Repository: ignite Updated Branches: refs/heads/ignite-2.5 b3f252666 -> 7173b0c48
http://git-wip-us.apache.org/repos/asf/ignite/blob/7173b0c4/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestSingleNode.java ---------------------------------------------------------------------- diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestSingleNode.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestSingleNode.java deleted file mode 100644 index 705db7a..0000000 --- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestSingleNode.java +++ /dev/null @@ -1,198 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.ignite.ml.clustering; - -import java.io.IOException; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.Collections; -import java.util.Comparator; -import java.util.HashMap; -import java.util.List; -import java.util.Map; -import java.util.Random; -import java.util.stream.Collectors; -import java.util.stream.IntStream; -import org.apache.ignite.Ignite; -import org.apache.ignite.internal.util.IgniteUtils; -import org.apache.ignite.ml.math.StorageConstants; -import org.apache.ignite.ml.math.Vector; -import org.apache.ignite.ml.math.VectorUtils; -import org.apache.ignite.ml.math.distances.DistanceMeasure; -import org.apache.ignite.ml.math.distances.EuclideanDistance; -import org.apache.ignite.ml.math.functions.Functions; -import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix; -import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector; -import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest; -import org.junit.Assert; - -import static org.apache.ignite.ml.clustering.KMeansUtil.checkIsInEpsilonNeighbourhood; - -/** - * This test checks logic of clustering (checks for clusters structures). - */ -public class KMeansDistributedClustererTestSingleNode extends GridCommonAbstractTest { - /** - * Number of nodes in grid. We should use 1 in this test because otherwise algorithm will be unstable - * (We cannot guarantee the order in which results are returned from each node). - */ - private static final int NODE_COUNT = 1; - - /** Grid instance. */ - private Ignite ignite; - - /** - * Default constructor. - */ - public KMeansDistributedClustererTestSingleNode() { - super(false); - } - - /** - * {@inheritDoc} - */ - @Override protected void beforeTest() throws Exception { - ignite = grid(NODE_COUNT); - } - - /** {@inheritDoc} */ - @Override protected void beforeTestsStarted() throws Exception { - for (int i = 1; i <= NODE_COUNT; i++) - startGrid(i); - } - - /** {@inheritDoc} */ - @Override protected void afterTestsStopped() throws Exception { - stopAllGrids(); - } - - /** */ - public void testPerformClusterAnalysisDegenerate() { - IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName()); - - KMeansDistributedClusterer clusterer = new KMeansDistributedClusterer(new EuclideanDistance(), 1, 1, 1L); - - double[] v1 = new double[] {1959, 325100}; - double[] v2 = new double[] {1960, 373200}; - - SparseDistributedMatrix points = new SparseDistributedMatrix(2, 2, StorageConstants.ROW_STORAGE_MODE, - StorageConstants.RANDOM_ACCESS_MODE); - - points.setRow(0, v1); - points.setRow(1, v2); - - KMeansModel mdl = clusterer.cluster(points, 1); - - Assert.assertEquals(1, mdl.centers().length); - Assert.assertEquals(2, mdl.centers()[0].size()); - } - - /** */ - public void testClusterizationOnDatasetWithObviousStructure() throws IOException { - IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName()); - - int ptsCnt = 10000; - int squareSideLen = 10000; - - Random rnd = new Random(123456L); - - // Let centers be in the vertices of square. - Map<Integer, Vector> centers = new HashMap<>(); - centers.put(100, new DenseLocalOnHeapVector(new double[] {0.0, 0.0})); - centers.put(900, new DenseLocalOnHeapVector(new double[] {squareSideLen, 0.0})); - centers.put(3000, new DenseLocalOnHeapVector(new double[] {0.0, squareSideLen})); - centers.put(6000, new DenseLocalOnHeapVector(new double[] {squareSideLen, squareSideLen})); - - int centersCnt = centers.size(); - - SparseDistributedMatrix points = new SparseDistributedMatrix(ptsCnt, 2, StorageConstants.ROW_STORAGE_MODE, - StorageConstants.RANDOM_ACCESS_MODE); - - List<Integer> permutation = IntStream.range(0, ptsCnt).boxed().collect(Collectors.toList()); - Collections.shuffle(permutation, rnd); - - Vector[] mc = new Vector[centersCnt]; - Arrays.fill(mc, VectorUtils.zeroes(2)); - - int centIdx = 0; - int totalCnt = 0; - - List<Vector> massCenters = new ArrayList<>(); - - for (Integer count : centers.keySet()) { - for (int i = 0; i < count; i++) { - Vector pnt = new DenseLocalOnHeapVector(2).assign(centers.get(count)); - // Perturbate point on random value. - pnt.map(val -> val + rnd.nextDouble() * squareSideLen / 100); - mc[centIdx] = mc[centIdx].plus(pnt); - points.assignRow(permutation.get(totalCnt), pnt); - totalCnt++; - } - massCenters.add(mc[centIdx].times(1 / (double)count)); - centIdx++; - } - - EuclideanDistance dist = new EuclideanDistance(); - OrderedNodesComparator comp = new OrderedNodesComparator(centers.values().toArray(new Vector[] {}), dist); - - massCenters.sort(comp); - KMeansDistributedClusterer clusterer = new KMeansDistributedClusterer(dist, 3, 100, 1L); - - KMeansModel mdl = clusterer.cluster(points, 4); - Vector[] resCenters = mdl.centers(); - Arrays.sort(resCenters, comp); - - checkIsInEpsilonNeighbourhood(resCenters, massCenters.toArray(new Vector[] {}), 30.0); - - points.destroy(); - } - - /** */ - private static class OrderedNodesComparator implements Comparator<Vector> { - /** */ - private final DistanceMeasure measure; - - /** */ - List<Vector> orderedNodes; - - /** */ - OrderedNodesComparator(Vector[] orderedNodes, DistanceMeasure measure) { - this.orderedNodes = Arrays.asList(orderedNodes); - this.measure = measure; - } - - /** */ - private int findClosestNodeIndex(Vector v) { - return Functions.argmin(orderedNodes, v1 -> measure.compute(v1, v)).get1(); - } - - /** */ - @Override public int compare(Vector v1, Vector v2) { - int ind1 = findClosestNodeIndex(v1); - int ind2 = findClosestNodeIndex(v2); - - int signum = (int)Math.signum(ind1 - ind2); - - if (signum != 0) - return signum; - - return (int)Math.signum(orderedNodes.get(ind1).minus(v1).kNorm(2) - - orderedNodes.get(ind2).minus(v2).kNorm(2)); - } - } -} http://git-wip-us.apache.org/repos/asf/ignite/blob/7173b0c4/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansLocalClustererTest.java ---------------------------------------------------------------------- diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansLocalClustererTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansLocalClustererTest.java deleted file mode 100644 index cd9b2ed..0000000 --- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansLocalClustererTest.java +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.ignite.ml.clustering; - -import org.apache.ignite.ml.math.distances.EuclideanDistance; -import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix; -import org.junit.Assert; -import org.junit.Test; - -/** */ -public class KMeansLocalClustererTest { - /** - * Two points, one cluster, one iteration - */ - @Test - public void testPerformClusterAnalysisDegenerate() { - KMeansLocalClusterer clusterer = new KMeansLocalClusterer(new EuclideanDistance(), 1, 1L); - - double[] v1 = new double[] {1959, 325100}; - double[] v2 = new double[] {1960, 373200}; - - DenseLocalOnHeapMatrix points = new DenseLocalOnHeapMatrix(new double[][] { - v1, - v2}); - - KMeansModel mdl = clusterer.cluster(points, 1); - - Assert.assertEquals(1, mdl.centers().length); - Assert.assertEquals(2, mdl.centers()[0].size()); - } -} http://git-wip-us.apache.org/repos/asf/ignite/blob/7173b0c4/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansModelTest.java ---------------------------------------------------------------------- diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansModelTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansModelTest.java new file mode 100644 index 0000000..5b3ad85 --- /dev/null +++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansModelTest.java @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.ignite.ml.clustering; + +import org.apache.ignite.ml.TestUtils; +import org.apache.ignite.ml.clustering.kmeans.KMeansModel; +import org.apache.ignite.ml.math.Vector; +import org.apache.ignite.ml.math.distances.DistanceMeasure; +import org.apache.ignite.ml.math.distances.EuclideanDistance; +import org.apache.ignite.ml.math.exceptions.CardinalityException; +import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector; +import org.apache.ignite.ml.regressions.linear.LinearRegressionModel; +import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationModel; +import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationModel; +import org.junit.Assert; +import org.junit.Test; + +/** + * Tests for {@link KMeansModel}. + */ +public class KMeansModelTest { + /** Precision in test checks. */ + private static final double PRECISION = 1e-6; + + /** */ + @Test + public void predictClusters() { + DistanceMeasure distanceMeasure = new EuclideanDistance(); + + Vector[] centers = new DenseLocalOnHeapVector[4]; + + centers[0] = new DenseLocalOnHeapVector(new double[]{1.0, 1.0}); + centers[1] = new DenseLocalOnHeapVector(new double[]{-1.0, 1.0}); + centers[2] = new DenseLocalOnHeapVector(new double[]{1.0, -1.0}); + centers[3] = new DenseLocalOnHeapVector(new double[]{-1.0, -1.0}); + + KMeansModel mdl = new KMeansModel(centers, distanceMeasure); + + Assert.assertEquals(mdl.apply(new DenseLocalOnHeapVector(new double[]{1.1, 1.1})), 0.0, PRECISION); + Assert.assertEquals(mdl.apply(new DenseLocalOnHeapVector(new double[]{-1.1, 1.1})), 1.0, PRECISION); + Assert.assertEquals(mdl.apply(new DenseLocalOnHeapVector(new double[]{1.1, -1.1})), 2.0, PRECISION); + Assert.assertEquals(mdl.apply(new DenseLocalOnHeapVector(new double[]{-1.1, -1.1})), 3.0, PRECISION); + + Assert.assertEquals(mdl.distanceMeasure(), distanceMeasure); + Assert.assertEquals(mdl.amountOfClusters(), 4); + Assert.assertArrayEquals(mdl.centers(), centers); + } +} http://git-wip-us.apache.org/repos/asf/ignite/blob/7173b0c4/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansTrainerTest.java ---------------------------------------------------------------------- diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansTrainerTest.java new file mode 100644 index 0000000..846d0de --- /dev/null +++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansTrainerTest.java @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.ignite.ml.clustering; + +import java.util.Arrays; +import java.util.HashMap; +import java.util.Map; +import org.apache.ignite.ml.clustering.kmeans.KMeansModel; +import org.apache.ignite.ml.clustering.kmeans.KMeansTrainer; +import org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder; +import org.apache.ignite.ml.math.Vector; +import org.apache.ignite.ml.math.distances.EuclideanDistance; +import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; + +/** + * Tests for {@link KMeansTrainer}. + */ +public class KMeansTrainerTest { + /** Precision in test checks. */ + private static final double PRECISION = 1e-2; + + /** + * A few points, one cluster, one iteration + */ + @Test + public void findOneClusters() { + + 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}); + + KMeansTrainer trainer = new KMeansTrainer() + .withDistance(new EuclideanDistance()) + .withK(1) + .withMaxIterations(1) + .withEpsilon(PRECISION); + + KMeansModel knnMdl = trainer.fit( + new LocalDatasetBuilder<>(data, 2), + (k, v) -> Arrays.copyOfRange(v, 0, v.length - 1), + (k, v) -> v[2] + ); + + Vector firstVector = new DenseLocalOnHeapVector(new double[] {2.0, 2.0}); + assertEquals(knnMdl.apply(firstVector), 0.0, PRECISION); + Vector secondVector = new DenseLocalOnHeapVector(new double[] {-2.0, -2.0}); + assertEquals(knnMdl.apply(secondVector), 0.0, PRECISION); + assertEquals(trainer.getMaxIterations(), 1); + assertEquals(trainer.getEpsilon(), PRECISION, PRECISION); + } +} http://git-wip-us.apache.org/repos/asf/ignite/blob/7173b0c4/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansUtil.java ---------------------------------------------------------------------- diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansUtil.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansUtil.java deleted file mode 100644 index 420678f..0000000 --- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansUtil.java +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.ignite.ml.clustering; - -import org.apache.ignite.ml.math.Vector; - -import static org.junit.Assert.assertTrue; - -/** Utilities for k-means tests. */ -class KMeansUtil { - /** */ - static void checkIsInEpsilonNeighbourhood(Vector[] v1s, Vector[] v2s, double epsilon) { - for (int i = 0; i < v1s.length; i++) { - assertTrue("Not in epsilon neighbourhood (index " + i + ") ", - v1s[i].minus(v2s[i]).kNorm(2) < epsilon); - } - } -}
