Repository: ignite
Updated Branches:
  refs/heads/master 9be3357c4 -> 8550d61b6


IGNITE-7829: Adopt kNN regression example to the new Partitioned Dataset

this closes #3798


Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/8550d61b
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/8550d61b
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/8550d61b

Branch: refs/heads/master
Commit: 8550d61b6b39625579eb7f69f4d1218b78f7cc5b
Parents: 9be3357
Author: zaleslaw <zaleslaw....@gmail.com>
Authored: Fri Apr 13 12:49:56 2018 +0300
Committer: YuriBabak <y.ch...@gmail.com>
Committed: Fri Apr 13 12:49:56 2018 +0300

----------------------------------------------------------------------
 .../ml/knn/KNNClassificationExample.java        |   4 +-
 .../examples/ml/knn/KNNRegressionExample.java   | 310 +++++++++++++++++++
 .../java/org/apache/ignite/ml/knn/KNNUtils.java |  10 +-
 .../classification/KNNClassificationModel.java  |   9 +-
 .../ml/knn/partitions/KNNPartitionContext.java  |  28 --
 .../ignite/ml/knn/partitions/package-info.java  |  22 --
 .../ml/knn/regression/KNNRegressionModel.java   |   7 +-
 .../partition/LabelPartitionContext.java        |  28 --
 .../LabelPartitionDataBuilderOnHeap.java        |   1 -
 .../svm/SVMLinearBinaryClassificationModel.java |   3 +
 .../SVMLinearBinaryClassificationTrainer.java   |   9 +-
 .../SVMLinearMultiClassClassificationModel.java |   3 +
 ...VMLinearMultiClassClassificationTrainer.java |   8 +-
 .../ignite/ml/svm/SVMPartitionContext.java      |  28 --
 .../org/apache/ignite/ml/knn/BaseKNNTest.java   |  89 ------
 .../ignite/ml/knn/KNNClassificationTest.java    | 110 +++----
 .../apache/ignite/ml/knn/KNNRegressionTest.java | 104 +++----
 .../ignite/ml/knn/LabeledDatasetHelper.java     |  87 ++++++
 .../ignite/ml/knn/LabeledDatasetTest.java       |   2 +-
 19 files changed, 536 insertions(+), 326 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
index 39a8431..15375a1 100644
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
@@ -80,7 +80,7 @@ public class KNNClassificationExample {
                         double prediction = knnMdl.apply(new 
DenseLocalOnHeapVector(inputs));
 
                         totalAmount++;
-                        if(groundTruth != prediction)
+                        if (groundTruth != prediction)
                             amountOfErrors++;
 
                         System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
@@ -89,7 +89,7 @@ public class KNNClassificationExample {
                     System.out.println(">>> 
---------------------------------");
 
                     System.out.println("\n>>> Absolute amount of errors " + 
amountOfErrors);
-                    System.out.println("\n>>> Accuracy " + (1 - amountOfErrors 
/ (double)totalAmount));
+                    System.out.println("\n>>> Accuracy " + (1 - amountOfErrors 
/ (double) totalAmount));
                 }
             });
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
new file mode 100644
index 0000000..76a07cd
--- /dev/null
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
@@ -0,0 +1,310 @@
+/*
+ * 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;
+
+import java.util.Arrays;
+import java.util.UUID;
+import javax.cache.Cache;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.cache.query.QueryCursor;
+import org.apache.ignite.cache.query.ScanQuery;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder;
+import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer;
+import org.apache.ignite.ml.knn.classification.KNNStrategy;
+import org.apache.ignite.ml.knn.regression.KNNRegressionModel;
+import org.apache.ignite.ml.knn.regression.KNNRegressionTrainer;
+import org.apache.ignite.ml.math.distances.ManhattanDistance;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * Run kNN regression trainer over distributed dataset.
+ *
+ * @see KNNClassificationTrainer
+ */
+public class KNNRegressionExample {
+    /** Run example. */
+    public static void main(String[] args) throws InterruptedException {
+        System.out.println();
+        System.out.println(">>> kNN regression over cached dataset usage 
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(), () -> {
+                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
+
+                KNNRegressionTrainer trainer = new KNNRegressionTrainer();
+
+                KNNRegressionModel knnMdl = (KNNRegressionModel) trainer.fit(
+                    new CacheBasedDatasetBuilder<>(ignite, dataCache),
+                    (k, v) -> Arrays.copyOfRange(v, 1, v.length),
+                    (k, v) -> v[0]
+                ).withK(5)
+                    .withDistanceMeasure(new ManhattanDistance())
+                    .withStrategy(KNNStrategy.WEIGHTED);
+
+                int totalAmount = 0;
+                // Calculate mean squared error (MSE)
+                double mse = 0.0;
+                // Calculate mean absolute error (MAE)
+                double mae = 0.0;
+
+                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
+                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
+                        double[] val = observation.getValue();
+                        double[] inputs = Arrays.copyOfRange(val, 1, 
val.length);
+                        double groundTruth = val[0];
+
+                        double prediction = knnMdl.apply(new 
DenseLocalOnHeapVector(inputs));
+
+                        mse += Math.pow(prediction - groundTruth, 2.0);
+                        mae += Math.abs(prediction - groundTruth);
+
+                        totalAmount++;
+                    }
+
+                    mse = mse / totalAmount;
+                    System.out.println("\n>>> Mean squared error (MSE) " + 
mse);
+
+                    mae = mae / totalAmount;
+                    System.out.println("\n>>> Mean absolute error (MAE) " + 
mae);
+                }
+            });
+
+            igniteThread.start();
+            igniteThread.join();
+        }
+    }
+
+    /**
+     * Fills cache with data and returns it.
+     *
+     * @param ignite Ignite instance.
+     * @return Filled Ignite Cache.
+     */
+    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
+        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
+        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
+        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
+
+        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
+
+        for (int i = 0; i < data.length; i++)
+            cache.put(i, data[i]);
+
+        return cache;
+    }
+
+    /** The Iris dataset. */
+    private static final double[][] data = {
+        {199, 125, 256, 6000, 256, 16, 128},
+        {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},
+        {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},
+        {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},
+        {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},
+        {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},
+        {181, 140, 2000, 32000, 32, 1, 54},
+        {32, 140, 2000, 4000, 8, 1, 20},
+        {82, 57, 4000, 16000, 1, 6, 12},
+        {171, 57, 4000, 24000, 64, 12, 16},
+        {361, 26, 16000, 32000, 64, 16, 24},
+        {350, 26, 16000, 32000, 64, 8, 24},
+        {220, 26, 8000, 32000, 0, 8, 24},
+        {113, 26, 8000, 16000, 0, 8, 16},
+        {15, 480, 96, 512, 0, 1, 1},
+        {21, 203, 1000, 2000, 0, 1, 5},
+        {35, 115, 512, 6000, 16, 1, 6},
+        {18, 1100, 512, 1500, 0, 1, 1},
+        {20, 1100, 768, 2000, 0, 1, 1},
+        {20, 600, 768, 2000, 0, 1, 1},
+        {28, 400, 2000, 4000, 0, 1, 1},
+        {45, 400, 4000, 8000, 0, 1, 1},
+        {18, 900, 1000, 1000, 0, 1, 2},
+        {17, 900, 512, 1000, 0, 1, 2},
+        {26, 900, 1000, 4000, 4, 1, 2},
+        {28, 900, 1000, 4000, 8, 1, 2},
+        {28, 900, 2000, 4000, 0, 3, 6},
+        {31, 225, 2000, 4000, 8, 3, 6},
+        {42, 180, 2000, 8000, 8, 1, 6},
+        {76, 185, 2000, 16000, 16, 1, 6},
+        {76, 180, 2000, 16000, 16, 1, 6},
+        {26, 225, 1000, 4000, 2, 3, 6},
+        {59, 25, 2000, 12000, 8, 1, 4},
+        {65, 25, 2000, 12000, 16, 3, 5},
+        {101, 17, 4000, 16000, 8, 6, 12},
+        {116, 17, 4000, 16000, 32, 6, 12},
+        {18, 1500, 768, 1000, 0, 0, 0},
+        {20, 1500, 768, 2000, 0, 0, 0},
+        {20, 800, 768, 2000, 0, 0, 0},
+        {30, 50, 2000, 4000, 0, 3, 6},
+        {44, 50, 2000, 8000, 8, 3, 6},
+        {82, 50, 2000, 16000, 24, 1, 6},
+        {128, 50, 8000, 16000, 48, 1, 10},
+        {37, 100, 1000, 8000, 0, 2, 6},
+        {46, 100, 1000, 8000, 24, 2, 6},
+        {46, 100, 1000, 8000, 24, 3, 6},
+        {80, 50, 2000, 16000, 12, 3, 16},
+        {88, 50, 2000, 16000, 24, 6, 16},
+        {33, 150, 512, 4000, 0, 8, 128},
+        {46, 115, 2000, 8000, 16, 1, 3},
+        {29, 115, 2000, 4000, 2, 1, 5},
+        {53, 92, 2000, 8000, 32, 1, 6},
+        {41, 92, 2000, 8000, 4, 1, 6},
+        {86, 75, 4000, 16000, 16, 1, 6},
+        {95, 60, 4000, 16000, 32, 1, 6},
+        {107, 60, 2000, 16000, 64, 5, 8},
+        {117, 60, 4000, 16000, 64, 5, 8},
+        {119, 50, 4000, 16000, 64, 5, 10},
+        {120, 72, 4000, 16000, 64, 8, 16},
+        {48, 72, 2000, 8000, 16, 6, 8},
+        {126, 40, 8000, 16000, 32, 8, 16},
+        {266, 40, 8000, 32000, 64, 8, 24},
+        {270, 35, 8000, 32000, 64, 8, 24},
+        {426, 38, 16000, 32000, 128, 16, 32},
+        {151, 48, 4000, 24000, 32, 8, 24},
+        {267, 38, 8000, 32000, 64, 8, 24},
+        {603, 30, 16000, 32000, 256, 16, 24},
+        {19, 112, 1000, 1000, 0, 1, 4},
+        {21, 84, 1000, 2000, 0, 1, 6},
+        {26, 56, 1000, 4000, 0, 1, 6},
+        {35, 56, 2000, 6000, 0, 1, 8},
+        {41, 56, 2000, 8000, 0, 1, 8},
+        {47, 56, 4000, 8000, 0, 1, 8},
+        {62, 56, 4000, 12000, 0, 1, 8},
+        {78, 56, 4000, 16000, 0, 1, 8},
+        {80, 38, 4000, 8000, 32, 16, 32},
+        {142, 38, 8000, 16000, 64, 4, 8},
+        {281, 38, 8000, 24000, 160, 4, 8},
+        {190, 38, 4000, 16000, 128, 16, 32},
+        {21, 200, 1000, 2000, 0, 1, 2},
+        {25, 200, 1000, 4000, 0, 1, 4},
+        {67, 200, 2000, 8000, 64, 1, 5},
+        {24, 250, 512, 4000, 0, 1, 7},
+        {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},
+        {53, 160, 2000, 8000, 32, 1, 13},
+        {19, 240, 512, 1000, 8, 1, 3},
+        {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},
+        {99, 52, 4000, 16000, 32, 4, 12},
+        {67, 70, 4000, 12000, 8, 6, 8},
+        {81, 59, 4000, 12000, 32, 6, 12},
+        {149, 59, 8000, 16000, 64, 12, 24},
+        {183, 26, 8000, 24000, 32, 8, 16},
+        {275, 26, 8000, 32000, 64, 12, 16},
+        {382, 26, 8000, 32000, 128, 24, 32},
+        {56, 116, 2000, 8000, 32, 5, 28},
+        {182, 50, 2000, 32000, 24, 6, 26},
+        {227, 50, 2000, 32000, 48, 26, 52},
+        {341, 50, 2000, 32000, 112, 52, 104},
+        {360, 50, 4000, 32000, 112, 52, 104},
+        {919, 30, 8000, 64000, 96, 12, 176},
+        {978, 30, 8000, 64000, 128, 12, 176},
+        {24, 180, 262, 4000, 0, 1, 3},
+        {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/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/knn/KNNUtils.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/knn/KNNUtils.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/KNNUtils.java
index 88fa70f..716eb52 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/knn/KNNUtils.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/knn/KNNUtils.java
@@ -20,7 +20,7 @@ package org.apache.ignite.ml.knn;
 import org.apache.ignite.ml.dataset.Dataset;
 import org.apache.ignite.ml.dataset.DatasetBuilder;
 import org.apache.ignite.ml.dataset.PartitionDataBuilder;
-import org.apache.ignite.ml.knn.partitions.KNNPartitionContext;
+import org.apache.ignite.ml.dataset.primitive.context.EmptyContext;
 import org.apache.ignite.ml.math.functions.IgniteBiFunction;
 import org.apache.ignite.ml.structures.LabeledDataset;
 import org.apache.ignite.ml.structures.LabeledVector;
@@ -39,18 +39,18 @@ public class KNNUtils {
      * @param lbExtractor Label extractor.
      * @return Dataset.
      */
-    @Nullable public static <K, V> Dataset<KNNPartitionContext, 
LabeledDataset<Double, LabeledVector>> buildDataset(DatasetBuilder<K, V> 
datasetBuilder, IgniteBiFunction<K, V, double[]> featureExtractor, 
IgniteBiFunction<K, V, Double> lbExtractor) {
-        PartitionDataBuilder<K, V, KNNPartitionContext, LabeledDataset<Double, 
LabeledVector>> partDataBuilder
+    @Nullable public static <K, V> Dataset<EmptyContext, 
LabeledDataset<Double, LabeledVector>> buildDataset(DatasetBuilder<K, V> 
datasetBuilder, IgniteBiFunction<K, V, double[]> featureExtractor, 
IgniteBiFunction<K, V, Double> lbExtractor) {
+        PartitionDataBuilder<K, V, EmptyContext, LabeledDataset<Double, 
LabeledVector>> partDataBuilder
             = new LabeledDatasetPartitionDataBuilderOnHeap<>(
             featureExtractor,
             lbExtractor
         );
 
-        Dataset<KNNPartitionContext, LabeledDataset<Double, LabeledVector>> 
dataset = null;
+        Dataset<EmptyContext, LabeledDataset<Double, LabeledVector>> dataset = 
null;
 
         if (datasetBuilder != null) {
             dataset = datasetBuilder.build(
-                (upstream, upstreamSize) -> new KNNPartitionContext(),
+                (upstream, upstreamSize) -> new EmptyContext(),
                 partDataBuilder
             );
         }

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/knn/classification/KNNClassificationModel.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/classification/KNNClassificationModel.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/classification/KNNClassificationModel.java
index 373f822..693b81d 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/classification/KNNClassificationModel.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/classification/KNNClassificationModel.java
@@ -32,7 +32,7 @@ import org.apache.ignite.ml.Exportable;
 import org.apache.ignite.ml.Exporter;
 import org.apache.ignite.ml.Model;
 import org.apache.ignite.ml.dataset.Dataset;
-import org.apache.ignite.ml.knn.partitions.KNNPartitionContext;
+import org.apache.ignite.ml.dataset.primitive.context.EmptyContext;
 import org.apache.ignite.ml.math.Vector;
 import org.apache.ignite.ml.math.distances.DistanceMeasure;
 import org.apache.ignite.ml.math.distances.EuclideanDistance;
@@ -44,6 +44,9 @@ import org.jetbrains.annotations.NotNull;
  * kNN algorithm model to solve multi-class classification task.
  */
 public class KNNClassificationModel<K, V> implements Model<Vector, Double>, 
Exportable<KNNModelFormat> {
+    /** */
+    private static final long serialVersionUID = -127386523291350345L;
+
     /** Amount of nearest neighbors. */
     protected int k = 5;
 
@@ -54,13 +57,13 @@ public class KNNClassificationModel<K, V> implements 
Model<Vector, Double>, Expo
     protected KNNStrategy stgy = KNNStrategy.SIMPLE;
 
     /** Dataset. */
-    private Dataset<KNNPartitionContext, LabeledDataset<Double, 
LabeledVector>> dataset;
+    private Dataset<EmptyContext, LabeledDataset<Double, LabeledVector>> 
dataset;
 
     /**
      * Builds the model via prepared dataset.
      * @param dataset Specially prepared object to run algorithm over it.
      */
-    public KNNClassificationModel(Dataset<KNNPartitionContext, 
LabeledDataset<Double, LabeledVector>> dataset) {
+    public KNNClassificationModel(Dataset<EmptyContext, LabeledDataset<Double, 
LabeledVector>> dataset) {
         this.dataset = dataset;
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/KNNPartitionContext.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/KNNPartitionContext.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/KNNPartitionContext.java
deleted file mode 100644
index 0081612..0000000
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/KNNPartitionContext.java
+++ /dev/null
@@ -1,28 +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.knn.partitions;
-
-import java.io.Serializable;
-
-/**
- * Partition context of the kNN classification algorithm.
- */
-public class KNNPartitionContext implements Serializable {
-    /** */
-    private static final long serialVersionUID = -7212307112344430126L;
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/package-info.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/package-info.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/package-info.java
deleted file mode 100644
index 951a849..0000000
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/partitions/package-info.java
+++ /dev/null
@@ -1,22 +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 description. -->
- * Contains helper classes for kNN classification algorithms.
- */
-package org.apache.ignite.ml.knn.partitions;

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
index cabc143..f5def43 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
@@ -17,8 +17,8 @@
 package org.apache.ignite.ml.knn.regression;
 
 import org.apache.ignite.ml.dataset.Dataset;
+import org.apache.ignite.ml.dataset.primitive.context.EmptyContext;
 import org.apache.ignite.ml.knn.classification.KNNClassificationModel;
-import org.apache.ignite.ml.knn.partitions.KNNPartitionContext;
 import org.apache.ignite.ml.math.Vector;
 import org.apache.ignite.ml.math.exceptions.UnsupportedOperationException;
 import org.apache.ignite.ml.structures.LabeledDataset;
@@ -38,11 +38,14 @@ import java.util.List;
  * </ul>
  */
 public class KNNRegressionModel<K,V> extends KNNClassificationModel<K,V> {
+    /** */
+    private static final long serialVersionUID = -721836321291120543L;
+
     /**
      * Builds the model via prepared dataset.
      * @param dataset Specially prepared object to run algorithm over it.
      */
-    public KNNRegressionModel(Dataset<KNNPartitionContext, 
LabeledDataset<Double, LabeledVector>> dataset) {
+    public KNNRegressionModel(Dataset<EmptyContext, LabeledDataset<Double, 
LabeledVector>> dataset) {
         super(dataset);
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionContext.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionContext.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionContext.java
deleted file mode 100644
index 1069ff8..0000000
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionContext.java
+++ /dev/null
@@ -1,28 +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.structures.partition;
-
-import java.io.Serializable;
-
-/**
- * Base partition context.
- */
-public class LabelPartitionContext implements Serializable {
-    /** */
-    private static final long serialVersionUID = -7412302212344430126L;
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionDataBuilderOnHeap.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionDataBuilderOnHeap.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionDataBuilderOnHeap.java
index 14c053e..4fba028 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionDataBuilderOnHeap.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/structures/partition/LabelPartitionDataBuilderOnHeap.java
@@ -22,7 +22,6 @@ import java.util.Iterator;
 import org.apache.ignite.ml.dataset.PartitionDataBuilder;
 import org.apache.ignite.ml.dataset.UpstreamEntry;
 import org.apache.ignite.ml.math.functions.IgniteBiFunction;
-import org.apache.ignite.ml.structures.LabeledDataset;
 
 /**
  * Partition data builder that builds {@link LabelPartitionDataOnHeap}.

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java
index dace8c6..f806fb8 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java
@@ -28,6 +28,9 @@ import org.apache.ignite.ml.math.Vector;
  * Base class for SVM linear classification model.
  */
 public class SVMLinearBinaryClassificationModel implements Model<Vector, 
Double>, Exportable<SVMLinearBinaryClassificationModel>, Serializable {
+    /** */
+    private static final long serialVersionUID = -996984622291440226L;
+
     /** Output label format. -1 and +1 for false value and raw distances from 
the separating hyperplane otherwise. */
     private boolean isKeepingRawLabels = false;
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java
index 7f11e20..d56848c 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java
@@ -18,6 +18,7 @@
 package org.apache.ignite.ml.svm;
 
 import java.util.concurrent.ThreadLocalRandom;
+import org.apache.ignite.ml.dataset.primitive.context.EmptyContext;
 import 
org.apache.ignite.ml.structures.partition.LabeledDatasetPartitionDataBuilderOnHeap;
 import org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer;
 import org.apache.ignite.ml.dataset.Dataset;
@@ -59,15 +60,15 @@ public class SVMLinearBinaryClassificationTrainer 
implements SingleLabelDatasetT
 
         assert datasetBuilder != null;
 
-        PartitionDataBuilder<K, V, SVMPartitionContext, LabeledDataset<Double, 
LabeledVector>> partDataBuilder = new 
LabeledDatasetPartitionDataBuilderOnHeap<>(
+        PartitionDataBuilder<K, V, EmptyContext, LabeledDataset<Double, 
LabeledVector>> partDataBuilder = new 
LabeledDatasetPartitionDataBuilderOnHeap<>(
             featureExtractor,
             lbExtractor
         );
 
         Vector weights;
 
-        try(Dataset<SVMPartitionContext, LabeledDataset<Double, 
LabeledVector>> dataset = datasetBuilder.build(
-            (upstream, upstreamSize) -> new SVMPartitionContext(),
+        try(Dataset<EmptyContext, LabeledDataset<Double, LabeledVector>> 
dataset = datasetBuilder.build(
+            (upstream, upstreamSize) -> new EmptyContext(),
             partDataBuilder
         )) {
             final int cols = dataset.compute(data -> data.colSize(), (a, b) -> 
a == null ? b : a);
@@ -90,7 +91,7 @@ public class SVMLinearBinaryClassificationTrainer implements 
SingleLabelDatasetT
     }
 
     /** */
-    private Vector calculateUpdates(Vector weights, 
Dataset<SVMPartitionContext, LabeledDataset<Double, LabeledVector>> dataset) {
+    private Vector calculateUpdates(Vector weights, Dataset<EmptyContext, 
LabeledDataset<Double, LabeledVector>> dataset) {
         return dataset.compute(data -> {
             Vector copiedWeights = weights.copy();
             Vector deltaWeights = initializeWeightsWithZeros(weights.size());

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java
index 5879ef0..bbec791 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java
@@ -29,6 +29,9 @@ import org.apache.ignite.ml.math.Vector;
 
 /** Base class for multi-classification model for set of SVM classifiers. */
 public class SVMLinearMultiClassClassificationModel implements Model<Vector, 
Double>, Exportable<SVMLinearMultiClassClassificationModel>, Serializable {
+    /** */
+    private static final long serialVersionUID = -667986511191350227L;
+
     /** List of models associated with each class. */
     private Map<Double, SVMLinearBinaryClassificationModel> models;
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationTrainer.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationTrainer.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationTrainer.java
index 88c342d..4e081c6 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationTrainer.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationTrainer.java
@@ -24,12 +24,12 @@ import java.util.List;
 import java.util.Set;
 import java.util.stream.Collectors;
 import java.util.stream.Stream;
+import org.apache.ignite.ml.dataset.primitive.context.EmptyContext;
 import org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer;
 import org.apache.ignite.ml.dataset.Dataset;
 import org.apache.ignite.ml.dataset.DatasetBuilder;
 import org.apache.ignite.ml.dataset.PartitionDataBuilder;
 import org.apache.ignite.ml.math.functions.IgniteBiFunction;
-import org.apache.ignite.ml.structures.partition.LabelPartitionContext;
 import 
org.apache.ignite.ml.structures.partition.LabelPartitionDataBuilderOnHeap;
 import org.apache.ignite.ml.structures.partition.LabelPartitionDataOnHeap;
 
@@ -89,12 +89,12 @@ public class SVMLinearMultiClassClassificationTrainer
     private <K, V> List<Double> extractClassLabels(DatasetBuilder<K, V> 
datasetBuilder, IgniteBiFunction<K, V, Double> lbExtractor) {
         assert datasetBuilder != null;
 
-        PartitionDataBuilder<K, V, LabelPartitionContext, 
LabelPartitionDataOnHeap> partDataBuilder = new 
LabelPartitionDataBuilderOnHeap<>(lbExtractor);
+        PartitionDataBuilder<K, V, EmptyContext, LabelPartitionDataOnHeap> 
partDataBuilder = new LabelPartitionDataBuilderOnHeap<>(lbExtractor);
 
         List<Double> res = new ArrayList<>();
 
-        try (Dataset<LabelPartitionContext, LabelPartitionDataOnHeap> dataset 
= datasetBuilder.build(
-            (upstream, upstreamSize) -> new LabelPartitionContext(),
+        try (Dataset<EmptyContext, LabelPartitionDataOnHeap> dataset = 
datasetBuilder.build(
+            (upstream, upstreamSize) -> new EmptyContext(),
             partDataBuilder
         )) {
             final Set<Double> clsLabels = dataset.compute(data -> {

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMPartitionContext.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMPartitionContext.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMPartitionContext.java
deleted file mode 100644
index 0aee0fb..0000000
--- a/modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMPartitionContext.java
+++ /dev/null
@@ -1,28 +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.svm;
-
-import java.io.Serializable;
-
-/**
- * Partition context of the SVM classification algorithm.
- */
-public class SVMPartitionContext implements Serializable {
-    /** */
-    private static final long serialVersionUID = -7212307112344430126L;
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/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
deleted file mode 100644
index aeac2cf..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/knn/BaseKNNTest.java
+++ /dev/null
@@ -1,89 +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.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.ml.structures.preprocessing.LabeledDatasetLoader;
-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";
-
-    /** 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 LabeledDatasetLoader.loadFromTxtFile(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/8550d61b/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
index b27fcba..0877fc0 100644
--- 
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
@@ -17,31 +17,35 @@
 
 package org.apache.ignite.ml.knn;
 
-import org.apache.ignite.internal.util.IgniteUtils;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Map;
+import org.junit.Assert;
+import org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder;
 import org.apache.ignite.ml.knn.classification.KNNClassificationModel;
 import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer;
 import org.apache.ignite.ml.knn.classification.KNNStrategy;
 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 java.util.Arrays;
-import java.util.HashMap;
-import java.util.Map;
+import org.junit.Test;
 
 /** Tests behaviour of KNNClassificationTest. */
-public class KNNClassificationTest extends BaseKNNTest {
+public class KNNClassificationTest {
+    /** Precision in test checks. */
+    private static final double PRECISION = 1e-2;
+
     /** */
-    public void testBinaryClassificationTest() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+    @Test
+    public void binaryClassificationTest() {
 
         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});
+        data.put(0, new double[]{1.0, 1.0, 1.0});
+        data.put(1, new double[]{1.0, 2.0, 1.0});
+        data.put(2, new double[]{2.0, 1.0, 1.0});
+        data.put(3, new double[]{-1.0, -1.0, 2.0});
+        data.put(4, new double[]{-1.0, -2.0, 2.0});
+        data.put(5, new double[]{-2.0, -1.0, 2.0});
 
         KNNClassificationTrainer trainer = new KNNClassificationTrainer();
 
@@ -54,23 +58,23 @@ public class KNNClassificationTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector firstVector = new DenseLocalOnHeapVector(new double[] {2.0, 
2.0});
-        assertEquals(knnMdl.apply(firstVector), 1.0);
-        Vector secondVector = new DenseLocalOnHeapVector(new double[] {-2.0, 
-2.0});
-        assertEquals(knnMdl.apply(secondVector), 2.0);
+        Vector firstVector = new DenseLocalOnHeapVector(new double[]{2.0, 
2.0});
+        Assert.assertEquals(knnMdl.apply(firstVector), 1.0, PRECISION);
+        Vector secondVector = new DenseLocalOnHeapVector(new double[]{-2.0, 
-2.0});
+        Assert.assertEquals(knnMdl.apply(secondVector), 2.0, PRECISION);
     }
 
     /** */
-    public void testBinaryClassificationWithSmallestKTest() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
+    @Test
+    public void binaryClassificationWithSmallestKTest() {
         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});
+
+        data.put(0, new double[]{1.0, 1.0, 1.0});
+        data.put(1, new double[]{1.0, 2.0, 1.0});
+        data.put(2, new double[]{2.0, 1.0, 1.0});
+        data.put(3, new double[]{-1.0, -1.0, 2.0});
+        data.put(4, new double[]{-1.0, -2.0, 2.0});
+        data.put(5, new double[]{-2.0, -1.0, 2.0});
 
         KNNClassificationTrainer trainer = new KNNClassificationTrainer();
 
@@ -83,23 +87,23 @@ public class KNNClassificationTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector firstVector = new DenseLocalOnHeapVector(new double[] {2.0, 
2.0});
-        assertEquals(knnMdl.apply(firstVector), 1.0);
-        Vector secondVector = new DenseLocalOnHeapVector(new double[] {-2.0, 
-2.0});
-        assertEquals(knnMdl.apply(secondVector), 2.0);
+        Vector firstVector = new DenseLocalOnHeapVector(new double[]{2.0, 
2.0});
+        Assert.assertEquals(knnMdl.apply(firstVector), 1.0, PRECISION);
+        Vector secondVector = new DenseLocalOnHeapVector(new double[]{-2.0, 
-2.0});
+        Assert.assertEquals(knnMdl.apply(secondVector), 2.0, PRECISION);
     }
 
     /** */
-    public void testBinaryClassificationFarPointsWithSimpleStrategy() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
+    @Test
+    public void binaryClassificationFarPointsWithSimpleStrategy() {
         Map<Integer, double[]> data = new HashMap<>();
-        data.put(0, new double[] {10.0, 10.0, 1.0});
-        data.put(1, new double[] {10.0, 20.0, 1.0});
-        data.put(2, new double[] {-1, -1, 1.0});
-        data.put(3, new double[] {-2, -2, 2.0});
-        data.put(4, new double[] {-1.0, -2.0, 2.0});
-        data.put(5, new double[] {-2.0, -1.0, 2.0});
+
+        data.put(0, new double[]{10.0, 10.0, 1.0});
+        data.put(1, new double[]{10.0, 20.0, 1.0});
+        data.put(2, new double[]{-1, -1, 1.0});
+        data.put(3, new double[]{-2, -2, 2.0});
+        data.put(4, new double[]{-1.0, -2.0, 2.0});
+        data.put(5, new double[]{-2.0, -1.0, 2.0});
 
         KNNClassificationTrainer trainer = new KNNClassificationTrainer();
 
@@ -112,21 +116,21 @@ public class KNNClassificationTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector vector = new DenseLocalOnHeapVector(new double[] {-1.01, 
-1.01});
-        assertEquals(knnMdl.apply(vector), 2.0);
+        Vector vector = new DenseLocalOnHeapVector(new double[]{-1.01, -1.01});
+        Assert.assertEquals(knnMdl.apply(vector), 2.0, PRECISION);
     }
 
     /** */
-    public void testBinaryClassificationFarPointsWithWeightedStrategy() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
+    @Test
+    public void binaryClassificationFarPointsWithWeightedStrategy() {
         Map<Integer, double[]> data = new HashMap<>();
-        data.put(0, new double[] {10.0, 10.0, 1.0});
-        data.put(1, new double[] {10.0, 20.0, 1.0});
-        data.put(2, new double[] {-1, -1, 1.0});
-        data.put(3, new double[] {-2, -2, 2.0});
-        data.put(4, new double[] {-1.0, -2.0, 2.0});
-        data.put(5, new double[] {-2.0, -1.0, 2.0});
+
+        data.put(0, new double[]{10.0, 10.0, 1.0});
+        data.put(1, new double[]{10.0, 20.0, 1.0});
+        data.put(2, new double[]{-1, -1, 1.0});
+        data.put(3, new double[]{-2, -2, 2.0});
+        data.put(4, new double[]{-1.0, -2.0, 2.0});
+        data.put(5, new double[]{-2.0, -1.0, 2.0});
 
         KNNClassificationTrainer trainer = new KNNClassificationTrainer();
 
@@ -139,7 +143,7 @@ public class KNNClassificationTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.WEIGHTED);
 
-        Vector vector = new DenseLocalOnHeapVector(new double[] {-1.01, 
-1.01});
-        assertEquals(knnMdl.apply(vector), 1.0);
+        Vector vector = new DenseLocalOnHeapVector(new double[]{-1.01, -1.01});
+        Assert.assertEquals(knnMdl.apply(vector), 1.0, PRECISION);
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNRegressionTest.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNRegressionTest.java 
b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNRegressionTest.java
index 66dbca9..ce9cae5 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNRegressionTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/knn/KNNRegressionTest.java
@@ -17,7 +17,6 @@
 
 package org.apache.ignite.ml.knn;
 
-import org.apache.ignite.internal.util.IgniteUtils;
 import org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder;
 import org.apache.ignite.ml.knn.classification.KNNStrategy;
 import org.apache.ignite.ml.knn.regression.KNNRegressionModel;
@@ -30,28 +29,23 @@ import org.junit.Assert;
 import java.util.Arrays;
 import java.util.HashMap;
 import java.util.Map;
+import org.junit.Test;
 
 /**
  * Tests for {@link KNNRegressionTrainer}.
  */
-public class KNNRegressionTest extends BaseKNNTest {
+public class KNNRegressionTest {
     /** */
-    private double[] y;
-
-    /** */
-    private double[][] x;
-
-    /** */
-    public void testSimpleRegressionWithOneNeighbour() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
+    @Test
+    public void simpleRegressionWithOneNeighbour() {
         Map<Integer, double[]> data = new HashMap<>();
-        data.put(0, new double[] {11.0, 0, 0, 0, 0, 0});
-        data.put(1, new double[] {12.0, 2.0, 0, 0, 0, 0});
-        data.put(2, new double[] {13.0, 0, 3.0, 0, 0, 0});
-        data.put(3, new double[] {14.0, 0, 0, 4.0, 0, 0});
-        data.put(4, new double[] {15.0, 0, 0, 0, 5.0, 0});
-        data.put(5, new double[] {16.0, 0, 0, 0, 0, 6.0});
+
+        data.put(0, new double[]{11.0, 0, 0, 0, 0, 0});
+        data.put(1, new double[]{12.0, 2.0, 0, 0, 0, 0});
+        data.put(2, new double[]{13.0, 0, 3.0, 0, 0, 0});
+        data.put(3, new double[]{14.0, 0, 0, 4.0, 0, 0});
+        data.put(4, new double[]{15.0, 0, 0, 0, 5.0, 0});
+        data.put(5, new double[]{16.0, 0, 0, 0, 0, 6.0});
 
         KNNRegressionTrainer trainer = new KNNRegressionTrainer();
 
@@ -63,32 +57,31 @@ public class KNNRegressionTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector vector = new DenseLocalOnHeapVector(new double[] {0, 0, 0, 5.0, 
0.0});
+        Vector vector = new DenseLocalOnHeapVector(new double[]{0, 0, 0, 5.0, 
0.0});
         System.out.println(knnMdl.apply(vector));
         Assert.assertEquals(15, knnMdl.apply(vector), 1E-12);
     }
 
     /** */
-    public void testLongly() {
-
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
+    @Test
+    public void longly() {
         Map<Integer, double[]> data = new HashMap<>();
-        data.put(0, new double[] {60323, 83.0, 234289, 2356, 1590, 107608, 
1947});
-        data.put(1, new double[] {61122, 88.5, 259426, 2325, 1456, 108632, 
1948});
-        data.put(2, new double[] {60171, 88.2, 258054, 3682, 1616, 109773, 
1949});
-        data.put(3, new double[] {61187, 89.5, 284599, 3351, 1650, 110929, 
1950});
-        data.put(4, new double[] {63221, 96.2, 328975, 2099, 3099, 112075, 
1951});
-        data.put(5, new double[] {63639, 98.1, 346999, 1932, 3594, 113270, 
1952});
-        data.put(6, new double[] {64989, 99.0, 365385, 1870, 3547, 115094, 
1953});
-        data.put(7, new double[] {63761, 100.0, 363112, 3578, 3350, 116219, 
1954});
-        data.put(8, new double[] {66019, 101.2, 397469, 2904, 3048, 117388, 
1955});
-        data.put(9, new double[] {68169, 108.4, 442769, 2936, 2798, 120445, 
1957});
-        data.put(10, new double[] {66513, 110.8, 444546, 4681, 2637, 121950, 
1958});
-        data.put(11, new double[] {68655, 112.6, 482704, 3813, 2552, 123366, 
1959});
-        data.put(12, new double[] {69564, 114.2, 502601, 3931, 2514, 125368, 
1960});
-        data.put(13, new double[] {69331, 115.7, 518173, 4806, 2572, 127852, 
1961});
-        data.put(14, new double[] {70551, 116.9, 554894, 4007, 2827, 130081, 
1962});
+
+        data.put(0, new double[]{60323, 83.0, 234289, 2356, 1590, 107608, 
1947});
+        data.put(1, new double[]{61122, 88.5, 259426, 2325, 1456, 108632, 
1948});
+        data.put(2, new double[]{60171, 88.2, 258054, 3682, 1616, 109773, 
1949});
+        data.put(3, new double[]{61187, 89.5, 284599, 3351, 1650, 110929, 
1950});
+        data.put(4, new double[]{63221, 96.2, 328975, 2099, 3099, 112075, 
1951});
+        data.put(5, new double[]{63639, 98.1, 346999, 1932, 3594, 113270, 
1952});
+        data.put(6, new double[]{64989, 99.0, 365385, 1870, 3547, 115094, 
1953});
+        data.put(7, new double[]{63761, 100.0, 363112, 3578, 3350, 116219, 
1954});
+        data.put(8, new double[]{66019, 101.2, 397469, 2904, 3048, 117388, 
1955});
+        data.put(9, new double[]{68169, 108.4, 442769, 2936, 2798, 120445, 
1957});
+        data.put(10, new double[]{66513, 110.8, 444546, 4681, 2637, 121950, 
1958});
+        data.put(11, new double[]{68655, 112.6, 482704, 3813, 2552, 123366, 
1959});
+        data.put(12, new double[]{69564, 114.2, 502601, 3931, 2514, 125368, 
1960});
+        data.put(13, new double[]{69331, 115.7, 518173, 4806, 2572, 127852, 
1961});
+        data.put(14, new double[]{70551, 116.9, 554894, 4007, 2827, 130081, 
1962});
 
         KNNRegressionTrainer trainer = new KNNRegressionTrainer();
 
@@ -100,31 +93,30 @@ public class KNNRegressionTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector vector = new DenseLocalOnHeapVector(new double[] {104.6, 
419180, 2822, 2857, 118734, 1956});
+        Vector vector = new DenseLocalOnHeapVector(new double[]{104.6, 419180, 
2822, 2857, 118734, 1956});
         System.out.println(knnMdl.apply(vector));
         Assert.assertEquals(67857, knnMdl.apply(vector), 2000);
     }
 
     /** */
     public void testLonglyWithWeightedStrategy() {
-        
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
         Map<Integer, double[]> data = new HashMap<>();
-        data.put(0, new double[] {60323, 83.0, 234289, 2356, 1590, 107608, 
1947});
-        data.put(1, new double[] {61122, 88.5, 259426, 2325, 1456, 108632, 
1948});
-        data.put(2, new double[] {60171, 88.2, 258054, 3682, 1616, 109773, 
1949});
-        data.put(3, new double[] {61187, 89.5, 284599, 3351, 1650, 110929, 
1950});
-        data.put(4, new double[] {63221, 96.2, 328975, 2099, 3099, 112075, 
1951});
-        data.put(5, new double[] {63639, 98.1, 346999, 1932, 3594, 113270, 
1952});
-        data.put(6, new double[] {64989, 99.0, 365385, 1870, 3547, 115094, 
1953});
-        data.put(7, new double[] {63761, 100.0, 363112, 3578, 3350, 116219, 
1954});
-        data.put(8, new double[] {66019, 101.2, 397469, 2904, 3048, 117388, 
1955});
-        data.put(9, new double[] {68169, 108.4, 442769, 2936, 2798, 120445, 
1957});
-        data.put(10, new double[] {66513, 110.8, 444546, 4681, 2637, 121950, 
1958});
-        data.put(11, new double[] {68655, 112.6, 482704, 3813, 2552, 123366, 
1959});
-        data.put(12, new double[] {69564, 114.2, 502601, 3931, 2514, 125368, 
1960});
-        data.put(13, new double[] {69331, 115.7, 518173, 4806, 2572, 127852, 
1961});
-        data.put(14, new double[] {70551, 116.9, 554894, 4007, 2827, 130081, 
1962});
+
+        data.put(0, new double[]{60323, 83.0, 234289, 2356, 1590, 107608, 
1947});
+        data.put(1, new double[]{61122, 88.5, 259426, 2325, 1456, 108632, 
1948});
+        data.put(2, new double[]{60171, 88.2, 258054, 3682, 1616, 109773, 
1949});
+        data.put(3, new double[]{61187, 89.5, 284599, 3351, 1650, 110929, 
1950});
+        data.put(4, new double[]{63221, 96.2, 328975, 2099, 3099, 112075, 
1951});
+        data.put(5, new double[]{63639, 98.1, 346999, 1932, 3594, 113270, 
1952});
+        data.put(6, new double[]{64989, 99.0, 365385, 1870, 3547, 115094, 
1953});
+        data.put(7, new double[]{63761, 100.0, 363112, 3578, 3350, 116219, 
1954});
+        data.put(8, new double[]{66019, 101.2, 397469, 2904, 3048, 117388, 
1955});
+        data.put(9, new double[]{68169, 108.4, 442769, 2936, 2798, 120445, 
1957});
+        data.put(10, new double[]{66513, 110.8, 444546, 4681, 2637, 121950, 
1958});
+        data.put(11, new double[]{68655, 112.6, 482704, 3813, 2552, 123366, 
1959});
+        data.put(12, new double[]{69564, 114.2, 502601, 3931, 2514, 125368, 
1960});
+        data.put(13, new double[]{69331, 115.7, 518173, 4806, 2572, 127852, 
1961});
+        data.put(14, new double[]{70551, 116.9, 554894, 4007, 2827, 130081, 
1962});
 
         KNNRegressionTrainer trainer = new KNNRegressionTrainer();
 
@@ -136,7 +128,7 @@ public class KNNRegressionTest extends BaseKNNTest {
             .withDistanceMeasure(new EuclideanDistance())
             .withStrategy(KNNStrategy.SIMPLE);
 
-        Vector vector = new DenseLocalOnHeapVector(new double[] {104.6, 
419180, 2822, 2857, 118734, 1956});
+        Vector vector = new DenseLocalOnHeapVector(new double[]{104.6, 419180, 
2822, 2857, 118734, 1956});
         System.out.println(knnMdl.apply(vector));
         Assert.assertEquals(67857, knnMdl.apply(vector), 2000);
     }

http://git-wip-us.apache.org/repos/asf/ignite/blob/8550d61b/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetHelper.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetHelper.java 
b/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetHelper.java
new file mode 100644
index 0000000..a25b303
--- /dev/null
+++ 
b/modules/ml/src/test/java/org/apache/ignite/ml/knn/LabeledDatasetHelper.java
@@ -0,0 +1,87 @@
+/*
+ * 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.ml.structures.preprocessing.LabeledDatasetLoader;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+
+/**
+ * Base class for decision trees test.
+ */
+public class LabeledDatasetHelper extends GridCommonAbstractTest {
+    /** Count of nodes. */
+    private static final int NODE_COUNT = 4;
+
+    /** Separator. */
+    private static final String SEPARATOR = "\t";
+
+    /** Grid instance. */
+    protected Ignite ignite;
+
+    /**
+     * Default constructor.
+     */
+    public LabeledDatasetHelper() {
+        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 LabeledDatasetLoader.loadFromTxtFile(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/8550d61b/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 cdd5dc4..77d40a6 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
@@ -34,7 +34,7 @@ import org.apache.ignite.ml.structures.LabeledVector;
 import org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader;
 
 /** Tests behaviour of KNNClassificationTest. */
-public class LabeledDatasetTest extends BaseKNNTest implements 
ExternalizableTest<LabeledDataset> {
+public class LabeledDatasetTest extends LabeledDatasetHelper implements 
ExternalizableTest<LabeledDataset> {
     /** */
     private static final String KNN_IRIS_TXT = "datasets/knn/iris.txt";
 

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