I have write a command line program proto type for RunAdaptiveLogistic,
1. How can I make it invokeable from mahout
2. Can you help to fine tune the AdaptiveLogisticRegression creating
and settings to make it make sense.
On Tue, May 10, 2011 at 11:30 PM, Ted Dunning <ted.dunn...@gmail.com> wrote:
> Great idea. Why don't you implement something like what you need? Others
> will be happy to contribute improvements.
>
> On Tue, May 10, 2011 at 8:26 AM, XiaoboGu <guxiaobo1...@gmail.com> wrote:
>
>> > There isn't a good command line for this, largely because it is difficult
>> to
>> > describe how to convert each CSV field. There is some beginnings of
>> efforts
>> > on this, but the results are still limit.
>>
>> In common usages the predictor variables are almost number or category
>> variables encoded into numbers, so an unify CSV file converter is possible
>> for data with only these data types.
>>
>
package org.apache.mahout.classifier.sgd;
import java.io.DataInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
public class AdaptiveLogisticModelParameters extends LogisticModelParameters {
private AdaptiveLogisticRegression alr;
public AdaptiveLogisticRegression createAdaptiveLogisticRegression() {
if (alr == null) {
alr = new
AdaptiveLogisticRegression(getMaxTargetCategories(),
getNumFeatures(), new L1());
}
return alr;
}
public static AdaptiveLogisticModelParameters
loadFromStream(InputStream in)
throws IOException {
AdaptiveLogisticModelParameters result = new
AdaptiveLogisticModelParameters();
result.readFields(new DataInputStream(in));
return result;
}
public static AdaptiveLogisticModelParameters loadFromFile(File in)
throws IOException {
InputStream input = new FileInputStream(in);
try {
return loadFromStream(input);
} finally {
input.close();
}
}
}
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 java.io.BufferedReader;
import java.io.File;
import java.io.IOException;
import java.io.PrintStream;
import java.util.Locale;
public final class RunAdaptiveLogistic {
private static String inputFile;
private static String modelFile;
private static boolean showAuc;
private static boolean showScores;
private static boolean showConfusion;
static PrintStream output = System.out;
private RunAdaptiveLogistic(){}
public static void main(String[] args) throws IOException {
if (parseArgs(args)) {
if (!showAuc && !showConfusion && !showScores) {
showAuc = true;
showConfusion = true;
}
//Auc collector = new Auc();
AdaptiveLogisticModelParameters lmp =
AdaptiveLogisticModelParameters.loadFromFile(new File(modelFile));
CsvRecordFactory csv = lmp.getCsvRecordFactory();
AdaptiveLogisticRegression lr =
lmp.createAdaptiveLogisticRegression();
BufferedReader in = TrainLogistic.open(inputFile);
String line = in.readLine();
csv.firstLine(line);
line = in.readLine();
if (showScores) {
output.printf(Locale.ENGLISH, "\"%s\",\"%s\",\"%s\"\n",
"target", "model-output", "log-likelihood");
}
while (line != null) {
Vector v = new
SequentialAccessSparseVector(lmp.getNumFeatures());
int target = csv.processLine(line, v);
lr.train(target, v);
//double score = lr.classifyScalar(v);
if (showScores) {
//output.printf(Locale.ENGLISH, "%d,%.3f,%.6f\n", target,
score, lr.logLikelihood(target, v));
output.printf(Locale.ENGLISH," sroce can't be shown
while using AdaptiveLogisticRegression");
}
//collector.add(target, score);
line = in.readLine();
}
if (showAuc) {
//output.printf(Locale.ENGLISH, "AUC = %.2f\n",
collector.auc());
}
/*
if (showConfusion) {
Matrix m = collector.confusion();
output.printf(Locale.ENGLISH, "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();
output.printf(Locale.ENGLISH, "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 inputFileOption = builder.withLongName("input")
.withRequired(true)
.withArgument(argumentBuilder.withName("input").withMaximum(1).create())
.withDescription("where to get training data")
.create();
Option modelFileOption = 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(inputFileOption)
.withOption(modelFileOption)
.create();
Parser parser = new Parser();
parser.setHelpOption(help);
parser.setHelpTrigger("--help");
parser.setGroup(normalArgs);
parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
CommandLine cmdLine = parser.parseAndHelp(args);
if (cmdLine == null) {
return false;
}
inputFile = getStringArgument(cmdLine, inputFileOption);
modelFile = getStringArgument(cmdLine, modelFileOption);
showAuc = getBooleanArgument(cmdLine, auc);
showScores = getBooleanArgument(cmdLine, scores);
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);
}
}
