Examples with Frame water.fvec.Frame used on opensource projects

Example 71 with Frame

use of water.fvec.Frame in project h2o-3 by h2oai.

the class GBMTest method testNfoldsOneVsRest.

@Test
public void testNfoldsOneVsRest() {
    Frame tfr = null;
    GBMModel gbm1 = null;
    GBMModel gbm2 = null;
    Scope.enter();
    try {
        tfr = parse_test_file("smalldata/junit/weights.csv");
        DKV.put(tfr);
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = tfr._key;
        parms._response_column = "response";
        parms._min_rows = 1;
        parms._max_depth = 2;
        parms._nfolds = (int) tfr.numRows();
        parms._fold_assignment = Model.Parameters.FoldAssignmentScheme.Modulo;
        parms._ntrees = 3;
        parms._seed = 12345;
        parms._learn_rate = 1e-3f;
        gbm1 = new GBM(parms).trainModel().get();
        //parms._nfolds = (int) tfr.numRows() + 1; //This is now an error
        gbm2 = new GBM(parms).trainModel().get();
        ModelMetricsBinomial mm1 = (ModelMetricsBinomial) gbm1._output._cross_validation_metrics;
        ModelMetricsBinomial mm2 = (ModelMetricsBinomial) gbm2._output._cross_validation_metrics;
        assertEquals(mm1.auc_obj()._auc, mm2.auc_obj()._auc, 1e-12);
        assertEquals(mm1.mse(), mm2.mse(), 1e-12);
        //assertEquals(mm1.r2(), mm2.r2(), 1e-12);
        assertEquals(mm1.logloss(), mm2.logloss(), 1e-12);
    //TODO: add check: the correct number of individual models were built. PUBDEV-1690
    } finally {
        if (tfr != null)
            tfr.remove();
        if (gbm1 != null) {
            gbm1.deleteCrossValidationModels();
            gbm1.delete();
        }
        if (gbm2 != null) {
            gbm2.deleteCrossValidationModels();
            gbm2.delete();
        }
        Scope.exit();
    }
}

Example 72 with Frame

use of water.fvec.Frame in project h2o-3 by h2oai.

the class GBMTest method testNFold.

@Test
public void testNFold() {
    Frame tfr = null, vfr = null;
    GBMModel gbm = null;
    Scope.enter();
    try {
        tfr = parse_test_file("smalldata/junit/weights.csv");
        DKV.put(tfr);
        GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
        parms._train = tfr._key;
        parms._response_column = "response";
        parms._weights_column = "weight";
        parms._seed = 123;
        parms._min_rows = 1;
        parms._max_depth = 2;
        parms._nfolds = 2;
        parms._ntrees = 3;
        parms._learn_rate = 1e-3f;
        parms._keep_cross_validation_predictions = true;
        // Build a first model; all remaining models should be equal
        gbm = new GBM(parms).trainModel().get();
        ModelMetricsBinomial mm = (ModelMetricsBinomial) gbm._output._cross_validation_metrics;
        assertEquals(0.6296296296296297, mm.auc_obj()._auc, 1e-8);
        assertEquals(0.28640022521234304, mm.mse(), 1e-8);
        assertEquals(0.7674117059335286, mm.logloss(), 1e-6);
    } finally {
        if (tfr != null)
            tfr.remove();
        if (vfr != null)
            vfr.remove();
        if (gbm != null) {
            gbm.deleteCrossValidationModels();
            gbm.delete();
            for (Key k : gbm._output._cross_validation_predictions) k.remove();
            gbm._output._cross_validation_holdout_predictions_frame_id.remove();
        }
        Scope.exit();
    }
}

Example 73 with Frame

use of water.fvec.Frame in project h2o-3 by h2oai.

the class GBMTest method testCatEncoding.

@Test
public void testCatEncoding() {
    for (Model.Parameters.CategoricalEncodingScheme c : Model.Parameters.CategoricalEncodingScheme.values()) {
        if (c == Model.Parameters.CategoricalEncodingScheme.OneHotInternal)
            continue;
        Frame tfr = null;
        GBMModel gbm = null;
        Frame fr2 = null;
        try {
            tfr = parse_test_file("./smalldata/junit/weather.csv");
            GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
            parms._train = tfr._key;
            parms._response_column = tfr.lastVecName();
            parms._ntrees = 5;
            parms._categorical_encoding = c;
            gbm = new GBM(parms).trainModel().get();
            // Done building model; produce a score column with predictions
            fr2 = gbm.score(tfr);
            // Build a POJO, validate same results
            Assert.assertTrue(gbm.testJavaScoring(tfr, fr2, 1e-15));
        } finally {
            if (tfr != null)
                tfr.delete();
            if (fr2 != null)
                fr2.delete();
            if (gbm != null)
                gbm.deleteCrossValidationModels();
            if (gbm != null)
                gbm.delete();
        }
    }
}

Example 74 with Frame

use of water.fvec.Frame in project h2o-3 by h2oai.

the class GBMTest method testNA.

// PUBDEV-2822
@Test
public void testNA() {
    String xy = ",0\n1,0\n2,0\n3,0\n4,-10\n,0";
    Key tr = Key.make("train");
    Frame df = ParseDataset.parse(tr, makeByteVec(Key.make("xy"), xy));
    GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
    parms._train = tr;
    parms._response_column = "C2";
    parms._min_rows = 1;
    parms._learn_rate = 1;
    parms._ntrees = 1;
    GBM job = new GBM(parms);
    GBMModel gbm = job.trainModel().get();
    Frame preds = gbm.score(df);
    Log.info(df);
    Log.info(preds);
    Assert.assertTrue(gbm.testJavaScoring(df, preds, 1e-15));
    Assert.assertTrue(Math.abs(preds.vec(0).at(0) - 0) < 1e-6);
    Assert.assertTrue(Math.abs(preds.vec(0).at(1) - 0) < 1e-6);
    Assert.assertTrue(Math.abs(preds.vec(0).at(2) - 0) < 1e-6);
    Assert.assertTrue(Math.abs(preds.vec(0).at(3) - 0) < 1e-6);
    Assert.assertTrue(Math.abs(preds.vec(0).at(4) - -10) < 1e-6);
    Assert.assertTrue(Math.abs(preds.vec(0).at(5) - 0) < 1e-6);
    preds.remove();
    gbm.remove();
    df.remove();
}

Example 75 with Frame

use of water.fvec.Frame in project h2o-3 by h2oai.

the class GBMTest method testGBMPredict.

// Predict with no actual, after training
@Test
public void testGBMPredict() {
    GBMModel gbm = null;
    GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
    Frame pred = null, res = null;
    Scope.enter();
    try {
        Frame train = parse_test_file("smalldata/gbm_test/ecology_model.csv");
        // Remove unique ID
        train.remove("Site").remove();
        int ci = train.find("Angaus");
        // Convert response 'Angaus' to categorical
        Scope.track(train.replace(ci, train.vecs()[ci].toCategoricalVec()));
        // Update frame after hacking it
        DKV.put(train);
        parms._train = train._key;
        // Train on the outcome
        parms._response_column = "Angaus";
        parms._distribution = DistributionFamily.multinomial;
        gbm = new GBM(parms).trainModel().get();
        pred = parse_test_file("smalldata/gbm_test/ecology_eval.csv");
        // No response column during scoring
        pred.remove("Angaus").remove();
        res = gbm.score(pred);
        // Build a POJO, validate same results
        Assert.assertTrue(gbm.testJavaScoring(pred, res, 1e-15));
    } finally {
        parms._train.remove();
        if (gbm != null)
            gbm.delete();
        if (pred != null)
            pred.remove();
        if (res != null)
            res.remove();
        Scope.exit();
    }
}