Examples with Rss org.apache.ignite.ml.selection.scoring.metric.regression.Rss used on opensource projects

Example 1 with Rss

use of org.apache.ignite.ml.selection.scoring.metric.regression.Rss in project ignite by apache.

the class RegressionEvaluatorTest method testEvaluatorWithFilter.

/**
 * Test evaluator and trainer with test-train splitting.
 */
@Test
public void testEvaluatorWithFilter() {
    Map<Integer, Vector> data = new HashMap<>();
    data.put(0, VectorUtils.of(60323, 83.0, 234289, 2356, 1590, 107608, 1947));
    data.put(1, VectorUtils.of(61122, 88.5, 259426, 2325, 1456, 108632, 1948));
    data.put(2, VectorUtils.of(60171, 88.2, 258054, 3682, 1616, 109773, 1949));
    data.put(3, VectorUtils.of(61187, 89.5, 284599, 3351, 1650, 110929, 1950));
    data.put(4, VectorUtils.of(63221, 96.2, 328975, 2099, 3099, 112075, 1951));
    data.put(5, VectorUtils.of(63639, 98.1, 346999, 1932, 3594, 113270, 1952));
    data.put(6, VectorUtils.of(64989, 99.0, 365385, 1870, 3547, 115094, 1953));
    data.put(7, VectorUtils.of(63761, 100.0, 363112, 3578, 3350, 116219, 1954));
    data.put(8, VectorUtils.of(66019, 101.2, 397469, 2904, 3048, 117388, 1955));
    data.put(9, VectorUtils.of(68169, 108.4, 442769, 2936, 2798, 120445, 1957));
    data.put(10, VectorUtils.of(66513, 110.8, 444546, 4681, 2637, 121950, 1958));
    data.put(11, VectorUtils.of(68655, 112.6, 482704, 3813, 2552, 123366, 1959));
    data.put(12, VectorUtils.of(69564, 114.2, 502601, 3931, 2514, 125368, 1960));
    data.put(13, VectorUtils.of(69331, 115.7, 518173, 4806, 2572, 127852, 1961));
    data.put(14, VectorUtils.of(70551, 116.9, 554894, 4007, 2827, 130081, 1962));
    KNNRegressionTrainer trainer = new KNNRegressionTrainer().withK(3).withDistanceMeasure(new EuclideanDistance());
    TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>(new SHA256UniformMapper<>(new Random(0))).split(0.5);
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
    KNNRegressionModel mdl = trainer.fit(data, split.getTestFilter(), parts, vectorizer);
    double score = Evaluator.evaluate(new LocalDatasetBuilder<>(data, split.getTrainFilter(), parts), mdl, vectorizer, new Rss()).getSingle();
    assertEquals(4800164.444444457, score, 1e-4);
}