This usually means that you have fed the ALR enough data for it to push a
batch of learning into the evolutionary algorithm. That means that there
isn't any best result yet.
Getting that null doesn't impact the model, but you have to watch out for
it.
On Sun, May 29, 2011 at 1:23 AM, XiaoboGu <guxiaobo1...@gmail.com> wrote:
> Hi,
>
> The main process for MAHOUT-696 is as following, but it will always
> cause a NullPointerException after the first call to getBest, can we
> continue training AdaptiveLogisticRegressions after using getBest() to score
> some new lines just as TrainLogistic does?
>
>
>
> double logPEstimate = 0;
> int k = 0;
>
> CsvRecordFactory csv = lmp.getCsvRecordFactory();
> model = lmp.createAdaptiveLogisticRegression();
> State<Wrapper, CrossFoldLearner> best = null;
> CrossFoldLearner learner = null;
>
> for (int pass = 0; pass < passes; pass++) {
> BufferedReader in = 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);
>
> // update model
> model.train(targetValue, input);
>
> k ++;
>
> if (scores && (k % (skipscorenum +
> 1) == 0) ) {
>
> best = model.getBest();
> if (null != best) {
> learner =
> best.getPayload().getLearner();
> }
> if (learner != null) {
> // check performance while
> this is still news
> double logP =
> learner.logLikelihood(targetValue, input);
> if
> (!Double.isInfinite(logP)) {
> if (k < 20) {
> logPEstimate
> = (k * logPEstimate + logP)
>
> / (k + 1);
> } else {
> logPEstimate
> = 0.95 * logPEstimate + 0.05
>
> * logP;
> }
> }
> double p =
> learner.classifyScalar(input);
>
> output.printf(Locale.ENGLISH,
> "%10d %2d
> %10.2f %2.4f %10.4f %10.4f\n",
> k,
> targetValue,
>
> learner.percentCorrect(), p, logP,
>
> logPEstimate);
> }else{
>
> output.printf(Locale.ENGLISH,
>
> "%10d %2d %s\n", k, targetValue,
>
> "AdaptiveLogisticRegression is not ready for scoring ... ");
> }
> }
>
>
> line = in.readLine();
> }
> in.close();
> }
>
>
>
>
> 100 1 AdaptiveLogisticRegression is not ready for scoring ...
> 200 0 AdaptiveLogisticRegression is not ready for scoring ...
> 300 1 AdaptiveLogisticRegression is not ready for scoring ...
> 400 0 AdaptiveLogisticRegression is not ready for scoring ...
> 500 1 AdaptiveLogisticRegression is not ready for scoring ...
> Exception in thread "main" java.lang.IllegalStateException:
> java.lang.NullPointerException
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.trainWithBufferedExamples(AdaptiveLogisticRegression.java:144)
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.train(AdaptiveLogisticRegression.java:117)
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.train(AdaptiveLogisticRegression.java:103)
> at
> org.apache.mahout.classifier.sgd.TrainAdaptiveLogistic.main(TrainAdaptiveLogistic.java:72)
> Caused by: java.lang.NullPointerException
> at
> org.apache.mahout.classifier.sgd.CrossFoldLearner.train(CrossFoldLearner.java:134)
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression$Wrapper.train(AdaptiveLogisticRegression.java:411)
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression$1.apply(AdaptiveLogisticRegression.java:128)
> at
> org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression$1.apply(AdaptiveLogisticRegression.java:1)
> at
> org.apache.mahout.ep.EvolutionaryProcess$1.call(EvolutionaryProcess.java:146)
> at
> org.apache.mahout.ep.EvolutionaryProcess$1.call(EvolutionaryProcess.java:1)
> at
> java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303)
> at java.util.concurrent.FutureTask.run(FutureTask.java:138)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
> at java.lang.Thread.run(Thread.java:662)
>
>
