Examples with Key water.Key used on opensource projects

Example 36 with Key

use of water.Key in project h2o-2 by h2oai.

the class Expr2Test method rbindTest.

@Test
public void rbindTest() {
    Key dest1 = Key.make("f1");
    float[] ratios = arf(0.5f);
    Frame[] splits = null;
    File file1 = TestUtil.find_test_file("smalldata/tnc3_10.csv");
    //File file = TestUtil.find_test_file("smalldata/iris/iris_wheader.csv");
    //File file = TestUtil.find_test_file("smalldata/cars.csv");
    Key fkey1 = NFSFileVec.make(file1);
    Frame f = ParseDataset2.parse(dest1, new Key[] { fkey1 });
    FrameSplitter fs = new FrameSplitter(f, ratios);
    H2O.submitTask(fs).join();
    splits = fs.getResult();
    Frame rbinded_frame;
    Env ev = Exec2.exec("rbind(" + splits[0]._key + "," + splits[1]._key + ")");
    try {
        rbinded_frame = ev.popAry();
    } finally {
        if (ev != null)
            ev.remove_and_unlock();
    }
    assertEquals(rbinded_frame.numRows(), f.numRows());
    rbinded_frame.delete();
    Lockable.delete(dest1);
    for (Frame s : splits) if (s != null)
        s.delete();
}

Example 37 with Key

use of water.Key in project h2o-2 by h2oai.

the class SparseTest method makeChunk.

protected Chunk makeChunk(double[] vals) {
    int nzs = 0;
    int[] nonzeros = new int[vals.length];
    int j = 0;
    for (double d : vals) if (d != 0)
        nonzeros[nzs++] = j++;
    Key key = Vec.newKey();
    AppendableVec av = new AppendableVec(key);
    NewChunk nv = new NewChunk(av, 0);
    for (double d : vals) {
        if (Double.isNaN(d))
            nv.addNA();
        else if ((long) d == d)
            nv.addNum((long) d, 0);
        else
            nv.addNum(d);
    }
    nv.close(0, null);
    Futures fs = new Futures();
    Vec vec = av.close(fs);
    fs.blockForPending();
    return vec.chunkForChunkIdx(0);
}

Example 38 with Key

use of water.Key in project h2o-2 by h2oai.

the class RunifSplitTest method test1.

@Test
public void test1() {
    Key file = NFSFileVec.make(find_test_file(PATH));
    Frame fr = ParseDataset2.parse(Key.make("iris_nn2"), new Key[] { file });
    Frame[] split = Frame.runifSplit(fr, .70f, -1);
    Assert.assertTrue(split[0].numRows() + split[1].numRows() == fr.numRows());
    fr.delete();
    split[0].delete();
    split[1].delete();
}

Example 39 with Key

use of water.Key in project h2o-3 by h2oai.

the class DeepLearningScoreTest method testPubDev928.

/** Load simple dataset, rebalance to a number of chunks > number of rows, and run deep learning */
@Test
public void testPubDev928() {
    // Create rebalanced dataset
    Key rebalancedKey = Key.make("rebalanced");
    NFSFileVec nfs = TestUtil.makeNfsFileVec("smalldata/logreg/prostate.csv");
    Frame fr = ParseDataset.parse(Key.make(), nfs._key);
    RebalanceDataSet rb = new RebalanceDataSet(fr, rebalancedKey, (int) (fr.numRows() + 1));
    H2O.submitTask(rb);
    rb.join();
    Frame rebalanced = DKV.get(rebalancedKey).get();
    // Assert that there is at least one 0-len chunk
    assertZeroLengthChunk("Rebalanced dataset should contain at least one 0-len chunk!", rebalanced.anyVec());
    DeepLearningModel dlModel = null;
    try {
        // Launch Deep Learning
        DeepLearningParameters dlParams = new DeepLearningParameters();
        dlParams._train = rebalancedKey;
        dlParams._epochs = 5;
        dlParams._response_column = "CAPSULE";
        dlModel = new DeepLearning(dlParams).trainModel().get();
    } finally {
        fr.delete();
        rebalanced.delete();
        if (dlModel != null)
            dlModel.delete();
    }
}

Example 40 with Key

use of water.Key in project h2o-3 by h2oai.

the class DeepLearningSpiralsTest method run.

@Test
public void run() {
    Scope.enter();
    NFSFileVec nfs = TestUtil.makeNfsFileVec("smalldata/junit/two_spiral.csv");
    Frame frame = ParseDataset.parse(Key.make(), nfs._key);
    Log.info(frame);
    int resp = frame.names().length - 1;
    for (boolean sparse : new boolean[] { true, false }) {
        for (boolean col_major : new boolean[] { false }) {
            if (!sparse && col_major)
                continue;
            Key model_id = Key.make();
            // build the model
            {
                DeepLearningParameters p = new DeepLearningParameters();
                p._epochs = 5000;
                p._hidden = new int[] { 100 };
                p._sparse = sparse;
                p._col_major = col_major;
                p._activation = DeepLearningParameters.Activation.Tanh;
                p._initial_weight_distribution = DeepLearningParameters.InitialWeightDistribution.Normal;
                p._initial_weight_scale = 2.5;
                p._loss = DeepLearningParameters.Loss.CrossEntropy;
                p._train = frame._key;
                p._response_column = frame.names()[resp];
                // Convert response to categorical
                Scope.track(frame.replace(resp, frame.vecs()[resp].toCategoricalVec()));
                DKV.put(frame);
                p._rho = 0.99;
                p._epsilon = 5e-3;
                //stop when reaching 0 classification error on training data
                p._classification_stop = 0;
                p._train_samples_per_iteration = 10000;
                p._stopping_rounds = 5;
                p._stopping_metric = ScoreKeeper.StoppingMetric.misclassification;
                p._score_each_iteration = true;
                p._reproducible = true;
                p._seed = 1234;
                new DeepLearning(p, model_id).trainModel().get();
            }
            // score and check result
            {
                DeepLearningModel mymodel = DKV.getGet(model_id);
                Frame pred = mymodel.score(frame);
                ModelMetricsBinomial mm = ModelMetricsBinomial.getFromDKV(mymodel, frame);
                double error = mm._auc.defaultErr();
                Log.info("Error: " + error);
                if (error > 0.1) {
                    Assert.fail("Test classification error is not <= 0.1, but " + error + ".");
                }
                Assert.assertTrue(mymodel.testJavaScoring(frame, pred, 1e-6));
                pred.delete();
                mymodel.delete();
            }
        }
    }
    frame.delete();
    Scope.exit();
}