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Posted to commits@ignite.apache.org by ch...@apache.org on 2018/08/31 08:37:19 UTC
[1/3] ignite git commit: IGNITE-9348: ML examples improvements,
follow-up to IGNITE-9297
Repository: ignite
Updated Branches:
refs/heads/master 2108b3b83 -> 145a28981
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/modules/ml/src/test/java/org/apache/ignite/ml/environment/EnvironmentTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/environment/EnvironmentTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/environment/EnvironmentTestSuite.java
index 636c603..ae9f490 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/environment/EnvironmentTestSuite.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/environment/EnvironmentTestSuite.java
@@ -26,6 +26,7 @@ import org.junit.runners.Suite;
@RunWith(Suite.class)
@Suite.SuiteClasses({
LearningEnvironmentBuilderTest.class,
+ LearningEnvironmentTest.class,
PromiseTest.class
})
public class EnvironmentTestSuite {
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/modules/ml/src/test/java/org/apache/ignite/ml/environment/LearningEnvironmentTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/environment/LearningEnvironmentTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/environment/LearningEnvironmentTest.java
new file mode 100644
index 0000000..f88fd3e
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/environment/LearningEnvironmentTest.java
@@ -0,0 +1,201 @@
+/*
+ * 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.environment;
+
+import java.util.Arrays;
+import java.util.UUID;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.concurrent.atomic.AtomicReference;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+import javax.cache.Cache;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+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.internal.util.IgniteUtils;
+import org.apache.ignite.ml.composition.ModelsComposition;
+import org.apache.ignite.ml.dataset.feature.FeatureMeta;
+import org.apache.ignite.ml.environment.logging.ConsoleLogger;
+import org.apache.ignite.ml.environment.logging.MLLogger;
+import org.apache.ignite.ml.environment.parallelism.ParallelismStrategy;
+import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
+import org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer;
+import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * Tests for {@link LearningEnvironment} that require to start the whole Ignite infrastructure. IMPL NOTE based on
+ * RandomForestRegressionExample example.
+ */
+public class LearningEnvironmentTest extends GridCommonAbstractTest {
+ /** Number of nodes in grid */
+ private static final int NODE_COUNT = 1;
+
+ /** 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() {
+ /* Grid instance. */
+ ignite = grid(NODE_COUNT);
+ ignite.configuration().setPeerClassLoadingEnabled(true);
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ }
+
+ /** */
+ public void testBasic() throws InterruptedException {
+ AtomicReference<Integer> actualAmount = new AtomicReference<>(null);
+ AtomicReference<Double> actualMse = new AtomicReference<>(null);
+ AtomicReference<Double> actualMae = new AtomicReference<>(null);
+
+ IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+ LearningEnvironmentTest.class.getSimpleName(), () -> {
+ IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
+
+ AtomicInteger idx = new AtomicInteger(0);
+ RandomForestRegressionTrainer trainer = new RandomForestRegressionTrainer(
+ IntStream.range(0, data[0].length - 1).mapToObj(
+ x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
+ ).withCountOfTrees(101)
+ .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
+ .withMaxDepth(4)
+ .withMinImpurityDelta(0.)
+ .withSubsampleSize(0.3)
+ .withSeed(0);
+
+ trainer.setEnvironment(LearningEnvironment.builder()
+ .withParallelismStrategy(ParallelismStrategy.Type.ON_DEFAULT_POOL)
+ .withLoggingFactory(ConsoleLogger.factory(MLLogger.VerboseLevel.LOW))
+ .build()
+ );
+
+ ModelsComposition randomForest = trainer.fit(ignite, dataCache,
+ (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
+ (k, v) -> v[v.length - 1]
+ );
+
+ double mse = 0.0;
+ double mae = 0.0;
+ int totalAmount = 0;
+
+ try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, double[]> observation : observations) {
+ double difference = estimatePrediction(randomForest, observation);
+
+ mse += Math.pow(difference, 2.0);
+ mae += Math.abs(difference);
+
+ totalAmount++;
+ }
+ }
+
+ actualAmount.set(totalAmount);
+
+ mse = mse / totalAmount;
+ actualMse.set(mse);
+
+ mae = mae / totalAmount;
+ actualMae.set(mae);
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+
+ assertEquals("Total amount", 23, (int)actualAmount.get());
+ assertTrue("Mean squared error (MSE)", actualMse.get() > 0);
+ assertTrue("Mean absolute error (MAE)", actualMae.get() > 0);
+ }
+
+ /** */
+ private double estimatePrediction(ModelsComposition randomForest, Cache.Entry<Integer, double[]> observation) {
+ double[] val = observation.getValue();
+ double[] inputs = Arrays.copyOfRange(val, 0, val.length - 1);
+ double groundTruth = val[val.length - 1];
+
+ double prediction = randomForest.apply(VectorUtils.of(inputs));
+
+ return prediction - groundTruth;
+ }
+
+ /**
+ * Fills cache with data and returns it.
+ *
+ * @param ignite Ignite instance.
+ * @return Filled Ignite Cache.
+ */
+ private IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
+ CacheConfiguration<Integer, double[]> cacheConfiguration = new CacheConfiguration<>();
+ cacheConfiguration.setName(UUID.randomUUID().toString());
+ 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;
+ }
+
+ /**
+ * Part of the Boston housing dataset.
+ */
+ private static final double[][] data = {
+ {0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60},
+ {0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70},
+ {0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40},
+ {0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20},
+ {0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70},
+ {0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90},
+ {0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10},
+ {0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50},
+ {0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90},
+ {0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45,15.00},
+ {0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90},
+ {0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70},
+ {0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40},
+ {0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20},
+ {0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47,19.90},
+ {1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10},
+ {0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67,17.50},
+ {0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69,20.20},
+ {0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28,18.20},
+ {1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02,13.60},
+ {0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83,19.60},
+ {1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72,15.20},
+ {0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88,14.50}
+ };
+
+}
+
[2/3] ignite git commit: IGNITE-9348: ML examples improvements,
follow-up to IGNITE-9297
Posted by ch...@apache.org.
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
index 96a87c2..e2bfe8b 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
@@ -30,21 +30,30 @@ import org.apache.ignite.cache.query.ScanQuery;
import org.apache.ignite.examples.ml.util.TestCache;
import org.apache.ignite.ml.composition.ModelsComposition;
import org.apache.ignite.ml.dataset.feature.FeatureMeta;
+import org.apache.ignite.ml.environment.LearningEnvironment;
+import org.apache.ignite.ml.environment.logging.ConsoleLogger;
+import org.apache.ignite.ml.environment.logging.MLLogger;
+import org.apache.ignite.ml.environment.parallelism.ParallelismStrategy;
import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
import org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer;
-import org.apache.ignite.ml.tree.randomforest.RandomForestTrainer;
import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
import org.apache.ignite.thread.IgniteThread;
/**
- * Example represents a solution for the task of price predictions for houses in Boston based on RandomForestTrainer
- * implementation for regression. It shows an initialization of {@link RandomForestTrainer}, +initialization of Ignite
- * Cache, learning step and evaluation of model quality in terms of Mean Squared Error (MSE) and Mean Absolute Error
- * (MAE).
- *
- * Dataset url: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
- *
- * @see RandomForestRegressionTrainer
+ * Example represents a solution for the task of price predictions for houses in Boston based on a
+ * <a href ="https://en.wikipedia.org/wiki/Random_forest">Random Forest</a> implementation for regression.
+ * <p>
+ * Code in this example launches Ignite grid and fills the cache with test data points (based on the
+ * <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">Boston Housing dataset</a>).</p>
+ * <p>
+ * After that it initializes the {@link RandomForestRegressionTrainer} and trains the model based on the specified data
+ * using random forest regression algorithm.</p>
+ * <p>
+ * Finally, this example loops over the test set of data points, compares prediction of the trained model to the
+ * expected outcome (ground truth), and evaluates model quality in terms of Mean Squared Error (MSE) and
+ * Mean Absolute Error (MAE).</p>
+ * <p>
+ * You can change the test data used in this example and re-run it to explore this algorithm further.</p>
*/
public class RandomForestRegressionExample {
/**
@@ -61,9 +70,10 @@ public class RandomForestRegressionExample {
RandomForestRegressionExample.class.getSimpleName(), () -> {
IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
- AtomicInteger indx = new AtomicInteger(0);
+ AtomicInteger idx = new AtomicInteger(0);
RandomForestRegressionTrainer trainer = new RandomForestRegressionTrainer(
- IntStream.range(0, data[0].length - 1).mapToObj(x -> new FeatureMeta("", indx.getAndIncrement(), false)).collect(Collectors.toList())
+ IntStream.range(0, data[0].length - 1).mapToObj(
+ x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
).withCountOfTrees(101)
.withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
.withMaxDepth(4)
@@ -71,11 +81,21 @@ public class RandomForestRegressionExample {
.withSubsampleSize(0.3)
.withSeed(0);
+ trainer.setEnvironment(LearningEnvironment.builder()
+ .withParallelismStrategy(ParallelismStrategy.Type.ON_DEFAULT_POOL)
+ .withLoggingFactory(ConsoleLogger.factory(MLLogger.VerboseLevel.LOW))
+ .build()
+ );
+
+ System.out.println(">>> Configured trainer: " + trainer.getClass().getSimpleName());
+
ModelsComposition randomForest = trainer.fit(ignite, dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
- (k, v) -> v[v.length - 1]
+ (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
+ (k, v) -> v[v.length - 1]
);
+ System.out.println(">>> Trained model: " + randomForest.toString(true));
+
double mse = 0.0;
double mae = 0.0;
int totalAmount = 0;
@@ -94,11 +114,15 @@ public class RandomForestRegressionExample {
totalAmount++;
}
+ System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
+
mse = mse / totalAmount;
System.out.println("\n>>> Mean squared error (MSE) " + mse);
mae = mae / totalAmount;
System.out.println("\n>>> Mean absolute error (MAE) " + mae);
+
+ System.out.println(">>> Random Forest regression algorithm over cached dataset usage example completed.");
}
});
@@ -107,514 +131,512 @@ public class RandomForestRegressionExample {
}
}
- /**
- * The Boston housing dataset.
- */
+ /** The Boston housing dataset. */
private static final double[][] data = {
- {0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60},
- {0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70},
- {0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40},
- {0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20},
- {0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70},
- {0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90},
- {0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10},
- {0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50},
- {0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90},
- {0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45,15.00},
- {0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90},
- {0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70},
- {0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40},
- {0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20},
- {0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47,19.90},
- {1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10},
- {0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67,17.50},
- {0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69,20.20},
- {0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28,18.20},
- {1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02,13.60},
- {0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83,19.60},
- {1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72,15.20},
- {0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88,14.50},
- {0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30,15.60},
- {0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51,13.90},
- {0.67191,0.00,8.140,0,0.5380,5.8130,90.30,4.6820,4,307.0,21.00,376.88,14.81,16.60},
- {0.95577,0.00,8.140,0,0.5380,6.0470,88.80,4.4534,4,307.0,21.00,306.38,17.28,14.80},
- {0.77299,0.00,8.140,0,0.5380,6.4950,94.40,4.4547,4,307.0,21.00,387.94,12.80,18.40},
- {1.00245,0.00,8.140,0,0.5380,6.6740,87.30,4.2390,4,307.0,21.00,380.23,11.98,21.00},
- {1.13081,0.00,8.140,0,0.5380,5.7130,94.10,4.2330,4,307.0,21.00,360.17,22.60,12.70},
- {1.35472,0.00,8.140,0,0.5380,6.0720,100.00,4.1750,4,307.0,21.00,376.73,13.04,14.50},
- {1.38799,0.00,8.140,0,0.5380,5.9500,82.00,3.9900,4,307.0,21.00,232.60,27.71,13.20},
- {1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35,13.10},
- {1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34,13.50},
- {0.06417,0.00,5.960,0,0.4990,5.9330,68.20,3.3603,5,279.0,19.20,396.90,9.68,18.90},
- {0.09744,0.00,5.960,0,0.4990,5.8410,61.40,3.3779,5,279.0,19.20,377.56,11.41,20.00},
- {0.08014,0.00,5.960,0,0.4990,5.8500,41.50,3.9342,5,279.0,19.20,396.90,8.77,21.00},
- {0.17505,0.00,5.960,0,0.4990,5.9660,30.20,3.8473,5,279.0,19.20,393.43,10.13,24.70},
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+ {0.03427,0.00,5.190,0,0.5150,5.8690,46.30,5.2311,5,224.0,20.20,396.90,9.80,19.50},
+ {0.03041,0.00,5.190,0,0.5150,5.8950,59.60,5.6150,5,224.0,20.20,394.81,10.56,18.50},
+ {0.03306,0.00,5.190,0,0.5150,6.0590,37.30,4.8122,5,224.0,20.20,396.14,8.51,20.60},
+ {0.05497,0.00,5.190,0,0.5150,5.9850,45.40,4.8122,5,224.0,20.20,396.90,9.74,19.00},
+ {0.06151,0.00,5.190,0,0.5150,5.9680,58.50,4.8122,5,224.0,20.20,396.90,9.29,18.70},
+ {0.01301,35.00,1.520,0,0.4420,7.2410,49.30,7.0379,1,284.0,15.50,394.74,5.49,32.70},
+ {0.02498,0.00,1.890,0,0.5180,6.5400,59.70,6.2669,1,422.0,15.90,389.96,8.65,16.50},
+ {0.02543,55.00,3.780,0,0.4840,6.6960,56.40,5.7321,5,370.0,17.60,396.90,7.18,23.90},
+ {0.03049,55.00,3.780,0,0.4840,6.8740,28.10,6.4654,5,370.0,17.60,387.97,4.61,31.20},
+ {0.03113,0.00,4.390,0,0.4420,6.0140,48.50,8.0136,3,352.0,18.80,385.64,10.53,17.50},
+ {0.06162,0.00,4.390,0,0.4420,5.8980,52.30,8.0136,3,352.0,18.80,364.61,12.67,17.20},
+ {0.01870,85.00,4.150,0,0.4290,6.5160,27.70,8.5353,4,351.0,17.90,392.43,6.36,23.10},
+ {0.01501,80.00,2.010,0,0.4350,6.6350,29.70,8.3440,4,280.0,17.00,390.94,5.99,24.50},
+ {0.02899,40.00,1.250,0,0.4290,6.9390,34.50,8.7921,1,335.0,19.70,389.85,5.89,26.60},
+ {0.06211,40.00,1.250,0,0.4290,6.4900,44.40,8.7921,1,335.0,19.70,396.90,5.98,22.90},
+ {0.07950,60.00,1.690,0,0.4110,6.5790,35.90,10.7103,4,411.0,18.30,370.78,5.49,24.10},
+ {0.07244,60.00,1.690,0,0.4110,5.8840,18.50,10.7103,4,411.0,18.30,392.33,7.79,18.60},
+ {0.01709,90.00,2.020,0,0.4100,6.7280,36.10,12.1265,5,187.0,17.00,384.46,4.50,30.10},
+ {0.04301,80.00,1.910,0,0.4130,5.6630,21.90,10.5857,4,334.0,22.00,382.80,8.05,18.20},
+ {0.10659,80.00,1.910,0,0.4130,5.9360,19.50,10.5857,4,334.0,22.00,376.04,5.57,20.60},
+ {8.98296,0.00,18.100,1,0.7700,6.2120,97.40,2.1222,24,666.0,20.20,377.73,17.60,17.80},
+ {3.84970,0.00,18.100,1,0.7700,6.3950,91.00,2.5052,24,666.0,20.20,391.34,13.27,21.70},
+ {5.20177,0.00,18.100,1,0.7700,6.1270,83.40,2.7227,24,666.0,20.20,395.43,11.48,22.70},
+ {4.26131,0.00,18.100,0,0.7700,6.1120,81.30,2.5091,24,666.0,20.20,390.74,12.67,22.60},
+ {4.54192,0.00,18.100,0,0.7700,6.3980,88.00,2.5182,24,666.0,20.20,374.56,7.79,25.00},
+ {3.83684,0.00,18.100,0,0.7700,6.2510,91.10,2.2955,24,666.0,20.20,350.65,14.19,19.90},
+ {3.67822,0.00,18.100,0,0.7700,5.3620,96.20,2.1036,24,666.0,20.20,380.79,10.19,20.80},
+ {4.22239,0.00,18.100,1,0.7700,5.8030,89.00,1.9047,24,666.0,20.20,353.04,14.64,16.80},
+ {3.47428,0.00,18.100,1,0.7180,8.7800,82.90,1.9047,24,666.0,20.20,354.55,5.29,21.90},
+ {4.55587,0.00,18.100,0,0.7180,3.5610,87.90,1.6132,24,666.0,20.20,354.70,7.12,27.50},
+ {3.69695,0.00,18.100,0,0.7180,4.9630,91.40,1.7523,24,666.0,20.20,316.03,14.00,21.90},
+
<TRUNCATED>
[3/3] ignite git commit: IGNITE-9348: ML examples improvements,
follow-up to IGNITE-9297
Posted by ch...@apache.org.
IGNITE-9348: ML examples improvements, follow-up to IGNITE-9297
this closes #4641
Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/145a2898
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/145a2898
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/145a2898
Branch: refs/heads/master
Commit: 145a28981017026c86ab509ac3d660449715bbb1
Parents: 2108b3b
Author: Oleg Ignatenko <oi...@gridgain.com>
Authored: Fri Aug 31 11:37:10 2018 +0300
Committer: Yury Babak <yb...@gridgain.com>
Committed: Fri Aug 31 11:37:10 2018 +0300
----------------------------------------------------------------------
.../clustering/KMeansClusterizationExample.java | 3 +-
.../AlgorithmSpecificDatasetExample.java | 5 +-
.../ml/dataset/LocalDatasetExample.java | 69 --
.../change/OptimizeMakeChangeGAExample.java | 14 +-
.../OptimizeMakeChangeTerminateCriteria.java | 42 +-
.../genetic/helloworld/HelloWorldGAExample.java | 13 +-
.../helloworld/HelloWorldTerminateCriteria.java | 34 +-
.../ml/genetic/knapsack/KnapsackGAExample.java | 12 +-
.../knapsack/KnapsackTerminateCriteria.java | 47 +-
.../ml/genetic/movie/MovieGAExample.java | 6 +-
.../genetic/movie/MovieTerminateCriteria.java | 42 +-
.../ml/knn/ANNClassificationExample.java | 3 +-
.../ml/knn/KNNClassificationExample.java | 3 +-
.../examples/ml/knn/KNNRegressionExample.java | 3 +-
.../examples/ml/nn/MLPTrainerExample.java | 3 +-
.../LinearRegressionLSQRTrainerExample.java | 3 +-
...ssionLSQRTrainerWithMinMaxScalerExample.java | 3 +-
.../LinearRegressionSGDTrainerExample.java | 5 +-
.../LogisticRegressionSGDTrainerExample.java | 4 +-
.../ml/selection/cv/CrossValidationExample.java | 6 +-
.../RandomForestClassificationExample.java | 51 +-
.../RandomForestRegressionExample.java | 1064 +++++++++---------
.../ml/environment/EnvironmentTestSuite.java | 1 +
.../ml/environment/LearningEnvironmentTest.java | 201 ++++
24 files changed, 920 insertions(+), 717 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
index c310302..b96cbce 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
@@ -38,7 +38,8 @@ import org.apache.ignite.thread.IgniteThread;
* Code in this example launches Ignite grid and fills the cache with test data points (based on the
* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
* <p>
- * After that it trains the model based on the specified data using KMeans algorithm.</p>
+ * After that it trains the model based on the specified data using
+ * <a href="https://en.wikipedia.org/wiki/K-means_clustering">KMeans</a> algorithm.</p>
* <p>
* Finally, this example loops over the test set of data points, applies the trained model to predict what cluster
* does this point belong to, and compares prediction to expected outcome (ground truth).</p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java
index de7f0e9..4d42d19 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java
@@ -34,8 +34,9 @@ import org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData;
import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
/**
- * Example that shows how to implement your own algorithm (gradient descent trainer for linear regression) which uses
- * dataset as an underlying infrastructure.
+ * Example that shows how to implement your own algorithm
+ * (<a href="https://en.wikipedia.org/wiki/Gradient_descent">gradient</a> descent trainer for linear regression)
+ * which uses dataset as an underlying infrastructure.
* <p>
* Code in this example launches Ignite grid and fills the cache with simple test data.</p>
* <p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java
deleted file mode 100644
index 282e76b..0000000
--- a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java
+++ /dev/null
@@ -1,69 +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.examples.ml.dataset;
-
-import java.util.HashMap;
-import java.util.Map;
-import org.apache.ignite.examples.ml.dataset.model.Person;
-import org.apache.ignite.examples.ml.util.DatasetHelper;
-import org.apache.ignite.ml.dataset.DatasetFactory;
-import org.apache.ignite.ml.dataset.primitive.SimpleDataset;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-
-/**
- * Example that shows how to create dataset based on an existing local storage and then use it to calculate {@code mean}
- * and {@code std} values as well as {@code covariance} and {@code correlation} matrices.
- * <p>
- * Code in this example the storage with simple test data.</p>
- * <p>
- * After that it creates the dataset based on the data in the storage and uses Dataset API to find and output
- * various statistical metrics of the data.</p>
- * <p>
- * You can change the test data used in this example and re-run it to explore this functionality further.</p>
- */
-public class LocalDatasetExample {
- /** Run example. */
- public static void main(String[] args) throws Exception {
- System.out.println(">>> Local Dataset example started.");
-
- Map<Integer, Person> persons = createCache();
-
- // Creates a local simple dataset containing features and providing standard dataset API.
- try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(
- persons,
- 2,
- (k, v) -> VectorUtils.of(v.getAge(), v.getSalary())
- )) {
- new DatasetHelper(dataset).describe();
- }
-
- System.out.println(">>> Local Dataset example completed.");
- }
-
- /** */
- private static Map<Integer, Person> createCache() {
- Map<Integer, Person> persons = new HashMap<>();
-
- persons.put(1, new Person("Mike", 42, 10000));
- persons.put(2, new Person("John", 32, 64000));
- persons.put(3, new Person("George", 53, 120000));
- persons.put(4, new Person("Karl", 24, 70000));
-
- return persons;
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java
index ed392c29..36a5f47 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java
@@ -20,7 +20,6 @@ package org.apache.ignite.examples.ml.genetic.change;
import java.util.ArrayList;
import java.util.List;
import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteLogger;
import org.apache.ignite.Ignition;
import org.apache.ignite.ml.genetic.Chromosome;
import org.apache.ignite.ml.genetic.GAGrid;
@@ -64,8 +63,6 @@ public class OptimizeMakeChangeGAExample {
public static void main(String args[]) {
System.out.println(">>> OptimizeMakeChange GA grid example started.");
- System.setProperty("IGNITE_QUIET", "false");
-
String sAmountChange = "75";
StringBuilder sbErrorMsg = new StringBuilder();
@@ -85,8 +82,6 @@ public class OptimizeMakeChangeGAExample {
// Create an Ignite instance as you would in any other use case.
Ignite ignite = Ignition.start("examples/config/example-ignite.xml");
- IgniteLogger log = ignite.log();
-
// Create GAConfiguration.
GAConfiguration gaCfg = new GAConfiguration();
@@ -120,7 +115,8 @@ public class OptimizeMakeChangeGAExample {
gaCfg.setFitnessFunction(function);
// Create and set TerminateCriteria.
- OptimizeMakeChangeTerminateCriteria termCriteria = new OptimizeMakeChangeTerminateCriteria(ignite);
+ OptimizeMakeChangeTerminateCriteria termCriteria = new OptimizeMakeChangeTerminateCriteria(ignite,
+ System.out::println);
ChromosomeCriteria chromosomeCriteria = new ChromosomeCriteria();
@@ -139,11 +135,11 @@ public class OptimizeMakeChangeGAExample {
// Initialize GAGrid.
GAGrid gaGrid = new GAGrid(gaCfg, ignite);
- log.info("##########################################################################################");
+ System.out.println("##########################################################################################");
- log.info("Calculating optimal set of coins where amount of change is " + sAmountChange);
+ System.out.println("Calculating optimal set of coins where amount of change is " + sAmountChange);
- log.info("##########################################################################################");
+ System.out.println("##########################################################################################");
Chromosome chromosome = gaGrid.evolve();
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java
index 1d6612c..2d65d3f 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java
@@ -18,8 +18,8 @@
package org.apache.ignite.examples.ml.genetic.change;
import java.util.List;
+import java.util.function.Consumer;
import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteLogger;
import org.apache.ignite.ml.genetic.Chromosome;
import org.apache.ignite.ml.genetic.Gene;
import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria;
@@ -29,19 +29,21 @@ import org.apache.ignite.ml.genetic.utils.GAGridUtils;
* Terminate Condition implementation for {@link OptimizeMakeChangeGAExample}.
*/
public class OptimizeMakeChangeTerminateCriteria implements ITerminateCriteria {
- /** Ignite logger. */
- private IgniteLogger igniteLog;
- /** Ignite instance. */
- private Ignite ignite;
+ /** */
+ private final Ignite ignite;
+
+ /** */
+ private final Consumer<String> logConsumer;
/**
* Create class instance.
*
* @param ignite Ignite instance.
+ * @param logConsumer Logging consumer.
*/
- public OptimizeMakeChangeTerminateCriteria(Ignite ignite) {
+ OptimizeMakeChangeTerminateCriteria(Ignite ignite, Consumer<String> logConsumer) {
this.ignite = ignite;
- this.igniteLog = ignite.log();
+ this.logConsumer = logConsumer;
}
/**
@@ -56,13 +58,14 @@ public class OptimizeMakeChangeTerminateCriteria implements ITerminateCriteria {
int currGeneration) {
boolean isTerminate = true;
- igniteLog.info("##########################################################################################");
- igniteLog.info("Generation: " + currGeneration);
- igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome);
- igniteLog.info("Chromosome: " + fittestChromosome);
- printCoins(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome));
- igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore);
- igniteLog.info("##########################################################################################");
+ logConsumer.accept(
+ "\n##########################################################################################"
+ + "\n Generation: " + currGeneration
+ + "\n Fittest is Chromosome Key: " + fittestChromosome
+ + "\n Chromosome: " + fittestChromosome
+ + "\n" + reportCoins(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))
+ + "\nAvg Chromosome Fitness: " + averageFitnessScore
+ + "\n##########################################################################################");
if (!(currGeneration > 5))
isTerminate = false;
@@ -74,11 +77,16 @@ public class OptimizeMakeChangeTerminateCriteria implements ITerminateCriteria {
* Helper to print change details.
*
* @param genes List if Genes.
+ * @return Details to print.
*/
- private void printCoins(List<Gene> genes) {
+ private String reportCoins(List<Gene> genes) {
+ StringBuilder sb = new StringBuilder();
+
for (Gene gene : genes) {
- igniteLog.info("Coin Type: " + ((Coin)gene.getVal()).getCoinType().toString());
- igniteLog.info("Number of Coins: " + ((Coin)gene.getVal()).getNumOfCoins());
+ sb.append("\nCoin Type: ").append(((Coin)gene.getVal()).getCoinType().toString())
+ .append("\nNumber of Coins: ").append(((Coin)gene.getVal()).getNumOfCoins());
}
+
+ return sb.toString();
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java
index 3182d2f..585cbb5 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java
@@ -19,6 +19,7 @@ package org.apache.ignite.examples.ml.genetic.helloworld;
import java.util.ArrayList;
import java.util.List;
+import java.util.concurrent.atomic.AtomicInteger;
import org.apache.ignite.Ignite;
import org.apache.ignite.Ignition;
import org.apache.ignite.ml.genetic.Chromosome;
@@ -56,8 +57,6 @@ public class HelloWorldGAExample {
public static void main(String args[]) {
System.out.println(">>> HelloWorld GA grid example started.");
- System.setProperty("IGNITE_QUIET", "false");
-
try {
// Create an Ignite instance as you would in any other use case.
Ignite ignite = Ignition.start("examples/config/example-ignite.xml");
@@ -79,10 +78,14 @@ public class HelloWorldGAExample {
gaCfg.setFitnessFunction(function);
// Create and set TerminateCriteria.
- HelloWorldTerminateCriteria termCriteria = new HelloWorldTerminateCriteria(ignite);
- gaCfg.setTerminateCriteria(termCriteria);
+ AtomicInteger cnt = new AtomicInteger(0);
+ HelloWorldTerminateCriteria termCriteria = new HelloWorldTerminateCriteria(ignite,
+ msg -> {
+ if (cnt.getAndIncrement() % 20 == 0)
+ System.out.println(msg);
+ });
- ignite.log();
+ gaCfg.setTerminateCriteria(termCriteria);
GAGrid gaGrid = new GAGrid(gaCfg, ignite);
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java
index f149da1..610d479 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java
@@ -18,8 +18,8 @@
package org.apache.ignite.examples.ml.genetic.helloworld;
import java.util.List;
+import java.util.function.Consumer;
import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteLogger;
import org.apache.ignite.ml.genetic.Chromosome;
import org.apache.ignite.ml.genetic.Gene;
import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria;
@@ -31,19 +31,21 @@ import org.apache.ignite.ml.genetic.utils.GAGridUtils;
* Class terminates Genetic algorithm when fitness score is more than 10.</p>
*/
public class HelloWorldTerminateCriteria implements ITerminateCriteria {
- /** Ignite logger. */
- private IgniteLogger igniteLog;
/** Ignite instance. */
- private Ignite ignite;
+ private final Ignite ignite;
+
+ /** */
+ private final Consumer<String> logConsumer;
/**
* Create class instance.
*
* @param ignite Ignite instance.
+ * @param logConsumer Logging consumer.
*/
- public HelloWorldTerminateCriteria(Ignite ignite) {
+ HelloWorldTerminateCriteria(Ignite ignite, Consumer<String> logConsumer) {
this.ignite = ignite;
- this.igniteLog = ignite.log();
+ this.logConsumer = logConsumer;
}
/**
@@ -58,13 +60,14 @@ public class HelloWorldTerminateCriteria implements ITerminateCriteria {
int currGeneration) {
boolean isTerminate = true;
- igniteLog.info("##########################################################################################");
- igniteLog.info("Generation: " + currGeneration);
- igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome);
- igniteLog.info("Chromosome: " + fittestChromosome);
- printPhrase(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome));
- igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore);
- igniteLog.info("##########################################################################################");
+ logConsumer.accept(
+ "\n##########################################################################################"
+ + "\n Generation: " + currGeneration
+ + "\n Fittest is Chromosome Key: " + fittestChromosome
+ + "\n Chromosome: " + fittestChromosome
+ + "\n" + printPhrase(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))
+ + "\nAvg Chromosome Fitness: " + averageFitnessScore
+ + "\n##########################################################################################");
if (!(fittestChromosome.getFitnessScore() > 10))
isTerminate = false;
@@ -76,13 +79,14 @@ public class HelloWorldTerminateCriteria implements ITerminateCriteria {
* Helper to print phrase.
*
* @param genes List of Genes.
+ * @return Phrase to print.
*/
- private void printPhrase(List<Gene> genes) {
+ private String printPhrase(List<Gene> genes) {
StringBuilder sbPhrase = new StringBuilder();
for (Gene gene : genes)
sbPhrase.append(((Character)gene.getVal()).toString());
- igniteLog.info(sbPhrase.toString());
+ return sbPhrase.toString();
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java
index 873c1c6..1631a95 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java
@@ -19,6 +19,7 @@ package org.apache.ignite.examples.ml.genetic.knapsack;
import java.util.ArrayList;
import java.util.List;
+import java.util.concurrent.atomic.AtomicInteger;
import org.apache.ignite.Ignite;
import org.apache.ignite.Ignition;
import org.apache.ignite.ml.genetic.Chromosome;
@@ -55,8 +56,6 @@ public class KnapsackGAExample {
public static void main(String args[]) {
System.out.println(">>> Knapsack GA grid example started.");
- System.setProperty("IGNITE_QUIET", "false");
-
try {
// Create an Ignite instance as you would in any other use case.
Ignite ignite = Ignition.start("examples/config/example-ignite.xml");
@@ -78,11 +77,14 @@ public class KnapsackGAExample {
gaCfg.setFitnessFunction(function);
// Create and set TerminateCriteria.
- KnapsackTerminateCriteria termCriteria = new KnapsackTerminateCriteria(ignite);
+ AtomicInteger cnt = new AtomicInteger(0);
+ KnapsackTerminateCriteria termCriteria = new KnapsackTerminateCriteria(ignite,
+ msg -> {
+ if (cnt.getAndIncrement() % 10 == 0)
+ System.out.println(msg);
+ });
gaCfg.setTerminateCriteria(termCriteria);
- ignite.log();
-
GAGrid gaGrid = new GAGrid(gaCfg, ignite);
// Evolve the population.
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java
index 1bba159..09e047b 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java
@@ -18,8 +18,8 @@
package org.apache.ignite.examples.ml.genetic.knapsack;
import java.util.List;
+import java.util.function.Consumer;
import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteLogger;
import org.apache.ignite.ml.genetic.Chromosome;
import org.apache.ignite.ml.genetic.Gene;
import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria;
@@ -32,19 +32,20 @@ import org.apache.ignite.ml.genetic.utils.GAGridUtils;
*/
public class KnapsackTerminateCriteria implements ITerminateCriteria {
/** Ignite instance. */
- private Ignite ignite;
+ private final Ignite ignite;
- /** Ignite logger. */
- private IgniteLogger igniteLog;
+ /** */
+ private final Consumer<String> logConsumer;
/**
* Create class instance.
*
* @param ignite Ignite instance.
+ * @param logConsumer Logging consumer.
*/
- public KnapsackTerminateCriteria(Ignite ignite) {
+ KnapsackTerminateCriteria(Ignite ignite, Consumer<String> logConsumer) {
this.ignite = ignite;
- this.igniteLog = this.ignite.log();
+ this.logConsumer = logConsumer;
}
/**
@@ -59,15 +60,16 @@ public class KnapsackTerminateCriteria implements ITerminateCriteria {
int currGeneration) {
boolean isTerminate = true;
- igniteLog.info("##########################################################################################");
- igniteLog.info("Generation: " + currGeneration);
- igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome);
- igniteLog.info("Total value is: " + fittestChromosome.getFitnessScore());
- igniteLog.info("Total weight is: " + calculateTotalWeight(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome)));
- igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore);
- igniteLog.info("Chromosome: " + fittestChromosome);
- printItems(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome));
- igniteLog.info("##########################################################################################");
+ logConsumer.accept(
+ "\n##########################################################################################"
+ + "\n Generation: " + currGeneration
+ + "\n Fittest is Chromosome Key: " + fittestChromosome
+ + "\nTotal value is: " + fittestChromosome.getFitnessScore()
+ + "\nTotal weight is: " + calculateTotalWeight(
+ GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))
+ + "\nChromosome: " + fittestChromosome
+ + "\n" + reportItems(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))
+ + "\n##########################################################################################");
if (!(currGeneration > 29))
isTerminate = false;
@@ -93,13 +95,18 @@ public class KnapsackTerminateCriteria implements ITerminateCriteria {
* Helper to print items in knapsack.
*
* @param genes List of Genes.
+ * @return Items to print.
*/
- private void printItems(List<Gene> genes) {
+ private String reportItems(List<Gene> genes) {
+ StringBuilder sb = new StringBuilder();
+
for (Gene gene : genes) {
- igniteLog.info("------------------------------------------------------------------------------------------");
- igniteLog.info("Name: " + ((Item)gene.getVal()).getName());
- igniteLog.info("Weight: " + ((Item)gene.getVal()).getWeight());
- igniteLog.info("Value: " + ((Item)gene.getVal()).getVal());
+ sb.append("\n------------------------------------------------------------------------------------------")
+ .append("\nName: ").append(((Item)gene.getVal()).getName())
+ .append("\nWeight: ").append(((Item)gene.getVal()).getWeight())
+ .append("\nValue: ").append(((Item)gene.getVal()).getVal());
}
+
+ return sb.toString();
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieGAExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieGAExample.java
index 51e28be..cec2719 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieGAExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieGAExample.java
@@ -61,8 +61,6 @@ public class MovieGAExample {
public static void main(String args[]) {
System.out.println(">>> Movie GA grid example started.");
- System.setProperty("IGNITE_QUIET", "false");
-
List<String> genres = new ArrayList<>();
String sGenres = "Action,Comedy,Romance";
@@ -110,14 +108,12 @@ public class MovieGAExample {
// Create an Ignite instance as you would in any other use case.
Ignite ignite = Ignition.start("examples/config/example-ignite.xml");
- MovieTerminateCriteria termCriteria = new MovieTerminateCriteria(ignite);
+ MovieTerminateCriteria termCriteria = new MovieTerminateCriteria(ignite, System.out::println);
gaCfg.setTerminateCriteria(termCriteria);
GAGrid gaGrid = new GAGrid(gaCfg, ignite);
- ignite.log();
-
Chromosome chromosome = gaGrid.evolve();
System.out.println(">>> Evolution result: " + chromosome);
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieTerminateCriteria.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieTerminateCriteria.java
index 7110a7c..68c577f 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieTerminateCriteria.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/MovieTerminateCriteria.java
@@ -18,8 +18,8 @@
package org.apache.ignite.examples.ml.genetic.movie;
import java.util.List;
+import java.util.function.Consumer;
import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteLogger;
import org.apache.ignite.ml.genetic.Chromosome;
import org.apache.ignite.ml.genetic.Gene;
import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria;
@@ -31,19 +31,21 @@ import org.apache.ignite.ml.genetic.utils.GAGridUtils;
* Class terminates Genetic algorithm when fitness score is more than 32.</p>
*/
public class MovieTerminateCriteria implements ITerminateCriteria {
- /** Ignite logger. */
- private IgniteLogger igniteLog;
/** Ignite instance. */
- private Ignite ignite;
+ private final Ignite ignite;
+
+ /** */
+ private final Consumer<String> logConsumer;
/**
* Create class instance.
*
* @param ignite Ignite instance.
+ * @param logConsumer Logging consumer.
*/
- public MovieTerminateCriteria(Ignite ignite) {
+ MovieTerminateCriteria(Ignite ignite, Consumer<String> logConsumer) {
this.ignite = ignite;
- this.igniteLog = ignite.log();
+ this.logConsumer = logConsumer;
}
@@ -59,12 +61,13 @@ public class MovieTerminateCriteria implements ITerminateCriteria {
int currGeneration) {
boolean isTerminate = true;
- igniteLog.info("##########################################################################################");
- igniteLog.info("Generation: " + currGeneration);
- igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome);
- igniteLog.info("Chromosome: " + fittestChromosome);
- printMovies(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome));
- igniteLog.info("##########################################################################################");
+ logConsumer.accept(
+ "\n##########################################################################################"
+ + "\n Generation: " + currGeneration
+ + "\n Fittest is Chromosome Key: " + fittestChromosome
+ + "\nChromosome: " + fittestChromosome
+ + "\n" + reportMovies(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))
+ + "\n##########################################################################################");
if (!(fittestChromosome.getFitnessScore() > 32))
isTerminate = false;
@@ -73,15 +76,20 @@ public class MovieTerminateCriteria implements ITerminateCriteria {
}
/**
- * Helper to print change details.
+ * Helper to print movies details.
*
* @param genes List of Genes.
+ * @return Movies details.
*/
- private void printMovies(List<Gene> genes) {
+ private String reportMovies(List<Gene> genes) {
+ StringBuilder sb = new StringBuilder();
+
for (Gene gene : genes) {
- igniteLog.info("Name: " + ((Movie)gene.getVal()).getName());
- igniteLog.info("Genres: " + ((Movie)gene.getVal()).getGenre().toString());
- igniteLog.info("IMDB Rating: " + ((Movie)gene.getVal()).getImdbRating());
+ sb.append("\nName: ").append(((Movie)gene.getVal()).getName())
+ .append("\nGenres: ").append(((Movie)gene.getVal()).getGenre().toString())
+ .append("\nIMDB Rating: ").append(((Movie)gene.getVal()).getImdbRating());
}
+
+ return sb.toString();
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
index 37cb231..8a2d786 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
@@ -42,7 +42,8 @@ import org.apache.ignite.thread.IgniteThread;
* Code in this example launches Ignite grid and fills the cache with test data points (based on the
* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
* <p>
- * After that it trains the model based on the specified data using kNN algorithm.</p>
+ * After that it trains the model based on the specified data using
+ * <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> algorithm.</p>
* <p>
* Finally, this example loops over the test set of data points, applies the trained model to predict what cluster
* does this point belong to, and compares prediction to expected outcome (ground truth).</p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/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 77a48ad..cf285a4 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
@@ -39,7 +39,8 @@ import org.apache.ignite.thread.IgniteThread;
* Code in this example launches Ignite grid and fills the cache with test data points (based on the
* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
* <p>
- * After that it trains the model based on the specified data using kNN algorithm.</p>
+ * After that it trains the model based on the specified data using
+ * <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> algorithm.</p>
* <p>
* Finally, this example loops over the test set of data points, applies the trained model to predict what cluster
* does this point belong to, and compares prediction to expected outcome (ground truth).</p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/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
index a28ecc4..78f38c8 100644
--- 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
@@ -39,7 +39,8 @@ import org.apache.ignite.thread.IgniteThread;
* Code in this example launches Ignite grid and fills the cache with test data points (based on the
* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
* <p>
- * After that it trains the model based on the specified data using kNN regression algorithm.</p>
+ * After that it trains the model based on the specified data using
+ * <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> regression algorithm.</p>
* <p>
* Finally, this example loops over the test set of data points, applies the trained model to predict what cluster
* does this point belong to, and compares prediction to expected outcome (ground truth).</p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
index 30f8769..3e5a98c 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
@@ -41,7 +41,8 @@ import org.apache.ignite.thread.IgniteThread;
* <p>
* Code in this example launches Ignite grid and fills the cache with simple test data.</p>
* <p>
- * After that it defines a layered architecture and a neural network trainer, trains neural network
+ * After that it defines a layered architecture and a
+ * <a href="https://en.wikipedia.org/wiki/Neural_network">neural network</a> trainer, trains neural network
* and obtains multilayer perceptron model.</p>
* <p>
* Finally, this example loops over the test set, applies the trained model to predict the value and
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
index 085a089..6ac445c 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
@@ -32,7 +32,8 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
import org.apache.ignite.thread.IgniteThread;
/**
- * Run linear regression model ({@link LinearRegressionLSQRTrainer}) over cached dataset.
+ * Run linear regression model based on <a href="http://web.stanford.edu/group/SOL/software/lsqr/">LSQR algorithm</a>
+ * ({@link LinearRegressionLSQRTrainer}) over cached dataset.
* <p>
* Code in this example launches Ignite grid and fills the cache with simple test data.</p>
* <p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
index 39f3771..320d464 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
@@ -35,7 +35,8 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
import org.apache.ignite.thread.IgniteThread;
/**
- * Run linear regression model ({@link LinearRegressionLSQRTrainer}) over cached dataset that was created using
+ * Run linear regression model based on <a href="http://web.stanford.edu/group/SOL/software/lsqr/">LSQR algorithm</a>
+ * ({@link LinearRegressionLSQRTrainer}) over cached dataset that was created using
* a minmaxscaling preprocessor ({@link MinMaxScalerTrainer}, {@link MinMaxScalerPreprocessor}).
* <p>
* Code in this example launches Ignite grid, fills the cache with simple test data, and defines minmaxscaling
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
index 0a1e966..9fdc0df 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
@@ -35,8 +35,9 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer;
import org.apache.ignite.thread.IgniteThread;
/**
- * Run linear regression model based on stochastic gradient descent algorithm ({@link LinearRegressionSGDTrainer})
- * over cached dataset.
+ * Run linear regression model based on based on
+ * <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent">stochastic gradient descent</a> algorithm
+ * ({@link LinearRegressionSGDTrainer}) over cached dataset.
* <p>
* Code in this example launches Ignite grid and fills the cache with simple test data.</p>
* <p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
index 7e1c773..0a6ff01 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
@@ -35,8 +35,8 @@ import org.apache.ignite.ml.regressions.logistic.binomial.LogisticRegressionSGDT
import org.apache.ignite.thread.IgniteThread;
/**
- * Run logistic regression model based on stochastic gradient descent algorithm ({@link LogisticRegressionSGDTrainer})
- * over distributed cache.
+ * Run logistic regression model based on <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent">
+ * stochastic gradient descent</a> algorithm ({@link LogisticRegressionSGDTrainer}) over distributed cache.
* <p>
* Code in this example launches Ignite grid and fills the cache with test data points (based on the
* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
index f1b5650..eb4c8f3 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
@@ -33,11 +33,13 @@ import org.apache.ignite.ml.tree.DecisionTreeNode;
import org.apache.ignite.thread.IgniteThread;
/**
- * Run decision tree classification with cross validation ({@link CrossValidation}).
+ * Run <a href="https://en.wikipedia.org/wiki/Decision_tree">decision tree</a> classification with
+ * <a href="https://en.wikipedia.org/wiki/Cross-validation_(statistics)">cross validation</a> ({@link CrossValidation}).
* <p>
* Code in this example launches Ignite grid and fills the cache with pseudo random training data points.</p>
* <p>
- * After that it creates classification trainer and computes cross-validated metrics based on the training set.</p>
+ * After that it creates classification trainer ({@link DecisionTreeClassificationTrainer}) and computes cross-validated
+ * metrics based on the training set.</p>
*/
public class CrossValidationExample {
/**
http://git-wip-us.apache.org/repos/asf/ignite/blob/145a2898/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
index 4ea471c..aa13943 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
@@ -32,18 +32,24 @@ import org.apache.ignite.ml.composition.ModelsComposition;
import org.apache.ignite.ml.dataset.feature.FeatureMeta;
import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
import org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer;
-import org.apache.ignite.ml.tree.randomforest.RandomForestTrainer;
import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
import org.apache.ignite.thread.IgniteThread;
/**
- * Example represents a solution for the task of wine classification based on RandomForestTrainer implementation for
- * multi-classification. It shows an initialization of {@link RandomForestTrainer} with thread pool for multi-thread
- * learning, initialization of Ignite Cache, learning step and evaluation of accuracy of model.
- *
- * Dataset url: https://archive.ics.uci.edu/ml/machine-learning-databases/wine/
- *
- * @see RandomForestClassifierTrainer
+ * Example represents a solution for the task of wine classification based on a
+ * <a href ="https://en.wikipedia.org/wiki/Random_forest">Random Forest</a> implementation for
+ * multi-classification.
+ * <p>
+ * Code in this example launches Ignite grid and fills the cache with test data points (based on the
+ * <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/">Wine recognition dataset</a>).</p>
+ * <p>
+ * After that it initializes the {@link RandomForestClassifierTrainer} with thread pool for multi-thread learning
+ * and trains the model based on the specified data using random forest regression algorithm.</p>
+ * <p>
+ * Finally, this example loops over the test set of data points, compares prediction of the trained model to the
+ * expected outcome (ground truth), and evaluates accuracy of the model.</p>
+ * <p>
+ * You can change the test data used in this example and re-run it to explore this algorithm further.</p>
*/
public class RandomForestClassificationExample {
/**
@@ -57,23 +63,29 @@ public class RandomForestClassificationExample {
System.out.println(">>> Ignite grid started.");
IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
- RandomForestClassificationExample.class.getSimpleName(), () -> {
+ RandomForestClassificationExample.class.getSimpleName(), () -> {
IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
- AtomicInteger indx = new AtomicInteger(0);
+ AtomicInteger idx = new AtomicInteger(0);
RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer(
- IntStream.range(0, 13).mapToObj(x -> new FeatureMeta("", indx.getAndIncrement(), false)).collect(Collectors.toList()))
- .withCountOfTrees(101)
- .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.SQRT)
- .withMaxDepth(3)
+ IntStream.range(0, data[0].length - 1).mapToObj(
+ x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
+ ).withCountOfTrees(101)
+ .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
+ .withMaxDepth(4)
.withMinImpurityDelta(0.)
- .withSubsampleSize(0.3);
+ .withSubsampleSize(0.3)
+ .withSeed(0);
+
+ System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName());
ModelsComposition randomForest = classifier.fit(ignite, dataCache,
(k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
(k, v) -> v[0]
);
+ System.out.println(">>> Trained model: " + randomForest.toString(true));
+
int amountOfErrors = 0;
int totalAmount = 0;
@@ -91,8 +103,11 @@ public class RandomForestClassificationExample {
}
+ System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
+
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));
+ System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example completed.");
}
});
@@ -101,9 +116,7 @@ public class RandomForestClassificationExample {
}
}
- /**
- * The Wine dataset.
- */
+ /** The Wine recognition dataset. */
private static final double[][] data = {
{1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065},
{1, 13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, .26, 1.28, 4.38, 1.05, 3.4, 1050},