Examples with LinearRegressionModel org.apache.ignite.ml.regressions.linear.LinearRegressionModel used on opensource projects

Example 16 with LinearRegressionModel

use of org.apache.ignite.ml.regressions.linear.LinearRegressionModel in project ignite by apache.

the class LinearRegressionSGDTrainerExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> Linear regression model over sparse distributed matrix API 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 = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionSGDTrainer<?> trainer = new LinearRegressionSGDTrainer<>(new UpdatesStrategy<>(new RPropUpdateCalculator(), RPropParameterUpdate.SUM_LOCAL, RPropParameterUpdate.AVG), 100000, 10, 100, 123L);
            System.out.println(">>> Perform the training to get the model.");
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            LinearRegressionModel mdl = trainer.fit(ignite, dataCache, vectorizer);
            System.out.println(">>> Linear regression model: " + mdl);
            double rmse = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.RMSE);
            System.out.println("\n>>> Rmse = " + rmse);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> Linear regression model over cache based dataset usage example completed.");
        } finally {
            if (dataCache != null)
                dataCache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 17 with LinearRegressionModel

use of org.apache.ignite.ml.regressions.linear.LinearRegressionModel in project ignite by apache.

the class LinearRegressionExportImportExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> Linear regression model over cache based 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;
        Path jsonMdlPath = null;
        try {
            dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
            System.out.println("\n>>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer();
            System.out.println("\n>>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(ignite, dataCache, new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST));
            System.out.println("\n>>> Exported LinearRegression model: " + mdl);
            double rmse = Evaluator.evaluate(dataCache, mdl, new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST), MetricName.RMSE);
            System.out.println("\n>>> RMSE for exported LinearRegression model: " + rmse);
            jsonMdlPath = Files.createTempFile(null, null);
            mdl.toJSON(jsonMdlPath);
            LinearRegressionModel modelImportedFromJSON = LinearRegressionModel.fromJSON(jsonMdlPath);
            System.out.println("\n>>> Imported LinearRegression model: " + modelImportedFromJSON);
            rmse = Evaluator.evaluate(dataCache, mdl, new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST), MetricName.RMSE);
            System.out.println("\n>>> RMSE for imported LinearRegression model: " + rmse);
            System.out.println("\n>>> Linear regression model over cache based dataset usage example completed.");
        } finally {
            if (dataCache != null)
                dataCache.destroy();
            if (jsonMdlPath != null)
                Files.deleteIfExists(jsonMdlPath);
        }
    } finally {
        System.out.flush();
    }
}

Example 18 with LinearRegressionModel

use of org.apache.ignite.ml.regressions.linear.LinearRegressionModel in project ignite by apache.

the class SparkModelParser method loadLinRegModel.

/**
 * Load linear regression model.
 *
 * @param pathToMdl Path to model.
 * @param learningEnvironment Learning environment.
 */
private static Model loadLinRegModel(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 = readLinRegInterceptor(g);
                coefficients = readLinRegCoefficients(g);
            }
        }
    } catch (IOException e) {
        String msg = "Error reading parquet file: " + e.getMessage();
        learningEnvironment.logger().log(MLLogger.VerboseLevel.HIGH, msg);
        e.printStackTrace();
    }
    return new LinearRegressionModel(coefficients, interceptor);
}

Example 19 with LinearRegressionModel

use of org.apache.ignite.ml.regressions.linear.LinearRegressionModel in project ignite by apache.

the class StackingTest method testSimpleVectorStack.

/**
 * Tests simple stack training.
 */
@Test
public void testSimpleVectorStack() {
    StackedVectorDatasetTrainer<Double, LinearRegressionModel, Double> trainer = new StackedVectorDatasetTrainer<>();
    UpdatesStrategy<SmoothParametrized, SimpleGDParameterUpdate> updatesStgy = new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG);
    MLPArchitecture arch = new MLPArchitecture(2).withAddedLayer(10, true, Activators.RELU).withAddedLayer(1, false, Activators.SIGMOID);
    DatasetTrainer<MultilayerPerceptron, Double> mlpTrainer = new MLPTrainer<>(arch, LossFunctions.MSE, updatesStgy, 3000, 10, 50, 123L).withConvertedLabels(VectorUtils::num2Arr);
    final double factor = 3;
    StackedModel<Vector, Vector, Double, LinearRegressionModel> mdl = trainer.withAggregatorTrainer(new LinearRegressionLSQRTrainer().withConvertedLabels(x -> x * factor)).addMatrix2MatrixTrainer(mlpTrainer).withEnvironmentBuilder(TestUtils.testEnvBuilder()).fit(getCacheMock(xor), parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(0.0, 0.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(0.0, 1.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(1.0, 0.0)), 0.3);
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(1.0, 1.0)), 0.3);
}