Examples with SVMLinearClassificationModel org.apache.ignite.ml.svm.SVMLinearClassificationModel used on opensource projects

Example 6 with SVMLinearClassificationModel

use of org.apache.ignite.ml.svm.SVMLinearClassificationModel in project ignite by apache.

the class SVMFromSparkExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws FileNotFoundException {
    System.out.println();
    System.out.println(">>> SVM model loaded from Spark through serialization over partitioned dataset usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        IgniteCache<Integer, Vector> dataCache = null;
        try {
            dataCache = TitanicUtils.readPassengers(ignite);
            final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 5, 6).labeled(1);
            SVMLinearClassificationModel mdl = (SVMLinearClassificationModel) SparkModelParser.parse(SPARK_MDL_PATH, SupportedSparkModels.LINEAR_SVM, env);
            System.out.println(">>> SVM: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
        } finally {
            dataCache.destroy();
        }
    }
}

Example 7 with SVMLinearClassificationModel

use of org.apache.ignite.ml.svm.SVMLinearClassificationModel in project ignite by apache.

the class SparkModelParser method loadLinearSVMModel.

/**
 * Load SVM model.
 *
 * @param pathToMdl Path to model.
 * @param learningEnvironment Learning environment.
 */
private static Model loadLinearSVMModel(String pathToMdl, LearningEnvironment learningEnvironment) {
    Vector coefficients = null;
    double interceptor = 0;
    try (ParquetFileReader r = ParquetFileReader.open(HadoopInputFile.fromPath(new Path(pathToMdl), new Configuration()))) {
        PageReadStore pages;
        final MessageType schema = r.getFooter().getFileMetaData().getSchema();
        final MessageColumnIO colIO = new ColumnIOFactory().getColumnIO(schema);
        while (null != (pages = r.readNextRowGroup())) {
            final long rows = pages.getRowCount();
            final RecordReader recordReader = colIO.getRecordReader(pages, new GroupRecordConverter(schema));
            for (int i = 0; i < rows; i++) {
                final SimpleGroup g = (SimpleGroup) recordReader.read();
                interceptor = readSVMInterceptor(g);
                coefficients = readSVMCoefficients(g);
            }
        }
    } catch (IOException e) {
        String msg = "Error reading parquet file: " + e.getMessage();
        learningEnvironment.logger().log(MLLogger.VerboseLevel.HIGH, msg);
        e.printStackTrace();
    }
    return new SVMLinearClassificationModel(coefficients, interceptor);
}

Example 8 with SVMLinearClassificationModel

use of org.apache.ignite.ml.svm.SVMLinearClassificationModel in project ignite by apache.

the class LocalModelsTest method importExportSVMBinaryClassificationModelTest.

/**
 */
@Test
public void importExportSVMBinaryClassificationModelTest() throws IOException {
    executeModelTest(mdlFilePath -> {
        SVMLinearClassificationModel mdl = new SVMLinearClassificationModel(new DenseVector(new double[] { 1, 2 }), 3);
        Exporter<SVMLinearClassificationModel, String> exporter = new FileExporter<>();
        mdl.saveModel(exporter, mdlFilePath);
        SVMLinearClassificationModel load = exporter.load(mdlFilePath);
        Assert.assertNotNull(load);
        Assert.assertEquals("", mdl, load);
        return null;
    });
}