Examples with CompoundNaiveBayesModel org.apache.ignite.ml.naivebayes.compound.CompoundNaiveBayesModel used on opensource projects

Example 1 with CompoundNaiveBayesModel

use of org.apache.ignite.ml.naivebayes.compound.CompoundNaiveBayesModel in project ignite by apache.

the class CompoundNaiveBayesExportImportExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> Compound Naive Bayes classification model 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;
        Path jsonMdlPath = null;
        try {
            dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MIXED_DATASET);
            double[] priorProbabilities = new double[] { .5, .5 };
            double[][] thresholds = new double[][] { { .5 }, { .5 }, { .5 }, { .5 }, { .5 } };
            System.out.println("\n>>> Create new naive Bayes classification trainer object.");
            CompoundNaiveBayesTrainer trainer = new CompoundNaiveBayesTrainer().withPriorProbabilities(priorProbabilities).withGaussianNaiveBayesTrainer(new GaussianNaiveBayesTrainer()).withGaussianFeatureIdsToSkip(asList(3, 4, 5, 6, 7)).withDiscreteNaiveBayesTrainer(new DiscreteNaiveBayesTrainer().setBucketThresholds(thresholds)).withDiscreteFeatureIdsToSkip(asList(0, 1, 2));
            System.out.println("\n>>> Perform the training to get the model.");
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            CompoundNaiveBayesModel mdl = trainer.fit(ignite, dataCache, vectorizer);
            System.out.println("\n>>> Exported Compound Naive Bayes model: " + mdl.toString(true));
            double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy for exported Compound Naive Bayes model:" + accuracy);
            jsonMdlPath = Files.createTempFile(null, null);
            mdl.toJSON(jsonMdlPath);
            CompoundNaiveBayesModel modelImportedFromJSON = CompoundNaiveBayesModel.fromJSON(jsonMdlPath);
            System.out.println("\n>>> Imported Compound Naive Bayes model: " + modelImportedFromJSON.toString(true));
            accuracy = Evaluator.evaluate(dataCache, modelImportedFromJSON, vectorizer, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy for imported Compound Naive Bayes model:" + accuracy);
            System.out.println("\n>>> Compound Naive Bayes model over partitioned dataset usage example completed.");
        } finally {
            if (dataCache != null)
                dataCache.destroy();
            if (jsonMdlPath != null)
                Files.deleteIfExists(jsonMdlPath);
        }
    } finally {
        System.out.flush();
    }
}

Example 2 with CompoundNaiveBayesModel

use of org.apache.ignite.ml.naivebayes.compound.CompoundNaiveBayesModel in project ignite by apache.

the class CompoundNaiveBayesExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> Compound Naive Bayes classification model 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 = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MIXED_DATASET);
        double[] priorProbabilities = new double[] { .5, .5 };
        double[][] thresholds = new double[][] { { .5 }, { .5 }, { .5 }, { .5 }, { .5 } };
        System.out.println(">>> Create new naive Bayes classification trainer object.");
        CompoundNaiveBayesTrainer trainer = new CompoundNaiveBayesTrainer().withPriorProbabilities(priorProbabilities).withGaussianNaiveBayesTrainer(new GaussianNaiveBayesTrainer()).withGaussianFeatureIdsToSkip(asList(3, 4, 5, 6, 7)).withDiscreteNaiveBayesTrainer(new DiscreteNaiveBayesTrainer().setBucketThresholds(thresholds)).withDiscreteFeatureIdsToSkip(asList(0, 1, 2));
        System.out.println(">>> Perform the training to get the model.");
        Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
        CompoundNaiveBayesModel mdl = trainer.fit(ignite, dataCache, vectorizer);
        System.out.println(">>> Compound Naive Bayes model: " + mdl);
        double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.ACCURACY);
        System.out.println("\n>>> Accuracy " + accuracy);
        System.out.println(">>> Compound Naive bayes model over partitioned dataset usage example completed.");
    }
}