Java Code Examples for smile.data.AttributeDataset#toArray()

/**
 * Test of learn method, of class DecisionTree.
 */
@Test
public void testIris() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    AttributeDataset iris = arffParser.parse(is);
    double[][] x = iris.toArray(new double[iris.size()][]);
    int[] y = iris.toArray(new int[iris.size()]);

    int n = x.length;
    LOOCV loocv = new LOOCV(n);
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(x, loocv.train[i]);
        int[] trainy = Math.slice(y, loocv.train[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(iris.attributes());
        DecisionTree tree = new DecisionTree(attrs,
            new RowMajorDenseMatrix2d(trainx, x[0].length), trainy, 4);
        assertEquals(tree.predict(x[loocv.test[i]]), evalPredict(tree, x[loocv.test[i]]));
    }
}
 

@Test
public void testIrisSparseDenseEquals() throws IOException, ParseException, HiveException {
    String urlString =
            "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff";
    DecisionTree.Node denseNode = getDecisionTreeFromDenseInput(urlString);
    DecisionTree.Node sparseNode = getDecisionTreeFromSparseInput(urlString);

    URL url = new URL(urlString);
    InputStream is = new BufferedInputStream(url.openStream());
    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);

    int diff = 0;
    for (int i = 0; i < size; i++) {
        if (denseNode.predict(x[i]) != sparseNode.predict(x[i])) {
            diff++;
        }
    }

    Assert.assertTrue("large diff " + diff + " between two predictions", diff < 10);
}
 

@Test
public void testIrisSerializedObj() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    AttributeDataset iris = arffParser.parse(is);
    double[][] x = iris.toArray(new double[iris.size()][]);
    int[] y = iris.toArray(new int[iris.size()]);

    int n = x.length;
    LOOCV loocv = new LOOCV(n);
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(x, loocv.train[i]);
        int[] trainy = Math.slice(y, loocv.train[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(iris.attributes());
        DecisionTree tree = new DecisionTree(attrs, matrix(trainx, true), trainy, 4);

        byte[] b = tree.serialize(false);
        Node node = DecisionTree.deserialize(b, b.length, false);
        assertEquals(tree.predict(x[loocv.test[i]]), node.predict(x[loocv.test[i]]));
    }
}
 

/**
 * Test of learn method, of class DecisionTree.
 */
@Test
public void testIris() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    AttributeDataset iris = arffParser.parse(is);
    double[][] x = iris.toArray(new double[iris.size()][]);
    int[] y = iris.toArray(new int[iris.size()]);

    int n = x.length;
    LOOCV loocv = new LOOCV(n);
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(x, loocv.train[i]);
        int[] trainy = Math.slice(y, loocv.train[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(iris.attributes());
        DecisionTree tree = new DecisionTree(attrs,
            new RowMajorDenseMatrix2d(trainx, x[0].length), trainy, 4);
        Assert.assertEquals(tree.predict(x[loocv.test[i]]),
            evalPredict(tree, x[loocv.test[i]]));
    }
}
 

@Test
public void testCpu() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/ef17aabecf0c0c5bcb69/raw/aac0575b4d43072c6f3c82d9072fdefb61892694/cpu.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(6);
    AttributeDataset data = arffParser.parse(is);
    double[] datay = data.toArray(new double[data.size()]);
    double[][] datax = data.toArray(new double[data.size()][]);

    int n = datax.length;
    int k = 10;

    CrossValidation cv = new CrossValidation(n, k);
    for (int i = 0; i < k; i++) {
        double[][] trainx = Math.slice(datax, cv.train[i]);
        double[] trainy = Math.slice(datay, cv.train[i]);
        double[][] testx = Math.slice(datax, cv.test[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(data.attributes());
        RegressionTree tree = new RegressionTree(attrs,
            new RowMajorDenseMatrix2d(trainx, trainx[0].length), trainy, 20);

        for (int j = 0; j < testx.length; j++) {
            assertEquals(tree.predict(testx[j]), evalPredict(tree, testx[j]), 1.0);
        }
    }
}
 

@Test
public void testSerialization() throws HiveException, IOException, ParseException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    final Object[][] rows = new Object[size][2];
    for (int i = 0; i < size; i++) {
        double[] row = x[i];
        final List<String> xi = new ArrayList<String>(x[0].length);
        for (int j = 0; j < row.length; j++) {
            xi.add(j + ":" + row[j]);
        }
        rows[i][0] = xi;
        rows[i][1] = y[i];
    }

    TestUtils.testGenericUDTFSerialization(GradientTreeBoostingClassifierUDTF.class,
        new ObjectInspector[] {
                ObjectInspectorFactory.getStandardListObjectInspector(
                    PrimitiveObjectInspectorFactory.javaStringObjectInspector),
                PrimitiveObjectInspectorFactory.javaIntObjectInspector,
                ObjectInspectorUtils.getConstantObjectInspector(
                    PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 490")},
        rows);
}
 

public static void trainModel(String trainingPath, String outputPath, String learner,
        int responseIndex, boolean skipCrossValidation) throws Exception {

    if (learner == null) {
        learner = "SVM";
    }

    System.out.println("Learning algorithm: " + learner);
    String modelFilePath = Paths.get(outputPath, "pageclassifier.model").toString();

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(responseIndex);

    Path arffFilePath = Paths.get(trainingPath, "/smile_input.arff");
    FileInputStream fis = new FileInputStream(arffFilePath.toFile());
    System.out.println("Writting temporarily data file to: " + arffFilePath.toString());

    AttributeDataset trainingData = arffParser.parse(fis);
    double[][] x = trainingData.toArray(new double[trainingData.size()][]);
    int[] y = trainingData.toArray(new int[trainingData.size()]);

    SoftClassifier<double[]> finalModel = null;
    if (skipCrossValidation) {
        System.out.println("Starting model training on whole dataset...");
        finalModel = trainClassifierNoCV(learner, x, y);
    } else {
        System.out.println("Starting cross-validation...");
        finalModel = trainModelCV(learner, x, y);
    }
    System.out.println("Writing model to file: " + modelFilePath);
    SmileUtil.writeSmileClassifier(modelFilePath, finalModel);
}
 

@Test
public void testIrisSerializeObjCompressed() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    AttributeDataset iris = arffParser.parse(is);
    double[][] x = iris.toArray(new double[iris.size()][]);
    int[] y = iris.toArray(new int[iris.size()]);

    int n = x.length;
    LOOCV loocv = new LOOCV(n);
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(x, loocv.train[i]);
        int[] trainy = Math.slice(y, loocv.train[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(iris.attributes());
        DecisionTree tree = new DecisionTree(attrs, matrix(trainx, true), trainy, 4);

        byte[] b1 = tree.serialize(true);
        byte[] b2 = tree.serialize(false);
        Assert.assertTrue("b1.length = " + b1.length + ", b2.length = " + b2.length,
            b1.length < b2.length);
        Node node = DecisionTree.deserialize(b1, b1.length, true);
        assertEquals(tree.predict(x[loocv.test[i]]), node.predict(x[loocv.test[i]]));
    }
}
 

private static int run(String datasetUrl, int responseIndex, int numLeafs, boolean dense)
        throws IOException, ParseException {
    URL url = new URL(datasetUrl);
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(responseIndex);

    AttributeDataset ds = arffParser.parse(is);
    double[][] x = ds.toArray(new double[ds.size()][]);
    int[] y = ds.toArray(new int[ds.size()]);

    int n = x.length;
    LOOCV loocv = new LOOCV(n);
    int error = 0;
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(x, loocv.train[i]);
        int[] trainy = Math.slice(y, loocv.train[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(ds.attributes());
        DecisionTree tree = new DecisionTree(attrs, matrix(trainx, dense), trainy, numLeafs,
            RandomNumberGeneratorFactory.createPRNG(i));
        if (y[loocv.test[i]] != tree.predict(x[loocv.test[i]])) {
            error++;
        }
    }

    debugPrint("Decision Tree error = " + error);
    return error;
}
 

@Test
public void testCpu2() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/ef17aabecf0c0c5bcb69/raw/aac0575b4d43072c6f3c82d9072fdefb61892694/cpu.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(6);
    AttributeDataset data = arffParser.parse(is);
    double[] datay = data.toArray(new double[data.size()]);
    double[][] datax = data.toArray(new double[data.size()][]);

    int n = datax.length;
    int m = 3 * n / 4;
    int[] index = Math.permutate(n);

    double[][] trainx = new double[m][];
    double[] trainy = new double[m];
    for (int i = 0; i < m; i++) {
        trainx[i] = datax[index[i]];
        trainy[i] = datay[index[i]];
    }

    double[][] testx = new double[n - m][];
    double[] testy = new double[n - m];
    for (int i = m; i < n; i++) {
        testx[i - m] = datax[index[i]];
        testy[i - m] = datay[index[i]];
    }

    RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(data.attributes());
    RegressionTree tree = new RegressionTree(attrs,
        new RowMajorDenseMatrix2d(trainx, trainx[0].length), trainy, 20);
    debugPrint(String.format("RMSE = %.4f\n", rmse(tree, testx, testy)));

    for (int i = m; i < n; i++) {
        assertEquals(tree.predict(testx[i - m]), evalPredict(tree, testx[i - m]), 1.0);
    }
}
 

@Test
public void testCpu2() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/ef17aabecf0c0c5bcb69/raw/aac0575b4d43072c6f3c82d9072fdefb61892694/cpu.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(6);
    AttributeDataset data = arffParser.parse(is);
    double[] datay = data.toArray(new double[data.size()]);
    double[][] datax = data.toArray(new double[data.size()][]);

    int n = datax.length;
    int m = 3 * n / 4;
    int[] index = Math.permutate(n);

    double[][] trainx = new double[m][];
    double[] trainy = new double[m];
    for (int i = 0; i < m; i++) {
        trainx[i] = datax[index[i]];
        trainy[i] = datay[index[i]];
    }

    double[][] testx = new double[n - m][];
    double[] testy = new double[n - m];
    for (int i = m; i < n; i++) {
        testx[i - m] = datax[index[i]];
        testy[i - m] = datay[index[i]];
    }

    RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(data.attributes());
    RegressionTree tree = new RegressionTree(attrs,
        new RowMajorDenseMatrix2d(trainx, trainx[0].length), trainy, 20);
    debugPrint(String.format("RMSE = %.4f\n", rmse(tree, testx, testy)));

    for (int i = m; i < n; i++) {
        Assert.assertEquals(tree.predict(testx[i - m]), evalPredict(tree, testx[i - m]), 1.0);
    }
}
 

@Test
public void testCpu() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/ef17aabecf0c0c5bcb69/raw/aac0575b4d43072c6f3c82d9072fdefb61892694/cpu.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(6);
    AttributeDataset data = arffParser.parse(is);
    double[] datay = data.toArray(new double[data.size()]);
    double[][] datax = data.toArray(new double[data.size()][]);

    int n = datax.length;
    int k = 10;

    CrossValidation cv = new CrossValidation(n, k);
    for (int i = 0; i < k; i++) {
        double[][] trainx = Math.slice(datax, cv.train[i]);
        double[] trainy = Math.slice(datay, cv.train[i]);
        double[][] testx = Math.slice(datax, cv.test[i]);

        RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(data.attributes());
        RegressionTree tree = new RegressionTree(attrs,
            new RowMajorDenseMatrix2d(trainx, trainx[0].length), trainy, 20);

        for (int j = 0; j < testx.length; j++) {
            Assert.assertEquals(tree.predict(testx[j]), evalPredict(tree, testx[j]), 1.0);
        }
    }
}
 

private static void runTracePredict(String datasetUrl, int responseIndex, int numLeafs)
        throws IOException, ParseException {
    URL url = new URL(datasetUrl);
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(responseIndex);

    AttributeDataset ds = arffParser.parse(is);
    final Attribute[] attrs = ds.attributes();
    final Attribute targetAttr = ds.response();

    double[][] x = ds.toArray(new double[ds.size()][]);
    int[] y = ds.toArray(new int[ds.size()]);

    Random rnd = new Random(43L);
    int numTrain = (int) (x.length * 0.7);
    int[] index = ArrayUtils.shuffle(MathUtils.permutation(x.length), rnd);
    int[] cvTrain = Arrays.copyOf(index, numTrain);
    int[] cvTest = Arrays.copyOfRange(index, numTrain, index.length);

    double[][] trainx = Math.slice(x, cvTrain);
    int[] trainy = Math.slice(y, cvTrain);
    double[][] testx = Math.slice(x, cvTest);

    DecisionTree tree = new DecisionTree(SmileExtUtils.convertAttributeTypes(attrs),
        matrix(trainx, false), trainy, numLeafs, RandomNumberGeneratorFactory.createPRNG(43L));

    final LinkedHashMap<String, Double> map = new LinkedHashMap<>();
    final StringBuilder buf = new StringBuilder();
    for (int i = 0; i < testx.length; i++) {
        final DenseVector test = new DenseVector(testx[i]);
        tree.predict(test, new PredictionHandler() {

            @Override
            public void visitBranch(Operator op, int splitFeatureIndex, double splitFeature,
                    double splitValue) {
                buf.append(attrs[splitFeatureIndex].name);
                buf.append(" [" + splitFeature + "] ");
                buf.append(op);
                buf.append(' ');
                buf.append(splitValue);
                buf.append('\n');

                map.put(attrs[splitFeatureIndex].name + " [" + splitFeature + "] " + op,
                    splitValue);
            }

            @Override
            public void visitLeaf(int output, double[] posteriori) {
                buf.append(targetAttr.toString(output));
            }
        });

        Assert.assertTrue(buf.length() > 0);
        Assert.assertFalse(map.isEmpty());

        StringUtils.clear(buf);
        map.clear();
    }

}
 

private static DecisionTree.Node getDecisionTreeFromDenseInput(String urlString)
        throws IOException, ParseException, HiveException {
    URL url = new URL(urlString);
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    RandomForestClassifierUDTF udtf = new RandomForestClassifierUDTF();
    ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector(
        PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 1 -seed 71");
    udtf.initialize(new ObjectInspector[] {
            ObjectInspectorFactory.getStandardListObjectInspector(
                PrimitiveObjectInspectorFactory.javaDoubleObjectInspector),
            PrimitiveObjectInspectorFactory.javaIntObjectInspector, param});

    final List<Double> xi = new ArrayList<Double>(x[0].length);
    for (int i = 0; i < size; i++) {
        for (int j = 0; j < x[i].length; j++) {
            xi.add(j, x[i][j]);
        }
        udtf.process(new Object[] {xi, y[i]});
        xi.clear();
    }

    final Text[] placeholder = new Text[1];
    Collector collector = new Collector() {
        public void collect(Object input) throws HiveException {
            Object[] forward = (Object[]) input;
            placeholder[0] = (Text) forward[2];
        }
    };

    udtf.setCollector(collector);
    udtf.close();

    Text modelTxt = placeholder[0];
    Assert.assertNotNull(modelTxt);

    byte[] b = Base91.decode(modelTxt.getBytes(), 0, modelTxt.getLength());
    DecisionTree.Node node = DecisionTree.deserialize(b, b.length, true);
    return node;
}
 

@Test
public void testTitanicPruning() throws IOException, ParseException {
    String datasetUrl =
            "https://gist.githubusercontent.com/myui/7cd82c443db84ba7e7add1523d0247a9/raw/f2d3e3051b0292577e8c01a1759edabaa95c5781/titanic_train.tsv";

    URL url = new URL(datasetUrl);
    InputStream is = new BufferedInputStream(url.openStream());

    DelimitedTextParser parser = new DelimitedTextParser();
    parser.setColumnNames(true);
    parser.setDelimiter(",");
    parser.setResponseIndex(new NominalAttribute("survived"), 0);

    AttributeDataset train = parser.parse("titanic train", is);
    double[][] x_ = train.toArray(new double[train.size()][]);
    int[] y = train.toArray(new int[train.size()]);

    // pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked
    // C,C,C,Q,Q,Q,C,Q,C,C
    RoaringBitmap nominalAttrs = new RoaringBitmap();
    nominalAttrs.add(0);
    nominalAttrs.add(1);
    nominalAttrs.add(2);
    nominalAttrs.add(6);
    nominalAttrs.add(8);
    nominalAttrs.add(9);

    int columns = x_[0].length;
    Matrix x = new RowMajorDenseMatrix2d(x_, columns);
    int numVars = (int) Math.ceil(Math.sqrt(columns));
    int maxDepth = Integer.MAX_VALUE;
    int maxLeafs = Integer.MAX_VALUE;
    int minSplits = 2;
    int minLeafSize = 1;
    int[] samples = null;
    PRNG rand = RandomNumberGeneratorFactory.createPRNG(43L);

    final String[] featureNames = new String[] {"pclass", "name", "sex", "age", "sibsp",
            "parch", "ticket", "fare", "cabin", "embarked"};
    final String[] classNames = new String[] {"yes", "no"};
    DecisionTree tree = new DecisionTree(nominalAttrs, x, y, numVars, maxDepth, maxLeafs,
        minSplits, minLeafSize, samples, SplitRule.GINI, rand) {
        @Override
        public String toString() {
            return predictJsCodegen(featureNames, classNames);
        }
    };
    tree.toString();
}
 

@Test
public void testIrisSparse() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    RandomForestClassifierUDTF udtf = new RandomForestClassifierUDTF();
    ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector(
        PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 49");
    udtf.initialize(new ObjectInspector[] {
            ObjectInspectorFactory.getStandardListObjectInspector(
                PrimitiveObjectInspectorFactory.javaStringObjectInspector),
            PrimitiveObjectInspectorFactory.javaIntObjectInspector, param});

    final List<String> xi = new ArrayList<String>(x[0].length);
    for (int i = 0; i < size; i++) {
        double[] row = x[i];
        for (int j = 0; j < row.length; j++) {
            xi.add(j + ":" + row[j]);
        }
        udtf.process(new Object[] {xi, y[i]});
        xi.clear();
    }

    final MutableInt count = new MutableInt(0);
    Collector collector = new Collector() {
        public void collect(Object input) throws HiveException {
            count.addValue(1);
        }
    };

    udtf.setCollector(collector);
    udtf.close();

    Assert.assertEquals(49, count.getValue());
}
 

@Test
public void testIrisSparse() throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    GradientTreeBoostingClassifierUDTF udtf = new GradientTreeBoostingClassifierUDTF();
    ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector(
        PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 490");
    udtf.initialize(new ObjectInspector[] {
            ObjectInspectorFactory.getStandardListObjectInspector(
                PrimitiveObjectInspectorFactory.javaStringObjectInspector),
            PrimitiveObjectInspectorFactory.javaIntObjectInspector, param});

    final List<String> xi = new ArrayList<String>(x[0].length);
    for (int i = 0; i < size; i++) {
        double[] row = x[i];
        for (int j = 0; j < row.length; j++) {
            xi.add(j + ":" + row[j]);
        }
        udtf.process(new Object[] {xi, y[i]});
        xi.clear();
    }

    final MutableInt count = new MutableInt(0);
    Collector collector = new Collector() {
        public void collect(Object input) throws HiveException {
            count.addValue(1);
        }
    };

    udtf.setCollector(collector);
    udtf.close();

    Assert.assertEquals(490, count.getValue());
}
 

@Test(expected = HiveException.class)
public void testIrisDenseAllNullFeaturesTest()
        throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    RandomForestClassifierUDTF udtf = new RandomForestClassifierUDTF();
    ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector(
        PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 49");
    udtf.initialize(new ObjectInspector[] {
            ObjectInspectorFactory.getStandardListObjectInspector(
                PrimitiveObjectInspectorFactory.javaDoubleObjectInspector),
            PrimitiveObjectInspectorFactory.javaIntObjectInspector, param});

    final List<Double> xi = new ArrayList<Double>(x[0].length);
    for (int i = 0; i < size; i++) {
        for (int j = 0; j < x[i].length; j++) {
            xi.add(j, null);
        }
        udtf.process(new Object[] {xi, y[i]});
        xi.clear();
    }

    final MutableInt count = new MutableInt(0);
    Collector collector = new Collector() {
        public void collect(Object input) throws HiveException {
            count.addValue(1);
        }
    };

    udtf.setCollector(collector);
    udtf.close();

    Assert.fail("should not be called");
}
 

@Test
public void testIrisDenseSomeNullFeaturesTest()
        throws IOException, ParseException, HiveException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);

    AttributeDataset iris = arffParser.parse(is);
    int size = iris.size();
    double[][] x = iris.toArray(new double[size][]);
    int[] y = iris.toArray(new int[size]);

    RandomForestClassifierUDTF udtf = new RandomForestClassifierUDTF();
    ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector(
        PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-trees 49");
    udtf.initialize(new ObjectInspector[] {
            ObjectInspectorFactory.getStandardListObjectInspector(
                PrimitiveObjectInspectorFactory.javaDoubleObjectInspector),
            PrimitiveObjectInspectorFactory.javaIntObjectInspector, param});

    final Random rand = new Random(43);
    final List<Double> xi = new ArrayList<Double>(x[0].length);
    for (int i = 0; i < size; i++) {
        for (int j = 0; j < x[i].length; j++) {
            if (rand.nextDouble() >= 0.7) {
                xi.add(j, null);
            } else {
                xi.add(j, x[i][j]);
            }
        }
        udtf.process(new Object[] {xi, y[i]});
        xi.clear();
    }

    final MutableInt count = new MutableInt(0);
    Collector collector = new Collector() {
        public void collect(Object input) throws HiveException {
            count.addValue(1);
        }
    };

    udtf.setCollector(collector);
    udtf.close();

    Assert.assertEquals(49, count.getValue());
}
 

@Test
public void testSerialization() throws HiveException, IOException, ParseException {
    URL url = new URL(
        "https://gist.githubusercontent.com/myui/ef17aabecf0c0c5bcb69/raw/aac0575b4d43072c6f3c82d9072fdefb61892694/cpu.arff");
    InputStream is = new BufferedInputStream(url.openStream());

    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(6);
    AttributeDataset data = arffParser.parse(is);
    double[] datay = data.toArray(new double[data.size()]);
    double[][] datax = data.toArray(new double[data.size()][]);

    int n = datax.length;
    int m = 3 * n / 4;
    int[] index = Math.permutate(n);

    double[][] trainx = new double[m][];
    double[] trainy = new double[m];
    for (int i = 0; i < m; i++) {
        trainx[i] = datax[index[i]];
        trainy[i] = datay[index[i]];
    }

    double[][] testx = new double[n - m][];
    double[] testy = new double[n - m];
    for (int i = m; i < n; i++) {
        testx[i - m] = datax[index[i]];
        testy[i - m] = datay[index[i]];
    }

    RoaringBitmap attrs = SmileExtUtils.convertAttributeTypes(data.attributes());
    RegressionTree tree = new RegressionTree(attrs,
        new RowMajorDenseMatrix2d(trainx, trainx[0].length), trainy, 20);
    String opScript = tree.predictOpCodegen(StackMachine.SEP);

    TestUtils.testGenericUDFSerialization(TreePredictUDFv1.class,
        new ObjectInspector[] {PrimitiveObjectInspectorFactory.javaStringObjectInspector,
                PrimitiveObjectInspectorFactory.javaIntObjectInspector,
                PrimitiveObjectInspectorFactory.javaStringObjectInspector,
                ObjectInspectorFactory.getStandardListObjectInspector(
                    PrimitiveObjectInspectorFactory.javaDoubleObjectInspector),
                ObjectInspectorUtils.getConstantObjectInspector(
                    PrimitiveObjectInspectorFactory.javaBooleanObjectInspector, false)},
        new Object[] {"model_id#1", ModelType.opscode.getId(), opScript,
                ArrayUtils.toList(testx[0])});
}