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Posted to commits@ignite.apache.org by sb...@apache.org on 2017/12/15 14:09:37 UTC
[07/50] [abbrv] ignite git commit: IGNITE-6880: KNN(k nearest
neighbor) algorithm
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/IgniteMLTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/IgniteMLTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/IgniteMLTestSuite.java
index 7a61bad..05c91bd 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/IgniteMLTestSuite.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/IgniteMLTestSuite.java
@@ -18,6 +18,7 @@
package org.apache.ignite.ml;
import org.apache.ignite.ml.clustering.ClusteringTestSuite;
+import org.apache.ignite.ml.knn.KNNTestSuite;
import org.apache.ignite.ml.math.MathImplMainTestSuite;
import org.apache.ignite.ml.regressions.RegressionsTestSuite;
import org.apache.ignite.ml.trees.DecisionTreesTestSuite;
@@ -33,6 +34,7 @@ import org.junit.runners.Suite;
RegressionsTestSuite.class,
ClusteringTestSuite.class,
DecisionTreesTestSuite.class,
+ KNNTestSuite.class,
LocalModelsTest.class
})
public class IgniteMLTestSuite {
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/LocalModelsTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/LocalModelsTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/LocalModelsTest.java
index d0d1247..37dec77 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/LocalModelsTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/LocalModelsTest.java
@@ -23,11 +23,15 @@ import java.nio.file.Path;
import java.util.function.Function;
import org.apache.ignite.ml.clustering.KMeansLocalClusterer;
import org.apache.ignite.ml.clustering.KMeansModel;
-import org.apache.ignite.ml.math.EuclideanDistance;
+import org.apache.ignite.ml.knn.models.KNNModel;
+import org.apache.ignite.ml.knn.models.KNNModelFormat;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
import org.apache.ignite.ml.regressions.OLSMultipleLinearRegressionModel;
import org.apache.ignite.ml.regressions.OLSMultipleLinearRegressionModelFormat;
import org.apache.ignite.ml.regressions.OLSMultipleLinearRegressionTrainer;
+import org.apache.ignite.ml.structures.LabeledDataset;
import org.junit.Assert;
import org.junit.Test;
@@ -126,4 +130,37 @@ public class LocalModelsTest {
return trainer.train(data);
}
+
+ /** */
+ @Test
+ public void importExportKNNModelTest() throws IOException {
+ executeModelTest(mdlFilePath -> {
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ KNNModel mdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+
+ Exporter<KNNModelFormat, String> exporter = new FileExporter<>();
+ mdl.saveModel(exporter, mdlFilePath);
+
+ KNNModelFormat load = exporter.load(mdlFilePath);
+
+ Assert.assertNotNull(load);
+
+ KNNModel importedMdl = new KNNModel(load.getK(), load.getDistanceMeasure(), load.getStgy(), load.getTraining());
+
+ Assert.assertTrue("", mdl.equals(importedMdl));
+
+ return null;
+ });
+ }
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
index 0cfa7b8..0aa8f83 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
@@ -22,10 +22,10 @@ import java.util.Comparator;
import java.util.Random;
import org.apache.ignite.Ignite;
import org.apache.ignite.internal.util.IgniteUtils;
-import org.apache.ignite.ml.math.DistanceMeasure;
-import org.apache.ignite.ml.math.EuclideanDistance;
import org.apache.ignite.ml.math.StorageConstants;
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.impls.matrix.SparseDistributedMatrix;
import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
index c58ffc7..2af94aa 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
@@ -21,10 +21,10 @@ import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
-import org.apache.ignite.ml.math.DistanceMeasure;
-import org.apache.ignite.ml.math.EuclideanDistance;
import org.apache.ignite.ml.math.Matrix;
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.MathIllegalArgumentException;
import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestMultiNode.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestMultiNode.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestMultiNode.java
index 1f71dee..71be8be 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestMultiNode.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/KMeansDistributedClustererTestMultiNode.java
@@ -27,9 +27,9 @@ 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.EuclideanDistance;
import org.apache.ignite.ml.math.StorageConstants;
import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
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;
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/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
index 19c328a..705db7a 100644
--- 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
@@ -30,11 +30,11 @@ 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.DistanceMeasure;
-import org.apache.ignite.ml.math.EuclideanDistance;
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;
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/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
index b396f5b..cd9b2ed 100644
--- 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
@@ -17,7 +17,7 @@
package org.apache.ignite.ml.clustering;
-import org.apache.ignite.ml.math.EuclideanDistance;
+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;
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/knn/BaseKNNTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/knn/BaseKNNTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/knn/BaseKNNTest.java
new file mode 100644
index 0000000..9075978
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/BaseKNNTest.java
@@ -0,0 +1,91 @@
+/*
+ * 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.knn;
+
+import java.io.IOException;
+import java.net.URISyntaxException;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+
+/**
+ * Base class for decision trees test.
+ */
+public class BaseKNNTest extends GridCommonAbstractTest {
+ /** Count of nodes. */
+ private static final int NODE_COUNT = 4;
+
+ /** Separator. */
+ private static final String SEPARATOR = "\t";
+
+ /** Path to the Iris dataset. */
+ static final String KNN_IRIS_TXT = "datasets/knn/iris.txt";
+
+ /** Grid instance. */
+ protected Ignite ignite;
+
+ /**
+ * Default constructor.
+ */
+ public BaseKNNTest() {
+ 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();
+ }
+
+ /**
+ * Loads labeled dataset from file with .txt extension.
+ *
+ * @param rsrcPath path to dataset.
+ * @return null if path is incorrect.
+ */
+ LabeledDataset loadDatasetFromTxt(String rsrcPath, boolean isFallOnBadData) {
+ try {
+ Path path = Paths.get(this.getClass().getClassLoader().getResource(rsrcPath).toURI());
+ try {
+ return LabeledDataset.loadTxt(path, SEPARATOR, false, isFallOnBadData);
+ }
+ catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+ catch (URISyntaxException e) {
+ e.printStackTrace();
+ return null;
+ }
+ return null;
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNClassificationTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNClassificationTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNClassificationTest.java
new file mode 100644
index 0000000..e010553
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNClassificationTest.java
@@ -0,0 +1,153 @@
+/*
+ * 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.knn;
+
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.knn.models.KNNModel;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.math.exceptions.knn.SmallTrainingDatasetSizeException;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import org.apache.ignite.ml.structures.LabeledDataset;
+
+/** Tests behaviour of KNNClassificationTest. */
+public class KNNClassificationTest extends BaseKNNTest {
+ /** */
+ public void testBinaryClassificationTest() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector firstVector = new DenseLocalOnHeapVector(new double[] {2.0, 2.0});
+ assertEquals(knnMdl.predict(firstVector), 1.0);
+ Vector secondVector = new DenseLocalOnHeapVector(new double[] {-2.0, -2.0});
+ assertEquals(knnMdl.predict(secondVector), 2.0);
+ }
+
+ /** */
+ public void testBinaryClassificationWithSmallestKTest() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ KNNModel knnMdl = new KNNModel(1, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector firstVector = new DenseLocalOnHeapVector(new double[] {2.0, 2.0});
+ assertEquals(knnMdl.predict(firstVector), 1.0);
+ Vector secondVector = new DenseLocalOnHeapVector(new double[] {-2.0, -2.0});
+ assertEquals(knnMdl.predict(secondVector), 2.0);
+ }
+
+ /** */
+ public void testBinaryClassificationFarPointsWithSimpleStrategy() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {10.0, 10.0},
+ {10.0, 20.0},
+ {-1, -1},
+ {-2, -2},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {-1.01, -1.01});
+ assertEquals(knnMdl.predict(vector), 2.0);
+ }
+
+ /** */
+ public void testBinaryClassificationFarPointsWithWeightedStrategy() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {10.0, 10.0},
+ {10.0, 20.0},
+ {-1, -1},
+ {-2, -2},
+ {-1.0, -2.0},
+ {-2.0, -1.0}
+ };
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.WEIGHTED, training);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {-1.01, -1.01});
+ assertEquals(knnMdl.predict(vector), 1.0);
+ }
+
+ /** */
+ public void testPredictOnIrisDataset() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ LabeledDataset training = loadDatasetFromTxt(KNN_IRIS_TXT, false);
+
+ KNNModel knnMdl = new KNNModel(7, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {5.15, 3.55, 1.45, 0.25});
+ assertEquals(knnMdl.predict(vector), 1.0);
+ }
+
+ /** */
+ public void testLargeKValue() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {10.0, 10.0},
+ {10.0, 20.0},
+ {-1, -1},
+ {-2, -2},
+ {-1.0, -2.0},
+ {-2.0, -1.0}
+ };
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+ LabeledDataset training = new LabeledDataset(mtx, lbs);
+
+ try {
+ new KNNModel(7, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ fail("SmallTrainingDatasetSizeException");
+ }
+ catch (SmallTrainingDatasetSizeException e) {
+ return;
+ }
+ fail("SmallTrainingDatasetSizeException");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNMultipleLinearRegressionTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNMultipleLinearRegressionTest.java
new file mode 100644
index 0000000..9a918b6
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNMultipleLinearRegressionTest.java
@@ -0,0 +1,157 @@
+/*
+ * 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.knn;
+
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.knn.models.Normalization;
+import org.apache.ignite.ml.knn.regression.KNNMultipleLinearRegression;
+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.apache.ignite.ml.math.impls.vector.SparseBlockDistributedVector;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.junit.Assert;
+
+/**
+ * Tests for {@link KNNMultipleLinearRegression}.
+ */
+public class KNNMultipleLinearRegressionTest extends BaseKNNTest {
+ /** */
+ private double[] y;
+
+ /** */
+ private double[][] x;
+
+ /** */
+ public void testSimpleRegressionWithOneNeighbour() {
+
+ y = new double[] {11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
+ x = new double[6][];
+ x[0] = new double[] {0, 0, 0, 0, 0};
+ x[1] = new double[] {2.0, 0, 0, 0, 0};
+ x[2] = new double[] {0, 3.0, 0, 0, 0};
+ x[3] = new double[] {0, 0, 4.0, 0, 0};
+ x[4] = new double[] {0, 0, 0, 5.0, 0};
+ x[5] = new double[] {0, 0, 0, 0, 6.0};
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ LabeledDataset training = new LabeledDataset(x, y);
+
+ KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(1, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector vector = new SparseBlockDistributedVector(new double[] {0, 0, 0, 5.0, 0.0});
+ System.out.println(knnMdl.predict(vector));
+ Assert.assertEquals(15, knnMdl.predict(vector), 1E-12);
+ }
+
+ /** */
+ public void testLongly() {
+
+ y = new double[] {60323, 61122, 60171, 61187, 63221, 63639, 64989, 63761, 66019, 68169, 66513, 68655, 69564, 69331, 70551};
+ x = new double[15][];
+ x[0] = new double[] {83.0, 234289, 2356, 1590, 107608, 1947};
+ x[1] = new double[] {88.5, 259426, 2325, 1456, 108632, 1948};
+ x[2] = new double[] {88.2, 258054, 3682, 1616, 109773, 1949};
+ x[3] = new double[] {89.5, 284599, 3351, 1650, 110929, 1950};
+ x[4] = new double[] {96.2, 328975, 2099, 3099, 112075, 1951};
+ x[5] = new double[] {98.1, 346999, 1932, 3594, 113270, 1952};
+ x[6] = new double[] {99.0, 365385, 1870, 3547, 115094, 1953};
+ x[7] = new double[] {100.0, 363112, 3578, 3350, 116219, 1954};
+ x[8] = new double[] {101.2, 397469, 2904, 3048, 117388, 1955};
+ x[9] = new double[] {108.4, 442769, 2936, 2798, 120445, 1957};
+ x[10] = new double[] {110.8, 444546, 4681, 2637, 121950, 1958};
+ x[11] = new double[] {112.6, 482704, 3813, 2552, 123366, 1959};
+ x[12] = new double[] {114.2, 502601, 3931, 2514, 125368, 1960};
+ x[13] = new double[] {115.7, 518173, 4806, 2572, 127852, 1961};
+ x[14] = new double[] {116.9, 554894, 4007, 2827, 130081, 1962};
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ LabeledDataset training = new LabeledDataset(x, y);
+
+ KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {104.6, 419180, 2822, 2857, 118734, 1956});
+ System.out.println(knnMdl.predict(vector));
+ Assert.assertEquals(67857, knnMdl.predict(vector), 2000);
+ }
+
+ /** */
+ public void testLonglyWithNormalization() {
+ y = new double[] {60323, 61122, 60171, 61187, 63221, 63639, 64989, 63761, 66019, 68169, 66513, 68655, 69564, 69331, 70551};
+ x = new double[15][];
+ x[0] = new double[] {83.0, 234289, 2356, 1590, 107608, 1947};
+ x[1] = new double[] {88.5, 259426, 2325, 1456, 108632, 1948};
+ x[2] = new double[] {88.2, 258054, 3682, 1616, 109773, 1949};
+ x[3] = new double[] {89.5, 284599, 3351, 1650, 110929, 1950};
+ x[4] = new double[] {96.2, 328975, 2099, 3099, 112075, 1951};
+ x[5] = new double[] {98.1, 346999, 1932, 3594, 113270, 1952};
+ x[6] = new double[] {99.0, 365385, 1870, 3547, 115094, 1953};
+ x[7] = new double[] {100.0, 363112, 3578, 3350, 116219, 1954};
+ x[8] = new double[] {101.2, 397469, 2904, 3048, 117388, 1955};
+ x[9] = new double[] {108.4, 442769, 2936, 2798, 120445, 1957};
+ x[10] = new double[] {110.8, 444546, 4681, 2637, 121950, 1958};
+ x[11] = new double[] {112.6, 482704, 3813, 2552, 123366, 1959};
+ x[12] = new double[] {114.2, 502601, 3931, 2514, 125368, 1960};
+ x[13] = new double[] {115.7, 518173, 4806, 2572, 127852, 1961};
+ x[14] = new double[] {116.9, 554894, 4007, 2827, 130081, 1962};
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ LabeledDataset training = new LabeledDataset(x, y);
+
+ final LabeledDataset normalizedTrainingDataset = training.normalizeWith(Normalization.MINIMAX);
+
+ KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(5, new EuclideanDistance(), KNNStrategy.SIMPLE, normalizedTrainingDataset);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {104.6, 419180, 2822, 2857, 118734, 1956});
+ System.out.println(knnMdl.predict(vector));
+ Assert.assertEquals(67857, knnMdl.predict(vector), 2000);
+ }
+
+ /** */
+ public void testLonglyWithWeightedStrategyAndNormalization() {
+ y = new double[] {60323, 61122, 60171, 61187, 63221, 63639, 64989, 63761, 66019, 68169, 66513, 68655, 69564, 69331, 70551};
+ x = new double[15][];
+ x[0] = new double[] {83.0, 234289, 2356, 1590, 107608, 1947};
+ x[1] = new double[] {88.5, 259426, 2325, 1456, 108632, 1948};
+ x[2] = new double[] {88.2, 258054, 3682, 1616, 109773, 1949};
+ x[3] = new double[] {89.5, 284599, 3351, 1650, 110929, 1950};
+ x[4] = new double[] {96.2, 328975, 2099, 3099, 112075, 1951};
+ x[5] = new double[] {98.1, 346999, 1932, 3594, 113270, 1952};
+ x[6] = new double[] {99.0, 365385, 1870, 3547, 115094, 1953};
+ x[7] = new double[] {100.0, 363112, 3578, 3350, 116219, 1954};
+ x[8] = new double[] {101.2, 397469, 2904, 3048, 117388, 1955};
+ x[9] = new double[] {108.4, 442769, 2936, 2798, 120445, 1957};
+ x[10] = new double[] {110.8, 444546, 4681, 2637, 121950, 1958};
+ x[11] = new double[] {112.6, 482704, 3813, 2552, 123366, 1959};
+ x[12] = new double[] {114.2, 502601, 3931, 2514, 125368, 1960};
+ x[13] = new double[] {115.7, 518173, 4806, 2572, 127852, 1961};
+ x[14] = new double[] {116.9, 554894, 4007, 2827, 130081, 1962};
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ LabeledDataset training = new LabeledDataset(x, y);
+
+ final LabeledDataset normalizedTrainingDataset = training.normalizeWith(Normalization.MINIMAX);
+
+ KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(5, new EuclideanDistance(), KNNStrategy.WEIGHTED, normalizedTrainingDataset);
+ Vector vector = new DenseLocalOnHeapVector(new double[] {104.6, 419180, 2822, 2857, 118734, 1956});
+ System.out.println(knnMdl.predict(vector));
+ Assert.assertEquals(67857, knnMdl.predict(vector), 2000);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNTestSuite.java
new file mode 100644
index 0000000..8b47e0a
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNTestSuite.java
@@ -0,0 +1,33 @@
+/*
+ * 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.knn;
+
+import org.junit.runner.RunWith;
+import org.junit.runners.Suite;
+
+/**
+ * Test suite for all tests located in org.apache.ignite.ml.trees package.
+ */
+@RunWith(Suite.class)
+@Suite.SuiteClasses({
+ KNNClassificationTest.class,
+ KNNMultipleLinearRegressionTest.class,
+ LabeledDatasetTest.class
+})
+public class KNNTestSuite {
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetTest.java
new file mode 100644
index 0000000..32bd37b
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetTest.java
@@ -0,0 +1,208 @@
+/*
+ * 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.knn;
+
+import java.io.IOException;
+import java.net.URISyntaxException;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.exceptions.CardinalityException;
+import org.apache.ignite.ml.math.exceptions.NoDataException;
+import org.apache.ignite.ml.math.exceptions.knn.EmptyFileException;
+import org.apache.ignite.ml.math.exceptions.knn.FileParsingException;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.ml.structures.LabeledVector;
+
+/** Tests behaviour of KNNClassificationTest. */
+public class LabeledDatasetTest extends BaseKNNTest {
+ /** */
+ private static final String KNN_IRIS_TXT = "datasets/knn/iris.txt";
+
+ /** */
+ private static final String NO_DATA_TXT = "datasets/knn/no_data.txt";
+
+ /** */
+ private static final String EMPTY_TXT = "datasets/knn/empty.txt";
+
+ /** */
+ private static final String IRIS_INCORRECT_TXT = "datasets/knn/iris_incorrect.txt";
+
+ /** */
+ private static final String IRIS_MISSED_DATA = "datasets/knn/missed_data.txt";
+
+
+ /** */
+ public void testFeatureNames() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ String[] featureNames = new String[] {"x", "y"};
+ final LabeledDataset dataset = new LabeledDataset(mtx, lbs, featureNames, false);
+
+ assertEquals(dataset.getFeatureName(0), "x");
+ }
+
+ /** */
+ public void testAccessMethods() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ final LabeledDataset dataset = new LabeledDataset(mtx, lbs, null, false);
+
+ assertEquals(dataset.colSize(), 2);
+ assertEquals(dataset.rowSize(), 6);
+
+ final LabeledVector<Vector, Double> row = dataset.getRow(0);
+
+ assertEquals(row.features().get(0), 1.0);
+ assertEquals(row.label(), 1.0);
+ dataset.setLabel(0, 2.0);
+ assertEquals(row.label(), 2.0);
+ }
+
+ /** */
+ public void testFailOnYNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {
+ {1.0, 1.0},
+ {1.0, 2.0},
+ {2.0, 1.0},
+ {-1.0, -1.0},
+ {-1.0, -2.0},
+ {-2.0, -1.0}};
+ double[] lbs = new double[] {};
+
+ try {
+ new LabeledDataset(mtx, lbs);
+ fail("CardinalityException");
+ }
+ catch (CardinalityException e) {
+ return;
+ }
+ fail("CardinalityException");
+ }
+
+ /** */
+ public void testFailOnXNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[][] mtx =
+ new double[][] {};
+ double[] lbs = new double[] {1.0, 1.0, 1.0, 2.0, 2.0, 2.0};
+
+ try {
+ new LabeledDataset(mtx, lbs);
+ fail("CardinalityException");
+ }
+ catch (CardinalityException e) {
+ return;
+ }
+ fail("CardinalityException");
+ }
+
+ /** */
+ public void testLoadingCorrectTxtFile() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ LabeledDataset training = loadDatasetFromTxt(KNN_IRIS_TXT, false);
+ assertEquals(training.rowSize(), 150);
+ }
+
+ /** */
+ public void testLoadingEmptyFile() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ loadDatasetFromTxt(EMPTY_TXT, false);
+ fail("EmptyFileException");
+ }
+ catch (EmptyFileException e) {
+ return;
+ }
+ fail("EmptyFileException");
+ }
+
+ /** */
+ public void testLoadingFileWithFirstEmptyRow() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ loadDatasetFromTxt(NO_DATA_TXT, false);
+ fail("NoDataException");
+ }
+ catch (NoDataException e) {
+ return;
+ }
+ fail("NoDataException");
+ }
+
+ /** */
+ public void testLoadingFileWithIncorrectData() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ LabeledDataset training = loadDatasetFromTxt(IRIS_INCORRECT_TXT, false);
+ assertEquals(149, training.rowSize());
+ }
+
+ /** */
+ public void testFailOnLoadingFileWithIncorrectData() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ loadDatasetFromTxt(IRIS_INCORRECT_TXT, true);
+ fail("FileParsingException");
+ }
+ catch (FileParsingException e) {
+ return;
+ }
+ fail("FileParsingException");
+
+ }
+
+ /** */
+ public void testLoadingFileWithMissedData() throws URISyntaxException, IOException {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ Path path = Paths.get(this.getClass().getClassLoader().getResource(IRIS_MISSED_DATA).toURI());
+
+ LabeledDataset training = LabeledDataset.loadTxt(path, ",", false, false);
+
+ assertEquals(training.features(2).get(1), 0.0);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/math/MathImplLocalTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/math/MathImplLocalTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/math/MathImplLocalTestSuite.java
index af2154e..bb41239 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/math/MathImplLocalTestSuite.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/math/MathImplLocalTestSuite.java
@@ -23,6 +23,7 @@ import org.apache.ignite.ml.math.decompositions.LUDecompositionTest;
import org.apache.ignite.ml.math.decompositions.QRDSolverTest;
import org.apache.ignite.ml.math.decompositions.QRDecompositionTest;
import org.apache.ignite.ml.math.decompositions.SingularValueDecompositionTest;
+import org.apache.ignite.ml.math.distances.DistanceTest;
import org.apache.ignite.ml.math.impls.matrix.DenseLocalOffHeapMatrixConstructorTest;
import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrixConstructorTest;
import org.apache.ignite.ml.math.impls.matrix.DiagonalMatrixTest;
@@ -117,8 +118,9 @@ import org.junit.runners.Suite;
EigenDecompositionTest.class,
CholeskyDecompositionTest.class,
QRDecompositionTest.class,
+ SingularValueDecompositionTest.class,
QRDSolverTest.class,
- SingularValueDecompositionTest.class
+ DistanceTest.class
})
public class MathImplLocalTestSuite {
// No-op.
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/math/distances/DistanceTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/math/distances/DistanceTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/math/distances/DistanceTest.java
new file mode 100644
index 0000000..022b86a
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/math/distances/DistanceTest.java
@@ -0,0 +1,75 @@
+/*
+ * 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.math.distances;
+
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+/** */
+public class DistanceTest {
+ /** Precision. */
+ private static final double PRECISION = 0.0;
+
+ /** */
+ private Vector v1;
+
+ /** */
+ private Vector v2;
+
+ /** */
+ @Before
+ public void setup() {
+ v1 = new DenseLocalOnHeapVector(new double[] {0.0, 0.0, 0.0});
+ v2 = new DenseLocalOnHeapVector(new double[] {2.0, 1.0, 0.0});
+ }
+
+ /** */
+ @Test
+ public void euclideanDistance() throws Exception {
+
+ double expRes = Math.pow(5, 0.5);
+
+ DistanceMeasure distanceMeasure = new EuclideanDistance();
+
+ Assert.assertEquals(expRes, distanceMeasure.compute(v1, v2), PRECISION);
+ }
+
+ /** */
+ @Test
+ public void manhattanDistance() throws Exception {
+ double expRes = 3;
+
+ DistanceMeasure distanceMeasure = new ManhattanDistance();
+
+ Assert.assertEquals(expRes, distanceMeasure.compute(v1, v2), PRECISION);
+ }
+
+ /** */
+ @Test
+ public void hammingDistance() throws Exception {
+ double expRes = 2;
+
+ DistanceMeasure distanceMeasure = new HammingDistance();
+
+ Assert.assertEquals(expRes, distanceMeasure.compute(v1, v2), PRECISION);
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
index 4be7336..2774028 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
@@ -418,6 +418,7 @@ public class OLSMultipleLinearRegressionTest extends AbstractMultipleLinearRegre
Matrix hat = mdl.calculateHat();
+
// Reference data is upper half of symmetric hat matrix
double[] refData = new double[] {
.418, -.002, .079, -.274, -.046, .181, .128, .222, .050, .242,
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
index 929ded9..9e81bea 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
@@ -183,9 +183,9 @@ public class ColumnDecisionTreeTrainerTest extends BaseDecisionTreeTest {
byRegion.keySet().forEach(k -> {
LabeledVectorDouble sp = byRegion.get(k).get(0);
- Tracer.showAscii(sp.vector());
- X.println("Actual and predicted vectors [act=" + sp.label() + " " + ", pred=" + mdl.predict(sp.vector()) + "]");
- assert mdl.predict(sp.vector()) == sp.doubleLabel();
+ Tracer.showAscii(sp.features());
+ X.println("Actual and predicted vectors [act=" + sp.label() + " " + ", pred=" + mdl.predict(sp.features()) + "]");
+ assert mdl.predict(sp.features()) == sp.doubleLabel();
});
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
index 7ca5d38..524a8ad 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
@@ -273,9 +273,9 @@ public class ColumnDecisionTreeTrainerBenchmark extends BaseDecisionTreeTest {
byRegion.keySet().forEach(k -> {
LabeledVectorDouble sp = byRegion.get(k).get(0);
- Tracer.showAscii(sp.vector());
- X.println("Predicted value and label [pred=" + mdl.predict(sp.vector()) + ", label=" + sp.doubleLabel() + "]");
- assert mdl.predict(sp.vector()) == sp.doubleLabel();
+ Tracer.showAscii(sp.features());
+ X.println("Predicted value and label [pred=" + mdl.predict(sp.features()) + ", label=" + sp.doubleLabel() + "]");
+ assert mdl.predict(sp.features()) == sp.doubleLabel();
});
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/README.md
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/resources/datasets/README.md b/modules/ml/src/test/resources/datasets/README.md
new file mode 100644
index 0000000..2f9c5ec
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/README.md
@@ -0,0 +1,2 @@
+iris.txt and cleared_machines are from Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
+Read more about machine dataset http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.names
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/resources/datasets/knn/cleared_machines.txt b/modules/ml/src/test/resources/datasets/knn/cleared_machines.txt
new file mode 100644
index 0000000..cf8b6b0
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/cleared_machines.txt
@@ -0,0 +1,209 @@
+199,125,256,6000,256,16,128
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+227,50,2000,32000,48,26,52
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/empty.txt
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diff --git a/modules/ml/src/test/resources/datasets/knn/empty.txt b/modules/ml/src/test/resources/datasets/knn/empty.txt
new file mode 100644
index 0000000..e69de29
http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/iris.txt
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diff --git a/modules/ml/src/test/resources/datasets/knn/iris.txt b/modules/ml/src/test/resources/datasets/knn/iris.txt
new file mode 100644
index 0000000..18f5f7c
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/iris.txt
@@ -0,0 +1,150 @@
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/iris_incorrect.txt
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diff --git a/modules/ml/src/test/resources/datasets/knn/iris_incorrect.txt b/modules/ml/src/test/resources/datasets/knn/iris_incorrect.txt
new file mode 100644
index 0000000..7bb42c6
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/iris_incorrect.txt
@@ -0,0 +1,150 @@
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/machine.data.txt
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/resources/datasets/knn/machine.data.txt b/modules/ml/src/test/resources/datasets/knn/machine.data.txt
new file mode 100644
index 0000000..656ed8c
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/machine.data.txt
@@ -0,0 +1,209 @@
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/missed_data.txt
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/resources/datasets/knn/missed_data.txt b/modules/ml/src/test/resources/datasets/knn/missed_data.txt
new file mode 100644
index 0000000..83ce9a5
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/missed_data.txt
@@ -0,0 +1,3 @@
+1.0,5.1,3.5,1.4,0.2
+1.0,4.9,3.0,1.4,0.2
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/modules/ml/src/test/resources/datasets/knn/no_data.txt
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/resources/datasets/knn/no_data.txt b/modules/ml/src/test/resources/datasets/knn/no_data.txt
new file mode 100644
index 0000000..d1d4c7b
--- /dev/null
+++ b/modules/ml/src/test/resources/datasets/knn/no_data.txt
@@ -0,0 +1,6 @@
+
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http://git-wip-us.apache.org/repos/asf/ignite/blob/8ba773bf/parent/pom.xml
----------------------------------------------------------------------
diff --git a/parent/pom.xml b/parent/pom.xml
index cb335d12..97983dd 100644
--- a/parent/pom.xml
+++ b/parent/pom.xml
@@ -838,6 +838,7 @@
<exclude>src/main/java/org/apache/ignite/examples/streaming/wordcount/*.txt</exclude><!--books examples-->
<exclude>examples/src/main/java/org/apache/ignite/examples/streaming/wordcount/*.txt</exclude><!--books examples-->
<exclude>src/main/resources/person.csv</exclude><!--CacheLoadOnlyStoreExample csv-->
+ <exclude>**/resources/datasets/knn/*</exclude><!--Dataset examples in ml module-->
<exclude>examples/src/main/resources/person.csv</exclude><!--CacheLoadOnlyStoreExample csv-->
<exclude>src/main/java/org/jetbrains/annotations/*.java</exclude><!--copyright-->
<exclude>dev-tools/IGNITE-*.patch</exclude>