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Posted to commits@ignite.apache.org by ch...@apache.org on 2017/12/14 17:27:24 UTC
ignite git commit: IGNITE-7079: Add examples for kNN classification
and for kNN regression
Repository: ignite
Updated Branches:
refs/heads/master 03bb55138 -> da782958a
IGNITE-7079: Add examples for kNN classification and for kNN regression
this closes #3218
Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/da782958
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/da782958
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/da782958
Branch: refs/heads/master
Commit: da782958adad85b750ffdc39266644c5750d0f2f
Parents: 03bb551
Author: zaleslaw <za...@gmail.com>
Authored: Thu Dec 14 20:27:15 2017 +0300
Committer: Yury Babak <yb...@gridgain.com>
Committed: Thu Dec 14 20:27:15 2017 +0300
----------------------------------------------------------------------
.../KNNClassificationExample.java | 151 ++++++++++++++
.../ml/knn/classification/package-info.java | 22 ++
.../ignite/examples/ml/knn/package-info.java | 22 ++
.../ml/knn/regression/KNNRegressionExample.java | 152 ++++++++++++++
.../ml/knn/regression/package-info.java | 22 ++
.../src/main/resources/datasets/knn/README.md | 2 +
.../resources/datasets/knn/cleared_machines.txt | 209 +++++++++++++++++++
.../src/main/resources/datasets/knn/iris.txt | 150 +++++++++++++
.../ignite/ml/structures/LabeledDataset.java | 18 ++
.../structures/LabeledDatasetTestTrainPair.java | 116 ++++++++++
.../ignite/ml/knn/LabeledDatasetTest.java | 58 +++++
11 files changed, 922 insertions(+)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
new file mode 100644
index 0000000..a92e9af
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
@@ -0,0 +1,151 @@
+/*
+ * 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.examples.ml.knn.classification;
+
+import java.io.IOException;
+import java.net.URISyntaxException;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.Arrays;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
+import org.apache.ignite.ml.knn.models.KNNModel;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.ml.structures.LabeledDatasetTestTrainPair;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <p>
+ * Example of using {@link KNNModel} with iris dataset.</p>
+ * <p>
+ * Note that in this example we cannot guarantee order in which nodes return results of intermediate
+ * computations and therefore algorithm can return different results.</p>
+ * <p>
+ * Remote nodes should always be started with special configuration file which
+ * enables P2P class loading: {@code 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p>
+ * <p>
+ * Alternatively you can run {@link ExampleNodeStartup} in another JVM which will start node
+ * with {@code examples/config/example-ignite.xml} configuration.</p>
+ */
+public class KNNClassificationExample {
+ /** Separator. */
+ private static final String SEPARATOR = "\t";
+
+ /** Path to the Iris dataset. */
+ static final String KNN_IRIS_TXT = "datasets/knn/iris.txt";
+
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) throws InterruptedException {
+ System.out.println(">>> kNN classification example started.");
+ // Start ignite grid.
+ try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
+ System.out.println(">>> Ignite grid started.");
+
+ IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+ KNNClassificationExample.class.getSimpleName(), () -> {
+
+ try {
+ // Prepare path to read
+ Path path = Paths.get(KNNClassificationExample.class.getClassLoader().getResource(KNN_IRIS_TXT).toURI());
+
+ // Read dataset from file
+ LabeledDataset dataset = LabeledDataset.loadTxt(path, SEPARATOR, true, false);
+
+ // Random splitting of iris data as 70% train and 30% test datasets
+ LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.3);
+
+ System.out.println("\n>>> Amount of observations in train dataset " + split.train().rowSize());
+ System.out.println("\n>>> Amount of observations in test dataset " + split.test().rowSize());
+
+ LabeledDataset test = split.test();
+ LabeledDataset train = split.train();
+
+ KNNModel knnMdl = new KNNModel(5, new EuclideanDistance(), KNNStrategy.SIMPLE, train);
+
+ // Clone labels
+ final double[] labels = test.labels();
+
+ // Save predicted classes to test dataset
+ for (int i = 0; i < test.rowSize(); i++) {
+ double predictedCls = knnMdl.predict(test.getRow(i).features());
+ test.setLabel(i, predictedCls);
+ }
+
+ // Calculate amount of errors on test dataset
+ int amountOfErrors = 0;
+ for (int i = 0; i < test.rowSize(); i++) {
+ if (test.label(i) != labels[i])
+ amountOfErrors++;
+ }
+
+ System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
+ System.out.println("\n>>> Accuracy " + amountOfErrors / (double)test.rowSize());
+
+ // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
+ int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
+ for (int i = 0; i < test.rowSize(); i++) {
+ int idx1 = (int)test.label(i);
+ int idx2 = (int)labels[i];
+ confusionMtx[idx1 - 1][idx2 - 1]++;
+ }
+ System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
+
+ // Calculate precision, recall and F-metric for each class
+ for (int i = 0; i < 3; i++) {
+ double precision = 0.0;
+ for (int j = 0; j < 3; j++)
+ precision += confusionMtx[i][j];
+ precision = confusionMtx[i][i] / precision;
+
+ double clsLb = (double)(i + 1);
+
+ System.out.println("\n>>> Precision for class " + clsLb + " is " + precision);
+
+ double recall = 0.0;
+ for (int j = 0; j < 3; j++)
+ recall += confusionMtx[j][i];
+ recall = confusionMtx[i][i] / recall;
+ System.out.println("\n>>> Recall for class " + clsLb + " is " + recall);
+
+ double fScore = 2 * precision * recall / (precision + recall);
+ System.out.println("\n>>> F-score for class " + clsLb + " is " + fScore);
+ }
+
+ }
+ catch (URISyntaxException | IOException e) {
+ e.printStackTrace();
+ System.out.println("\n>>> Check resources");
+ }
+ finally {
+ System.out.println("\n>>> kNN classification example completed.");
+ }
+
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/package-info.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/package-info.java
new file mode 100644
index 0000000..d853f0d
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN classification examples.
+ */
+package org.apache.ignite.examples.ml.knn.classification;
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/ml/org/apache/ignite/examples/ml/knn/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/package-info.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/package-info.java
new file mode 100644
index 0000000..8de4656
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN examples.
+ */
+package org.apache.ignite.examples.ml.knn;
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
new file mode 100644
index 0000000..f4a9e1c
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
@@ -0,0 +1,152 @@
+/*
+ * 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.examples.ml.knn.regression;
+
+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.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
+import org.apache.ignite.examples.ml.knn.classification.KNNClassificationExample;
+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.distances.ManhattanDistance;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.ml.structures.LabeledDatasetTestTrainPair;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <p>
+ * Example of using {@link KNNMultipleLinearRegression} with iris dataset.</p>
+ * <p>
+ * Note that in this example we cannot guarantee order in which nodes return results of intermediate
+ * computations and therefore algorithm can return different results.</p>
+ * <p>
+ * Remote nodes should always be started with special configuration file which
+ * enables P2P class loading: {@code 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p>
+ * <p>
+ * Alternatively you can run {@link ExampleNodeStartup} in another JVM which will start node
+ * with {@code examples/config/example-ignite.xml} configuration.</p>
+ */
+public class KNNRegressionExample {
+ /** Separator. */
+ private static final String SEPARATOR = ",";
+
+ /** */
+ public static final String KNN_CLEARED_MACHINES_TXT = "datasets/knn/cleared_machines.txt";
+
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) throws InterruptedException {
+ System.out.println(">>> kNN regression example started.");
+ // Start ignite grid.
+ try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
+ System.out.println(">>> Ignite grid started.");
+
+ IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+ KNNRegressionExample.class.getSimpleName(), () -> {
+
+ try {
+ // Prepare path to read
+ Path path = Paths.get(KNNClassificationExample.class.getClassLoader().getResource(KNN_CLEARED_MACHINES_TXT).toURI());
+
+ // Read dataset from file
+ LabeledDataset dataset = LabeledDataset.loadTxt(path, SEPARATOR, false, false);
+
+ // Normalize dataset
+ dataset.normalizeWith(Normalization.MINIMAX);
+
+ // Random splitting of iris data as 80% train and 20% test datasets
+ LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.2);
+
+ System.out.println("\n>>> Amount of observations in train dataset " + split.train().rowSize());
+ System.out.println("\n>>> Amount of observations in test dataset " + split.test().rowSize());
+
+ LabeledDataset test = split.test();
+ LabeledDataset train = split.train();
+
+ // Builds weighted kNN-regression with Manhattan Distance
+ KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(7, new ManhattanDistance(), KNNStrategy.WEIGHTED, train);
+
+ // Clone labels
+ final double[] labels = test.labels();
+
+ // Save predicted classes to test dataset
+ for (int i = 0; i < test.rowSize(); i++) {
+ double predictedCls = knnMdl.predict(test.getRow(i).features());
+ test.setLabel(i, predictedCls);
+ }
+
+ // Calculate mean squared error (MSE)
+ double mse = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ mse += Math.pow(test.label(i) - labels[i], 2.0);
+ mse = mse / test.rowSize();
+
+ System.out.println("\n>>> Mean squared error (MSE) " + mse);
+
+ // Calculate mean absolute error (MAE)
+ double mae = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ mae += Math.abs(test.label(i) - labels[i]);
+ mae = mae / test.rowSize();
+
+ System.out.println("\n>>> Mean absolute error (MAE) " + mae);
+
+ // Calculate R^2 as 1 - RSS/TSS
+ double avg = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ avg += test.label(i);
+
+ avg = avg / test.rowSize();
+
+ double detCf = 0.0;
+ double tss = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++) {
+ detCf += Math.pow(test.label(i) - labels[i], 2.0);
+ tss += Math.pow(test.label(i) - avg, 2.0);
+ }
+
+ detCf = 1 - detCf / tss;
+
+ System.out.println("\n>>> R^2 " + detCf);
+ }
+ catch (URISyntaxException | IOException e) {
+ e.printStackTrace();
+ System.out.println("\n>>> Check resources");
+ }
+ finally {
+ System.out.println("\n>>> kNN regression example completed.");
+ }
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/package-info.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/package-info.java
new file mode 100644
index 0000000..e7ac336
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN regression examples.
+ */
+package org.apache.ignite.examples.ml.knn.regression;
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/resources/datasets/knn/README.md
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/README.md b/examples/src/main/resources/datasets/knn/README.md
new file mode 100644
index 0000000..2f9c5ec
--- /dev/null
+++ b/examples/src/main/resources/datasets/knn/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/da782958/examples/src/main/resources/datasets/knn/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/cleared_machines.txt b/examples/src/main/resources/datasets/knn/cleared_machines.txt
new file mode 100644
index 0000000..cf8b6b0
--- /dev/null
+++ b/examples/src/main/resources/datasets/knn/cleared_machines.txt
@@ -0,0 +1,209 @@
+199,125,256,6000,256,16,128
+253,29,8000,32000,32,8,32
+253,29,8000,32000,32,8,32
+253,29,8000,32000,32,8,32
+132,29,8000,16000,32,8,16
+290,26,8000,32000,64,8,32
+381,23,16000,32000,64,16,32
+381,23,16000,32000,64,16,32
+749,23,16000,64000,64,16,32
+1238,23,32000,64000,128,32,64
+23,400,1000,3000,0,1,2
+24,400,512,3500,4,1,6
+70,60,2000,8000,65,1,8
+117,50,4000,16000,65,1,8
+15,350,64,64,0,1,4
+64,200,512,16000,0,4,32
+23,167,524,2000,8,4,15
+29,143,512,5000,0,7,32
+22,143,1000,2000,0,5,16
+124,110,5000,5000,142,8,64
+35,143,1500,6300,0,5,32
+39,143,3100,6200,0,5,20
+40,143,2300,6200,0,6,64
+45,110,3100,6200,0,6,64
+28,320,128,6000,0,1,12
+21,320,512,2000,4,1,3
+28,320,256,6000,0,1,6
+22,320,256,3000,4,1,3
+28,320,512,5000,4,1,5
+27,320,256,5000,4,1,6
+102,25,1310,2620,131,12,24
+102,25,1310,2620,131,12,24
+74,50,2620,10480,30,12,24
+74,50,2620,10480,30,12,24
+138,56,5240,20970,30,12,24
+136,64,5240,20970,30,12,24
+23,50,500,2000,8,1,4
+29,50,1000,4000,8,1,5
+44,50,2000,8000,8,1,5
+30,50,1000,4000,8,3,5
+41,50,1000,8000,8,3,5
+74,50,2000,16000,8,3,5
+74,50,2000,16000,8,3,6
+74,50,2000,16000,8,3,6
+54,133,1000,12000,9,3,12
+41,133,1000,8000,9,3,12
+18,810,512,512,8,1,1
+28,810,1000,5000,0,1,1
+36,320,512,8000,4,1,5
+38,200,512,8000,8,1,8
+34,700,384,8000,0,1,1
+19,700,256,2000,0,1,1
+72,140,1000,16000,16,1,3
+36,200,1000,8000,0,1,2
+30,110,1000,4000,16,1,2
+56,110,1000,12000,16,1,2
+42,220,1000,8000,16,1,2
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+19,125,512,1000,0,8,20
+75,75,2000,8000,64,1,38
+113,75,2000,16000,64,1,38
+157,75,2000,16000,128,1,38
+18,90,256,1000,0,3,10
+20,105,256,2000,0,3,10
+28,105,1000,4000,0,3,24
+33,105,2000,4000,8,3,19
+47,75,2000,8000,8,3,24
+54,75,3000,8000,8,3,48
+20,175,256,2000,0,3,24
+23,300,768,3000,0,6,24
+25,300,768,3000,6,6,24
+52,300,768,12000,6,6,24
+27,300,768,4500,0,1,24
+50,300,384,12000,6,1,24
+18,300,192,768,6,6,24
+53,180,768,12000,6,1,31
+23,330,1000,3000,0,2,4
+30,300,1000,4000,8,3,64
+73,300,1000,16000,8,2,112
+20,330,1000,2000,0,1,2
+25,330,1000,4000,0,3,6
+28,140,2000,4000,0,3,6
+29,140,2000,4000,0,4,8
+32,140,2000,4000,8,1,20
+175,140,2000,32000,32,1,20
+57,140,2000,8000,32,1,54
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+33,150,512,4000,0,8,128
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+29,115,2000,4000,2,1,5
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+190,38,4000,16000,128,16,32
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+25,200,1000,4000,0,1,4
+67,200,2000,8000,64,1,5
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+24,250,512,4000,0,4,7
+64,250,1000,16000,1,1,8
+25,160,512,4000,2,1,5
+20,160,512,2000,2,3,8
+29,160,1000,4000,8,1,14
+43,160,1000,8000,16,1,14
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+22,240,512,2000,8,1,5
+31,105,2000,4000,8,3,8
+41,105,2000,6000,16,6,16
+47,105,2000,8000,16,4,14
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+227,50,2000,32000,48,26,52
+341,50,2000,32000,112,52,104
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+919,30,8000,64000,96,12,176
+978,30,8000,64000,128,12,176
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+37,124,1000,8000,0,1,8
+50,98,1000,8000,32,2,8
+41,125,2000,8000,0,2,14
+47,480,512,8000,32,0,0
+25,480,1000,4000,0,0,0
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/examples/src/main/resources/datasets/knn/iris.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/iris.txt b/examples/src/main/resources/datasets/knn/iris.txt
new file mode 100644
index 0000000..18f5f7c
--- /dev/null
+++ b/examples/src/main/resources/datasets/knn/iris.txt
@@ -0,0 +1,150 @@
+1.0 5.1 3.5 1.4 0.2
+1.0 4.9 3.0 1.4 0.2
+1.0 4.7 3.2 1.3 0.2
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+1.0 5.4 3.9 1.7 0.4
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+1.0 5.0 3.4 1.5 0.2
+1.0 4.4 2.9 1.4 0.2
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+1.0 4.8 3.4 1.6 0.2
+1.0 4.8 3.0 1.4 0.1
+1.0 4.3 3.0 1.1 0.1
+1.0 5.8 4.0 1.2 0.2
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+1.0 5.4 3.9 1.3 0.4
+1.0 5.1 3.5 1.4 0.3
+1.0 5.7 3.8 1.7 0.3
+1.0 5.1 3.8 1.5 0.3
+1.0 5.4 3.4 1.7 0.2
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+3.0 6.4 3.1 5.5 1.8
+3.0 6.0 3.0 4.8 1.8
+3.0 6.9 3.1 5.4 2.1
+3.0 6.7 3.1 5.6 2.4
+3.0 6.9 3.1 5.1 2.3
+3.0 5.8 2.7 5.1 1.9
+3.0 6.8 3.2 5.9 2.3
+3.0 6.7 3.3 5.7 2.5
+3.0 6.7 3.0 5.2 2.3
+3.0 6.3 2.5 5.0 1.9
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+3.0 5.9 3.0 5.1 1.8
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
index 81f7607..ee2f442 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
@@ -270,6 +270,24 @@ public class LabeledDataset implements Serializable {
}
/**
+ * Returns new copy of labels of all labeled vectors NOTE: This method is useful for copying labels from test
+ * dataset.
+ *
+ * @return Copy of labels.
+ */
+ public double[] labels() {
+ assert data != null;
+ assert data.length > 0;
+
+ double[] labels = new double[data.length];
+
+ for (int i = 0; i < data.length; i++)
+ labels[i] = (double)data[i].label();
+
+ return labels;
+ }
+
+ /**
* Fill the label with given value.
*
* @param idx Index of observation.
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDatasetTestTrainPair.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDatasetTestTrainPair.java b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDatasetTestTrainPair.java
new file mode 100644
index 0000000..dd3d244
--- /dev/null
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDatasetTestTrainPair.java
@@ -0,0 +1,116 @@
+/*
+ * 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.structures;
+
+import java.io.Serializable;
+import java.util.Map;
+import java.util.Random;
+import java.util.TreeMap;
+import java.util.TreeSet;
+import org.jetbrains.annotations.NotNull;
+
+/**
+ * Class for splitting Labeled Dataset on train and test sets.
+ */
+public class LabeledDatasetTestTrainPair implements Serializable {
+ /** Data to keep train set. */
+ private LabeledDataset train;
+
+ /** Data to keep test set. */
+ private LabeledDataset test;
+
+ /**
+ * Creates two subsets of given dataset.
+ * <p>
+ * NOTE: This method uses next algorithm with O(n log n) by calculations and O(n) by memory.
+ * </p>
+ * @param dataset The dataset to split on train and test subsets.
+ * @param testPercentage The percentage of the test subset.
+ */
+ public LabeledDatasetTestTrainPair(LabeledDataset dataset, double testPercentage) {
+ assert testPercentage > 0.0;
+ assert testPercentage < 1.0;
+ final int datasetSize = dataset.rowSize();
+ assert datasetSize > 2;
+
+ final int testSize = (int)Math.floor(datasetSize * testPercentage);
+ final int trainSize = datasetSize - testSize;
+
+ final TreeSet<Integer> sortedTestIndices = getSortedIndices(datasetSize, testSize);
+
+
+ LabeledVector[] testVectors = new LabeledVector[testSize];
+ LabeledVector[] trainVectors = new LabeledVector[trainSize];
+
+
+ int datasetCntr = 0;
+ int trainCntr = 0;
+ int testCntr = 0;
+
+ for (Integer idx: sortedTestIndices){ // guarantee order as iterator
+ testVectors[testCntr] = dataset.getRow(idx);
+ testCntr++;
+
+ for (int i = datasetCntr; i < idx; i++) {
+ trainVectors[trainCntr] = dataset.getRow(i);
+ trainCntr++;
+ }
+
+ datasetCntr = idx + 1;
+ }
+ if(datasetCntr < datasetSize){
+ for (int i = datasetCntr; i < datasetSize; i++) {
+ trainVectors[trainCntr] = dataset.getRow(i);
+ trainCntr++;
+ }
+ }
+
+ test = new LabeledDataset(testVectors, testSize);
+ train = new LabeledDataset(trainVectors, trainSize);
+ }
+
+ /** This method generates "random double, integer" pairs, sort them, gets first "testSize" elements and returns appropriate indices */
+ @NotNull private TreeSet<Integer> getSortedIndices(int datasetSize, int testSize) {
+ Random rnd = new Random();
+ TreeMap<Double, Integer> randomIdxPairs = new TreeMap<>();
+ for (int i = 0; i < datasetSize; i++)
+ randomIdxPairs.put(rnd.nextDouble(), i);
+
+ final TreeMap<Double, Integer> testIdxPairs = randomIdxPairs.entrySet().stream()
+ .limit(testSize)
+ .collect(TreeMap::new, (m, e) -> m.put(e.getKey(), e.getValue()), Map::putAll);
+
+ return new TreeSet<>(testIdxPairs.values());
+ }
+
+ /**
+ * Train subset of the whole dataset.
+ * @return Train subset.
+ */
+ public LabeledDataset train() {
+ return train;
+ }
+
+ /**
+ * Test subset of the whole dataset.
+ * @return Test subset.
+ */
+ public LabeledDataset test() {
+ return test;
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/da782958/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
index 32bd37b..c64a8d8 100644
--- 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
@@ -28,6 +28,7 @@ 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.LabeledDatasetTestTrainPair;
import org.apache.ignite.ml.structures.LabeledVector;
/** Tests behaviour of KNNClassificationTest. */
@@ -205,4 +206,61 @@ public class LabeledDatasetTest extends BaseKNNTest {
assertEquals(training.features(2).get(1), 0.0);
}
+
+ /** */
+ public void testSplitting() {
+ 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);
+
+ LabeledDatasetTestTrainPair split1 = new LabeledDatasetTestTrainPair(training, 0.67);
+
+ assertEquals(4, split1.test().rowSize());
+ assertEquals(2, split1.train().rowSize());
+
+ LabeledDatasetTestTrainPair split2 = new LabeledDatasetTestTrainPair(training, 0.65);
+
+ assertEquals(3, split2.test().rowSize());
+ assertEquals(3, split2.train().rowSize());
+
+ LabeledDatasetTestTrainPair split3 = new LabeledDatasetTestTrainPair(training, 0.4);
+
+ assertEquals(2, split3.test().rowSize());
+ assertEquals(4, split3.train().rowSize());
+
+ LabeledDatasetTestTrainPair split4 = new LabeledDatasetTestTrainPair(training, 0.3);
+
+ assertEquals(1, split4.test().rowSize());
+ assertEquals(5, split4.train().rowSize());
+ }
+
+ /** */
+ public void testLabels() {
+ 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 dataset = new LabeledDataset(mtx, lbs);
+ final double[] labels = dataset.labels();
+ for (int i = 0; i < lbs.length; i++)
+ assertEquals(lbs[i], labels[i]);
+ }
}