key.make("test.hex") });
        // Invoke the job
        sf.exec().get();
        Key[] ksplits = sf._destination_frames;
        tr = DKV.get(ksplits[0]).get();
        te = DKV.get(ksplits[1]).get();
        SVDModel.SVDParameters parms = new SVDModel.SVDParameters();
        parms._train = ksplits[0];
        parms._valid = ksplits[1];
        parms._nv = 4;
        parms._max_iterations = 1000;
        parms._svd_method = SVDParameters.Method.Power;
        parms._save_v_frame = false;
        model = new SVD(parms).trainModel().get();
        // Done building model; produce a score column with cluster choices
        fr2 = model.score(te);
        Assert.assertTrue(model.testJavaScoring(te, fr2, 1e-5));
    } finally {
        if (fr != null)
            fr.delete();
        if (fr2 != null)
            fr2.delete();
        if (tr != null)
            tr.delete();
        if (te != null)
            te.delete();
        if (model != null)
            model.delete();
    }
}