Unfortunately not, 100 iterations ~ 30 minutes 300 iterations > 2 days and
it is still running... i will block it
i still do not understand what number should i set as *folds*. Ok i will
set a number > 1 but, should i have to pay more attention to this
parameter? if i set 8 or 10 does it matter anything?
2017-03-06 12:19 GMT+01:00 Joern Kottmann <kottm...@gmail.com>:
> test.evaluate(samples, 1), here the second parameter is the number of
> folds, usually you use 10 or a number larger than 1.
>
> The amount of times you need for training with perceptron is linear to the
> iterations, if you use 300 instead of 100 it should take three times as
> long.
>
> Jörn
>
> On Mon, Mar 6, 2017 at 11:12 AM, Damiano Porta <damianopo...@gmail.com>
> wrote:
>
> > Jorn,
> > I am training and testing the model via api. If it is not a training
> > problem. How is that possible that the evaluation is taking 2 days (and
> > still running) to evaluate the model? As i told you with 100 iterations i
> > can get the model and the test in ~30 minutes.
> >
> > I only have a doubt about evaluation, this is the code:
> >
> > try (ObjectStream<NameSample> samples =
> > ObjectStreamUtils.createObjectStream(evaluation)) {
> >
> > TrainingParameters mlParams = new TrainingParameters();
> > mlParams.put(TrainingParameters.ALGORITHM_PARAM,
> > PerceptronTrainer.PERCEPTRON_VALUE);
> > mlParams.put(TrainingParameters.ITERATIONS_PARAM,
> > Integer.toString(100));
> > mlParams.put(TrainingParameters.CUTOFF_PARAM,
> > Integer.toString(0));
> >
> > TokenNameFinderCrossValidator test = new
> > TokenNameFinderCrossValidator("it",
> > null, mlParams, null,
> > (TokenNameFinderEvaluationMonitor)null);
> >
> > test.evaluate(samples, 1); *// <---- SECOND PARAMETER HERE*
> >
> > FMeasure result = test.getFMeasure();
> >
> > System.out.println(result.toString());
> > }
> >
> > What should i put on the second parameter of test.evaluate() ? Each
> sample
> > (in samples variable) represents a document. There are no relations with
> > other samples.
> >
> > 2017-03-06 10:56 GMT+01:00 Joern Kottmann <kottm...@gmail.com>:
> >
> > > Hello,
> > >
> > > the model is only available after the training finished, hard to guess
> > what
> > > you are doing.
> > >
> > > Do you use the command line? Which command?
> > >
> > > Jörn
> > >
> > > On Mon, Mar 6, 2017 at 10:29 AM, Damiano Porta <damianopo...@gmail.com
> >
> > > wrote:
> > >
> > > > Hello Jorn,
> > > > I tried with 300 iterations and it takes forever, reducing that
> number
> > to
> > > > 100 i can finally get the model in half an hour.
> > > >
> > > > The problem with 300 iterations is that i can see the model (.bin) in
> > > half
> > > > an hour too but the computations are still running. So i do not
> really
> > > > understand what it is doing.
> > > >
> > > > Damiano
> > > >
> > > > 2017-03-06 10:19 GMT+01:00 Joern Kottmann <kottm...@gmail.com>:
> > > >
> > > > > Hello,
> > > > >
> > > > > this looks like output from the cross validator.
> > > > >
> > > > > Jörn
> > > > >
> > > > > On Sun, Mar 5, 2017 at 11:34 AM, Damiano Porta <
> > damianopo...@gmail.com
> > > >
> > > > > wrote:
> > > > >
> > > > > > Hello,
> > > > > >
> > > > > > I am training a NER model with perceptron classifier (using
> OpenNLP
> > > > > 1.7.0)
> > > > > >
> > > > > > the output of the training is:
> > > > > >
> > > > > > Indexing events using cutoff of 0
> > > > > >
> > > > > > Computing event counts... done. 11861603 events
> > > > > > Indexing... done.
> > > > > > Collecting events... Done indexing.
> > > > > > Incorporating indexed data for training...
> > > > > > done.
> > > > > > Number of Event Tokens: 11861603
> > > > > > Number of Outcomes: 23
> > > > > > Number of Predicates: 6623489
> > > > > > Computing model parameters...
> > > > > > Performing 300 iterations.
> > > > > > 1: . (11795234/11861603) 0.9944047191597966
> > > > > > 2: . (11820243/11861603) 0.9965131188423689
> > > > > > 3: . (11829329/11861603) 0.9972791198626357
> > > > > > 4: . (11834935/11861603) 0.9977517372651908
> > > > > > 5: . (11838996/11861603) 0.9980941024581584
> > > > > > 6: . (11841501/11861603) 0.9983052880795286
> > > > > > 7: . (11843704/11861603) 0.998491013398442
> > > > > > 8: . (11845304/11861603) 0.9986259024180796
> > > > > > 9: . (11846421/11861603) 0.9987200718149141
> > > > > > 10: . (11847181/11861603) 0.9987841440992419
> > > > > > 20: . (11852226/11861603) 0.9992094660392866
> > > > > > 30: . (11853947/11861603) 0.9993545560410343
> > > > > > 40: . (11854831/11861603) 0.999429082224384
> > > > > > 50: . (11855471/11861603) 0.999483037832239
> > > > > > Stopping: change in training set accuracy less than 1.0E-5
> > > > > > Stats: (11846242/11861603) 0.998704981105842
> > > > > > ...done.
> > > > > > Compressed 6623489 parameters to 554312
> > > > > > 6892 outcome patterns
> > > > > > Indexing events using cutoff of 0
> > > > > >
> > > > > > Computing event counts... done. 6370206 events
> > > > > > Indexing... done.
> > > > > > Collecting events... Done indexing.
> > > > > > Incorporating indexed data for training...
> > > > > > done.
> > > > > > Number of Event Tokens: 6370206
> > > > > > Number of Outcomes: 23
> > > > > > Number of Predicates: 3737425
> > > > > > Computing model parameters...
> > > > > > Performing 300 iterations.
> > > > > > 1: . (6330365/6370206) 0.9937457281601254
> > > > > > 2: . (6345859/6370206) 0.9961779885925196
> > > > > > 3: . (6351552/6370206) 0.9970716802564941
> > > > > > 4: . (6354847/6370206) 0.9975889319748843
> > > > > > 5: . (6356872/6370206) 0.997906818084062
> > > > > > 6: . (6358350/6370206) 0.998138835698563
> > > > > > 7: . (6359611/6370206) 0.9983367884806237
> > > > > > 8: . (6360473/6370206) 0.9984721059256169
> > > > > > 9: . (6361138/6370206) 0.9985764981540628
> > > > > > 10: . (6361532/6370206) 0.9986383485871572
> > > > > > 20: . (6364161/6370206) 0.9990510510963068
> > > > > > 30: . (6365106/6370206) 0.9991993979472563
> > > > > > Stopping: change in training set accuracy less than 1.0E-5
> > > > > > Stats: (6360617/6370206) 0.9984947111600473
> > > > > > ...done.
> > > > > > Indexing events using cutoff of 0
> > > > > >
> > > > > > Computing event counts... done. 6370114 events
> > > > > > Indexing... done.
> > > > > > Collecting events... Done indexing.
> > > > > > Incorporating indexed data for training...
> > > > > > done.
> > > > > > Number of Event Tokens: 6370114
> > > > > > Number of Outcomes: 23
> > > > > > Number of Predicates: 3737390
> > > > > > Computing model parameters...
> > > > > > Performing 300 iterations.
> > > > > > 1: . (6330266/6370114) 0.9937445389517362
> > > > > > 2: . (6345810/6370114) 0.9961846836650019
> > > > > > 3: . (6351374/6370114) 0.9970581374210885
> > > > > > 4: . (6354747/6370114) 0.9975876412886803
> > > > > > 5: . (6356872/6370114) 0.9979212302950936
> > > > > > 6: . (6358429/6370114) 0.998165652922381
> > > > > > 7: . (6359417/6370114) 0.9983207521874805
> > > > > > 8: . (6360292/6370114) 0.9984581123665919
> > > > > > 9: . (6361076/6370114) 0.9985811870870757
> > > > > > 10: . (6361693/6370114) 0.998678045636232
> > > > > > 20: . (6364109/6370114) 0.9990573167136413
> > > > > > 30: . (6365008/6370114) 0.9991984444862368
> > > > > > 40: . (6365478/6370114) 0.9992722265253023
> > > > > > Stopping: change in training set accuracy less than 1.0E-5
> > > > > > Stats: (6359985/6370114) 0.9984099185666065
> > > > > > ...done.
> > > > > > Indexing events using cutoff of 0
> > > > > >
> > > > > > Computing event counts... done. 6370480 events
> > > > > > Indexing... done.
> > > > > > Collecting events... Done indexing.
> > > > > > Incorporating indexed data for training...
> > > > > > done.
> > > > > > Number of Event Tokens: 6370480
> > > > > > Number of Outcomes: 23
> > > > > > Number of Predicates: 3737798
> > > > > > Computing model parameters...
> > > > > > Performing 300 iterations.
> > > > > > 1: . (6330685/6370480) 0.9937532179678769
> > > > > > 2: . (6346153/6370480) 0.9961812924614786
> > > > > > 3: . (6351726/6370480) 0.9970561088018485
> > > > > > 4: . (6355089/6370480) 0.9975840125076917
> > > > > > 5: . (6357173/6370480) 0.9979111464128292
> > > > > > 6: . (6358780/6370480) 0.9981634036995642
> > > > > > 7: . (6359845/6370480) 0.9983305810551167
> > > > > > 8: . (6360827/6370480) 0.9984847295651191
> > > > > > 9: . (6361316/6370480) 0.9985614898720347
> > > > > > 10: . (6362076/6370480) 0.9986807901445417
> > > > > > 20: . (6364506/6370480) 0.9990622370684784
> > > > > > 30: . (6365415/6370480) 0.9992049264733583
> > > > > > Stopping: change in training set accuracy less than 1.0E-5
> > > > > > Stats: (6362594/6370480) 0.9987621026986977
> > > > > > ...done.
> > > > > > Indexing events using cutoff of 0
> > > > > >
> > > > > > Computing event counts... done. 6370008 events
> > > > > > Indexing... done.
> > > > > > Collecting events... Done indexing.
> > > > > > Incorporating indexed data for training...
> > > > > > done.
> > > > > > Number of Event Tokens: 6370008
> > > > > > Number of Outcomes: 23
> > > > > > Number of Predicates: 3737824
> > > > > > Computing model parameters...
> > > > > > Performing 300 iterations.
> > > > > > 1: . (6330200/6370008) 0.9937507142848172
> > > > > > 2: . (6345643/6370008) 0.9961750440501802
> > > > > > 3: . (6351415/6370008) 0.9970811653611737
> > > > > > 4: . (6354522/6370008) 0.9975689198506501
> > > > > > 5: . (6356723/6370008) 0.9979144453193779
> > > > > > 6: . (6358164/6370008) 0.9981406616757781
> > > > > > 7: . (6359399/6370008) 0.9983345389833106
> > > > > > 8: . (6360274/6370008) 0.9984719014481614
> > > > > > 9: . (6360694/6370008) 0.9985378354312899
> > > > > > 10: . (6361531/6370008) 0.9986692324405244
> > > > > > ....
> > > > > > ....
> > > > > > ....
> > > > > >
> > > > > > etc etc is that normal ? The parameters are; *0 cutoff* and *300
> > > > > > iterators*.
> > > > > >
> > > > > > The corpus is relative small, it has 20k sentences.
> > > > > >
> > > > > > I do not remember an output like that using MAXENT classifier.
> > > > > >
> > > > > > Damiano
> > > > > >
> > > > >
> > > >
> > >
> >
>
