Hi Jörn,
Currently, GIS is a wrapper for the GISTrainer, which ironically is NOT an
EventTrainer. There are several ways to handle this.
First, don’t touch GIS. This is the class that people know best. It is easy
to use if you use the parameter hard coded in the class. We can make
GISTrainer an Event trainer and refactor it appropriately to handle parameters
in the map.
Second, refactor GIS, but keep the static methods in the code. This allows user
to keep their code working.
Third, refactor GIS and deprecate the static methods.
I don’t see any value to the GIS class other than an Adapter. My vote would be
the fix the original problem of GISTrainer NOT being an event trainer.
What is your opinion?
Daniel
On 10/31/16, 10:18 AM, "Russ, Daniel (NIH/CIT) [E]" <dr...@mail.nih.gov> wrote:
Ok, I will send you patch with a refactored GIS (can you find the jira
number). If we refactor DataIndexer, I have no problem using a factory. I am
happy to extend OnePassDataIndexer. I won’t have to copy code. I will have to
look at DataIndexer again to before I comment on specifics.
Daniel
On 10/29/16, 8:45 AM, "Joern Kottmann" <kottm...@gmail.com> wrote:
On Fri, 2016-10-28 at 14:16 +0000, Russ, Daniel (NIH/CIT) [E] wrote:
> Hi Jörn,
> 1) I agree that the field values should be set in the init method for
> the QNTrainer. Other minor changes I would make include adding a
> getNumberOfThreads() method to AbstractTrainer, and a default of
> 1. I would modify GIS to use the value set in the
> TrainingParameters. I also think this needs to be documented more
> clearly. I can take a stab at some text and send it out to the
> community.
Sounds good.
I had a look at the code and it looks like it was never refactored to
fit properly in the new structure we created. I think the multi-
threaded GIS training is actually working, but couldn't see how the
parameter is hooked up to the training code.
Suggestion: We open a jira issue to refactor the GIS training to make
use of the init method, have all variables externalized (e.g. smoothing
factor, print debug output to the console, etc.) and other clean up we
think make sense.
The problem with GIS is that people take it as an example on how things
should be done when they implement a new trainer.
Another issue is that we should have some validation of parameters,
e.g. threads can only be one for perceptron.
> 2) The One/TwoPassDataIndexers are very rigid classes because they do
> the indexing in the constructor. The trainers deal with the rigidity
> by overloading methods that take a cutoff/sort/#Threads. In my
> opinion, that was not the best way to do it. There should have been
> a constructor and an index method. At this point it may be a large
> refactoring effort for little gain.
Yes, I agree with the first part. I don't think it will be so much
effort to add a new consructor and the index method. The old
constructors can stay and be deprecated until we remove them.
Should we open a jira issue for this task?
> As of 1.6.0, I see that there are 4 EventTrainers (GIS,
> NaiveBayesTrainer, PerceptronTrainer, QNTrainer). All of these are
> AbstractEventTrainers. So if we add a public MaxentModel
> train(DataIndexer di) to both the interface and the abstract class,
> nothing should break. Now, if you are concerned, the
> train(ObjectStream<Event>) method calls doTrain(DataIndexer), So the
> behavior is exactly the same for the two methods, I just call the
> doTrain(DataIndexer). This would not be used in the 1.5.x versions.
>
> I don’t like the idea of using a factory because the DataIndexer
> requires you pass parameters into the constructor. It may be
> possible to use reflection, but why make like so difficult. Let the
> client give the trainer the dataindexer.
We use this kind of factory in various places already, I think it would
be a good solution, because then people can switch the Data Indexer
without writing training code and it will work with all components we
have out of the box. I agree it would only work properly if we do the
refactoring.
In OPENNLP-830 someone said the TwoPassDataIndexer can be implemented
better performing these days with non-blocking IO. I don't think it
make sense to change the current implementation. But it would be nice
to add an improved version as a third option. If we have plugable Data
Indexer support this idea could be easily explored.
This could be done exactly like it is done for the trainer in:
TrainerFactory.getEventTrainer.
And we also need a jira for this.
> In my not-so-expert opinion, I think adding a train(Dataindexer)
> method to EventTrainer is the best way forward.
We have might have the case that certain trainers don't support the
Data Indexer, for example deeplearning libraries, but for them we
probably have to add a new trainer type anyway.
Would this work also when we refactor the Data Indexer? Then a user a
would create a Data Indexer instance, calls the index method and passes
it in. Should be fine.
I have time I can dedicate to help out with refactoring things.
Jörn
