Examples with KNNClassificationTrainer org.apache.ignite.ml.knn.classification.KNNClassificationTrainer used on opensource projects

Example 6 with KNNClassificationTrainer

use of org.apache.ignite.ml.knn.classification.KNNClassificationTrainer in project ignite by apache.

the class IrisClassificationExample method main.

/**
 * Runs example.
 */
public static void main(String[] args) throws IOException {
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        IgniteCache<Integer, Vector> dataCache = null;
        try {
            System.out.println(">>> Fill dataset cache.");
            dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.IRIS);
            KNNClassificationTrainer trainer = ((KNNClassificationTrainer) new KNNClassificationTrainer().withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(0))).withK(3).withDistanceMeasure(new EuclideanDistance()).withWeighted(true);
            // This vectorizer works with values in cache of Vector class.
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(// FIRST means "label are stored at first coordinate of vector"
            Vectorizer.LabelCoordinate.FIRST);
            // Splits dataset to train and test samples with 60/40 proportion.
            TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>().split(0.6);
            System.out.println(">>> Start traininig.");
            KNNClassificationModel mdl = trainer.fit(ignite, dataCache, split.getTrainFilter(), vectorizer);
            System.out.println(">>> Perform scoring.");
            double accuracy = Evaluator.evaluate(dataCache, split.getTestFilter(), mdl, vectorizer, new Accuracy<>());
            System.out.println(">> Model accuracy: " + accuracy);
        } finally {
            if (dataCache != null)
                dataCache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 7 with KNNClassificationTrainer

use of org.apache.ignite.ml.knn.classification.KNNClassificationTrainer in project ignite by apache.

the class BinaryClassificationEvaluatorTest method testEvaluatorWithFilter.

/**
 * Test evaluator and trainer on classification model y = x.
 */
@Test
public void testEvaluatorWithFilter() {
    Map<Integer, Vector> cacheMock = new HashMap<>();
    for (int i = 0; i < twoLinearlySeparableClasses.length; i++) cacheMock.put(i, VectorUtils.of(twoLinearlySeparableClasses[i]));
    KNNClassificationTrainer trainer = new KNNClassificationTrainer().withK(3);
    TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>(new SHA256UniformMapper<>(new Random(100))).split(0.75);
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
    KNNClassificationModel mdl = trainer.fit(cacheMock, split.getTrainFilter(), parts, vectorizer);
    double score = Evaluator.evaluate(cacheMock, split.getTestFilter(), mdl, vectorizer, MetricName.ACCURACY);
    assertEquals(0.9769230769230769, score, 1e-12);
}

Example 8 with KNNClassificationTrainer

use of org.apache.ignite.ml.knn.classification.KNNClassificationTrainer in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationFarPointsWithWeightedStrategy.

/**
 */
@Test
public void testBinaryClassificationFarPointsWithWeightedStrategy() {
    Map<Integer, double[]> data = new HashMap<>();
    data.put(0, new double[] { 10.0, 10.0, 1.0 });
    data.put(1, new double[] { 10.0, 20.0, 1.0 });
    data.put(2, new double[] { -1.0, -1.0, 1.0 });
    data.put(3, new double[] { -2.0, -2.0, 2.0 });
    data.put(4, new double[] { -1.0, -2.0, 2.0 });
    data.put(5, new double[] { -2.0, -1.0, 2.0 });
    KNNClassificationTrainer trainer = new KNNClassificationTrainer().withK(3).withDistanceMeasure(new EuclideanDistance()).withWeighted(true);
    KNNClassificationModel knnMdl = trainer.fit(data, parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    assertEquals(1.0, knnMdl.predict(VectorUtils.of(-1.01, -1.01)), 0);
}

Example 9 with KNNClassificationTrainer

use of org.apache.ignite.ml.knn.classification.KNNClassificationTrainer in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationWithSmallestK.

/**
 */
@Test
public void testBinaryClassificationWithSmallestK() {
    Map<Integer, double[]> data = new HashMap<>();
    data.put(0, new double[] { 1.0, 1.0, 1.0 });
    data.put(1, new double[] { 1.0, 2.0, 1.0 });
    data.put(2, new double[] { 2.0, 1.0, 1.0 });
    data.put(3, new double[] { -1.0, -1.0, 2.0 });
    data.put(4, new double[] { -1.0, -2.0, 2.0 });
    data.put(5, new double[] { -2.0, -1.0, 2.0 });
    KNNClassificationTrainer trainer = new KNNClassificationTrainer().withK(1).withDistanceMeasure(new EuclideanDistance()).withWeighted(false);
    KNNClassificationModel knnMdl = trainer.fit(data, parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    assertEquals(1.0, knnMdl.predict(VectorUtils.of(2.0, 2.0)), 0);
    assertEquals(2.0, knnMdl.predict(VectorUtils.of(-2.0, -2.0)), 0);
}

Example 10 with KNNClassificationTrainer

use of org.apache.ignite.ml.knn.classification.KNNClassificationTrainer in project ignite by apache.

the class KNNClassificationTest method testUpdate.

/**
 */
@Test
public void testUpdate() {
    Map<Integer, double[]> data = new HashMap<>();
    data.put(0, new double[] { 10.0, 10.0, 1.0 });
    data.put(1, new double[] { 10.0, 20.0, 1.0 });
    data.put(2, new double[] { -1, -1, 1.0 });
    data.put(3, new double[] { -2, -2, 2.0 });
    data.put(4, new double[] { -1.0, -2.0, 2.0 });
    data.put(5, new double[] { -2.0, -1.0, 2.0 });
    KNNClassificationTrainer trainer = new KNNClassificationTrainer().withK(3).withDistanceMeasure(new EuclideanDistance()).withWeighted(false);
    KNNClassificationModel originalMdlOnEmptyDataset = trainer.fit(new HashMap<>(), parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    KNNClassificationModel updatedOnData = trainer.update(originalMdlOnEmptyDataset, data, parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    Vector vector = VectorUtils.of(-1.01, -1.01);
    assertNull(originalMdlOnEmptyDataset.predict(vector));
    assertEquals(Double.valueOf(2.0), updatedOnData.predict(vector));
}