Examples with DeepLearningModel hex.deeplearning.DeepLearningModel used on opensource projects

Example 11 with DeepLearningModel

use of hex.deeplearning.DeepLearningModel in project h2o-2 by h2oai.

the class DeepLearningAutoEncoderCategoricalTest method run.

@Test
public void run() {
    long seed = 0xDECAF;
    Key file_train = NFSFileVec.make(find_test_file(PATH));
    Frame train = ParseDataset2.parse(Key.make(), new Key[] { file_train });
    DeepLearning p = new DeepLearning();
    p.source = train;
    p.autoencoder = true;
    p.response = train.lastVec();
    p.seed = seed;
    p.hidden = new int[] { 100, 50, 20 };
    //    p.ignored_cols = new int[]{0,1,2,3,6,7,8,10}; //Optional: ignore all categoricals
    //    p.ignored_cols = new int[]{4,5,9}; //Optional: ignore all numericals
    p.adaptive_rate = true;
    p.l1 = 1e-4;
    p.activation = DeepLearning.Activation.Tanh;
    p.train_samples_per_iteration = -1;
    p.loss = DeepLearning.Loss.MeanSquare;
    p.epochs = 2;
    //    p.shuffle_training_data = true;
    p.force_load_balance = true;
    p.score_training_samples = 0;
    p.score_validation_samples = 0;
    //    p.reproducible = true;
    p.invoke();
    // Verification of results
    StringBuilder sb = new StringBuilder();
    sb.append("Verifying results.\n");
    DeepLearningModel mymodel = UKV.get(p.dest());
    sb.append("Reported mean reconstruction error: " + mymodel.mse() + "\n");
    // Training data
    // Reconstruct data using the same helper functions and verify that self-reported MSE agrees
    final Frame l2 = mymodel.scoreAutoEncoder(train);
    final Vec l2vec = l2.anyVec();
    sb.append("Actual   mean reconstruction error: " + l2vec.mean() + "\n");
    // print stats and potential outliers
    double quantile = 1 - 5. / train.numRows();
    sb.append("The following training points are reconstructed with an error above the " + quantile * 100 + "-th percentile - potential \"outliers\" in testing data.\n");
    double thresh = mymodel.calcOutlierThreshold(l2vec, quantile);
    for (long i = 0; i < l2vec.length(); i++) {
        if (l2vec.at(i) > thresh) {
            sb.append(String.format("row %d : l2vec error = %5f\n", i, l2vec.at(i)));
        }
    }
    Log.info(sb.toString());
    Assert.assertEquals(mymodel.mse(), l2vec.mean(), 1e-8);
    // Create reconstruction
    Log.info("Creating full reconstruction.");
    final Frame recon_train = mymodel.score(train);
    // cleanup
    recon_train.delete();
    train.delete();
    p.delete();
    mymodel.delete();
    l2.delete();
}

Example 12 with DeepLearningModel

use of hex.deeplearning.DeepLearningModel 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();
    }
}