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Posted to commits@hama.apache.org by yx...@apache.org on 2014/02/03 20:15:16 UTC
svn commit: r1564008 - in /hama/trunk: ./
commons/src/main/java/org/apache/hama/commons/math/ core/
examples/src/main/java/org/apache/hama/examples/
examples/src/main/java/org/apache/hama/examples/util/
examples/src/test/java/org/apache/hama/examples/ ...
Author: yxjiang
Date: Mon Feb 3 19:15:15 2014
New Revision: 1564008
URL: http://svn.apache.org/r1564008
Log:
remove cli2
Removed:
hama/trunk/examples/src/main/java/org/apache/hama/examples/util/ParserUtil.java
Modified:
hama/trunk/CHANGES.txt
hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
hama/trunk/core/pom.xml
hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
hama/trunk/pom.xml
Modified: hama/trunk/CHANGES.txt
URL: http://svn.apache.org/viewvc/hama/trunk/CHANGES.txt?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/CHANGES.txt (original)
+++ hama/trunk/CHANGES.txt Mon Feb 3 19:15:15 2014
@@ -3,7 +3,8 @@ Hama Change Log
Release 0.7.0 (unreleased changes)
NEW FEATURES
-
+
+ HAMA-864: Fix/improve DoubleVector and DenseDoubleVector (Yexi Jiang)
HAMA-842: Add persistent queue option to JobConf (edwardyoon)
HAMA-839: Support NullWritable in Hama Pipes (Martin Illecker)
HAMA-837: Add sort behaviour to runtime partitioner (edwardyoon)
@@ -26,7 +27,6 @@ Release 0.7.0 (unreleased changes)
IMPROVEMENTS
- HAMA-859: Leverage commons cli2 to parse the input argument for NeuralNetwork Example (Yexi Jiang)
HAMA-853: Refactor Outgoing message manager (edwardyoon)
HAMA-852: Add MessageClass property in BSPJob (Martin Illecker)
HAMA-843: Message communication overhead between master aggregation and vertex computation supersteps (edwardyoon)
Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java Mon Feb 3 19:15:15 2014
@@ -127,36 +127,6 @@ public final class DenseDoubleVector imp
/*
* (non-Javadoc)
- * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.function.
- * DoubleVectorFunction)
- */
- @Deprecated
- @Override
- public DoubleVector apply(DoubleVectorFunction func) {
- DenseDoubleVector newV = new DenseDoubleVector(this.vector);
- for (int i = 0; i < vector.length; i++) {
- newV.vector[i] = func.calculate(i, vector[i]);
- }
- return newV;
- }
-
- /*
- * (non-Javadoc)
- * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.DoubleVector,
- * de.jungblut.math.function.DoubleDoubleVectorFunction)
- */
- @Deprecated
- @Override
- public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
- DenseDoubleVector newV = (DenseDoubleVector) deepCopy();
- for (int i = 0; i < vector.length; i++) {
- newV.vector[i] = func.calculate(i, vector[i], other.get(i));
- }
- return newV;
- }
-
- /*
- * (non-Javadoc)
* @see de.jungblut.math.DoubleVector#add(de.jungblut.math.DoubleVector)
*/
@Override
Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java Mon Feb 3 19:15:15 2014
@@ -66,27 +66,6 @@ public interface DoubleVector {
* @param func the function to apply.
* @return a new vector with the applied function.
*/
- @Deprecated
- public DoubleVector apply(DoubleVectorFunction func);
-
- /**
- * Apply a given {@link DoubleDoubleVectorFunction} to this vector and the
- * other given vector.
- *
- * @param other the other vector.
- * @param func the function to apply on this and the other vector.
- * @return a new vector with the result of the function of the two vectors.
- */
- @Deprecated
- public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func);
-
- /**
- * Apply a given {@link DoubleVectorFunction} to this vector and return a new
- * one.
- *
- * @param func the function to apply.
- * @return a new vector with the applied function.
- */
public DoubleVector applyToElements(DoubleFunction func);
/**
@@ -331,18 +310,22 @@ public interface DoubleVector {
public Iterator<DoubleVectorElement> iterate();
/**
+ * Return whether the vector is a sparse vector.
* @return true if this instance is a sparse vector. Smarter and faster than
* instanceof.
*/
public boolean isSparse();
/**
+ * Return whether the vector is a named vector.
* @return true if this instance is a named vector.Smarter and faster than
* instanceof.
*/
public boolean isNamed();
/**
+ * Get the name of the vector.
+ *
* @return If this vector is a named instance, this will return its name. Or
* null if this is not a named instance.
*
Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java Mon Feb 3 19:15:15 2014
@@ -51,18 +51,6 @@ public final class NamedDoubleVector imp
}
@Override
- @Deprecated
- public DoubleVector apply(DoubleVectorFunction func) {
- return vector.apply(func);
- }
-
- @Override
- @Deprecated
- public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
- return vector.apply(other, func);
- }
-
- @Override
public DoubleVector applyToElements(DoubleFunction func) {
return vector.applyToElements(func);
}
@@ -238,8 +226,9 @@ public final class NamedDoubleVector imp
return name;
}
+ @Override
public String toString() {
- return name + ": " + vector.toString();
+ return String.format("%s: %s", name, vector.toArray());
}
}
Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java Mon Feb 3 19:15:15 2014
@@ -17,12 +17,22 @@
*/
package org.apache.hama.commons.math;
-@SuppressWarnings("deprecation")
-public class SquareVectorFunction implements DoubleVectorFunction {
+public class SquareVectorFunction extends DoubleFunction {
+ /* (non-Javadoc)
+ * @see org.apache.hama.commons.math.DoubleFunction#apply(double)
+ */
@Override
- public double calculate(int index, double value) {
- return Math.pow(value, 2);
+ public double apply(double value) {
+ return value * value;
+ }
+
+ /* (non-Javadoc)
+ * @see org.apache.hama.commons.math.DoubleFunction#applyDerivative(double)
+ */
+ @Override
+ public double applyDerivative(double value) {
+ throw new UnsupportedOperationException();
}
}
Modified: hama/trunk/core/pom.xml
URL: http://svn.apache.org/viewvc/hama/trunk/core/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/core/pom.xml (original)
+++ hama/trunk/core/pom.xml Mon Feb 3 19:15:15 2014
@@ -51,6 +51,10 @@
<artifactId>commons-logging</artifactId>
</dependency>
<dependency>
+ <groupId>commons-cli</groupId>
+ <artifactId>commons-cli</artifactId>
+ </dependency>
+ <dependency>
<groupId>commons-configuration</groupId>
<artifactId>commons-configuration</artifactId>
</dependency>
Modified: hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java (original)
+++ hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java Mon Feb 3 19:15:15 2014
@@ -23,288 +23,194 @@ import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.net.URI;
import java.util.HashMap;
-import java.util.List;
import java.util.Map;
-import org.apache.commons.cli2.CommandLine;
-import org.apache.commons.cli2.Group;
-import org.apache.commons.cli2.Option;
-import org.apache.commons.cli2.builder.ArgumentBuilder;
-import org.apache.commons.cli2.builder.DefaultOptionBuilder;
-import org.apache.commons.cli2.builder.GroupBuilder;
-import org.apache.commons.cli2.commandline.Parser;
-import org.apache.commons.cli2.util.HelpFormatter;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hama.HamaConfiguration;
import org.apache.hama.commons.math.DenseDoubleVector;
import org.apache.hama.commons.math.DoubleVector;
import org.apache.hama.commons.math.FunctionFactory;
-import org.apache.hama.examples.util.ParserUtil;
import org.apache.hama.ml.ann.SmallLayeredNeuralNetwork;
-import com.google.common.io.Closeables;
-
/**
* The example of using {@link SmallLayeredNeuralNetwork}, including the
* training phase and labeling phase.
*/
public class NeuralNetwork {
- // either train or label
- private static String mode;
-
- // arguments for labeling
- private static String featureDataPath;
- private static String resultDataPath;
- private static String modelPath;
-
- // arguments for training
- private static String trainingDataPath;
- private static int featureDimension;
- private static int labelDimension;
- private static List<Integer> hiddenLayerDimension;
- private static int iterations;
- private static double learningRate;
- private static double momemtumWeight;
- private static double regularizationWeight;
-
- public static boolean parseArgs(String[] args) {
- DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
- GroupBuilder groupBuilder = new GroupBuilder();
- ArgumentBuilder argumentBuilder = new ArgumentBuilder();
-
- // the feature data (unlabeled data) path argument
- Option featureDataPathOption = optionBuilder
- .withLongName("feature-data-path")
- .withShortName("fp")
- .withDescription("the path of the feature data (unlabeled data).")
- .withArgument(
- argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the result data path argument
- Option resultDataPathOption = optionBuilder
- .withLongName("result-data-path")
- .withShortName("rp")
- .withDescription("the path to store the result.")
- .withArgument(
- argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the path to store the model
- Option modelPathOption = optionBuilder
- .withLongName("model-data-path")
- .withShortName("mp")
- .withDescription("the path to store the trained model.")
- .withArgument(
- argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the path of the training data
- Option trainingDataPathOption = optionBuilder
- .withLongName("training-data-path")
- .withShortName("tp")
- .withDescription("the path to store the trained model.")
- .withArgument(
- argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the dimension of the features
- Option featureDimensionOption = optionBuilder
- .withLongName("feature dimension")
- .withShortName("fd")
- .withDescription("the dimension of the features.")
- .withArgument(
- argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the dimension of the hidden layers, at most two hidden layers
- Option hiddenLayerOption = optionBuilder
- .withLongName("hidden layer dimension(s)")
- .withShortName("hd")
- .withDescription("the dimension of the hidden layer(s).")
- .withArgument(
- argumentBuilder.withName("dimension").withMinimum(0).withMaximum(2)
- .create()).withRequired(true).create();
-
- // the dimension of the labels
- Option labelDimensionOption = optionBuilder
- .withLongName("label dimension")
- .withShortName("ld")
- .withDescription("the dimension of the label(s).")
- .withArgument(
- argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
- .create()).withRequired(true).create();
-
- // the number of iterations for training
- Option iterationOption = optionBuilder
- .withLongName("iterations")
- .withShortName("itr")
- .withDescription("the iterations for training.")
- .withArgument(
- argumentBuilder.withName("iterations").withMinimum(1)
- .withMaximum(1).withDefault(1000).create()).create();
-
- // the learning rate
- Option learningRateOption = optionBuilder
- .withLongName("learning-rate")
- .withShortName("l")
- .withDescription("the learning rate for training, default 0.1.")
- .withArgument(
- argumentBuilder.withName("learning-rate").withMinimum(1)
- .withMaximum(1).withDefault(0.1).create()).create();
-
- // the momemtum weight
- Option momentumWeightOption = optionBuilder
- .withLongName("momemtum-weight")
- .withShortName("m")
- .withDescription("the momemtum weight for training, default 0.1.")
- .withArgument(
- argumentBuilder.withName("momemtum weight").withMinimum(1)
- .withMaximum(1).withDefault(0.1).create()).create();
-
- // the regularization weight
- Option regularizationWeightOption = optionBuilder
- .withLongName("regularization-weight")
- .withShortName("r")
- .withDescription("the regularization weight for training, default 0.")
- .withArgument(
- argumentBuilder.withName("regularization weight").withMinimum(1)
- .withMaximum(1).withDefault(0).create()).create();
-
- // the parameters related to train mode
- Group trainModeGroup = groupBuilder.withOption(trainingDataPathOption)
- .withOption(modelPathOption).withOption(featureDimensionOption)
- .withOption(labelDimensionOption).withOption(hiddenLayerOption)
- .withOption(iterationOption).withOption(learningRateOption)
- .withOption(momentumWeightOption)
- .withOption(regularizationWeightOption).create();
-
- // the parameters related to label mode
- Group labelModeGroup = groupBuilder.withOption(modelPathOption)
- .withOption(featureDataPathOption).withOption(resultDataPathOption)
- .create();
-
- Option trainModeOption = optionBuilder.withLongName("train")
- .withShortName("train").withDescription("the train mode")
- .withChildren(trainModeGroup).create();
-
- Option labelModeOption = optionBuilder.withLongName("label")
- .withShortName("label").withChildren(labelModeGroup)
- .withDescription("the label mode").create();
-
- Group normalGroup = groupBuilder.withOption(trainModeOption)
- .withOption(labelModeOption).create();
-
- Parser parser = new Parser();
- parser.setGroup(normalGroup);
- parser.setHelpFormatter(new HelpFormatter());
- parser.setHelpTrigger("--help");
- CommandLine cli = parser.parseAndHelp(args);
- if (cli == null) {
- return false;
- }
- // get the arguments
- boolean hasTrainMode = cli.hasOption(trainModeOption);
- boolean hasLabelMode = cli.hasOption(labelModeOption);
- if (hasTrainMode && hasLabelMode) {
- return false;
+ public static void main(String[] args) throws Exception {
+ if (args.length < 3) {
+ printUsage();
+ return;
}
+ String mode = args[0];
+ if (mode.equalsIgnoreCase("label")) {
+ if (args.length < 4) {
+ printUsage();
+ return;
+ }
+ HamaConfiguration conf = new HamaConfiguration();
- mode = hasTrainMode ? "train" : "label";
- if (mode.equals("train")) {
- trainingDataPath = ParserUtil.getString(cli, trainingDataPathOption);
- modelPath = ParserUtil.getString(cli, modelPathOption);
- featureDimension = ParserUtil.getInteger(cli, featureDimensionOption);
- labelDimension = ParserUtil.getInteger(cli, labelDimensionOption);
- hiddenLayerDimension = ParserUtil.getInts(cli, hiddenLayerOption);
- iterations = ParserUtil.getInteger(cli, iterationOption);
- learningRate = ParserUtil.getDouble(cli, learningRateOption);
- momemtumWeight = ParserUtil.getDouble(cli, momentumWeightOption);
- regularizationWeight = ParserUtil.getDouble(cli,
- regularizationWeightOption);
- } else {
- featureDataPath = ParserUtil.getString(cli, featureDataPathOption);
- modelPath = ParserUtil.getString(cli, modelPathOption);
- resultDataPath = ParserUtil.getString(cli, resultDataPathOption);
- }
+ String featureDataPath = args[1];
+ String resultDataPath = args[2];
+ String modelPath = args[3];
+
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
+
+ // process data in streaming approach
+ FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
+ BufferedReader br = new BufferedReader(new InputStreamReader(
+ fs.open(new Path(featureDataPath))));
+ Path outputPath = new Path(resultDataPath);
+ if (fs.exists(outputPath)) {
+ fs.delete(outputPath, true);
+ }
+ BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
+ fs.create(outputPath)));
- return true;
- }
+ String line = null;
- public static void main(String[] args) throws Exception {
- if (parseArgs(args)) {
- if (mode.equals("label")) {
- HamaConfiguration conf = new HamaConfiguration();
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
-
- // process data in streaming approach
- FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
- BufferedReader br = new BufferedReader(new InputStreamReader(
- fs.open(new Path(featureDataPath))));
- Path outputPath = new Path(resultDataPath);
- if (fs.exists(outputPath)) {
- fs.delete(outputPath, true);
+ while ((line = br.readLine()) != null) {
+ if (line.trim().length() == 0) {
+ continue;
}
- BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
- fs.create(outputPath)));
-
- String line = null;
-
- while ((line = br.readLine()) != null) {
- if (line.trim().length() == 0) {
- continue;
- }
- String[] tokens = line.trim().split(",");
- double[] vals = new double[tokens.length];
- for (int i = 0; i < tokens.length; ++i) {
- vals[i] = Double.parseDouble(tokens[i]);
- }
- DoubleVector instance = new DenseDoubleVector(vals);
- DoubleVector result = ann.getOutput(instance);
- double[] arrResult = result.toArray();
- StringBuilder sb = new StringBuilder();
- for (int i = 0; i < arrResult.length; ++i) {
- sb.append(arrResult[i]);
- if (i != arrResult.length - 1) {
- sb.append(",");
- } else {
- sb.append("\n");
- }
+ String[] tokens = line.trim().split(",");
+ double[] vals = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ vals[i] = Double.parseDouble(tokens[i]);
+ }
+ DoubleVector instance = new DenseDoubleVector(vals);
+ DoubleVector result = ann.getOutput(instance);
+ double[] arrResult = result.toArray();
+ StringBuilder sb = new StringBuilder();
+ for (int i = 0; i < arrResult.length; ++i) {
+ sb.append(arrResult[i]);
+ if (i != arrResult.length - 1) {
+ sb.append(",");
+ } else {
+ sb.append("\n");
}
- bw.write(sb.toString());
}
+ bw.write(sb.toString());
+ }
- Closeables.close(br, true);
- Closeables.close(bw, true);
- } else { // train the model
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
- ann.setLearningRate(learningRate);
- ann.setMomemtumWeight(momemtumWeight);
- ann.setRegularizationWeight(regularizationWeight);
- ann.addLayer(featureDimension, false,
- FunctionFactory.createDoubleFunction("Sigmoid"));
- if (hiddenLayerDimension != null) {
- for (int dimension : hiddenLayerDimension) {
- ann.addLayer(dimension, false,
- FunctionFactory.createDoubleFunction("Sigmoid"));
- }
+ br.close();
+ bw.close();
+ } else if (mode.equals("train")) {
+ if (args.length < 5) {
+ printUsage();
+ return;
+ }
+
+ String trainingDataPath = args[1];
+ String trainedModelPath = args[2];
+
+ int featureDimension = Integer.parseInt(args[3]);
+ int labelDimension = Integer.parseInt(args[4]);
+
+ int iteration = 1000;
+ double learningRate = 0.4;
+ double momemtumWeight = 0.2;
+ double regularizationWeight = 0.01;
+
+ // parse parameters
+ if (args.length >= 6) {
+ try {
+ iteration = Integer.parseInt(args[5]);
+ System.out.printf("Iteration: %d\n", iteration);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("MAX_ITERATION format invalid. It should be a positive number.");
+ return;
+ }
+ }
+ if (args.length >= 7) {
+ try {
+ learningRate = Double.parseDouble(args[6]);
+ System.out.printf("Learning rate: %f\n", learningRate);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("LEARNING_RATE format invalid. It should be a positive double in range (0, 1.0)");
+ return;
}
- ann.addLayer(labelDimension, true,
- FunctionFactory.createDoubleFunction("Sigmoid"));
- ann.setCostFunction(FunctionFactory
- .createDoubleDoubleFunction("CrossEntropy"));
- ann.setModelPath(modelPath);
-
- Map<String, String> trainingParameters = new HashMap<String, String>();
- trainingParameters.put("tasks", "5");
- trainingParameters.put("training.max.iterations", "" + iterations);
- trainingParameters.put("training.batch.size", "300");
- trainingParameters.put("convergence.check.interval", "1000");
- ann.train(new Path(trainingDataPath), trainingParameters);
}
+ if (args.length >= 8) {
+ try {
+ momemtumWeight = Double.parseDouble(args[7]);
+ System.out.printf("Momemtum weight: %f\n", momemtumWeight);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("MOMEMTUM_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+ return;
+ }
+ }
+ if (args.length >= 9) {
+ try {
+ regularizationWeight = Double.parseDouble(args[8]);
+ System.out
+ .printf("Regularization weight: %f\n", regularizationWeight);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("REGULARIZATION_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+ return;
+ }
+ }
+
+ // train the model
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.setLearningRate(learningRate);
+ ann.setMomemtumWeight(momemtumWeight);
+ ann.setRegularizationWeight(regularizationWeight);
+ ann.addLayer(featureDimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(featureDimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(labelDimension, true,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("CrossEntropy"));
+ ann.setModelPath(trainedModelPath);
+
+ Map<String, String> trainingParameters = new HashMap<String, String>();
+ trainingParameters.put("tasks", "5");
+ trainingParameters.put("training.max.iterations", "" + iteration);
+ trainingParameters.put("training.batch.size", "300");
+ trainingParameters.put("convergence.check.interval", "1000");
+ ann.train(new Path(trainingDataPath), trainingParameters);
}
+
+ }
+
+ private static void printUsage() {
+ System.out
+ .println("USAGE: <MODE> <INPUT_PATH> <OUTPUT_PATH> <MODEL_PATH>|<FEATURE_DIMENSION> <LABEL_DIMENSION> [<MAX_ITERATION> <LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT>]");
+ System.out
+ .println("\tMODE\t- train: train the model with given training data.");
+ System.out
+ .println("\t\t- label: obtain the result by feeding the features to the neural network.");
+ System.out
+ .println("\tINPUT_PATH\tin 'train' mode, it is the path of the training data; in 'label' mode, it is the path of the to be evaluated data that lacks the label.");
+ System.out
+ .println("\tOUTPUT_PATH\tin 'train' mode, it is where the trained model is stored; in 'label' mode, it is where the labeled data is stored.");
+ System.out.println("\n\tConditional Parameters:");
+ System.out
+ .println("\tMODEL_PATH\tonly required in 'label' mode. It specifies where to load the trained neural network model.");
+ System.out
+ .println("\tMAX_ITERATION\tonly used in 'train' mode. It specifies how many iterations for the neural network to run. Default is 0.01.");
+ System.out
+ .println("\tLEARNING_RATE\tonly used to 'train' mode. It specifies the degree of aggregation for learning, usually in range (0, 1.0). Default is 0.1.");
+ System.out
+ .println("\tMOMEMTUM_WEIGHT\tonly used to 'train' mode. It specifies the weight of momemtum. Default is 0.");
+ System.out
+ .println("\tREGULARIZATION_WEIGHT\tonly required in 'train' model. It specifies the weight of reqularization.");
+ System.out.println("\nExample:");
+ System.out
+ .println("Train a neural network with with feature dimension 8, label dimension 1 and default setting:\n\tneuralnets train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1");
+ System.out
+ .println("Train a neural network with with feature dimension 8, label dimension 1 and specify learning rate as 0.1, momemtum rate as 0.2, and regularization weight as 0.01:\n\tneuralnets.train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1 0.1 0.2 0.01");
+ System.out
+ .println("Label the data with trained model:\n\tneuralnets evaluate hdfs://localhost:30002/unlabeled_data hdfs://localhost:30002/result hdfs://localhost:30002/model");
}
}
Modified: hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java (original)
+++ hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java Mon Feb 3 19:15:15 2014
@@ -23,6 +23,8 @@ import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
+import junit.framework.TestCase;
+
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
@@ -31,34 +33,32 @@ import org.apache.hadoop.io.SequenceFile
import org.apache.hama.HamaConfiguration;
import org.apache.hama.commons.io.VectorWritable;
import org.apache.hama.commons.math.DenseDoubleVector;
-import org.junit.Before;
-import org.junit.Test;
/**
* Test the functionality of NeuralNetwork Example.
*
*/
-public class NeuralNetworkTest {
+public class NeuralNetworkTest extends TestCase {
private Configuration conf = new HamaConfiguration();
private FileSystem fs;
private String MODEL_PATH = "/tmp/neuralnets.model";
private String RESULT_PATH = "/tmp/neuralnets.txt";
private String SEQTRAIN_DATA = "/tmp/test-neuralnets.data";
-
- @Before
- public void setup() throws Exception {
+
+ @Override
+ protected void setUp() throws Exception {
+ super.setUp();
fs = FileSystem.get(conf);
}
- @Test
public void testNeuralnetsLabeling() throws IOException {
this.neuralNetworkTraining();
String dataPath = "src/test/resources/neuralnets_classification_test.txt";
- String mode = "-label";
+ String mode = "label";
try {
NeuralNetwork
- .main(new String[] { mode, "-fp", dataPath, "-rp", RESULT_PATH, "-mp", MODEL_PATH });
+ .main(new String[] { mode, dataPath, RESULT_PATH, MODEL_PATH });
// compare results with ground-truth
BufferedReader groundTruthReader = new BufferedReader(new FileReader(
@@ -98,7 +98,7 @@ public class NeuralNetworkTest {
}
private void neuralNetworkTraining() {
- String mode = "-train";
+ String mode = "train";
String strTrainingDataPath = "src/test/resources/neuralnets_classification_training.txt";
int featureDimension = 8;
int labelDimension = 1;
@@ -130,9 +130,8 @@ public class NeuralNetworkTest {
}
try {
- NeuralNetwork.main(new String[] { mode, "-tp", SEQTRAIN_DATA, "-mp",
- MODEL_PATH, "-fd", "" + featureDimension, "-hd",
- "" + featureDimension, "-ld", "" + labelDimension, "-itr", "3000", "-m", "0.2", "-l", "0.2" });
+ NeuralNetwork.main(new String[] { mode, SEQTRAIN_DATA,
+ MODEL_PATH, "" + featureDimension, "" + labelDimension });
} catch (Exception e) {
e.printStackTrace();
}
Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java (original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java Mon Feb 3 19:15:15 2014
@@ -473,7 +473,7 @@ public class OnlineCF implements Recomme
// Euclidean distance
return Math.pow( usr1Vector
.subtract(usr2Vector)
- .apply(new SquareVectorFunction())
+ .applyToElements(new SquareVectorFunction())
.sum() , 0.5);
}
@@ -514,7 +514,7 @@ public class OnlineCF implements Recomme
// Euclidean distance
return Math.pow( itm1Vector
.subtract(itm2Vector)
- .apply(new SquareVectorFunction())
+ .applyToElements(new SquareVectorFunction())
.sum() , 0.5);
}
Modified: hama/trunk/pom.xml
URL: http://svn.apache.org/viewvc/hama/trunk/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/pom.xml (original)
+++ hama/trunk/pom.xml Mon Feb 3 19:15:15 2014
@@ -87,7 +87,7 @@
<properties>
<!-- Dependencies -->
<commons-logging.version>1.1.1</commons-logging.version>
- <commons-cli.version>2.0-SNAPSHOT</commons-cli.version>
+ <commons-cli.version>1.2</commons-cli.version>
<commons-configuration>1.7</commons-configuration>
<commons-lang>2.6</commons-lang>
<commons-httpclient>3.0.1</commons-httpclient>
Re: svn commit: r1564008 - in /hama/trunk: ./ commons/src/main/java/org/apache/hama/commons/math/
core/ examples/src/main/java/org/apache/hama/examples/ examples/src/main/java/org/apache/hama/examples/util/
examples/src/test/java/org/apache/hama/examples/ ...
Posted by "Edward J. Yoon" <ed...@apache.org>.
+ @Override
public String toString() {
- return name + ": " + vector.toString();
+ return String.format("%s: %s", name, vector.toArray());
}
I'm change like below:
+ return String.format("%s: %s", name, vector.toString());
On Tue, Feb 4, 2014 at 4:15 AM, <yx...@apache.org> wrote:
> Author: yxjiang
> Date: Mon Feb 3 19:15:15 2014
> New Revision: 1564008
>
> URL: http://svn.apache.org/r1564008
> Log:
> remove cli2
>
> Removed:
> hama/trunk/examples/src/main/java/org/apache/hama/examples/util/ParserUtil.java
> Modified:
> hama/trunk/CHANGES.txt
> hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
> hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
> hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
> hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
> hama/trunk/core/pom.xml
> hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
> hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
> hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
> hama/trunk/pom.xml
>
> Modified: hama/trunk/CHANGES.txt
> URL: http://svn.apache.org/viewvc/hama/trunk/CHANGES.txt?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/CHANGES.txt (original)
> +++ hama/trunk/CHANGES.txt Mon Feb 3 19:15:15 2014
> @@ -3,7 +3,8 @@ Hama Change Log
> Release 0.7.0 (unreleased changes)
>
> NEW FEATURES
> -
> +
> + HAMA-864: Fix/improve DoubleVector and DenseDoubleVector (Yexi Jiang)
> HAMA-842: Add persistent queue option to JobConf (edwardyoon)
> HAMA-839: Support NullWritable in Hama Pipes (Martin Illecker)
> HAMA-837: Add sort behaviour to runtime partitioner (edwardyoon)
> @@ -26,7 +27,6 @@ Release 0.7.0 (unreleased changes)
>
> IMPROVEMENTS
>
> - HAMA-859: Leverage commons cli2 to parse the input argument for NeuralNetwork Example (Yexi Jiang)
> HAMA-853: Refactor Outgoing message manager (edwardyoon)
> HAMA-852: Add MessageClass property in BSPJob (Martin Illecker)
> HAMA-843: Message communication overhead between master aggregation and vertex computation supersteps (edwardyoon)
>
> Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
> URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java (original)
> +++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java Mon Feb 3 19:15:15 2014
> @@ -127,36 +127,6 @@ public final class DenseDoubleVector imp
>
> /*
> * (non-Javadoc)
> - * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.function.
> - * DoubleVectorFunction)
> - */
> - @Deprecated
> - @Override
> - public DoubleVector apply(DoubleVectorFunction func) {
> - DenseDoubleVector newV = new DenseDoubleVector(this.vector);
> - for (int i = 0; i < vector.length; i++) {
> - newV.vector[i] = func.calculate(i, vector[i]);
> - }
> - return newV;
> - }
> -
> - /*
> - * (non-Javadoc)
> - * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.DoubleVector,
> - * de.jungblut.math.function.DoubleDoubleVectorFunction)
> - */
> - @Deprecated
> - @Override
> - public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
> - DenseDoubleVector newV = (DenseDoubleVector) deepCopy();
> - for (int i = 0; i < vector.length; i++) {
> - newV.vector[i] = func.calculate(i, vector[i], other.get(i));
> - }
> - return newV;
> - }
> -
> - /*
> - * (non-Javadoc)
> * @see de.jungblut.math.DoubleVector#add(de.jungblut.math.DoubleVector)
> */
> @Override
>
> Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
> URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java (original)
> +++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java Mon Feb 3 19:15:15 2014
> @@ -66,27 +66,6 @@ public interface DoubleVector {
> * @param func the function to apply.
> * @return a new vector with the applied function.
> */
> - @Deprecated
> - public DoubleVector apply(DoubleVectorFunction func);
> -
> - /**
> - * Apply a given {@link DoubleDoubleVectorFunction} to this vector and the
> - * other given vector.
> - *
> - * @param other the other vector.
> - * @param func the function to apply on this and the other vector.
> - * @return a new vector with the result of the function of the two vectors.
> - */
> - @Deprecated
> - public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func);
> -
> - /**
> - * Apply a given {@link DoubleVectorFunction} to this vector and return a new
> - * one.
> - *
> - * @param func the function to apply.
> - * @return a new vector with the applied function.
> - */
> public DoubleVector applyToElements(DoubleFunction func);
>
> /**
> @@ -331,18 +310,22 @@ public interface DoubleVector {
> public Iterator<DoubleVectorElement> iterate();
>
> /**
> + * Return whether the vector is a sparse vector.
> * @return true if this instance is a sparse vector. Smarter and faster than
> * instanceof.
> */
> public boolean isSparse();
>
> /**
> + * Return whether the vector is a named vector.
> * @return true if this instance is a named vector.Smarter and faster than
> * instanceof.
> */
> public boolean isNamed();
>
> /**
> + * Get the name of the vector.
> + *
> * @return If this vector is a named instance, this will return its name. Or
> * null if this is not a named instance.
> *
>
> Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
> URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java (original)
> +++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java Mon Feb 3 19:15:15 2014
> @@ -51,18 +51,6 @@ public final class NamedDoubleVector imp
> }
>
> @Override
> - @Deprecated
> - public DoubleVector apply(DoubleVectorFunction func) {
> - return vector.apply(func);
> - }
> -
> - @Override
> - @Deprecated
> - public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
> - return vector.apply(other, func);
> - }
> -
> - @Override
> public DoubleVector applyToElements(DoubleFunction func) {
> return vector.applyToElements(func);
> }
> @@ -238,8 +226,9 @@ public final class NamedDoubleVector imp
> return name;
> }
>
> + @Override
> public String toString() {
> - return name + ": " + vector.toString();
> + return String.format("%s: %s", name, vector.toArray());
> }
>
> }
>
> Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
> URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java (original)
> +++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java Mon Feb 3 19:15:15 2014
> @@ -17,12 +17,22 @@
> */
> package org.apache.hama.commons.math;
>
> -@SuppressWarnings("deprecation")
> -public class SquareVectorFunction implements DoubleVectorFunction {
> +public class SquareVectorFunction extends DoubleFunction {
>
> + /* (non-Javadoc)
> + * @see org.apache.hama.commons.math.DoubleFunction#apply(double)
> + */
> @Override
> - public double calculate(int index, double value) {
> - return Math.pow(value, 2);
> + public double apply(double value) {
> + return value * value;
> + }
> +
> + /* (non-Javadoc)
> + * @see org.apache.hama.commons.math.DoubleFunction#applyDerivative(double)
> + */
> + @Override
> + public double applyDerivative(double value) {
> + throw new UnsupportedOperationException();
> }
>
> }
>
> Modified: hama/trunk/core/pom.xml
> URL: http://svn.apache.org/viewvc/hama/trunk/core/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/core/pom.xml (original)
> +++ hama/trunk/core/pom.xml Mon Feb 3 19:15:15 2014
> @@ -51,6 +51,10 @@
> <artifactId>commons-logging</artifactId>
> </dependency>
> <dependency>
> + <groupId>commons-cli</groupId>
> + <artifactId>commons-cli</artifactId>
> + </dependency>
> + <dependency>
> <groupId>commons-configuration</groupId>
> <artifactId>commons-configuration</artifactId>
> </dependency>
>
> Modified: hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
> URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java (original)
> +++ hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java Mon Feb 3 19:15:15 2014
> @@ -23,288 +23,194 @@ import java.io.InputStreamReader;
> import java.io.OutputStreamWriter;
> import java.net.URI;
> import java.util.HashMap;
> -import java.util.List;
> import java.util.Map;
>
> -import org.apache.commons.cli2.CommandLine;
> -import org.apache.commons.cli2.Group;
> -import org.apache.commons.cli2.Option;
> -import org.apache.commons.cli2.builder.ArgumentBuilder;
> -import org.apache.commons.cli2.builder.DefaultOptionBuilder;
> -import org.apache.commons.cli2.builder.GroupBuilder;
> -import org.apache.commons.cli2.commandline.Parser;
> -import org.apache.commons.cli2.util.HelpFormatter;
> import org.apache.hadoop.fs.FileSystem;
> import org.apache.hadoop.fs.Path;
> import org.apache.hama.HamaConfiguration;
> import org.apache.hama.commons.math.DenseDoubleVector;
> import org.apache.hama.commons.math.DoubleVector;
> import org.apache.hama.commons.math.FunctionFactory;
> -import org.apache.hama.examples.util.ParserUtil;
> import org.apache.hama.ml.ann.SmallLayeredNeuralNetwork;
>
> -import com.google.common.io.Closeables;
> -
> /**
> * The example of using {@link SmallLayeredNeuralNetwork}, including the
> * training phase and labeling phase.
> */
> public class NeuralNetwork {
> - // either train or label
> - private static String mode;
> -
> - // arguments for labeling
> - private static String featureDataPath;
> - private static String resultDataPath;
> - private static String modelPath;
> -
> - // arguments for training
> - private static String trainingDataPath;
> - private static int featureDimension;
> - private static int labelDimension;
> - private static List<Integer> hiddenLayerDimension;
> - private static int iterations;
> - private static double learningRate;
> - private static double momemtumWeight;
> - private static double regularizationWeight;
> -
> - public static boolean parseArgs(String[] args) {
> - DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
> - GroupBuilder groupBuilder = new GroupBuilder();
> - ArgumentBuilder argumentBuilder = new ArgumentBuilder();
> -
> - // the feature data (unlabeled data) path argument
> - Option featureDataPathOption = optionBuilder
> - .withLongName("feature-data-path")
> - .withShortName("fp")
> - .withDescription("the path of the feature data (unlabeled data).")
> - .withArgument(
> - argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the result data path argument
> - Option resultDataPathOption = optionBuilder
> - .withLongName("result-data-path")
> - .withShortName("rp")
> - .withDescription("the path to store the result.")
> - .withArgument(
> - argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the path to store the model
> - Option modelPathOption = optionBuilder
> - .withLongName("model-data-path")
> - .withShortName("mp")
> - .withDescription("the path to store the trained model.")
> - .withArgument(
> - argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the path of the training data
> - Option trainingDataPathOption = optionBuilder
> - .withLongName("training-data-path")
> - .withShortName("tp")
> - .withDescription("the path to store the trained model.")
> - .withArgument(
> - argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the dimension of the features
> - Option featureDimensionOption = optionBuilder
> - .withLongName("feature dimension")
> - .withShortName("fd")
> - .withDescription("the dimension of the features.")
> - .withArgument(
> - argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the dimension of the hidden layers, at most two hidden layers
> - Option hiddenLayerOption = optionBuilder
> - .withLongName("hidden layer dimension(s)")
> - .withShortName("hd")
> - .withDescription("the dimension of the hidden layer(s).")
> - .withArgument(
> - argumentBuilder.withName("dimension").withMinimum(0).withMaximum(2)
> - .create()).withRequired(true).create();
> -
> - // the dimension of the labels
> - Option labelDimensionOption = optionBuilder
> - .withLongName("label dimension")
> - .withShortName("ld")
> - .withDescription("the dimension of the label(s).")
> - .withArgument(
> - argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
> - .create()).withRequired(true).create();
> -
> - // the number of iterations for training
> - Option iterationOption = optionBuilder
> - .withLongName("iterations")
> - .withShortName("itr")
> - .withDescription("the iterations for training.")
> - .withArgument(
> - argumentBuilder.withName("iterations").withMinimum(1)
> - .withMaximum(1).withDefault(1000).create()).create();
> -
> - // the learning rate
> - Option learningRateOption = optionBuilder
> - .withLongName("learning-rate")
> - .withShortName("l")
> - .withDescription("the learning rate for training, default 0.1.")
> - .withArgument(
> - argumentBuilder.withName("learning-rate").withMinimum(1)
> - .withMaximum(1).withDefault(0.1).create()).create();
> -
> - // the momemtum weight
> - Option momentumWeightOption = optionBuilder
> - .withLongName("momemtum-weight")
> - .withShortName("m")
> - .withDescription("the momemtum weight for training, default 0.1.")
> - .withArgument(
> - argumentBuilder.withName("momemtum weight").withMinimum(1)
> - .withMaximum(1).withDefault(0.1).create()).create();
> -
> - // the regularization weight
> - Option regularizationWeightOption = optionBuilder
> - .withLongName("regularization-weight")
> - .withShortName("r")
> - .withDescription("the regularization weight for training, default 0.")
> - .withArgument(
> - argumentBuilder.withName("regularization weight").withMinimum(1)
> - .withMaximum(1).withDefault(0).create()).create();
> -
> - // the parameters related to train mode
> - Group trainModeGroup = groupBuilder.withOption(trainingDataPathOption)
> - .withOption(modelPathOption).withOption(featureDimensionOption)
> - .withOption(labelDimensionOption).withOption(hiddenLayerOption)
> - .withOption(iterationOption).withOption(learningRateOption)
> - .withOption(momentumWeightOption)
> - .withOption(regularizationWeightOption).create();
> -
> - // the parameters related to label mode
> - Group labelModeGroup = groupBuilder.withOption(modelPathOption)
> - .withOption(featureDataPathOption).withOption(resultDataPathOption)
> - .create();
> -
> - Option trainModeOption = optionBuilder.withLongName("train")
> - .withShortName("train").withDescription("the train mode")
> - .withChildren(trainModeGroup).create();
> -
> - Option labelModeOption = optionBuilder.withLongName("label")
> - .withShortName("label").withChildren(labelModeGroup)
> - .withDescription("the label mode").create();
> -
> - Group normalGroup = groupBuilder.withOption(trainModeOption)
> - .withOption(labelModeOption).create();
> -
> - Parser parser = new Parser();
> - parser.setGroup(normalGroup);
> - parser.setHelpFormatter(new HelpFormatter());
> - parser.setHelpTrigger("--help");
> - CommandLine cli = parser.parseAndHelp(args);
> - if (cli == null) {
> - return false;
> - }
>
> - // get the arguments
> - boolean hasTrainMode = cli.hasOption(trainModeOption);
> - boolean hasLabelMode = cli.hasOption(labelModeOption);
> - if (hasTrainMode && hasLabelMode) {
> - return false;
> + public static void main(String[] args) throws Exception {
> + if (args.length < 3) {
> + printUsage();
> + return;
> }
> + String mode = args[0];
> + if (mode.equalsIgnoreCase("label")) {
> + if (args.length < 4) {
> + printUsage();
> + return;
> + }
> + HamaConfiguration conf = new HamaConfiguration();
>
> - mode = hasTrainMode ? "train" : "label";
> - if (mode.equals("train")) {
> - trainingDataPath = ParserUtil.getString(cli, trainingDataPathOption);
> - modelPath = ParserUtil.getString(cli, modelPathOption);
> - featureDimension = ParserUtil.getInteger(cli, featureDimensionOption);
> - labelDimension = ParserUtil.getInteger(cli, labelDimensionOption);
> - hiddenLayerDimension = ParserUtil.getInts(cli, hiddenLayerOption);
> - iterations = ParserUtil.getInteger(cli, iterationOption);
> - learningRate = ParserUtil.getDouble(cli, learningRateOption);
> - momemtumWeight = ParserUtil.getDouble(cli, momentumWeightOption);
> - regularizationWeight = ParserUtil.getDouble(cli,
> - regularizationWeightOption);
> - } else {
> - featureDataPath = ParserUtil.getString(cli, featureDataPathOption);
> - modelPath = ParserUtil.getString(cli, modelPathOption);
> - resultDataPath = ParserUtil.getString(cli, resultDataPathOption);
> - }
> + String featureDataPath = args[1];
> + String resultDataPath = args[2];
> + String modelPath = args[3];
> +
> + SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
> +
> + // process data in streaming approach
> + FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
> + BufferedReader br = new BufferedReader(new InputStreamReader(
> + fs.open(new Path(featureDataPath))));
> + Path outputPath = new Path(resultDataPath);
> + if (fs.exists(outputPath)) {
> + fs.delete(outputPath, true);
> + }
> + BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
> + fs.create(outputPath)));
>
> - return true;
> - }
> + String line = null;
>
> - public static void main(String[] args) throws Exception {
> - if (parseArgs(args)) {
> - if (mode.equals("label")) {
> - HamaConfiguration conf = new HamaConfiguration();
> - SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
> -
> - // process data in streaming approach
> - FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
> - BufferedReader br = new BufferedReader(new InputStreamReader(
> - fs.open(new Path(featureDataPath))));
> - Path outputPath = new Path(resultDataPath);
> - if (fs.exists(outputPath)) {
> - fs.delete(outputPath, true);
> + while ((line = br.readLine()) != null) {
> + if (line.trim().length() == 0) {
> + continue;
> }
> - BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
> - fs.create(outputPath)));
> -
> - String line = null;
> -
> - while ((line = br.readLine()) != null) {
> - if (line.trim().length() == 0) {
> - continue;
> - }
> - String[] tokens = line.trim().split(",");
> - double[] vals = new double[tokens.length];
> - for (int i = 0; i < tokens.length; ++i) {
> - vals[i] = Double.parseDouble(tokens[i]);
> - }
> - DoubleVector instance = new DenseDoubleVector(vals);
> - DoubleVector result = ann.getOutput(instance);
> - double[] arrResult = result.toArray();
> - StringBuilder sb = new StringBuilder();
> - for (int i = 0; i < arrResult.length; ++i) {
> - sb.append(arrResult[i]);
> - if (i != arrResult.length - 1) {
> - sb.append(",");
> - } else {
> - sb.append("\n");
> - }
> + String[] tokens = line.trim().split(",");
> + double[] vals = new double[tokens.length];
> + for (int i = 0; i < tokens.length; ++i) {
> + vals[i] = Double.parseDouble(tokens[i]);
> + }
> + DoubleVector instance = new DenseDoubleVector(vals);
> + DoubleVector result = ann.getOutput(instance);
> + double[] arrResult = result.toArray();
> + StringBuilder sb = new StringBuilder();
> + for (int i = 0; i < arrResult.length; ++i) {
> + sb.append(arrResult[i]);
> + if (i != arrResult.length - 1) {
> + sb.append(",");
> + } else {
> + sb.append("\n");
> }
> - bw.write(sb.toString());
> }
> + bw.write(sb.toString());
> + }
>
> - Closeables.close(br, true);
> - Closeables.close(bw, true);
> - } else { // train the model
> - SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
> - ann.setLearningRate(learningRate);
> - ann.setMomemtumWeight(momemtumWeight);
> - ann.setRegularizationWeight(regularizationWeight);
> - ann.addLayer(featureDimension, false,
> - FunctionFactory.createDoubleFunction("Sigmoid"));
> - if (hiddenLayerDimension != null) {
> - for (int dimension : hiddenLayerDimension) {
> - ann.addLayer(dimension, false,
> - FunctionFactory.createDoubleFunction("Sigmoid"));
> - }
> + br.close();
> + bw.close();
> + } else if (mode.equals("train")) {
> + if (args.length < 5) {
> + printUsage();
> + return;
> + }
> +
> + String trainingDataPath = args[1];
> + String trainedModelPath = args[2];
> +
> + int featureDimension = Integer.parseInt(args[3]);
> + int labelDimension = Integer.parseInt(args[4]);
> +
> + int iteration = 1000;
> + double learningRate = 0.4;
> + double momemtumWeight = 0.2;
> + double regularizationWeight = 0.01;
> +
> + // parse parameters
> + if (args.length >= 6) {
> + try {
> + iteration = Integer.parseInt(args[5]);
> + System.out.printf("Iteration: %d\n", iteration);
> + } catch (NumberFormatException e) {
> + System.err
> + .println("MAX_ITERATION format invalid. It should be a positive number.");
> + return;
> + }
> + }
> + if (args.length >= 7) {
> + try {
> + learningRate = Double.parseDouble(args[6]);
> + System.out.printf("Learning rate: %f\n", learningRate);
> + } catch (NumberFormatException e) {
> + System.err
> + .println("LEARNING_RATE format invalid. It should be a positive double in range (0, 1.0)");
> + return;
> }
> - ann.addLayer(labelDimension, true,
> - FunctionFactory.createDoubleFunction("Sigmoid"));
> - ann.setCostFunction(FunctionFactory
> - .createDoubleDoubleFunction("CrossEntropy"));
> - ann.setModelPath(modelPath);
> -
> - Map<String, String> trainingParameters = new HashMap<String, String>();
> - trainingParameters.put("tasks", "5");
> - trainingParameters.put("training.max.iterations", "" + iterations);
> - trainingParameters.put("training.batch.size", "300");
> - trainingParameters.put("convergence.check.interval", "1000");
> - ann.train(new Path(trainingDataPath), trainingParameters);
> }
> + if (args.length >= 8) {
> + try {
> + momemtumWeight = Double.parseDouble(args[7]);
> + System.out.printf("Momemtum weight: %f\n", momemtumWeight);
> + } catch (NumberFormatException e) {
> + System.err
> + .println("MOMEMTUM_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
> + return;
> + }
> + }
> + if (args.length >= 9) {
> + try {
> + regularizationWeight = Double.parseDouble(args[8]);
> + System.out
> + .printf("Regularization weight: %f\n", regularizationWeight);
> + } catch (NumberFormatException e) {
> + System.err
> + .println("REGULARIZATION_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
> + return;
> + }
> + }
> +
> + // train the model
> + SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
> + ann.setLearningRate(learningRate);
> + ann.setMomemtumWeight(momemtumWeight);
> + ann.setRegularizationWeight(regularizationWeight);
> + ann.addLayer(featureDimension, false,
> + FunctionFactory.createDoubleFunction("Sigmoid"));
> + ann.addLayer(featureDimension, false,
> + FunctionFactory.createDoubleFunction("Sigmoid"));
> + ann.addLayer(labelDimension, true,
> + FunctionFactory.createDoubleFunction("Sigmoid"));
> + ann.setCostFunction(FunctionFactory
> + .createDoubleDoubleFunction("CrossEntropy"));
> + ann.setModelPath(trainedModelPath);
> +
> + Map<String, String> trainingParameters = new HashMap<String, String>();
> + trainingParameters.put("tasks", "5");
> + trainingParameters.put("training.max.iterations", "" + iteration);
> + trainingParameters.put("training.batch.size", "300");
> + trainingParameters.put("convergence.check.interval", "1000");
> + ann.train(new Path(trainingDataPath), trainingParameters);
> }
> +
> + }
> +
> + private static void printUsage() {
> + System.out
> + .println("USAGE: <MODE> <INPUT_PATH> <OUTPUT_PATH> <MODEL_PATH>|<FEATURE_DIMENSION> <LABEL_DIMENSION> [<MAX_ITERATION> <LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT>]");
> + System.out
> + .println("\tMODE\t- train: train the model with given training data.");
> + System.out
> + .println("\t\t- label: obtain the result by feeding the features to the neural network.");
> + System.out
> + .println("\tINPUT_PATH\tin 'train' mode, it is the path of the training data; in 'label' mode, it is the path of the to be evaluated data that lacks the label.");
> + System.out
> + .println("\tOUTPUT_PATH\tin 'train' mode, it is where the trained model is stored; in 'label' mode, it is where the labeled data is stored.");
> + System.out.println("\n\tConditional Parameters:");
> + System.out
> + .println("\tMODEL_PATH\tonly required in 'label' mode. It specifies where to load the trained neural network model.");
> + System.out
> + .println("\tMAX_ITERATION\tonly used in 'train' mode. It specifies how many iterations for the neural network to run. Default is 0.01.");
> + System.out
> + .println("\tLEARNING_RATE\tonly used to 'train' mode. It specifies the degree of aggregation for learning, usually in range (0, 1.0). Default is 0.1.");
> + System.out
> + .println("\tMOMEMTUM_WEIGHT\tonly used to 'train' mode. It specifies the weight of momemtum. Default is 0.");
> + System.out
> + .println("\tREGULARIZATION_WEIGHT\tonly required in 'train' model. It specifies the weight of reqularization.");
> + System.out.println("\nExample:");
> + System.out
> + .println("Train a neural network with with feature dimension 8, label dimension 1 and default setting:\n\tneuralnets train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1");
> + System.out
> + .println("Train a neural network with with feature dimension 8, label dimension 1 and specify learning rate as 0.1, momemtum rate as 0.2, and regularization weight as 0.01:\n\tneuralnets.train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1 0.1 0.2 0.01");
> + System.out
> + .println("Label the data with trained model:\n\tneuralnets evaluate hdfs://localhost:30002/unlabeled_data hdfs://localhost:30002/result hdfs://localhost:30002/model");
> }
>
> }
>
> Modified: hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
> URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java (original)
> +++ hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java Mon Feb 3 19:15:15 2014
> @@ -23,6 +23,8 @@ import java.io.IOException;
> import java.util.ArrayList;
> import java.util.List;
>
> +import junit.framework.TestCase;
> +
> import org.apache.hadoop.conf.Configuration;
> import org.apache.hadoop.fs.FileSystem;
> import org.apache.hadoop.fs.Path;
> @@ -31,34 +33,32 @@ import org.apache.hadoop.io.SequenceFile
> import org.apache.hama.HamaConfiguration;
> import org.apache.hama.commons.io.VectorWritable;
> import org.apache.hama.commons.math.DenseDoubleVector;
> -import org.junit.Before;
> -import org.junit.Test;
>
> /**
> * Test the functionality of NeuralNetwork Example.
> *
> */
> -public class NeuralNetworkTest {
> +public class NeuralNetworkTest extends TestCase {
> private Configuration conf = new HamaConfiguration();
> private FileSystem fs;
> private String MODEL_PATH = "/tmp/neuralnets.model";
> private String RESULT_PATH = "/tmp/neuralnets.txt";
> private String SEQTRAIN_DATA = "/tmp/test-neuralnets.data";
> -
> - @Before
> - public void setup() throws Exception {
> +
> + @Override
> + protected void setUp() throws Exception {
> + super.setUp();
> fs = FileSystem.get(conf);
> }
>
> - @Test
> public void testNeuralnetsLabeling() throws IOException {
> this.neuralNetworkTraining();
>
> String dataPath = "src/test/resources/neuralnets_classification_test.txt";
> - String mode = "-label";
> + String mode = "label";
> try {
> NeuralNetwork
> - .main(new String[] { mode, "-fp", dataPath, "-rp", RESULT_PATH, "-mp", MODEL_PATH });
> + .main(new String[] { mode, dataPath, RESULT_PATH, MODEL_PATH });
>
> // compare results with ground-truth
> BufferedReader groundTruthReader = new BufferedReader(new FileReader(
> @@ -98,7 +98,7 @@ public class NeuralNetworkTest {
> }
>
> private void neuralNetworkTraining() {
> - String mode = "-train";
> + String mode = "train";
> String strTrainingDataPath = "src/test/resources/neuralnets_classification_training.txt";
> int featureDimension = 8;
> int labelDimension = 1;
> @@ -130,9 +130,8 @@ public class NeuralNetworkTest {
> }
>
> try {
> - NeuralNetwork.main(new String[] { mode, "-tp", SEQTRAIN_DATA, "-mp",
> - MODEL_PATH, "-fd", "" + featureDimension, "-hd",
> - "" + featureDimension, "-ld", "" + labelDimension, "-itr", "3000", "-m", "0.2", "-l", "0.2" });
> + NeuralNetwork.main(new String[] { mode, SEQTRAIN_DATA,
> + MODEL_PATH, "" + featureDimension, "" + labelDimension });
> } catch (Exception e) {
> e.printStackTrace();
> }
>
> Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
> URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java (original)
> +++ hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java Mon Feb 3 19:15:15 2014
> @@ -473,7 +473,7 @@ public class OnlineCF implements Recomme
> // Euclidean distance
> return Math.pow( usr1Vector
> .subtract(usr2Vector)
> - .apply(new SquareVectorFunction())
> + .applyToElements(new SquareVectorFunction())
> .sum() , 0.5);
> }
>
> @@ -514,7 +514,7 @@ public class OnlineCF implements Recomme
> // Euclidean distance
> return Math.pow( itm1Vector
> .subtract(itm2Vector)
> - .apply(new SquareVectorFunction())
> + .applyToElements(new SquareVectorFunction())
> .sum() , 0.5);
> }
>
>
> Modified: hama/trunk/pom.xml
> URL: http://svn.apache.org/viewvc/hama/trunk/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
> ==============================================================================
> --- hama/trunk/pom.xml (original)
> +++ hama/trunk/pom.xml Mon Feb 3 19:15:15 2014
> @@ -87,7 +87,7 @@
> <properties>
> <!-- Dependencies -->
> <commons-logging.version>1.1.1</commons-logging.version>
> - <commons-cli.version>2.0-SNAPSHOT</commons-cli.version>
> + <commons-cli.version>1.2</commons-cli.version>
> <commons-configuration>1.7</commons-configuration>
> <commons-lang>2.6</commons-lang>
> <commons-httpclient>3.0.1</commons-httpclient>
>
>
--
Edward J. Yoon (@eddieyoon)
Chief Executive Officer
DataSayer, Inc.