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

Example 1 with LinearRegressionModel

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

the class DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    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.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            System.out.println(">>> Create new normalization trainer object.");
            NormalizationTrainer<Integer, double[]> normalizationTrainer = new NormalizationTrainer<>();
            System.out.println(">>> Perform the training to get the normalization preprocessor.");
            NormalizationPreprocessor<Integer, double[]> preprocessor = normalizationTrainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), 4);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer<Integer, double[]> trainer = new LinearRegressionLSQRTrainer<>();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), preprocessor, (k, v) -> v[0], 4);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    Integer key = observation.getKey();
                    double[] val = observation.getValue();
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(preprocessor.apply(key, val)));
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 2 with LinearRegressionModel

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

the class DistributedLinearRegressionWithLSQRTrainerExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    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.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer<Integer, double[]> trainer = new LinearRegressionLSQRTrainer<>();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), (k, v) -> v[0], 4);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    double[] val = observation.getValue();
                    double[] inputs = Arrays.copyOfRange(val, 1, val.length);
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(inputs));
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 3 with LinearRegressionModel

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

the class DistributedLinearRegressionWithSGDTrainerExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    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.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            // Create SparseDistributedMatrix, new cache will be created automagically.
            System.out.println(">>> Create new SparseDistributedMatrix inside IgniteThread.");
            SparseDistributedMatrix distributedMatrix = new SparseDistributedMatrix(data);
            System.out.println(">>> Create new linear regression trainer object.");
            Trainer<LinearRegressionModel, Matrix> trainer = new LinearRegressionSGDTrainer(100_000, 1e-12);
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel model = trainer.train(distributedMatrix);
            System.out.println(">>> Linear regression model: " + model);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            for (double[] observation : data) {
                Vector inputs = new SparseDistributedVector(Arrays.copyOfRange(observation, 1, observation.length));
                double prediction = model.apply(inputs);
                double groundTruth = observation[0];
                System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 4 with LinearRegressionModel

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

the class IgniteModelDistributedInferenceExample method main.

/**
 * Run example.
 */
public static void main(String... args) throws IOException, ExecutionException, InterruptedException {
    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;
        try {
            dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(ignite, dataCache, new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST));
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> Preparing model reader and model parser.");
            ModelReader reader = new InMemoryModelReader(mdl);
            ModelParser<Vector, Double, ?> parser = new IgniteModelParser<>();
            try (Model<Vector, Future<Double>> infMdl = new IgniteDistributedModelBuilder(ignite, 4, 4).build(reader, parser)) {
                System.out.println(">>> Inference model is ready.");
                System.out.println(">>> ---------------------------------");
                System.out.println(">>> | Prediction\t| Ground Truth\t|");
                System.out.println(">>> ---------------------------------");
                try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
                    for (Cache.Entry<Integer, Vector> observation : observations) {
                        Vector val = observation.getValue();
                        Vector inputs = val.copyOfRange(1, val.size());
                        double groundTruth = val.get(0);
                        double prediction = infMdl.predict(inputs).get();
                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                    }
                }
            }
            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 5 with LinearRegressionModel

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

the class LinearRegressionFromSparkExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws FileNotFoundException {
    System.out.println();
    System.out.println(">>> Linear regression 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, 1, 5, 6).labeled(4);
            LinearRegressionModel mdl = (LinearRegressionModel) SparkModelParser.parse(SPARK_MDL_PATH, SupportedSparkModels.LINEAR_REGRESSION, env);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, Vector> observation : observations) {
                    LabeledVector<Double> lv = vectorizer.apply(observation.getKey(), observation.getValue());
                    Vector inputs = lv.features();
                    double groundTruth = lv.label();
                    double prediction = mdl.predict(inputs);
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        } finally {
            dataCache.destroy();
        }
    }
}