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

Example 1 with SVMLinearClassificationModel

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

the class SVMBinaryClassificationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> SVM Binary classification model over cached 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.TWO_CLASSED_IRIS);
            SVMLinearClassificationTrainer trainer = new SVMLinearClassificationTrainer();
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            SVMLinearClassificationModel mdl = trainer.fit(ignite, dataCache, vectorizer);
            System.out.println(">>> SVM model " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println(">>> SVM Binary classification model over cache based dataset usage example completed.");
        } finally {
            if (dataCache != null)
                dataCache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 2 with SVMLinearClassificationModel

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

the class CollectionsTest method test.

/**
 */
@Test
@SuppressWarnings("unchecked")
public void test() {
    test(new VectorizedViewMatrix(new DenseMatrix(2, 2), 1, 1, 1, 1), new VectorizedViewMatrix(new DenseMatrix(3, 2), 2, 1, 1, 1));
    specialTest(new ManhattanDistance(), new ManhattanDistance());
    specialTest(new HammingDistance(), new HammingDistance());
    specialTest(new EuclideanDistance(), new EuclideanDistance());
    FeatureMetadata data = new FeatureMetadata("name2");
    data.setName("name1");
    test(data, new FeatureMetadata("name2"));
    test(new DatasetRow<>(new DenseVector()), new DatasetRow<>(new DenseVector(1)));
    test(new LabeledVector<>(new DenseVector(), null), new LabeledVector<>(new DenseVector(1), null));
    test(new Dataset<DatasetRow<Vector>>(new DatasetRow[] {}, new FeatureMetadata[] {}), new Dataset<DatasetRow<Vector>>(new DatasetRow[] { new DatasetRow() }, new FeatureMetadata[] { new FeatureMetadata() }));
    test(new LogisticRegressionModel(new DenseVector(), 1.0), new LogisticRegressionModel(new DenseVector(), 0.5));
    test(new KMeansModelFormat(new Vector[] {}, new ManhattanDistance()), new KMeansModelFormat(new Vector[] {}, new HammingDistance()));
    test(new KMeansModel(new Vector[] {}, new ManhattanDistance()), new KMeansModel(new Vector[] {}, new HammingDistance()));
    test(new SVMLinearClassificationModel(null, 1.0), new SVMLinearClassificationModel(null, 0.5));
    test(new ANNClassificationModel(new LabeledVectorSet<>(), new ANNClassificationTrainer.CentroidStat()), new ANNClassificationModel(new LabeledVectorSet<>(1, 1), new ANNClassificationTrainer.CentroidStat()));
    test(new ANNModelFormat(1, new ManhattanDistance(), false, new LabeledVectorSet<>(), new ANNClassificationTrainer.CentroidStat()), new ANNModelFormat(2, new ManhattanDistance(), false, new LabeledVectorSet<>(), new ANNClassificationTrainer.CentroidStat()));
}

Example 3 with SVMLinearClassificationModel

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

the class OneVsRestClassificationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> One-vs-Rest SVM Multi-class classification model over cached 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.GLASS_IDENTIFICATION);
            OneVsRestTrainer<SVMLinearClassificationModel> trainer = new OneVsRestTrainer<>(new SVMLinearClassificationTrainer().withAmountOfIterations(20).withAmountOfLocIterations(50).withLambda(0.2).withSeed(1234L));
            MultiClassModel<SVMLinearClassificationModel> mdl = trainer.fit(ignite, dataCache, new DummyVectorizer<Integer>().labeled(0));
            System.out.println(">>> One-vs-Rest SVM Multi-class model");
            System.out.println(mdl.toString());
            MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>();
            Preprocessor<Integer, Vector> preprocessor = minMaxScalerTrainer.fit(ignite, dataCache, new DummyVectorizer<Integer>().labeled(0));
            MultiClassModel<SVMLinearClassificationModel> mdlWithScaling = trainer.fit(ignite, dataCache, preprocessor);
            System.out.println(">>> One-vs-Rest SVM Multi-class model with MinMaxScaling");
            System.out.println(mdlWithScaling.toString());
            System.out.println(">>> ----------------------------------------------------------------");
            System.out.println(">>> | Prediction\t| Prediction with MinMaxScaling\t| Ground Truth\t|");
            System.out.println(">>> ----------------------------------------------------------------");
            int amountOfErrors = 0;
            int amountOfErrorsWithMinMaxScaling = 0;
            int totalAmount = 0;
            // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
            int[][] confusionMtx = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
            int[][] confusionMtxWithMinMaxScaling = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
            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 = mdl.predict(inputs);
                    double predictionWithMinMaxScaling = mdlWithScaling.predict(inputs);
                    totalAmount++;
                    // Collect data for model
                    if (!Precision.equals(groundTruth, prediction, Precision.EPSILON))
                        amountOfErrors++;
                    int idx1 = (int) prediction == 1 ? 0 : ((int) prediction == 3 ? 1 : 2);
                    int idx2 = (int) groundTruth == 1 ? 0 : ((int) groundTruth == 3 ? 1 : 2);
                    confusionMtx[idx1][idx2]++;
                    // Collect data for model with min-max scaling
                    if (!Precision.equals(groundTruth, predictionWithMinMaxScaling, Precision.EPSILON))
                        amountOfErrorsWithMinMaxScaling++;
                    idx1 = (int) predictionWithMinMaxScaling == 1 ? 0 : ((int) predictionWithMinMaxScaling == 3 ? 1 : 2);
                    idx2 = (int) groundTruth == 1 ? 0 : ((int) groundTruth == 3 ? 1 : 2);
                    confusionMtxWithMinMaxScaling[idx1][idx2]++;
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithMinMaxScaling, groundTruth);
                }
                System.out.println(">>> ----------------------------------------------------------------");
                System.out.println("\n>>> -----------------One-vs-Rest SVM model-------------");
                System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
                System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double) totalAmount));
                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
                System.out.println("\n>>> -----------------One-vs-Rest SVM model with MinMaxScaling-------------");
                System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithMinMaxScaling);
                System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithMinMaxScaling / (double) totalAmount));
                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithMinMaxScaling));
                System.out.println(">>> One-vs-Rest SVM model over cache based dataset usage example completed.");
            }
        } finally {
            if (dataCache != null)
                dataCache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 4 with SVMLinearClassificationModel

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

the class EvaluatorExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> Evaluation of SVM binary classification algorithm over cached 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.TWO_CLASSED_IRIS);
            SVMLinearClassificationTrainer trainer = new SVMLinearClassificationTrainer();
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            SVMLinearClassificationModel mdl = trainer.fit(ignite, dataCache, vectorizer);
            System.out.println(Evaluator.evaluateBinaryClassification(dataCache, mdl, vectorizer));
        } finally {
            if (dataCache != null)
                dataCache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 5 with SVMLinearClassificationModel

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

the class SVMExportImportExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> SVM Binary classification model over cached dataset usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println("\n>>> Ignite grid started.");
        IgniteCache<Integer, Vector> dataCache = null;
        Path jsonMdlPath = null;
        try {
            dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
            SVMLinearClassificationTrainer trainer = new SVMLinearClassificationTrainer();
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            SVMLinearClassificationModel mdl = trainer.fit(ignite, dataCache, vectorizer);
            System.out.println("\n>>> Exported SVM model: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy for exported SVM model: " + accuracy);
            jsonMdlPath = Files.createTempFile(null, null);
            mdl.toJSON(jsonMdlPath);
            SVMLinearClassificationModel modelImportedFromJSON = SVMLinearClassificationModel.fromJSON(jsonMdlPath);
            System.out.println("\n>>> Imported SVM model: " + modelImportedFromJSON);
            accuracy = Evaluator.evaluate(dataCache, modelImportedFromJSON, vectorizer, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy for imported SVM model: " + accuracy);
            System.out.println("\n>>> SVM Binary classification model over cache based dataset usage example completed.");
        } finally {
            if (dataCache != null)
                dataCache.destroy();
            if (jsonMdlPath != null)
                Files.deleteIfExists(jsonMdlPath);
        }
    } finally {
        System.out.flush();
    }
}