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Posted to commits@ignite.apache.org by ak...@apache.org on 2018/04/11 07:19:56 UTC
[02/14] ignite git commit: IGNITE-8059: Integrate decision tree with
partition based dataset.
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/mse/MSEImpurityMeasureTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/mse/MSEImpurityMeasureTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/mse/MSEImpurityMeasureTest.java
new file mode 100644
index 0000000..3d11d9d
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/mse/MSEImpurityMeasureTest.java
@@ -0,0 +1,109 @@
+/*
+ * 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.tree.impurity.mse;
+
+import java.util.Random;
+import org.junit.Test;
+
+import static junit.framework.TestCase.assertEquals;
+
+/**
+ * Tests for {@link MSEImpurityMeasure}.
+ */
+public class MSEImpurityMeasureTest {
+ /** */
+ @Test
+ public void testImpurityOnEmptyData() {
+ MSEImpurityMeasure impurity = new MSEImpurityMeasure(0, 0, 0, 0, 0, 0);
+
+ assertEquals(0.0, impurity.impurity(), 1e-10);
+ }
+
+ /** */
+ @Test
+ public void testImpurityLeftPart() {
+ // Test on left part [1, 2, 2, 1, 1, 1].
+ MSEImpurityMeasure impurity = new MSEImpurityMeasure(8, 12, 6, 0, 0, 0);
+
+ assertEquals(1.333, impurity.impurity(), 1e-3);
+ }
+
+ /** */
+ @Test
+ public void testImpurityRightPart() {
+ // Test on right part [1, 2, 2, 1, 1, 1].
+ MSEImpurityMeasure impurity = new MSEImpurityMeasure(0, 0, 0, 8, 12, 6);
+
+ assertEquals(1.333, impurity.impurity(), 1e-3);
+ }
+
+ /** */
+ @Test
+ public void testImpurityLeftAndRightPart() {
+ // Test on left part [1, 2, 2] and right part [1, 1, 1].
+ MSEImpurityMeasure impurity = new MSEImpurityMeasure(5, 9, 3, 3, 3, 3);
+
+ assertEquals(0.666, impurity.impurity(), 1e-3);
+ }
+
+ /** */
+ @Test
+ public void testAdd() {
+ Random rnd = new Random(0);
+
+ MSEImpurityMeasure a = new MSEImpurityMeasure(
+ rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt(), rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt()
+ );
+
+ MSEImpurityMeasure b = new MSEImpurityMeasure(
+ rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt(), rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt()
+ );
+
+ MSEImpurityMeasure c = a.add(b);
+
+ assertEquals(a.getLeftY() + b.getLeftY(), c.getLeftY(), 1e-10);
+ assertEquals(a.getLeftY2() + b.getLeftY2(), c.getLeftY2(), 1e-10);
+ assertEquals(a.getLeftCnt() + b.getLeftCnt(), c.getLeftCnt());
+ assertEquals(a.getRightY() + b.getRightY(), c.getRightY(), 1e-10);
+ assertEquals(a.getRightY2() + b.getRightY2(), c.getRightY2(), 1e-10);
+ assertEquals(a.getRightCnt() + b.getRightCnt(), c.getRightCnt());
+ }
+
+ /** */
+ @Test
+ public void testSubtract() {
+ Random rnd = new Random(0);
+
+ MSEImpurityMeasure a = new MSEImpurityMeasure(
+ rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt(), rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt()
+ );
+
+ MSEImpurityMeasure b = new MSEImpurityMeasure(
+ rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt(), rnd.nextDouble(), rnd.nextDouble(), rnd.nextInt()
+ );
+
+ MSEImpurityMeasure c = a.subtract(b);
+
+ assertEquals(a.getLeftY() - b.getLeftY(), c.getLeftY(), 1e-10);
+ assertEquals(a.getLeftY2() - b.getLeftY2(), c.getLeftY2(), 1e-10);
+ assertEquals(a.getLeftCnt() - b.getLeftCnt(), c.getLeftCnt());
+ assertEquals(a.getRightY() - b.getRightY(), c.getRightY(), 1e-10);
+ assertEquals(a.getRightY2() - b.getRightY2(), c.getRightY2(), 1e-10);
+ assertEquals(a.getRightCnt() - b.getRightCnt(), c.getRightCnt());
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/SimpleStepFunctionCompressorTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/SimpleStepFunctionCompressorTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/SimpleStepFunctionCompressorTest.java
new file mode 100644
index 0000000..001404f
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/SimpleStepFunctionCompressorTest.java
@@ -0,0 +1,75 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.tree.impurity.util;
+
+import org.junit.Test;
+
+import static org.junit.Assert.assertArrayEquals;
+
+/**
+ * Tests for {@link SimpleStepFunctionCompressor}.
+ */
+public class SimpleStepFunctionCompressorTest {
+ /** */
+ @Test
+ public void testCompressSmallFunction() {
+ StepFunction<TestImpurityMeasure> function = new StepFunction<>(
+ new double[]{1, 2, 3, 4},
+ TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3, 4)
+ );
+
+ SimpleStepFunctionCompressor<TestImpurityMeasure> compressor = new SimpleStepFunctionCompressor<>(5, 0, 0);
+
+ StepFunction<TestImpurityMeasure> resFunction = compressor.compress(function);
+
+ assertArrayEquals(new double[]{1, 2, 3, 4}, resFunction.getX(), 1e-10);
+ assertArrayEquals(TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3, 4), resFunction.getY());
+ }
+
+ /** */
+ @Test
+ public void testCompressIncreasingFunction() {
+ StepFunction<TestImpurityMeasure> function = new StepFunction<>(
+ new double[]{1, 2, 3, 4, 5},
+ TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3, 4, 5)
+ );
+
+ SimpleStepFunctionCompressor<TestImpurityMeasure> compressor = new SimpleStepFunctionCompressor<>(1, 0.4, 0);
+
+ StepFunction<TestImpurityMeasure> resFunction = compressor.compress(function);
+
+ assertArrayEquals(new double[]{1, 3, 5}, resFunction.getX(), 1e-10);
+ assertArrayEquals(TestImpurityMeasure.asTestImpurityMeasures(1, 3, 5), resFunction.getY());
+ }
+
+ /** */
+ @Test
+ public void testCompressDecreasingFunction() {
+ StepFunction<TestImpurityMeasure> function = new StepFunction<>(
+ new double[]{1, 2, 3, 4, 5},
+ TestImpurityMeasure.asTestImpurityMeasures(5, 4, 3, 2, 1)
+ );
+
+ SimpleStepFunctionCompressor<TestImpurityMeasure> compressor = new SimpleStepFunctionCompressor<>(1, 0, 0.4);
+
+ StepFunction<TestImpurityMeasure> resFunction = compressor.compress(function);
+
+ assertArrayEquals(new double[]{1, 3, 5}, resFunction.getX(), 1e-10);
+ assertArrayEquals(TestImpurityMeasure.asTestImpurityMeasures(5, 3, 1), resFunction.getY());
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/StepFunctionTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/StepFunctionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/StepFunctionTest.java
new file mode 100644
index 0000000..2a0279c
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/StepFunctionTest.java
@@ -0,0 +1,71 @@
+/*
+ * 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.tree.impurity.util;
+
+import org.junit.Test;
+
+import static org.junit.Assert.assertArrayEquals;
+
+/**
+ * Tests for {@link StepFunction}.
+ */
+public class StepFunctionTest {
+ /** */
+ @Test
+ public void testAddIncreasingFunctions() {
+ StepFunction<TestImpurityMeasure> a = new StepFunction<>(
+ new double[]{1, 3, 5},
+ TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3)
+ );
+
+ StepFunction<TestImpurityMeasure> b = new StepFunction<>(
+ new double[]{0, 2, 4},
+ TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3)
+ );
+
+ StepFunction<TestImpurityMeasure> c = a.add(b);
+
+ assertArrayEquals(new double[]{0, 1, 2, 3, 4, 5}, c.getX(), 1e-10);
+ assertArrayEquals(
+ TestImpurityMeasure.asTestImpurityMeasures(1, 2, 3, 4, 5, 6),
+ c.getY()
+ );
+ }
+
+ /** */
+ @Test
+ public void testAddDecreasingFunctions() {
+ StepFunction<TestImpurityMeasure> a = new StepFunction<>(
+ new double[]{1, 3, 5},
+ TestImpurityMeasure.asTestImpurityMeasures(3, 2, 1)
+ );
+
+ StepFunction<TestImpurityMeasure> b = new StepFunction<>(
+ new double[]{0, 2, 4},
+ TestImpurityMeasure.asTestImpurityMeasures(3, 2, 1)
+ );
+
+ StepFunction<TestImpurityMeasure> c = a.add(b);
+
+ assertArrayEquals(new double[]{0, 1, 2, 3, 4, 5}, c.getX(), 1e-10);
+ assertArrayEquals(
+ TestImpurityMeasure.asTestImpurityMeasures(3, 6, 5, 4, 3, 2),
+ c.getY()
+ );
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/TestImpurityMeasure.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/TestImpurityMeasure.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/TestImpurityMeasure.java
new file mode 100644
index 0000000..c0d1911
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/impurity/util/TestImpurityMeasure.java
@@ -0,0 +1,88 @@
+/*
+ * 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.tree.impurity.util;
+
+import java.util.Objects;
+import org.apache.ignite.ml.tree.impurity.ImpurityMeasure;
+
+/**
+ * Utils class used as impurity measure in tests.
+ */
+class TestImpurityMeasure implements ImpurityMeasure<TestImpurityMeasure> {
+ /** */
+ private static final long serialVersionUID = 2414020770162797847L;
+
+ /** Impurity. */
+ private final double impurity;
+
+ /**
+ * Constructs a new instance of test impurity measure.
+ *
+ * @param impurity Impurity.
+ */
+ private TestImpurityMeasure(double impurity) {
+ this.impurity = impurity;
+ }
+
+ /**
+ * Convert doubles to array of test impurity measures.
+ *
+ * @param impurity Impurity as array of doubles.
+ * @return Test impurity measure objects as array.
+ */
+ static TestImpurityMeasure[] asTestImpurityMeasures(double... impurity) {
+ TestImpurityMeasure[] res = new TestImpurityMeasure[impurity.length];
+
+ for (int i = 0; i < impurity.length; i++)
+ res[i] = new TestImpurityMeasure(impurity[i]);
+
+ return res;
+ }
+
+ /** {@inheritDoc} */
+ @Override public double impurity() {
+ return impurity;
+ }
+
+ /** {@inheritDoc} */
+ @Override public TestImpurityMeasure add(TestImpurityMeasure measure) {
+ return new TestImpurityMeasure(impurity + measure.impurity);
+ }
+
+ /** {@inheritDoc} */
+ @Override public TestImpurityMeasure subtract(TestImpurityMeasure measure) {
+ return new TestImpurityMeasure(impurity - measure.impurity);
+ }
+
+ /** */
+ @Override public boolean equals(Object o) {
+ if (this == o)
+ return true;
+ if (o == null || getClass() != o.getClass())
+ return false;
+ TestImpurityMeasure measure = (TestImpurityMeasure)o;
+
+ return Double.compare(measure.impurity, impurity) == 0;
+ }
+
+ /** */
+ @Override public int hashCode() {
+
+ return Objects.hash(impurity);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTIntegrationTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTIntegrationTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTIntegrationTest.java
new file mode 100644
index 0000000..b259ec9
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTIntegrationTest.java
@@ -0,0 +1,105 @@
+/*
+ * 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.tree.performance;
+
+import java.io.IOException;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder;
+import org.apache.ignite.ml.nn.performance.MnistMLPTestUtil;
+import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
+import org.apache.ignite.ml.tree.DecisionTreeNode;
+import org.apache.ignite.ml.tree.impurity.util.SimpleStepFunctionCompressor;
+import org.apache.ignite.ml.util.MnistUtils;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+
+/**
+ * Tests {@link DecisionTreeClassificationTrainer} on the MNIST dataset that require to start the whole Ignite
+ * infrastructure. For manual run.
+ */
+public class DecisionTreeMNISTIntegrationTest extends GridCommonAbstractTest {
+ /** Number of nodes in grid */
+ private static final int NODE_COUNT = 3;
+
+ /** Ignite instance. */
+ private Ignite ignite;
+
+ /** {@inheritDoc} */
+ @Override protected void beforeTestsStarted() throws Exception {
+ for (int i = 1; i <= NODE_COUNT; i++)
+ startGrid(i);
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void afterTestsStopped() {
+ stopAllGrids();
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override protected void beforeTest() throws Exception {
+ /* Grid instance. */
+ ignite = grid(NODE_COUNT);
+ ignite.configuration().setPeerClassLoadingEnabled(true);
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ }
+
+ /** Tests on the MNIST dataset. For manual run. */
+ public void testMNIST() throws IOException {
+ CacheConfiguration<Integer, MnistUtils.MnistLabeledImage> trainingSetCacheCfg = new CacheConfiguration<>();
+ trainingSetCacheCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
+ trainingSetCacheCfg.setName("MNIST_TRAINING_SET");
+
+ IgniteCache<Integer, MnistUtils.MnistLabeledImage> trainingSet = ignite.createCache(trainingSetCacheCfg);
+
+ int i = 0;
+ for (MnistUtils.MnistLabeledImage e : MnistMLPTestUtil.loadTrainingSet(60_000))
+ trainingSet.put(i++, e);
+
+ DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(
+ 8,
+ 0,
+ new SimpleStepFunctionCompressor<>());
+
+ DecisionTreeNode mdl = trainer.fit(
+ new CacheBasedDatasetBuilder<>(ignite, trainingSet),
+ (k, v) -> v.getPixels(),
+ (k, v) -> (double) v.getLabel()
+ );
+
+ int correctAnswers = 0;
+ int incorrectAnswers = 0;
+
+ for (MnistUtils.MnistLabeledImage e : MnistMLPTestUtil.loadTestSet(10_000)) {
+ double res = mdl.apply(e.getPixels());
+
+ if (res == e.getLabel())
+ correctAnswers++;
+ else
+ incorrectAnswers++;
+ }
+
+ double accuracy = 1.0 * correctAnswers / (correctAnswers + incorrectAnswers);
+
+ assertTrue(accuracy > 0.8);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTTest.java
new file mode 100644
index 0000000..6dbd44c
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/tree/performance/DecisionTreeMNISTTest.java
@@ -0,0 +1,74 @@
+/*
+ * 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.tree.performance;
+
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.Map;
+import org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder;
+import org.apache.ignite.ml.nn.performance.MnistMLPTestUtil;
+import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
+import org.apache.ignite.ml.tree.DecisionTreeNode;
+import org.apache.ignite.ml.tree.impurity.util.SimpleStepFunctionCompressor;
+import org.apache.ignite.ml.util.MnistUtils;
+import org.junit.Test;
+
+import static junit.framework.TestCase.assertTrue;
+
+/**
+ * Tests {@link DecisionTreeClassificationTrainer} on the MNIST dataset using locally stored data. For manual run.
+ */
+public class DecisionTreeMNISTTest {
+ /** Tests on the MNIST dataset. For manual run. */
+ @Test
+ public void testMNIST() throws IOException {
+ Map<Integer, MnistUtils.MnistLabeledImage> trainingSet = new HashMap<>();
+
+ int i = 0;
+ for (MnistUtils.MnistLabeledImage e : MnistMLPTestUtil.loadTrainingSet(60_000))
+ trainingSet.put(i++, e);
+
+
+ DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(
+ 8,
+ 0,
+ new SimpleStepFunctionCompressor<>());
+
+ DecisionTreeNode mdl = trainer.fit(
+ new LocalDatasetBuilder<>(trainingSet, 10),
+ (k, v) -> v.getPixels(),
+ (k, v) -> (double) v.getLabel()
+ );
+
+ int correctAnswers = 0;
+ int incorrectAnswers = 0;
+
+ for (MnistUtils.MnistLabeledImage e : MnistMLPTestUtil.loadTestSet(10_000)) {
+ double res = mdl.apply(e.getPixels());
+
+ if (res == e.getLabel())
+ correctAnswers++;
+ else
+ incorrectAnswers++;
+ }
+
+ double accuracy = 1.0 * correctAnswers / (correctAnswers + incorrectAnswers);
+
+ assertTrue(accuracy > 0.8);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/BaseDecisionTreeTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/BaseDecisionTreeTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/BaseDecisionTreeTest.java
deleted file mode 100644
index 65f0ae4..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/BaseDecisionTreeTest.java
+++ /dev/null
@@ -1,70 +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.trees;
-
-import java.util.Arrays;
-import org.apache.ignite.Ignite;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.structures.LabeledVectorDouble;
-import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
-
-/**
- * Base class for decision trees test.
- */
-public class BaseDecisionTreeTest extends GridCommonAbstractTest {
- /** Count of nodes. */
- private static final int NODE_COUNT = 4;
-
- /** Grid instance. */
- protected Ignite ignite;
-
- /**
- * Default constructor.
- */
- public BaseDecisionTreeTest() {
- 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();
- }
-
- /**
- * Convert double array to {@link LabeledVectorDouble}
- *
- * @param arr Array for conversion.
- * @return LabeledVectorDouble.
- */
- protected static LabeledVectorDouble<DenseLocalOnHeapVector> asLabeledVector(double arr[]) {
- return new LabeledVectorDouble<>(new DenseLocalOnHeapVector(Arrays.copyOf(arr, arr.length - 1)), arr[arr.length - 1]);
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
deleted file mode 100644
index b090f43..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/ColumnDecisionTreeTrainerTest.java
+++ /dev/null
@@ -1,191 +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.trees;
-
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Map;
-import java.util.Random;
-import java.util.stream.Collectors;
-import java.util.stream.DoubleStream;
-import org.apache.ignite.internal.util.IgniteUtils;
-import org.apache.ignite.internal.util.typedef.X;
-import org.apache.ignite.lang.IgniteBiTuple;
-import org.apache.ignite.ml.math.StorageConstants;
-import org.apache.ignite.ml.math.Tracer;
-import org.apache.ignite.ml.math.functions.IgniteFunction;
-import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.structures.LabeledVectorDouble;
-import org.apache.ignite.ml.trees.models.DecisionTreeModel;
-import org.apache.ignite.ml.trees.trainers.columnbased.ColumnDecisionTreeTrainer;
-import org.apache.ignite.ml.trees.trainers.columnbased.ColumnDecisionTreeTrainerInput;
-import org.apache.ignite.ml.trees.trainers.columnbased.MatrixColumnDecisionTreeTrainerInput;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.ContinuousSplitCalculators;
-import org.apache.ignite.ml.trees.trainers.columnbased.regcalcs.RegionCalculators;
-
-/** Tests behaviour of ColumnDecisionTreeTrainer. */
-public class ColumnDecisionTreeTrainerTest extends BaseDecisionTreeTest {
- /**
- * Test {@link ColumnDecisionTreeTrainerTest} for mixed (continuous and categorical) data with Gini impurity.
- */
- public void testCacheMixedGini() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int totalPts = 1 << 10;
- int featCnt = 2;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(1, 3);
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 1, new int[] {0, 2}).
- split(1, 0, -10.0);
-
- testByGen(totalPts, catsInfo, gen, ContinuousSplitCalculators.GINI.apply(ignite), RegionCalculators.GINI, RegionCalculators.MEAN, rnd);
- }
-
- /**
- * Test {@link ColumnDecisionTreeTrainerTest} for mixed (continuous and categorical) data with Variance impurity.
- */
- public void testCacheMixed() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int totalPts = 1 << 10;
- int featCnt = 2;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(1, 3);
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 1, new int[] {0, 2}).
- split(1, 0, -10.0);
-
- testByGen(totalPts, catsInfo, gen, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, RegionCalculators.MEAN, rnd);
- }
-
- /**
- * Test {@link ColumnDecisionTreeTrainerTest} for continuous data with Variance impurity.
- */
- public void testCacheCont() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int totalPts = 1 << 10;
- int featCnt = 12;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 0, -10.0).
- split(1, 0, 0.0).
- split(1, 1, 2.0).
- split(3, 7, 50.0);
-
- testByGen(totalPts, catsInfo, gen, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, RegionCalculators.MEAN, rnd);
- }
-
- /**
- * Test {@link ColumnDecisionTreeTrainerTest} for continuous data with Gini impurity.
- */
- public void testCacheContGini() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int totalPts = 1 << 10;
- int featCnt = 12;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 0, -10.0).
- split(1, 0, 0.0).
- split(1, 1, 2.0).
- split(3, 7, 50.0);
-
- testByGen(totalPts, catsInfo, gen, ContinuousSplitCalculators.GINI.apply(ignite), RegionCalculators.GINI, RegionCalculators.MEAN, rnd);
- }
-
- /**
- * Test {@link ColumnDecisionTreeTrainerTest} for categorical data with Variance impurity.
- */
- public void testCacheCat() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int totalPts = 1 << 10;
- int featCnt = 12;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(5, 7);
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 5, new int[] {0, 2, 5});
-
- testByGen(totalPts, catsInfo, gen, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, RegionCalculators.MEAN, rnd);
- }
-
- /** */
- private <D extends ContinuousRegionInfo> void testByGen(int totalPts, HashMap<Integer, Integer> catsInfo,
- SplitDataGenerator<DenseLocalOnHeapVector> gen,
- IgniteFunction<ColumnDecisionTreeTrainerInput, ? extends ContinuousSplitCalculator<D>> calc,
- IgniteFunction<ColumnDecisionTreeTrainerInput, IgniteFunction<DoubleStream, Double>> catImpCalc,
- IgniteFunction<DoubleStream, Double> regCalc, Random rnd) {
-
- List<IgniteBiTuple<Integer, DenseLocalOnHeapVector>> lst = gen.
- points(totalPts, (i, rn) -> i).
- collect(Collectors.toList());
-
- int featCnt = gen.featuresCnt();
-
- Collections.shuffle(lst, rnd);
-
- SparseDistributedMatrix m = new SparseDistributedMatrix(totalPts, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
-
- Map<Integer, List<LabeledVectorDouble>> byRegion = new HashMap<>();
-
- int i = 0;
- for (IgniteBiTuple<Integer, DenseLocalOnHeapVector> bt : lst) {
- byRegion.putIfAbsent(bt.get1(), new LinkedList<>());
- byRegion.get(bt.get1()).add(asLabeledVector(bt.get2().getStorage().data()));
- m.setRow(i, bt.get2().getStorage().data());
- i++;
- }
-
- ColumnDecisionTreeTrainer<D> trainer =
- new ColumnDecisionTreeTrainer<>(3, calc, catImpCalc, regCalc, ignite);
-
- DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, catsInfo));
-
- byRegion.keySet().forEach(k -> {
- LabeledVectorDouble sp = byRegion.get(k).get(0);
- Tracer.showAscii(sp.features());
- X.println("Actual and predicted vectors [act=" + sp.label() + " " + ", pred=" + mdl.apply(sp.features()) + "]");
- assert mdl.apply(sp.features()) == sp.doubleLabel();
- });
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/DecisionTreesTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/DecisionTreesTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/DecisionTreesTestSuite.java
deleted file mode 100644
index 3343503..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/DecisionTreesTestSuite.java
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.ignite.ml.trees;
-
-import org.junit.runner.RunWith;
-import org.junit.runners.Suite;
-
-/**
- * Test suite for all tests located in org.apache.ignite.ml.trees package
- */
-@RunWith(Suite.class)
-@Suite.SuiteClasses({
- ColumnDecisionTreeTrainerTest.class,
- GiniSplitCalculatorTest.class,
- VarianceSplitCalculatorTest.class
-})
-public class DecisionTreesTestSuite {
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/GiniSplitCalculatorTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/GiniSplitCalculatorTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/GiniSplitCalculatorTest.java
deleted file mode 100644
index c92b4f5..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/GiniSplitCalculatorTest.java
+++ /dev/null
@@ -1,141 +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.trees;
-
-import java.util.stream.DoubleStream;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.GiniSplitCalculator;
-import org.apache.ignite.ml.trees.trainers.columnbased.vectors.SplitInfo;
-import org.junit.Test;
-
-/**
- * Test of {@link GiniSplitCalculator}.
- */
-public class GiniSplitCalculatorTest {
- /** Test calculation of region info consisting from one point. */
- @Test
- public void testCalculateRegionInfoSimple() {
- double labels[] = new double[] {0.0};
-
- assert new GiniSplitCalculator(labels).calculateRegionInfo(DoubleStream.of(labels), 0).impurity() == 0.0;
- }
-
- /** Test calculation of region info consisting from two distinct classes. */
- @Test
- public void testCalculateRegionInfoTwoClasses() {
- double labels[] = new double[] {0.0, 1.0};
-
- assert new GiniSplitCalculator(labels).calculateRegionInfo(DoubleStream.of(labels), 0).impurity() == 0.5;
- }
-
- /** Test calculation of region info consisting from three distinct classes. */
- @Test
- public void testCalculateRegionInfoThreeClasses() {
- double labels[] = new double[] {0.0, 1.0, 2.0};
-
- assert Math.abs(new GiniSplitCalculator(labels).calculateRegionInfo(DoubleStream.of(labels), 0).impurity() - 2.0 / 3) < 1E-5;
- }
-
- /** Test calculation of split of region consisting from one point. */
- @Test
- public void testSplitSimple() {
- double labels[] = new double[] {0.0};
- double values[] = new double[] {0.0};
- Integer[] samples = new Integer[] {0};
-
- int cnts[] = new int[] {1};
-
- GiniSplitCalculator.GiniData data = new GiniSplitCalculator.GiniData(0.0, 1, cnts, 1);
-
- assert new GiniSplitCalculator(labels).splitRegion(samples, values, labels, 0, data) == null;
- }
-
- /** Test calculation of split of region consisting from two points. */
- @Test
- public void testSplitTwoClassesTwoPoints() {
- double labels[] = new double[] {0.0, 1.0};
- double values[] = new double[] {0.0, 1.0};
- Integer[] samples = new Integer[] {0, 1};
-
- int cnts[] = new int[] {1, 1};
-
- GiniSplitCalculator.GiniData data = new GiniSplitCalculator.GiniData(0.5, 2, cnts, 1.0 * 1.0 + 1.0 * 1.0);
-
- SplitInfo<GiniSplitCalculator.GiniData> split = new GiniSplitCalculator(labels).splitRegion(samples, values, labels, 0, data);
-
- assert split.leftData().impurity() == 0;
- assert split.leftData().counts()[0] == 1;
- assert split.leftData().counts()[1] == 0;
- assert split.leftData().getSize() == 1;
-
- assert split.rightData().impurity() == 0;
- assert split.rightData().counts()[0] == 0;
- assert split.rightData().counts()[1] == 1;
- assert split.rightData().getSize() == 1;
- }
-
- /** Test calculation of split of region consisting from four distinct values. */
- @Test
- public void testSplitTwoClassesFourPoints() {
- double labels[] = new double[] {0.0, 0.0, 1.0, 1.0};
- double values[] = new double[] {0.0, 1.0, 2.0, 3.0};
-
- Integer[] samples = new Integer[] {0, 1, 2, 3};
-
- int[] cnts = new int[] {2, 2};
-
- GiniSplitCalculator.GiniData data = new GiniSplitCalculator.GiniData(0.5, 4, cnts, 2.0 * 2.0 + 2.0 * 2.0);
-
- SplitInfo<GiniSplitCalculator.GiniData> split = new GiniSplitCalculator(labels).splitRegion(samples, values, labels, 0, data);
-
- assert split.leftData().impurity() == 0;
- assert split.leftData().counts()[0] == 2;
- assert split.leftData().counts()[1] == 0;
- assert split.leftData().getSize() == 2;
-
- assert split.rightData().impurity() == 0;
- assert split.rightData().counts()[0] == 0;
- assert split.rightData().counts()[1] == 2;
- assert split.rightData().getSize() == 2;
- }
-
- /** Test calculation of split of region consisting from three distinct values. */
- @Test
- public void testSplitThreePoints() {
- double labels[] = new double[] {0.0, 1.0, 2.0};
- double values[] = new double[] {0.0, 1.0, 2.0};
- Integer[] samples = new Integer[] {0, 1, 2};
-
- int[] cnts = new int[] {1, 1, 1};
-
- GiniSplitCalculator.GiniData data = new GiniSplitCalculator.GiniData(2.0 / 3, 3, cnts, 1.0 * 1.0 + 1.0 * 1.0 + 1.0 * 1.0);
-
- SplitInfo<GiniSplitCalculator.GiniData> split = new GiniSplitCalculator(labels).splitRegion(samples, values, labels, 0, data);
-
- assert split.leftData().impurity() == 0.0;
- assert split.leftData().counts()[0] == 1;
- assert split.leftData().counts()[1] == 0;
- assert split.leftData().counts()[2] == 0;
- assert split.leftData().getSize() == 1;
-
- assert split.rightData().impurity() == 0.5;
- assert split.rightData().counts()[0] == 0;
- assert split.rightData().counts()[1] == 1;
- assert split.rightData().counts()[2] == 1;
- assert split.rightData().getSize() == 2;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/SplitDataGenerator.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/SplitDataGenerator.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/SplitDataGenerator.java
deleted file mode 100644
index 279e685..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/SplitDataGenerator.java
+++ /dev/null
@@ -1,390 +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.trees;
-
-import java.io.Serializable;
-import java.util.Arrays;
-import java.util.BitSet;
-import java.util.HashMap;
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Map;
-import java.util.Random;
-import java.util.function.BiFunction;
-import java.util.function.Function;
-import java.util.function.Supplier;
-import java.util.stream.Collectors;
-import java.util.stream.IntStream;
-import java.util.stream.Stream;
-import org.apache.ignite.lang.IgniteBiTuple;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException;
-import org.apache.ignite.ml.util.Utils;
-
-/**
- * Utility class for generating data which has binary tree split structure.
- *
- * @param <V>
- */
-public class SplitDataGenerator<V extends Vector> {
- /** */
- private static final double DELTA = 100.0;
-
- /** Map of the form of (is categorical -> list of region indexes). */
- private final Map<Boolean, List<Integer>> di;
-
- /** List of regions. */
- private final List<Region> regs;
-
- /** Data of bounds of regions. */
- private final Map<Integer, IgniteBiTuple<Double, Double>> boundsData;
-
- /** Random numbers generator. */
- private final Random rnd;
-
- /** Supplier of vectors. */
- private final Supplier<V> supplier;
-
- /** Features count. */
- private final int featCnt;
-
- /**
- * Create SplitDataGenerator.
- *
- * @param featCnt Features count.
- * @param catFeaturesInfo Information about categorical features in form of map (feature index -> categories
- * count).
- * @param supplier Supplier of vectors.
- * @param rnd Random numbers generator.
- */
- public SplitDataGenerator(int featCnt, Map<Integer, Integer> catFeaturesInfo, Supplier<V> supplier, Random rnd) {
- regs = new LinkedList<>();
- boundsData = new HashMap<>();
- this.rnd = rnd;
- this.supplier = supplier;
- this.featCnt = featCnt;
-
- // Divide indexes into indexes of categorical coordinates and indexes of continuous coordinates.
- di = IntStream.range(0, featCnt).
- boxed().
- collect(Collectors.partitioningBy(catFeaturesInfo::containsKey));
-
- // Categorical coordinates info.
- Map<Integer, CatCoordInfo> catCoords = new HashMap<>();
- di.get(true).forEach(i -> {
- BitSet bs = new BitSet();
- bs.set(0, catFeaturesInfo.get(i));
- catCoords.put(i, new CatCoordInfo(bs));
- });
-
- // Continuous coordinates info.
- Map<Integer, ContCoordInfo> contCoords = new HashMap<>();
- di.get(false).forEach(i -> {
- contCoords.put(i, new ContCoordInfo());
- boundsData.put(i, new IgniteBiTuple<>(-1.0, 1.0));
- });
-
- Region firstReg = new Region(catCoords, contCoords, 0);
- regs.add(firstReg);
- }
-
- /**
- * Categorical coordinate info.
- */
- private static class CatCoordInfo implements Serializable {
- /**
- * Defines categories which are included in this region
- */
- private final BitSet bs;
-
- /**
- * Construct CatCoordInfo.
- *
- * @param bs Bitset.
- */
- CatCoordInfo(BitSet bs) {
- this.bs = bs;
- }
-
- /** {@inheritDoc} */
- @Override public String toString() {
- return "CatCoordInfo [" +
- "bs=" + bs +
- ']';
- }
- }
-
- /**
- * Continuous coordinate info.
- */
- private static class ContCoordInfo implements Serializable {
- /**
- * Left (min) bound of region.
- */
- private double left;
-
- /**
- * Right (max) bound of region.
- */
- private double right;
-
- /**
- * Construct ContCoordInfo.
- */
- ContCoordInfo() {
- left = Double.NEGATIVE_INFINITY;
- right = Double.POSITIVE_INFINITY;
- }
-
- /** {@inheritDoc} */
- @Override public String toString() {
- return "ContCoordInfo [" +
- "left=" + left +
- ", right=" + right +
- ']';
- }
- }
-
- /**
- * Class representing information about region.
- */
- private static class Region implements Serializable {
- /**
- * Information about categorical coordinates restrictions of this region in form of
- * (coordinate index -> restriction)
- */
- private final Map<Integer, CatCoordInfo> catCoords;
-
- /**
- * Information about continuous coordinates restrictions of this region in form of
- * (coordinate index -> restriction)
- */
- private final Map<Integer, ContCoordInfo> contCoords;
-
- /**
- * Region should contain {@code 1/2^twoPow * totalPoints} points.
- */
- private int twoPow;
-
- /**
- * Construct region by information about restrictions on coordinates (features) values.
- *
- * @param catCoords Restrictions on categorical coordinates.
- * @param contCoords Restrictions on continuous coordinates
- * @param twoPow Region should contain {@code 1/2^twoPow * totalPoints} points.
- */
- Region(Map<Integer, CatCoordInfo> catCoords, Map<Integer, ContCoordInfo> contCoords, int twoPow) {
- this.catCoords = catCoords;
- this.contCoords = contCoords;
- this.twoPow = twoPow;
- }
-
- /** */
- int divideBy() {
- return 1 << twoPow;
- }
-
- /** */
- void incTwoPow() {
- twoPow++;
- }
-
- /** {@inheritDoc} */
- @Override public String toString() {
- return "Region [" +
- "catCoords=" + catCoords +
- ", contCoords=" + contCoords +
- ", twoPow=" + twoPow +
- ']';
- }
-
- /**
- * Generate continuous coordinate for this region.
- *
- * @param coordIdx Coordinate index.
- * @param boundsData Data with bounds
- * @param rnd Random numbers generator.
- * @return Categorical coordinate value.
- */
- double generateContCoord(int coordIdx, Map<Integer, IgniteBiTuple<Double, Double>> boundsData,
- Random rnd) {
- ContCoordInfo cci = contCoords.get(coordIdx);
- double left = cci.left;
- double right = cci.right;
-
- if (left == Double.NEGATIVE_INFINITY)
- left = boundsData.get(coordIdx).get1() - DELTA;
-
- if (right == Double.POSITIVE_INFINITY)
- right = boundsData.get(coordIdx).get2() + DELTA;
-
- double size = right - left;
-
- return left + rnd.nextDouble() * size;
- }
-
- /**
- * Generate categorical coordinate value for this region.
- *
- * @param coordIdx Coordinate index.
- * @param rnd Random numbers generator.
- * @return Categorical coordinate value.
- */
- double generateCatCoord(int coordIdx, Random rnd) {
- // Pick random bit.
- BitSet bs = catCoords.get(coordIdx).bs;
- int j = rnd.nextInt(bs.length());
-
- int i = 0;
- int bn = 0;
- int bnp = 0;
-
- while ((bn = bs.nextSetBit(bn)) != -1 && i <= j) {
- i++;
- bnp = bn;
- bn++;
- }
-
- return bnp;
- }
-
- /**
- * Generate points for this region.
- *
- * @param ptsCnt Count of points to generate.
- * @param val Label for all points in this region.
- * @param boundsData Data about bounds of continuous coordinates.
- * @param catCont Data about which categories can be in this region in the form (coordinate index -> list of
- * categories indexes).
- * @param s Vectors supplier.
- * @param rnd Random numbers generator.
- * @param <V> Type of vectors.
- * @return Stream of generated points for this region.
- */
- <V extends Vector> Stream<V> generatePoints(int ptsCnt, double val,
- Map<Integer, IgniteBiTuple<Double, Double>> boundsData, Map<Boolean, List<Integer>> catCont,
- Supplier<V> s,
- Random rnd) {
- return IntStream.range(0, ptsCnt / divideBy()).mapToObj(i -> {
- V v = s.get();
- int coordsCnt = v.size();
- catCont.get(false).forEach(ci -> v.setX(ci, generateContCoord(ci, boundsData, rnd)));
- catCont.get(true).forEach(ci -> v.setX(ci, generateCatCoord(ci, rnd)));
-
- v.setX(coordsCnt - 1, val);
- return v;
- });
- }
- }
-
- /**
- * Split region by continuous coordinate.using given threshold.
- *
- * @param regIdx Region index.
- * @param coordIdx Coordinate index.
- * @param threshold Threshold.
- * @return {@code this}.
- */
- public SplitDataGenerator<V> split(int regIdx, int coordIdx, double threshold) {
- Region regToSplit = regs.get(regIdx);
- ContCoordInfo cci = regToSplit.contCoords.get(coordIdx);
-
- double left = cci.left;
- double right = cci.right;
-
- if (threshold < left || threshold > right)
- throw new MathIllegalArgumentException("Threshold is out of region bounds.");
-
- regToSplit.incTwoPow();
-
- Region newReg = Utils.copy(regToSplit);
- newReg.contCoords.get(coordIdx).left = threshold;
-
- regs.add(regIdx + 1, newReg);
- cci.right = threshold;
-
- IgniteBiTuple<Double, Double> bounds = boundsData.get(coordIdx);
- double min = bounds.get1();
- double max = bounds.get2();
- boundsData.put(coordIdx, new IgniteBiTuple<>(Math.min(threshold, min), Math.max(max, threshold)));
-
- return this;
- }
-
- /**
- * Split region by categorical coordinate.
- *
- * @param regIdx Region index.
- * @param coordIdx Coordinate index.
- * @param cats Categories allowed for the left sub region.
- * @return {@code this}.
- */
- public SplitDataGenerator<V> split(int regIdx, int coordIdx, int[] cats) {
- BitSet subset = new BitSet();
- Arrays.stream(cats).forEach(subset::set);
- Region regToSplit = regs.get(regIdx);
- CatCoordInfo cci = regToSplit.catCoords.get(coordIdx);
-
- BitSet ssc = (BitSet)subset.clone();
- BitSet set = cci.bs;
- ssc.and(set);
- if (ssc.length() != subset.length())
- throw new MathIllegalArgumentException("Splitter set is not a subset of a parent subset.");
-
- ssc.xor(set);
- set.and(subset);
-
- regToSplit.incTwoPow();
- Region newReg = Utils.copy(regToSplit);
- newReg.catCoords.put(coordIdx, new CatCoordInfo(ssc));
-
- regs.add(regIdx + 1, newReg);
-
- return this;
- }
-
- /**
- * Get stream of points generated by this generator.
- *
- * @param ptsCnt Points count.
- */
- public Stream<IgniteBiTuple<Integer, V>> points(int ptsCnt, BiFunction<Double, Random, Double> f) {
- regs.forEach(System.out::println);
-
- return IntStream.range(0, regs.size()).
- boxed().
- map(i -> regs.get(i).generatePoints(ptsCnt, f.apply((double)i, rnd), boundsData, di, supplier, rnd).map(v -> new IgniteBiTuple<>(i, v))).flatMap(Function.identity());
- }
-
- /**
- * Count of regions.
- *
- * @return Count of regions.
- */
- public int regsCount() {
- return regs.size();
- }
-
- /**
- * Get features count.
- *
- * @return Features count.
- */
- public int featuresCnt() {
- return featCnt;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/VarianceSplitCalculatorTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/VarianceSplitCalculatorTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/VarianceSplitCalculatorTest.java
deleted file mode 100644
index d67cbc6..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/VarianceSplitCalculatorTest.java
+++ /dev/null
@@ -1,84 +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.trees;
-
-import java.util.stream.DoubleStream;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.VarianceSplitCalculator;
-import org.apache.ignite.ml.trees.trainers.columnbased.vectors.SplitInfo;
-import org.junit.Test;
-
-/**
- * Test for {@link VarianceSplitCalculator}.
- */
-public class VarianceSplitCalculatorTest {
- /** Test calculation of region info consisting from one point. */
- @Test
- public void testCalculateRegionInfoSimple() {
- double labels[] = new double[] {0.0};
-
- assert new VarianceSplitCalculator().calculateRegionInfo(DoubleStream.of(labels), 1).impurity() == 0.0;
- }
-
- /** Test calculation of region info consisting from two classes. */
- @Test
- public void testCalculateRegionInfoTwoClasses() {
- double labels[] = new double[] {0.0, 1.0};
-
- assert new VarianceSplitCalculator().calculateRegionInfo(DoubleStream.of(labels), 2).impurity() == 0.25;
- }
-
- /** Test calculation of region info consisting from three classes. */
- @Test
- public void testCalculateRegionInfoThreeClasses() {
- double labels[] = new double[] {1.0, 2.0, 3.0};
-
- assert Math.abs(new VarianceSplitCalculator().calculateRegionInfo(DoubleStream.of(labels), 3).impurity() - 2.0 / 3) < 1E-10;
- }
-
- /** Test calculation of split of region consisting from one point. */
- @Test
- public void testSplitSimple() {
- double labels[] = new double[] {0.0};
- double values[] = new double[] {0.0};
- Integer[] samples = new Integer[] {0};
-
- VarianceSplitCalculator.VarianceData data = new VarianceSplitCalculator.VarianceData(0.0, 1, 0.0);
-
- assert new VarianceSplitCalculator().splitRegion(samples, values, labels, 0, data) == null;
- }
-
- /** Test calculation of split of region consisting from two classes. */
- @Test
- public void testSplitTwoClassesTwoPoints() {
- double labels[] = new double[] {0.0, 1.0};
- double values[] = new double[] {0.0, 1.0};
- Integer[] samples = new Integer[] {0, 1};
-
- VarianceSplitCalculator.VarianceData data = new VarianceSplitCalculator.VarianceData(0.25, 2, 0.5);
-
- SplitInfo<VarianceSplitCalculator.VarianceData> split = new VarianceSplitCalculator().splitRegion(samples, values, labels, 0, data);
-
- assert split.leftData().impurity() == 0;
- assert split.leftData().mean() == 0;
- assert split.leftData().getSize() == 1;
-
- assert split.rightData().impurity() == 0;
- assert split.rightData().mean() == 1;
- assert split.rightData().getSize() == 1;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java b/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
deleted file mode 100644
index 21fd692..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/trees/performance/ColumnDecisionTreeTrainerBenchmark.java
+++ /dev/null
@@ -1,456 +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.trees.performance;
-
-import it.unimi.dsi.fastutil.ints.Int2DoubleOpenHashMap;
-import java.io.IOException;
-import java.io.InputStream;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.Iterator;
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Map;
-import java.util.Properties;
-import java.util.Random;
-import java.util.UUID;
-import java.util.function.Function;
-import java.util.stream.Collectors;
-import java.util.stream.DoubleStream;
-import java.util.stream.IntStream;
-import java.util.stream.Stream;
-import org.apache.ignite.IgniteCache;
-import org.apache.ignite.IgniteDataStreamer;
-import org.apache.ignite.Ignition;
-import org.apache.ignite.cache.CacheAtomicityMode;
-import org.apache.ignite.cache.CacheMode;
-import org.apache.ignite.cache.CacheWriteSynchronizationMode;
-import org.apache.ignite.configuration.CacheConfiguration;
-import org.apache.ignite.configuration.IgniteConfiguration;
-import org.apache.ignite.internal.processors.cache.GridCacheProcessor;
-import org.apache.ignite.internal.util.IgniteUtils;
-import org.apache.ignite.internal.util.typedef.X;
-import org.apache.ignite.lang.IgniteBiTuple;
-import org.apache.ignite.ml.Model;
-import org.apache.ignite.ml.estimators.Estimators;
-import org.apache.ignite.ml.math.StorageConstants;
-import org.apache.ignite.ml.math.Tracer;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.distributed.keys.impl.SparseMatrixKey;
-import org.apache.ignite.ml.math.functions.IgniteFunction;
-import org.apache.ignite.ml.math.functions.IgniteTriFunction;
-import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
-import org.apache.ignite.ml.math.impls.storage.matrix.SparseDistributedMatrixStorage;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.structures.LabeledVectorDouble;
-import org.apache.ignite.ml.trees.BaseDecisionTreeTest;
-import org.apache.ignite.ml.trees.SplitDataGenerator;
-import org.apache.ignite.ml.trees.models.DecisionTreeModel;
-import org.apache.ignite.ml.trees.trainers.columnbased.BiIndex;
-import org.apache.ignite.ml.trees.trainers.columnbased.BiIndexedCacheColumnDecisionTreeTrainerInput;
-import org.apache.ignite.ml.trees.trainers.columnbased.ColumnDecisionTreeTrainer;
-import org.apache.ignite.ml.trees.trainers.columnbased.MatrixColumnDecisionTreeTrainerInput;
-import org.apache.ignite.ml.trees.trainers.columnbased.caches.ContextCache;
-import org.apache.ignite.ml.trees.trainers.columnbased.caches.FeaturesCache;
-import org.apache.ignite.ml.trees.trainers.columnbased.caches.ProjectionsCache;
-import org.apache.ignite.ml.trees.trainers.columnbased.caches.SplitCache;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.ContinuousSplitCalculators;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.GiniSplitCalculator;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.VarianceSplitCalculator;
-import org.apache.ignite.ml.trees.trainers.columnbased.regcalcs.RegionCalculators;
-import org.apache.ignite.ml.util.MnistUtils;
-import org.apache.ignite.stream.StreamTransformer;
-import org.apache.ignite.testframework.junits.IgniteTestResources;
-import org.apache.log4j.Level;
-import org.junit.Assert;
-
-/**
- * Various benchmarks for hand runs.
- */
-public class ColumnDecisionTreeTrainerBenchmark extends BaseDecisionTreeTest {
- /** Name of the property specifying path to training set images. */
- private static final String PROP_TRAINING_IMAGES = "mnist.training.images";
-
- /** Name of property specifying path to training set labels. */
- private static final String PROP_TRAINING_LABELS = "mnist.training.labels";
-
- /** Name of property specifying path to test set images. */
- private static final String PROP_TEST_IMAGES = "mnist.test.images";
-
- /** Name of property specifying path to test set labels. */
- private static final String PROP_TEST_LABELS = "mnist.test.labels";
-
- /** Function to approximate. */
- private static final Function<Vector, Double> f1 = v -> v.get(0) * v.get(0) + 2 * Math.sin(v.get(1)) + v.get(2);
-
- /** {@inheritDoc} */
- @Override protected long getTestTimeout() {
- return 6000000;
- }
-
- /** {@inheritDoc} */
- @Override protected IgniteConfiguration getConfiguration(String igniteInstanceName,
- IgniteTestResources rsrcs) throws Exception {
- IgniteConfiguration configuration = super.getConfiguration(igniteInstanceName, rsrcs);
- // We do not need any extra event types.
- configuration.setIncludeEventTypes();
- configuration.setPeerClassLoadingEnabled(false);
-
- resetLog4j(Level.INFO, false, GridCacheProcessor.class.getPackage().getName());
-
- return configuration;
- }
-
- /**
- * This test is for manual run only.
- * To run this test rename this method so it starts from 'test'.
- */
- public void tstCacheMixed() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int ptsPerReg = 150;
- int featCnt = 10;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(1, 3);
-
- Random rnd = new Random(12349L);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1), rnd).
- split(0, 1, new int[] {0, 2}).
- split(1, 0, -10.0).
- split(0, 0, 0.0);
-
- testByGenStreamerLoad(ptsPerReg, catsInfo, gen, rnd);
- }
-
- /**
- * Run decision tree classifier on MNIST using bi-indexed cache as a storage for dataset.
- * To run this test rename this method so it starts from 'test'.
- *
- * @throws IOException In case of loading MNIST dataset errors.
- */
- public void tstMNISTBiIndexedCache() throws IOException {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- int ptsCnt = 40_000;
- int featCnt = 28 * 28;
-
- Properties props = loadMNISTProperties();
-
- Stream<DenseLocalOnHeapVector> trainingMnistStream = MnistUtils.mnistAsStream(props.getProperty(PROP_TRAINING_IMAGES), props.getProperty(PROP_TRAINING_LABELS), new Random(123L), ptsCnt);
- Stream<DenseLocalOnHeapVector> testMnistStream = MnistUtils.mnistAsStream(props.getProperty(PROP_TEST_IMAGES), props.getProperty(PROP_TEST_LABELS), new Random(123L), 10_000);
-
- IgniteCache<BiIndex, Double> cache = createBiIndexedCache();
-
- loadVectorsIntoBiIndexedCache(cache.getName(), trainingMnistStream.iterator(), featCnt + 1);
-
- ColumnDecisionTreeTrainer<GiniSplitCalculator.GiniData> trainer =
- new ColumnDecisionTreeTrainer<>(10, ContinuousSplitCalculators.GINI.apply(ignite), RegionCalculators.GINI, RegionCalculators.MOST_COMMON, ignite);
-
- X.println("Training started.");
- long before = System.currentTimeMillis();
- DecisionTreeModel mdl = trainer.train(new BiIndexedCacheColumnDecisionTreeTrainerInput(cache, new HashMap<>(), ptsCnt, featCnt));
- X.println("Training finished in " + (System.currentTimeMillis() - before));
-
- IgniteTriFunction<Model<Vector, Double>, Stream<IgniteBiTuple<Vector, Double>>, Function<Double, Double>, Double> mse = Estimators.errorsPercentage();
- Double accuracy = mse.apply(mdl, testMnistStream.map(v -> new IgniteBiTuple<>(v.viewPart(0, featCnt), v.getX(featCnt))), Function.identity());
- X.println("Errors percentage: " + accuracy);
-
- Assert.assertEquals(0, SplitCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, FeaturesCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, ContextCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, ProjectionsCache.getOrCreate(ignite).size());
- }
-
- /**
- * Run decision tree classifier on MNIST using sparse distributed matrix as a storage for dataset.
- * To run this test rename this method so it starts from 'test'.
- *
- * @throws IOException In case of loading MNIST dataset errors.
- */
- public void tstMNISTSparseDistributedMatrix() throws IOException {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- int ptsCnt = 30_000;
- int featCnt = 28 * 28;
-
- Properties props = loadMNISTProperties();
-
- Stream<DenseLocalOnHeapVector> trainingMnistStream = MnistUtils.mnistAsStream(props.getProperty(PROP_TRAINING_IMAGES), props.getProperty(PROP_TRAINING_LABELS), new Random(123L), ptsCnt);
- Stream<DenseLocalOnHeapVector> testMnistStream = MnistUtils.mnistAsStream(props.getProperty(PROP_TEST_IMAGES), props.getProperty(PROP_TEST_LABELS), new Random(123L), 10_000);
-
- SparseDistributedMatrix m = new SparseDistributedMatrix(ptsCnt, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
-
- SparseDistributedMatrixStorage sto = (SparseDistributedMatrixStorage)m.getStorage();
-
- loadVectorsIntoSparseDistributedMatrixCache(sto.cache().getName(), sto.getUUID(), trainingMnistStream.iterator(), featCnt + 1);
-
- ColumnDecisionTreeTrainer<GiniSplitCalculator.GiniData> trainer =
- new ColumnDecisionTreeTrainer<>(10, ContinuousSplitCalculators.GINI.apply(ignite), RegionCalculators.GINI, RegionCalculators.MOST_COMMON, ignite);
-
- X.println("Training started");
- long before = System.currentTimeMillis();
- DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, new HashMap<>()));
- X.println("Training finished in " + (System.currentTimeMillis() - before));
-
- IgniteTriFunction<Model<Vector, Double>, Stream<IgniteBiTuple<Vector, Double>>, Function<Double, Double>, Double> mse = Estimators.errorsPercentage();
- Double accuracy = mse.apply(mdl, testMnistStream.map(v -> new IgniteBiTuple<>(v.viewPart(0, featCnt), v.getX(featCnt))), Function.identity());
- X.println("Errors percentage: " + accuracy);
-
- Assert.assertEquals(0, SplitCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, FeaturesCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, ContextCache.getOrCreate(ignite).size());
- Assert.assertEquals(0, ProjectionsCache.getOrCreate(ignite).size());
- }
-
- /** Load properties for MNIST tests. */
- private static Properties loadMNISTProperties() throws IOException {
- Properties res = new Properties();
-
- InputStream is = ColumnDecisionTreeTrainerBenchmark.class.getClassLoader().getResourceAsStream("manualrun/trees/columntrees.manualrun.properties");
-
- res.load(is);
-
- return res;
- }
-
- /** */
- private void testByGenStreamerLoad(int ptsPerReg, HashMap<Integer, Integer> catsInfo,
- SplitDataGenerator<DenseLocalOnHeapVector> gen, Random rnd) {
-
- List<IgniteBiTuple<Integer, DenseLocalOnHeapVector>> lst = gen.
- points(ptsPerReg, (i, rn) -> i).
- collect(Collectors.toList());
-
- int featCnt = gen.featuresCnt();
-
- Collections.shuffle(lst, rnd);
-
- int numRegs = gen.regsCount();
-
- SparseDistributedMatrix m = new SparseDistributedMatrix(numRegs * ptsPerReg, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
-
- IgniteFunction<DoubleStream, Double> regCalc = s -> s.average().orElse(0.0);
-
- Map<Integer, List<LabeledVectorDouble>> byRegion = new HashMap<>();
-
- SparseDistributedMatrixStorage sto = (SparseDistributedMatrixStorage)m.getStorage();
- long before = System.currentTimeMillis();
- X.println("Batch loading started...");
- loadVectorsIntoSparseDistributedMatrixCache(sto.cache().getName(), sto.getUUID(), gen.
- points(ptsPerReg, (i, rn) -> i).map(IgniteBiTuple::get2).iterator(), featCnt + 1);
- X.println("Batch loading took " + (System.currentTimeMillis() - before) + " ms.");
-
- for (IgniteBiTuple<Integer, DenseLocalOnHeapVector> bt : lst) {
- byRegion.putIfAbsent(bt.get1(), new LinkedList<>());
- byRegion.get(bt.get1()).add(asLabeledVector(bt.get2().getStorage().data()));
- }
-
- ColumnDecisionTreeTrainer<VarianceSplitCalculator.VarianceData> trainer =
- new ColumnDecisionTreeTrainer<>(2, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, regCalc, ignite);
-
- before = System.currentTimeMillis();
- DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, catsInfo));
-
- X.println("Training took: " + (System.currentTimeMillis() - before) + " ms.");
-
- byRegion.keySet().forEach(k -> {
- LabeledVectorDouble sp = byRegion.get(k).get(0);
- Tracer.showAscii(sp.features());
- X.println("Predicted value and label [pred=" + mdl.apply(sp.features()) + ", label=" + sp.doubleLabel() + "]");
- assert mdl.apply(sp.features()) == sp.doubleLabel();
- });
- }
-
- /**
- * Test decision tree regression.
- * To run this test rename this method so it starts from 'test'.
- */
- public void tstF1() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- int ptsCnt = 10000;
- Map<Integer, double[]> ranges = new HashMap<>();
-
- ranges.put(0, new double[] {-100.0, 100.0});
- ranges.put(1, new double[] {-100.0, 100.0});
- ranges.put(2, new double[] {-100.0, 100.0});
-
- int featCnt = 100;
- double[] defRng = {-1.0, 1.0};
-
- Vector[] trainVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), ptsCnt, f1);
-
- SparseDistributedMatrix m = new SparseDistributedMatrix(ptsCnt, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
-
- SparseDistributedMatrixStorage sto = (SparseDistributedMatrixStorage)m.getStorage();
-
- loadVectorsIntoSparseDistributedMatrixCache(sto.cache().getName(), sto.getUUID(), Arrays.stream(trainVectors).iterator(), featCnt + 1);
-
- IgniteFunction<DoubleStream, Double> regCalc = s -> s.average().orElse(0.0);
-
- ColumnDecisionTreeTrainer<VarianceSplitCalculator.VarianceData> trainer =
- new ColumnDecisionTreeTrainer<>(10, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, regCalc, ignite);
-
- X.println("Training started.");
- long before = System.currentTimeMillis();
- DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, new HashMap<>()));
- X.println("Training finished in: " + (System.currentTimeMillis() - before) + " ms.");
-
- Vector[] testVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), 20, f1);
-
- IgniteTriFunction<Model<Vector, Double>, Stream<IgniteBiTuple<Vector, Double>>, Function<Double, Double>, Double> mse = Estimators.MSE();
- Double accuracy = mse.apply(mdl, Arrays.stream(testVectors).map(v -> new IgniteBiTuple<>(v.viewPart(0, featCnt), v.getX(featCnt))), Function.identity());
- X.println("MSE: " + accuracy);
- }
-
- /**
- * Load vectors into sparse distributed matrix.
- *
- * @param cacheName Name of cache where matrix is stored.
- * @param uuid UUID of matrix.
- * @param iter Iterator over vectors.
- * @param vectorSize size of vectors.
- */
- private void loadVectorsIntoSparseDistributedMatrixCache(String cacheName, UUID uuid,
- Iterator<? extends org.apache.ignite.ml.math.Vector> iter, int vectorSize) {
- try (IgniteDataStreamer<SparseMatrixKey, Map<Integer, Double>> streamer =
- Ignition.localIgnite().dataStreamer(cacheName)) {
- int sampleIdx = 0;
- streamer.allowOverwrite(true);
-
- streamer.receiver(StreamTransformer.from((e, arg) -> {
- Map<Integer, Double> val = e.getValue();
-
- if (val == null)
- val = new Int2DoubleOpenHashMap();
-
- val.putAll((Map<Integer, Double>)arg[0]);
-
- e.setValue(val);
-
- return null;
- }));
-
- // Feature index -> (sample index -> value)
- Map<Integer, Map<Integer, Double>> batch = new HashMap<>();
- IntStream.range(0, vectorSize).forEach(i -> batch.put(i, new HashMap<>()));
- int batchSize = 1000;
-
- while (iter.hasNext()) {
- org.apache.ignite.ml.math.Vector next = iter.next();
-
- for (int i = 0; i < vectorSize; i++)
- batch.get(i).put(sampleIdx, next.getX(i));
-
- X.println("Sample index: " + sampleIdx);
- if (sampleIdx % batchSize == 0) {
- batch.keySet().forEach(fi -> streamer.addData(new SparseMatrixKey(fi, uuid, fi), batch.get(fi)));
- IntStream.range(0, vectorSize).forEach(i -> batch.put(i, new HashMap<>()));
- }
- sampleIdx++;
- }
- if (sampleIdx % batchSize != 0) {
- batch.keySet().forEach(fi -> streamer.addData(new SparseMatrixKey(fi, uuid, fi), batch.get(fi)));
- IntStream.range(0, vectorSize).forEach(i -> batch.put(i, new HashMap<>()));
- }
- }
- }
-
- /**
- * Load vectors into bi-indexed cache.
- *
- * @param cacheName Name of cache.
- * @param iter Iterator over vectors.
- * @param vectorSize size of vectors.
- */
- private void loadVectorsIntoBiIndexedCache(String cacheName,
- Iterator<? extends org.apache.ignite.ml.math.Vector> iter, int vectorSize) {
- try (IgniteDataStreamer<BiIndex, Double> streamer =
- Ignition.localIgnite().dataStreamer(cacheName)) {
- int sampleIdx = 0;
-
- streamer.perNodeBufferSize(10000);
-
- while (iter.hasNext()) {
- org.apache.ignite.ml.math.Vector next = iter.next();
-
- for (int i = 0; i < vectorSize; i++)
- streamer.addData(new BiIndex(sampleIdx, i), next.getX(i));
-
- sampleIdx++;
-
- if (sampleIdx % 1000 == 0)
- System.out.println("Loaded: " + sampleIdx + " vectors.");
- }
- }
- }
-
- /**
- * Create bi-indexed cache for tests.
- *
- * @return Bi-indexed cache.
- */
- private IgniteCache<BiIndex, Double> createBiIndexedCache() {
- CacheConfiguration<BiIndex, Double> cfg = new CacheConfiguration<>();
-
- // Write to primary.
- cfg.setWriteSynchronizationMode(CacheWriteSynchronizationMode.PRIMARY_SYNC);
-
- // Atomic transactions only.
- cfg.setAtomicityMode(CacheAtomicityMode.ATOMIC);
-
- // No eviction.
- cfg.setEvictionPolicy(null);
-
- // No copying of values.
- cfg.setCopyOnRead(false);
-
- // Cache is partitioned.
- cfg.setCacheMode(CacheMode.PARTITIONED);
-
- cfg.setBackups(0);
-
- cfg.setName("TMP_BI_INDEXED_CACHE");
-
- return Ignition.localIgnite().getOrCreateCache(cfg);
- }
-
- /** */
- private Vector[] vecsFromRanges(Map<Integer, double[]> ranges, int featCnt, double[] defRng, Random rnd, int ptsCnt,
- Function<Vector, Double> f) {
- int vs = featCnt + 1;
- DenseLocalOnHeapVector[] res = new DenseLocalOnHeapVector[ptsCnt];
- for (int pt = 0; pt < ptsCnt; pt++) {
- DenseLocalOnHeapVector v = new DenseLocalOnHeapVector(vs);
- for (int i = 0; i < featCnt; i++) {
- double[] range = ranges.getOrDefault(i, defRng);
- double from = range[0];
- double to = range[1];
- double rng = to - from;
-
- v.setX(i, rnd.nextDouble() * rng);
- }
- v.setX(featCnt, f.apply(v));
- res[pt] = v;
- }
-
- return res;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeGiniBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeGiniBenchmark.java b/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeGiniBenchmark.java
deleted file mode 100644
index f8a7c08..0000000
--- a/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeGiniBenchmark.java
+++ /dev/null
@@ -1,70 +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.yardstick.ml.trees;
-
-import java.util.HashMap;
-import java.util.Map;
-import org.apache.ignite.Ignite;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.ContinuousSplitCalculators;
-import org.apache.ignite.ml.trees.trainers.columnbased.regcalcs.RegionCalculators;
-import org.apache.ignite.resources.IgniteInstanceResource;
-import org.apache.ignite.thread.IgniteThread;
-import org.apache.ignite.yardstick.IgniteAbstractBenchmark;
-
-/**
- * Ignite benchmark that performs ML Grid operations.
- */
-@SuppressWarnings("unused")
-public class IgniteColumnDecisionTreeGiniBenchmark extends IgniteAbstractBenchmark {
- /** */
- @IgniteInstanceResource
- private Ignite ignite;
-
- /** {@inheritDoc} */
- @Override public boolean test(Map<Object, Object> ctx) throws Exception {
- // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
- // because we create ignite cache internally.
- IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
- this.getClass().getSimpleName(), new Runnable() {
- /** {@inheritDoc} */
- @Override public void run() {
- // IMPL NOTE originally taken from ColumnDecisionTreeTrainerTest#testCacheMixedGini
- int totalPts = 1 << 10;
- int featCnt = 2;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(1, 3);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1)).
- split(0, 1, new int[] {0, 2}).
- split(1, 0, -10.0);
-
- gen.testByGen(totalPts, ContinuousSplitCalculators.GINI.apply(ignite),
- RegionCalculators.GINI, RegionCalculators.MEAN, ignite);
- }
- });
-
- igniteThread.start();
-
- igniteThread.join();
-
- return true;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/139c2af6/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeVarianceBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeVarianceBenchmark.java b/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeVarianceBenchmark.java
deleted file mode 100644
index f9d417f..0000000
--- a/modules/yardstick/src/main/java/org/apache/ignite/yardstick/ml/trees/IgniteColumnDecisionTreeVarianceBenchmark.java
+++ /dev/null
@@ -1,71 +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.yardstick.ml.trees;
-
-import java.util.HashMap;
-import java.util.Map;
-import org.apache.ignite.Ignite;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.trees.trainers.columnbased.contsplitcalcs.ContinuousSplitCalculators;
-import org.apache.ignite.ml.trees.trainers.columnbased.regcalcs.RegionCalculators;
-import org.apache.ignite.resources.IgniteInstanceResource;
-import org.apache.ignite.thread.IgniteThread;
-import org.apache.ignite.yardstick.IgniteAbstractBenchmark;
-
-/**
- * Ignite benchmark that performs ML Grid operations.
- */
-@SuppressWarnings("unused")
-public class IgniteColumnDecisionTreeVarianceBenchmark extends IgniteAbstractBenchmark {
- /** */
- @IgniteInstanceResource
- private Ignite ignite;
-
- /** {@inheritDoc} */
- @Override public boolean test(Map<Object, Object> ctx) throws Exception {
- // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
- // because we create ignite cache internally.
- IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
- this.getClass().getSimpleName(), new Runnable() {
- /** {@inheritDoc} */
- @Override public void run() {
- // IMPL NOTE originally taken from ColumnDecisionTreeTrainerTest#testCacheMixed
- int totalPts = 1 << 10;
- int featCnt = 2;
-
- HashMap<Integer, Integer> catsInfo = new HashMap<>();
- catsInfo.put(1, 3);
-
- SplitDataGenerator<DenseLocalOnHeapVector> gen
- = new SplitDataGenerator<>(
- featCnt, catsInfo, () -> new DenseLocalOnHeapVector(featCnt + 1)).
- split(0, 1, new int[] {0, 2}).
- split(1, 0, -10.0);
-
- gen.testByGen(totalPts,
- ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, RegionCalculators.MEAN, ignite);
- }
- });
-
- igniteThread.start();
-
- igniteThread.join();
-
- return true;
- }
-}