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Posted to commits@mahout.apache.org by td...@apache.org on 2010/07/25 01:38:30 UTC
svn commit: r978948 [3/4] - in /mahout/trunk: conf/ core/ core/doc/
core/src/main/java/org/apache/mahout/classifier/
core/src/main/java/org/apache/mahout/classifier/evaluation/
core/src/main/java/org/apache/mahout/classifier/sgd/
core/src/test/java/org...
Added: mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/LogisticModelParameters.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/LogisticModelParameters.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/LogisticModelParameters.java (added)
+++ mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/LogisticModelParameters.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,276 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+import com.google.common.collect.Maps;
+import com.google.gson.*;
+import org.apache.mahout.math.DenseMatrix;
+import org.apache.mahout.math.Matrix;
+
+import java.io.*;
+import java.lang.reflect.Type;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Encapsulates everything we need to know about a model and how it reads and vectorizes its input.
+ * This encapsulation allows us to coherently save and restore a model from a file. This also
+ * allows us to keep command line arguments that affect learning in a coherent way.
+ */
+public class LogisticModelParameters {
+ private String targetVariable;
+ private Map<String, String> typeMap;
+ private int numFeatures;
+ private boolean useBias;
+ private int maxTargetCategories;
+ private List<String> targetCategories = null;
+ private double lambda;
+ private double learningRate;
+ private transient CsvRecordFactory csv = null;
+ private OnlineLogisticRegression lr = null;
+
+ public LogisticModelParameters() {
+ }
+
+ /**
+ * Returns a CsvRecordFactory compatible with this logistic model. The reason that this is tied
+ * in here is so that we have access to the list of target categories when it comes time to save
+ * the model. If the input isn't CSV, then calling setTargetCategories before calling saveTo will
+ * suffice.
+ *
+ * @return
+ */
+ public CsvRecordFactory getCsvRecordFactory() {
+ if (csv == null) {
+ csv = new CsvRecordFactory(getTargetVariable(), getTypeMap())
+ .maxTargetValue(getMaxTargetCategories())
+ .includeBiasTerm(useBias());
+ if (targetCategories != null) {
+ csv.defineTargetCategories(targetCategories);
+ }
+ }
+ return csv;
+ }
+
+ /**
+ * Creates a logistic regression trainer using the parameters collected here.
+ *
+ * @return
+ */
+ public OnlineLogisticRegression createRegression() {
+ if (lr == null) {
+ lr = new OnlineLogisticRegression(getMaxTargetCategories(), getNumFeatures(), new L1())
+ .lambda(getLambda())
+ .learningRate(getLearningRate())
+ .alpha(1 - 1e-3);
+ }
+ return lr;
+ }
+
+ public static void saveModel(Writer out, OnlineLogisticRegression model, List<String> targetCategories) throws IOException {
+ LogisticModelParameters x = new LogisticModelParameters();
+ x.setTargetCategories(targetCategories);
+ x.setLambda(model.getLambda());
+ x.setLearningRate(model.currentLearningRate());
+ x.setNumFeatures(model.numFeatures());
+ x.setUseBias(true);
+ x.setTargetCategories(targetCategories);
+ x.saveTo(out);
+ }
+
+ /**
+ * Saves a model in JSON format. This includes the current state of the logistic regression
+ * trainer and the dictionary for the target categories.
+ *
+ * @param out Where to write the model.
+ * @throws IOException
+ */
+ public void saveTo(Writer out) throws IOException {
+ if (lr != null) {
+ lr.close();
+ }
+ targetCategories = csv.getTargetCategories();
+ GsonBuilder gb = new GsonBuilder();
+ gb.registerTypeAdapter(Matrix.class, new MatrixTypeAdapter());
+ Gson gson = gb.setPrettyPrinting().create();
+
+ String savedForm = gson.toJson(this);
+ out.write(savedForm);
+ }
+
+ /**
+ * Reads a model in JSON format.
+ *
+ * @param in Where to read the model from.
+ * @return The LogisticModelParameters object that we read.
+ */
+ public static LogisticModelParameters loadFrom(Reader in) {
+ GsonBuilder gb = new GsonBuilder();
+ gb.registerTypeAdapter(Matrix.class, new MatrixTypeAdapter());
+ return gb.create().fromJson(in, LogisticModelParameters.class);
+ }
+
+ /**
+ * Reads a model in JSON format from a File.
+ *
+ * @param in Where to read the model from.
+ * @return The LogisticModelParameters object that we read.
+ * @throws IOException If there is an error opening or closing the file.
+ */
+ public static LogisticModelParameters loadFrom(File in) throws IOException {
+ FileReader input = new FileReader(in);
+ LogisticModelParameters r = loadFrom(input);
+ input.close();
+ return r;
+ }
+
+ /**
+ * Sets the types of the predictors. This will later be used when reading CSV data. If you don't
+ * use the CSV data and convert to vectors on your own, you don't need to call this.
+ *
+ * @param predictorList The list of variable names.
+ * @param typeList The list of types in the format preferred by CsvRecordFactory.
+ */
+ public void setTypeMap(List predictorList, List typeList) {
+ typeMap = Maps.newHashMap();
+ if (typeList.size() == 0) {
+ throw new IllegalArgumentException("Must have at least one type specifier");
+ }
+ Iterator iTypes = typeList.iterator();
+ String lastType = null;
+ for (Object x : predictorList) {
+ // type list can be short .. we just repeat last spec
+ if (iTypes.hasNext()) {
+ lastType = iTypes.next().toString();
+ }
+ typeMap.put(x.toString(), lastType);
+ }
+ }
+
+ /**
+ * Sets the target variable. If you don't use the CSV record factory, then this is irrelevant.
+ *
+ * @param targetVariable
+ */
+ public void setTargetVariable(String targetVariable) {
+ this.targetVariable = targetVariable;
+ }
+
+ /**
+ * Sets the number of target categories to be considered.
+ *
+ * @param maxTargetCategories
+ */
+ public void setMaxTargetCategories(int maxTargetCategories) {
+ this.maxTargetCategories = maxTargetCategories;
+ }
+
+ public void setNumFeatures(int numFeatures) {
+ this.numFeatures = numFeatures;
+ }
+
+ public void setTargetCategories(List<String> targetCategories) {
+ this.targetCategories = targetCategories;
+ maxTargetCategories = targetCategories.size();
+ }
+
+ public void setUseBias(boolean useBias) {
+ this.useBias = useBias;
+ }
+
+ public boolean useBias() {
+ return useBias;
+ }
+
+ public String getTargetVariable() {
+ return targetVariable;
+ }
+
+ public Map<String, String> getTypeMap() {
+ return typeMap;
+ }
+
+ public int getNumFeatures() {
+ return numFeatures;
+ }
+
+ public int getMaxTargetCategories() {
+ return maxTargetCategories;
+ }
+
+ public double getLambda() {
+ return lambda;
+ }
+
+ public void setLambda(double lambda) {
+ this.lambda = lambda;
+ }
+
+ public double getLearningRate() {
+ return learningRate;
+ }
+
+ public void setLearningRate(double learningRate) {
+ this.learningRate = learningRate;
+ }
+
+ /**
+ * Tells GSON how to (de)serialize a Mahout matrix. We assume on deserialization that
+ * the matrix is dense.
+ */
+ public static class MatrixTypeAdapter implements JsonDeserializer<Matrix>, JsonSerializer<Matrix>, InstanceCreator<Matrix> {
+ @Override
+ public JsonElement serialize(Matrix m, Type type, JsonSerializationContext jsonSerializationContext) {
+ JsonObject r = new JsonObject();
+ r.add("rows", new JsonPrimitive(m.numRows()));
+ r.add("cols", new JsonPrimitive(m.numCols()));
+ JsonArray v = new JsonArray();
+ for (int row = 0; row < m.numRows(); row++) {
+ JsonArray rowData = new JsonArray();
+ for (int col = 0; col < m.numCols(); col++) {
+ rowData.add(new JsonPrimitive(m.get(row, col)));
+ }
+ v.add(rowData);
+ }
+ r.add("data", v);
+ return r;
+ }
+
+ @Override
+ public Matrix deserialize(JsonElement x, Type type, JsonDeserializationContext jsonDeserializationContext) throws JsonParseException {
+ JsonObject data = x.getAsJsonObject();
+ Matrix r = new DenseMatrix(data.get("rows").getAsInt(), data.get("cols").getAsInt());
+ int i = 0;
+ for (JsonElement row : data.get("data").getAsJsonArray()) {
+ int j = 0;
+ for (JsonElement element : row.getAsJsonArray()) {
+ r.set(i, j, element.getAsDouble());
+ j++;
+ }
+ i++;
+ }
+ return r;
+ }
+
+ @Override
+ public Matrix createInstance(Type type) {
+ return new DenseMatrix();
+ }
+ }
+}
Added: mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/PrintResourceOrFile.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/PrintResourceOrFile.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/PrintResourceOrFile.java (added)
+++ mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/PrintResourceOrFile.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,39 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+
+/**
+ * Uses the same logic as TrainLogistic and RunLogistic for finding an input, but instead
+ * of processing the input, this class just prints the input to standard out.
+ */
+public class PrintResourceOrFile {
+ public static void main(String[] args) throws IOException {
+ if (args.length != 1) {
+ throw new IllegalArgumentException("Must have a single argument that names a file or resource.");
+ }
+ BufferedReader in = TrainLogistic.InputOpener.open(args[0]);
+ String line = in.readLine();
+ while (line != null) {
+ System.out.println(line);
+ line = in.readLine();
+ }
+ }
+}
Added: mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java (added)
+++ mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,156 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+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.mahout.math.Matrix;
+import org.apache.mahout.math.SequentialAccessSparseVector;
+import org.apache.mahout.math.Vector;
+import org.apache.mahout.classifier.evaluation.Auc;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.IOException;
+
+/**
+ *
+ */
+public class RunLogistic {
+ private static final Logger log = LoggerFactory.getLogger(RunLogistic.class);
+ private static String inputFile;
+ private static String modelFile;
+ private static boolean showAuc = false;
+ private static boolean showScores = false;
+ private static boolean showConfusion = false;
+
+ public static void main(String[] args) throws IOException {
+ if (parseArgs(args)) {
+ if (!showAuc && !showConfusion && !showScores) {
+ showAuc = true;
+ showConfusion = true;
+ }
+
+ Auc collector = new Auc();
+ LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));
+
+ CsvRecordFactory csv = lmp.getCsvRecordFactory();
+ OnlineLogisticRegression lr = lmp.createRegression();
+ BufferedReader in = TrainLogistic.InputOpener.open(inputFile);
+ String line = in.readLine();
+ csv.firstLine(line);
+ line = in.readLine();
+ if (showScores) {
+ System.out.printf("\"%s\",\"%s\",\"%s\"\n", "target", "model-output", "log-likelihood");
+ }
+ while (line != null) {
+ Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
+ int target = csv.processLine(line, v);
+ double score = lr.classifyScalar(v);
+ if (showScores) {
+ System.out.printf("%d,%.3f,%.6f\n", target, score, lr.logLikelihood(target, v));
+ }
+ collector.add(target, score);
+ line = in.readLine();
+ }
+
+ if (showAuc) {
+ System.out.printf("AUC = %.2f\n", collector.auc());
+ }
+ if (showConfusion) {
+ Matrix m = collector.confusion();
+ System.out.printf("confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
+ m = collector.entropy();
+ System.out.printf("entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
+ }
+ }
+ }
+
+ private static boolean parseArgs(String[] args) {
+ DefaultOptionBuilder builder = new DefaultOptionBuilder();
+
+ Option help = builder.withLongName("help").withDescription("print this list").create();
+
+ Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create();
+
+ Option auc = builder.withLongName("auc").withDescription("print AUC").create();
+ Option confusion = builder.withLongName("confusion").withDescription("print confusion matrix").create();
+
+ Option scores = builder.withLongName("scores").withDescription("print scores").create();
+
+ ArgumentBuilder argumentBuilder = new ArgumentBuilder();
+ Option inputFile = builder.withLongName("input")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
+ .withDescription("where to get training data")
+ .create();
+
+ Option modelFile = builder.withLongName("model")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("model").withMaximum(1).create())
+ .withDescription("where to get a model")
+ .create();
+
+ Group normalArgs = new GroupBuilder()
+ .withOption(help)
+ .withOption(quiet)
+ .withOption(auc)
+ .withOption(scores)
+ .withOption(confusion)
+ .withOption(inputFile)
+ .withOption(modelFile)
+ .create();
+
+ Parser parser = new Parser();
+ parser.setHelpOption(help);
+ parser.setHelpTrigger("--help");
+ parser.setGroup(normalArgs);
+ parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
+ CommandLine cmdLine;
+ cmdLine = parser.parseAndHelp(args);
+
+ if (cmdLine == null) {
+ return false;
+ }
+
+ RunLogistic.inputFile = getStringArgument(cmdLine, inputFile);
+ RunLogistic.modelFile = getStringArgument(cmdLine, modelFile);
+ RunLogistic.showAuc = getBooleanArgument(cmdLine, auc);
+ RunLogistic.showScores = getBooleanArgument(cmdLine, scores);
+ RunLogistic.showConfusion = getBooleanArgument(cmdLine, confusion);
+
+ return true;
+ }
+
+ private static boolean getBooleanArgument(CommandLine cmdLine, Option option) {
+ return cmdLine.hasOption(option);
+ }
+
+ private static String getStringArgument(CommandLine cmdLine, Option inputFile) {
+ return (String) cmdLine.getValue(inputFile);
+ }
+
+}
Added: mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java (added)
+++ mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,294 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+import com.google.common.collect.Lists;
+import com.google.common.io.Resources;
+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.mahout.math.RandomAccessSparseVector;
+import org.apache.mahout.math.Vector;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.*;
+import java.net.URL;
+import java.util.List;
+
+
+/**
+ * Train a logistic regression for the examples from Chapter 13 of Mahout in Action
+ */
+public class TrainLogistic {
+ private static final Logger log = LoggerFactory.getLogger(TrainLogistic.class);
+ private static String inputFile;
+ private static String outputFile;
+ private static LogisticModelParameters lmp;
+
+ private static int passes;
+ private static boolean scores = false;
+
+
+ public static void main(String[] args) throws IOException {
+ if (parseArgs(args)) {
+ double logPEstimate = 0;
+ int samples = 0;
+
+ CsvRecordFactory csv = lmp.getCsvRecordFactory();
+ OnlineLogisticRegression lr = lmp.createRegression();
+ for (int pass = 0; pass < passes; pass++) {
+ BufferedReader in = InputOpener.open(inputFile);
+
+ // read variable names
+ csv.firstLine(in.readLine());
+
+ String line = in.readLine();
+ while (line != null) {
+ // for each new line, get target and predictors
+ Vector input = new RandomAccessSparseVector(lmp.getNumFeatures());
+ int targetValue = csv.processLine(line, input);
+
+ // check performance while this is still news
+ double logP = lr.logLikelihood(targetValue, input);
+ if (!Double.isInfinite(logP)) {
+ if (samples < 20) {
+ logPEstimate = (samples * logPEstimate + logP) / (samples + 1);
+ } else {
+ logPEstimate = 0.95 * logPEstimate + 0.05 * logP;
+ }
+ samples++;
+ }
+ double p = lr.classifyScalar(input);
+ if (scores) {
+ System.out.printf("%10d %2d %10.2f %2.4f %10.4f %10.4f\n", samples, targetValue, lr.currentLearningRate(), p, logP, logPEstimate);
+ }
+
+ // now update model
+ lr.train(targetValue, input);
+
+ line = in.readLine();
+ }
+ in.close();
+ }
+
+ FileWriter modelOutput = new FileWriter(outputFile);
+ lmp.saveTo(modelOutput);
+ modelOutput.close();
+
+ System.out.printf("%d\n", lmp.getNumFeatures());
+ System.out.printf("%s ~ ", lmp.getTargetVariable());
+ String sep = "";
+ for (String v : csv.getPredictors()) {
+ double weight = predictorWeight(lr, 0, csv, v);
+ if (weight != 0) {
+ System.out.printf("%s%.3f*%s", sep, weight, v);
+ sep = " + ";
+ }
+ }
+ System.out.printf("\n");
+ for (int row = 0; row < lr.getBeta().numRows(); row++) {
+ for (String key : csv.getTraceDictionary().keySet()) {
+ double weight = predictorWeight(lr, row, csv, key);
+ if (weight != 0) {
+ System.out.printf("%20s %.5f\n", key, weight);
+ }
+ }
+ for (int column = 0; column < lr.getBeta().numCols(); column++) {
+ System.out.printf("%15.9f ", lr.getBeta().get(row, column));
+ }
+ System.out.println();
+ }
+ }
+ }
+
+ private static double predictorWeight(OnlineLogisticRegression lr, int row, RecordFactory csv, String predictor) {
+ double weight = 0;
+ for (Integer column : csv.getTraceDictionary().get(predictor)) {
+ weight += lr.getBeta().get(row, column);
+ }
+ return weight;
+ }
+
+ private static boolean parseArgs(String[] args) {
+ DefaultOptionBuilder builder = new DefaultOptionBuilder();
+
+ Option help = builder.withLongName("help").withDescription("print this list").create();
+
+ Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create();
+ Option scores = builder.withLongName("scores").withDescription("output score diagnostics during training").create();
+
+ ArgumentBuilder argumentBuilder = new ArgumentBuilder();
+ Option inputFile = builder.withLongName("input")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
+ .withDescription("where to get training data")
+ .create();
+
+ Option outputFile = builder.withLongName("output")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("output").withMaximum(1).create())
+ .withDescription("where to get training data")
+ .create();
+
+ Option predictors = builder.withLongName("predictors")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("p").create())
+ .withDescription("a list of predictor variables")
+ .create();
+
+ Option types = builder.withLongName("types")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("t").create())
+ .withDescription("a list of predictor variable types (numeric, word, or text)")
+ .create();
+
+ Option target = builder.withLongName("target")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("target").withMaximum(1).create())
+ .withDescription("the name of the target variable")
+ .create();
+
+ Option features = builder.withLongName("features")
+ .withArgument(
+ argumentBuilder.withName("numFeatures")
+ .withDefault("1000")
+ .withMaximum(1).create())
+ .withDescription("the number of internal hashed features to use")
+ .create();
+
+ Option passes = builder.withLongName("passes")
+ .withArgument(
+ argumentBuilder.withName("passes")
+ .withDefault("2")
+ .withMaximum(1).create())
+ .withDescription("the number of times to pass over the input data")
+ .create();
+
+ Option lambda = builder.withLongName("lambda")
+ .withArgument(argumentBuilder.withName("lambda").withDefault("1e-4").withMaximum(1).create())
+ .withDescription("the amount of coefficient decay to use")
+ .create();
+
+ Option rate = builder.withLongName("rate")
+ .withArgument(argumentBuilder.withName("learningRate").withDefault("1e-3").withMaximum(1).create())
+ .withDescription("the learning rate")
+ .create();
+
+ Option noBias = builder.withLongName("noBias")
+ .withDescription("don't include a bias term")
+ .create();
+
+ Option targetCategories = builder.withLongName("categories")
+ .withRequired(true)
+ .withArgument(argumentBuilder.withName("number").withMaximum(1).create())
+ .withDescription("the number of target categories to be considered")
+ .create();
+
+ Group normalArgs = new GroupBuilder()
+ .withOption(help)
+ .withOption(quiet)
+ .withOption(inputFile)
+ .withOption(outputFile)
+ .withOption(target)
+ .withOption(targetCategories)
+ .withOption(predictors)
+ .withOption(types)
+ .withOption(passes)
+ .withOption(lambda)
+ .withOption(rate)
+ .withOption(noBias)
+ .withOption(features)
+ .create();
+
+ Parser parser = new Parser();
+ parser.setHelpOption(help);
+ parser.setHelpTrigger("--help");
+ parser.setGroup(normalArgs);
+ parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
+ CommandLine cmdLine;
+ cmdLine = parser.parseAndHelp(args);
+
+ if (cmdLine == null) {
+ return false;
+ }
+
+ TrainLogistic.inputFile = getStringArgument(cmdLine, inputFile);
+ TrainLogistic.outputFile = getStringArgument(cmdLine, outputFile);
+
+ List<String> typeList = Lists.newArrayList();
+ for (Object x : cmdLine.getValues(types)) {
+ typeList.add(x.toString());
+ }
+
+ List<String> predictorList = Lists.newArrayList();
+ for (Object x : cmdLine.getValues(predictors)) {
+ predictorList.add(x.toString());
+ }
+
+ lmp = new LogisticModelParameters();
+ lmp.setTargetVariable(getStringArgument(cmdLine, target));
+ lmp.setMaxTargetCategories(getIntegerArgument(cmdLine, targetCategories));
+ lmp.setNumFeatures(getIntegerArgument(cmdLine, features));
+ lmp.setUseBias(!getBooleanArgument(cmdLine, noBias));
+ lmp.setTypeMap(predictorList, typeList);
+
+ lmp.setLambda(getDoubleArgument(cmdLine, lambda));
+ lmp.setLearningRate(getDoubleArgument(cmdLine, rate));
+
+ TrainLogistic.scores = getBooleanArgument(cmdLine, scores);
+ TrainLogistic.passes = getIntegerArgument(cmdLine, passes);
+
+ return true;
+ }
+
+ private static String getStringArgument(CommandLine cmdLine, Option inputFile) {
+ return (String) cmdLine.getValue(inputFile);
+ }
+
+ private static boolean getBooleanArgument(CommandLine cmdLine, Option option) {
+ return cmdLine.hasOption(option);
+ }
+
+ private static int getIntegerArgument(CommandLine cmdLine, Option features) {
+ return Integer.parseInt((String) cmdLine.getValue(features));
+ }
+
+ private static double getDoubleArgument(CommandLine cmdLine, Option op) {
+ return Double.parseDouble((String) cmdLine.getValue(op));
+ }
+
+ public static class InputOpener {
+ public static BufferedReader open(String inputFile) throws IOException {
+ InputStreamReader s;
+ try {
+ URL resource = Resources.getResource(inputFile);
+ s = new InputStreamReader(resource.openStream());
+ } catch (IllegalArgumentException e) {
+ s = new FileReader(inputFile);
+ }
+
+ return new BufferedReader(s);
+ }
+ }
+}
Added: mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TwentyNewsGroupTrain.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TwentyNewsGroupTrain.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TwentyNewsGroupTrain.java (added)
+++ mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TwentyNewsGroupTrain.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,224 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+import com.google.common.base.Splitter;
+import com.google.common.collect.ConcurrentHashMultiset;
+import com.google.common.collect.Lists;
+import com.google.common.collect.Maps;
+import com.google.common.collect.Multiset;
+import com.google.gson.Gson;
+import com.google.gson.GsonBuilder;
+import org.apache.lucene.analysis.Analyzer;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.standard.StandardAnalyzer;
+import org.apache.lucene.analysis.tokenattributes.TermAttribute;
+import org.apache.lucene.util.Version;
+import org.apache.mahout.math.DenseVector;
+import org.apache.mahout.math.Matrix;
+import org.apache.mahout.math.RandomAccessSparseVector;
+import org.apache.mahout.math.Vector;
+
+import java.io.*;
+import java.util.*;
+
+/**
+ * Simple training program that reads newsgroup articles, one per file and trains an SGD model using
+ * that data.
+ */
+public class TwentyNewsGroupTrain {
+ private static final int FEATURES = 200000;
+ private static final int PASSES = 1;
+ private static Splitter onColon = Splitter.on(":").trimResults();
+
+
+ public static void main(String[] args) throws IOException {
+ File base = new File(args[0]);
+
+ Map<String, Set<Integer>> traceDictionary = Maps.newTreeMap();
+ RecordValueEncoder encoder = new StaticWordValueEncoder("body");
+ RecordValueEncoder bias = new ConstantValueEncoder("Intercept");
+ bias.setTraceDictionary(traceDictionary);
+ bias.setTraceDictionary(traceDictionary);
+ RecordValueEncoder lines = new ConstantValueEncoder("Lines");
+ RecordValueEncoder logLines = new ConstantValueEncoder("LogLines");
+ encoder.setProbes(2);
+ encoder.setTraceDictionary(traceDictionary);
+
+ OnlineLogisticRegression learningAlgorithm = new OnlineLogisticRegression(20, FEATURES, new L1())
+ .alpha(1)
+ .stepOffset(1000)
+ .decayExponent(0.9)
+ .lambda(0)
+ .learningRate(10);
+
+ Dictionary newsGroups = new Dictionary();
+
+ List<File> files = Lists.newArrayList();
+ for (File newsgroup : base.listFiles()) {
+ newsGroups.intern(newsgroup.getName());
+ files.addAll(Arrays.asList(newsgroup.listFiles()));
+ }
+ System.out.printf("%d files\n", files.size());
+
+ Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_30);
+ Random rand = new Random();
+ double averageLL = 0;
+ double averageCorrect = 0;
+ double averageLineCount = 0;
+
+ int k = 0;
+ double step = 0;
+ int[] bumps = new int[]{1, 2, 5};
+ PrintWriter lineCounts = new PrintWriter(new File("lineCounts.tsv"));
+ for (File file : permute(files, rand)) {
+ BufferedReader reader = new BufferedReader(new FileReader(file));
+
+ String ng = file.getParentFile().getName();
+ int actual = newsGroups.intern(ng);
+
+ Multiset<String> words = ConcurrentHashMultiset.create();
+
+ double lineCount = averageLineCount;
+
+ // read headers
+ String line = reader.readLine();
+ while (line != null && line.length() > 0) {
+ if (line.startsWith("Lines:")) {
+ String count = Lists.newArrayList(onColon.split(line)).get(1);
+ try {
+ lineCount = Integer.parseInt(count);
+ averageLineCount = averageLineCount + (lineCount - averageLineCount) / Math.min(k + 1, 1000);
+ lineCounts.printf("%s\t%.1f\n", ng, lineCount);
+ } catch (NumberFormatException e) {
+ // ignore bogus data, use average value
+ }
+ }
+
+ boolean countHeader = (
+ false
+// ||line.startsWith("From:")
+// ||line.startsWith("Subject:")
+// ||line.startsWith("Keywords:")
+ ||line.startsWith("Summary:")
+ );
+ do {
+ StringReader in = new StringReader(line);
+ if (countHeader) {
+ countWords(analyzer, words, in);
+ }
+ line = reader.readLine();
+ } while (line.startsWith(" "));
+ }
+
+ // read body of document
+ // countWords(analyzer, words, reader);
+ reader.close();
+
+ // now encode words as vector
+ Vector v = new RandomAccessSparseVector(FEATURES);
+
+ // encode constant term
+ bias.addToVector(null, 1, v);
+
+ lines.addToVector(null, lineCount / 30, v);
+ logLines.addToVector(null, Math.log(lineCount + 1), v);
+
+ // and then all other words
+ for (String word : words.elementSet()) {
+ encoder.addToVector(word, Math.log(1 + words.count(word)), v);
+ }
+
+ double ll = learningAlgorithm.logLikelihood(actual, v);
+ averageLL = (Math.min(k, 100) * averageLL + ll) / (Math.min(k, 100) + 1);
+ Vector p = new DenseVector(20);
+ learningAlgorithm.classifyFull(p, v);
+ int estimated = p.maxValueIndex();
+ boolean correct = estimated == actual;
+ averageCorrect = (Math.min(k, 500) * averageCorrect + (correct ? 1 : 0)) / (Math.min(k, 500) + 1);
+ learningAlgorithm.train(actual, v);
+
+ k++;
+ if (k % (bumps[(int) Math.floor(step) % bumps.length] * Math.pow(10, Math.floor(step / bumps.length))) == 0) {
+ step += 0.25;
+ if (estimated == -1) {
+ System.out.printf("%d\n", estimated);
+ }
+ System.out.printf("%10d %10.3f %10.3f %10.2f %s %s\n",
+ k, ll, averageLL, averageCorrect * 100, ng, newsGroups.values().get(estimated));
+
+ }
+ lineCounts.close();
+ }
+
+ learningAlgorithm.close();
+
+ GsonBuilder gb = new GsonBuilder();
+ gb.registerTypeAdapter(Matrix.class, new LogisticModelParameters.MatrixTypeAdapter());
+ Gson gson = gb.setPrettyPrinting().create();
+
+ Writer output = new FileWriter("model");
+
+ Model x = new Model();
+ x.lr = learningAlgorithm;
+ x.targetCategories = newsGroups.values();
+ gson.toJson(x, output);
+
+ output.close();
+ }
+
+ private static void checkVector(Vector v) {
+ Iterator<Vector.Element> i = v.iterateNonZero();
+ while (i.hasNext()) {
+ Vector.Element element = i.next();
+ if (Double.isInfinite(element.get()) || Double.isNaN(element.get())) {
+ System.out.printf("Found invalid value at %d: %.0f\n", element.index(), element.get());
+ }
+ }
+ }
+
+ private static class Model {
+ OnlineLogisticRegression lr;
+ List<String> targetCategories;
+ }
+
+ private static void countWords(Analyzer analyzer, Multiset<String> words, Reader in) throws IOException {
+ TokenStream ts = analyzer.tokenStream("body", in);
+ TermAttribute termAtt = ts.addAttribute(TermAttribute.class);
+ while (ts.incrementToken()) {
+ char[] termBuffer = termAtt.termBuffer();
+ int termLen = termAtt.termLength();
+ words.add(new String(termBuffer, 0, termLen));
+ }
+ }
+
+ private static <T> List<T> permute(Iterable<T> values, Random rand) {
+ ArrayList<T> r = Lists.newArrayList();
+ for (T value : values) {
+ int i = rand.nextInt(r.size() + 1);
+ if (i < r.size()) {
+ T t = r.get(i);
+ r.set(i, value);
+ r.add(t);
+ } else {
+ r.add(value);
+ }
+ }
+ return r;
+ }
+}
Added: mahout/trunk/examples/src/main/resources/donut-test.csv
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/resources/donut-test.csv?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/resources/donut-test.csv (added)
+++ mahout/trunk/examples/src/main/resources/donut-test.csv Sat Jul 24 23:38:28 2010
@@ -0,0 +1,41 @@
+"x","y","shape","color","xx","xy","yy","c","a","b"
+0.802415437065065,0.0978854028508067,21,2,0.643870533640319,0.07854475831082,0.00958155209126472,0.503141377562721,0.808363832523192,0.220502180491382
+0.97073650965467,0.989339149091393,23,2,0.942329371176533,0.96038763245370,0.978791951924881,0.67900343471543,1.38604520961670,0.989771844311643
+0.566630310611799,0.369259539060295,25,1,0.321069908904024,0.209233647314105,0.136352607187021,0.146740132271139,0.676330182744379,0.569352171215186
+0.377948862500489,0.500907538458705,24,1,0.142845342665413,0.189317434378387,0.250908362084759,0.122054511555201,0.62749797190921,0.79865886318828
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+0.395229484187439,0.385281964903697,25,1,0.156206345171069,0.152274792255611,0.148442192480054,0.155361155247979,0.551949760078871,0.717070128562224
+0.757145672803745,0.416044564917684,21,1,0.573269569845435,0.315006342020941,0.173093079997545,0.270503996498299,0.863922826323613,0.481737796145881
+0.589166145538911,0.971624446567148,24,2,0.347116747049177,0.572448230095344,0.944054065166917,0.479979395505718,1.13629697360157,1.05491161769044
+0.843438957352191,0.218833807157353,25,2,0.711389274779351,0.184572958142208,0.0478882351549814,0.443852166182378,0.871365313708512,0.269071728782402
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+0.374636142514646,0.197784703457728,21,2,0.140352239278254,0.0740972983518064,0.0391187889218614,0.327185241457712,0.423640210792266,0.655895375171089
+0.482126326300204,0.841961156809703,22,1,0.232445794511731,0.405931639420132,0.708898589576332,0.342427950053959,0.970229036922758,0.988479504839456
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+0.0772640188224614,0.437956433976069,22,2,0.00596972860459766,0.0338382741581451,0.191805838061035,0.427264688298837,0.444719649515999,1.02139489377063
+0.998469967395067,0.464829172473401,25,2,0.996942275789907,0.464117968683793,0.216066159582307,0.499709210945471,1.10136662168971,0.464831690595724
Added: mahout/trunk/examples/src/main/resources/donut.csv
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/resources/donut.csv?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/resources/donut.csv (added)
+++ mahout/trunk/examples/src/main/resources/donut.csv Sat Jul 24 23:38:28 2010
@@ -0,0 +1,41 @@
+"x","y","shape","color","k","k0","xx","xy","yy","a","b","c","bias"
+0.923307513352484,0.0135197141207755,21,2,4,8,0.852496764213146,0.0124828536260896,0.000182782669907495,0.923406490600458,0.0778750292332978,0.644866125183976,1
+0.711011884035543,0.909141522599384,22,2,3,9,0.505537899239772,0.64641042683833,0.826538308114327,1.15415605849213,0.953966686673604,0.46035073663368,1
+0.75118898646906,0.836567111080512,23,2,3,9,0.564284893392414,0.62842000028592,0.699844531341594,1.12433510339845,0.872783737128441,0.419968245447719,1
+0.308209649519995,0.418023289414123,24,1,5,1,0.094993188057238,0.128838811521522,0.174743470492603,0.519361780024138,0.808280495564412,0.208575453051705,1
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Added: mahout/trunk/examples/src/main/resources/test-data.csv
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/resources/test-data.csv?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/main/resources/test-data.csv (added)
+++ mahout/trunk/examples/src/main/resources/test-data.csv Sat Jul 24 23:38:28 2010
@@ -0,0 +1,61 @@
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Added: mahout/trunk/examples/src/test/java/org/apache/mahout/classifier/sgd/LogisticModelParametersTest.java
URL: http://svn.apache.org/viewvc/mahout/trunk/examples/src/test/java/org/apache/mahout/classifier/sgd/LogisticModelParametersTest.java?rev=978948&view=auto
==============================================================================
--- mahout/trunk/examples/src/test/java/org/apache/mahout/classifier/sgd/LogisticModelParametersTest.java (added)
+++ mahout/trunk/examples/src/test/java/org/apache/mahout/classifier/sgd/LogisticModelParametersTest.java Sat Jul 24 23:38:28 2010
@@ -0,0 +1,188 @@
+/*
+ * 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.mahout.classifier.sgd;
+
+import com.google.common.collect.ImmutableList;
+import org.apache.mahout.math.DenseVector;
+import org.apache.mahout.math.Vector;
+import org.junit.Test;
+
+import java.io.IOException;
+import java.io.StringReader;
+import java.io.StringWriter;
+
+import static org.junit.Assert.assertEquals;
+
+
+public class LogisticModelParametersTest {
+ @Test
+ public void testSaveTo() throws IOException {
+ LogisticModelParameters lmp = new LogisticModelParameters();
+ lmp.setTargetVariable("target");
+ lmp.setMaxTargetCategories(3);
+
+ lmp.setTypeMap(ImmutableList.of("x", "y", "z"), ImmutableList.of("n", "word", "numeric"));
+ lmp.setUseBias(true);
+
+ lmp.setLambda(123.4);
+ lmp.setLearningRate(5.2);
+
+ lmp.setNumFeatures(214);
+
+ lmp.getCsvRecordFactory().firstLine("x,target,q,z,r,y");
+ Vector v = new DenseVector(20);
+ assertEquals(0, lmp.getCsvRecordFactory().processLine("3,t_1,foo,5,r,cat", v));
+ assertEquals(1, lmp.getCsvRecordFactory().processLine("3,t_2,foo,5,r,dog", v));
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_3,foo,5,r,pig", v));
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_4,foo,5,r,pig", v));
+
+ assertEquals(3, lmp.getMaxTargetCategories());
+ assertEquals("[t_1, t_2, t_3]", lmp.getCsvRecordFactory().getTargetCategories().toString());
+
+ StringWriter s = new StringWriter();
+ lmp.saveTo(s);
+ s.close();
+ assertEquals("{\"targetVariable\":\"target\",\"typeMap\":{\"z\":\"numeric\",\"y\":\"word\",\"x\":\"n\"},\n" +
+ " \"numFeatures\":214,\"useBias\":true,\"maxTargetCategories\":3,\n" +
+ " \"targetCategories\":[\"t_1\",\"t_2\",\"t_3\"],\"lambda\":123.4,\"learningRate\":5.2}", s.toString().trim());
+ }
+
+ @Test
+ public void testSaveWithRegression() throws IOException {
+ LogisticModelParameters lmp = new LogisticModelParameters();
+ lmp.setTargetVariable("target");
+ lmp.setMaxTargetCategories(3);
+
+ lmp.setTypeMap(ImmutableList.of("x", "y", "z"), ImmutableList.of("n", "word", "numeric"));
+ lmp.setUseBias(true);
+
+ lmp.setLambda(123.4);
+ lmp.setLearningRate(5.2);
+
+ lmp.setNumFeatures(214);
+
+ OnlineLogisticRegression lr = lmp.createRegression();
+ lr.getBeta().set(0, 4, 5.0);
+ lr.getBeta().set(1, 3, 7.0);
+ lmp.getCsvRecordFactory().firstLine("x,target,q,z,r,y");
+ Vector v = new DenseVector(20);
+ assertEquals(0, lmp.getCsvRecordFactory().processLine("3,t_1,foo,5,r,cat", v));
+ assertEquals(1, lmp.getCsvRecordFactory().processLine("3,t_2,foo,5,r,dog", v));
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_3,foo,5,r,pig", v));
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_4,foo,5,r,pig", v));
+
+ assertEquals(3, lmp.getMaxTargetCategories());
+ assertEquals("[t_1, t_2, t_3]", lmp.getCsvRecordFactory().getTargetCategories().toString());
+
+ StringWriter s = new StringWriter();
+ lmp.saveTo(s);
+ s.close();
+ assertEquals("{\"targetVariable\":\"target\",\"typeMap\":{\"z\":\"numeric\",\"y\":\"word\",\"x\":\"n\"},\n" +
+ " \"numFeatures\":214,\"useBias\":true,\"maxTargetCategories\":3,\n" +
+ " \"targetCategories\":[\"t_1\",\"t_2\",\"t_3\"],\"lambda\":123.4,\"learningRate\":5.2,\n" +
+ " \"lr\":{\"beta\":{\"rows\":2,\"cols\":214,\"data\":[[0.0,0.0,0.0,0.0,5.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[\n" +
+ " 0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0]]},\"numCategories\":3,\"step\":1,\"mu_0\":5.2,\n" +
+ " \"decayFactor\":0.999,\"stepOffset\":10,\"decayExponent\":-0.5,\"lambda\":\n" +
+ " 123.4,\"sealed\":true}}", s.toString().trim());
+ }
+
+ @Test
+ public void testLoadFrom() {
+ LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new StringReader(
+ "{\"targetVariable\":\"target\",\"typeMap\":{\"z\":\"numeric\",\"y\":\"word\",\"x\":\"n\"},\n" +
+ " \"numFeatures\":214,\"useBias\":true,\"maxTargetCategories\":3,\n" +
+ " \"targetCategories\":[\"t_1\",\"t_2\",\"t_3\"],\"lambda\":123.4,\"learningRate\":5.2,\n" +
+ " \"lr\":{\"beta\":{\"rows\":2,\"cols\":214,\"data\":[[0.0,0.0,0.0,0.0,5.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[\n" +
+ " 0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,\n" +
+ " 0.0,0.0,0.0,0.0,0.0,0.0]]},\"numCategories\":3,\"step\":1,\"mu_0\":5.2,\n" +
+ " \"decayFactor\":0.999,\"stepOffset\":10,\"decayExponent\":-0.5,\"lambda\":\n" +
+ " 123.4,\"sealed\":true}}"));
+
+ assertEquals(5.0, lmp.createRegression().getBeta().get(0, 4), 0);
+ assertEquals(7.0, lmp.createRegression().getBeta().get(1, 3), 0);
+
+ assertEquals(123.4, lmp.getLambda(), 0.0);
+ assertEquals(true, lmp.useBias());
+ assertEquals(5.2, lmp.getLearningRate(), 0.0);
+ assertEquals(214, lmp.getNumFeatures());
+
+ lmp.getCsvRecordFactory().firstLine("x,target,q,z,r,y");
+ Vector v = new DenseVector(20);
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_3,foo,5,r,pig", v));
+ assertEquals(1, lmp.getCsvRecordFactory().processLine("3,t_2,foo,5,r,dog", v));
+ assertEquals(2, lmp.getCsvRecordFactory().processLine("3,t_4,foo,5,r,pig", v));
+ assertEquals(0, lmp.getCsvRecordFactory().processLine("3,t_1,foo,5,r,cat", v));
+
+ assertEquals(3, lmp.getMaxTargetCategories());
+ assertEquals("[t_1, t_2, t_3]", lmp.getCsvRecordFactory().getTargetCategories().toString());
+
+ assertEquals(214, lmp.createRegression().getBeta().numCols());
+ assertEquals(2, lmp.createRegression().getBeta().numRows());
+ }
+}
Modified: mahout/trunk/math/src/main/java/org/apache/mahout/math/jet/random/NegativeBinomial.java
URL: http://svn.apache.org/viewvc/mahout/trunk/math/src/main/java/org/apache/mahout/math/jet/random/NegativeBinomial.java?rev=978948&r1=978947&r2=978948&view=diff
==============================================================================
--- mahout/trunk/math/src/main/java/org/apache/mahout/math/jet/random/NegativeBinomial.java (original)
+++ mahout/trunk/math/src/main/java/org/apache/mahout/math/jet/random/NegativeBinomial.java Sat Jul 24 23:38:28 2010
@@ -50,7 +50,8 @@ public class NegativeBinomial extends Ab
*/
public NegativeBinomial(int n, double p, RandomEngine randomGenerator) {
setRandomGenerator(randomGenerator);
- setNandP(n, p);
+ this.n = n;
+ this.p = p;
this.gamma = new Gamma(n, 1, randomGenerator);
this.poisson = new Poisson(0.0, randomGenerator);
}