[developers] [itsdb] Howto train a model on more than one profile

Berthold Crysmann crysmann at dfki.de
Fri Nov 10 13:25:51 CET 2006

On Fri, 2006-11-10 at 12:06 +0100, Stephan Oepen wrote:

> hi berthold,
> > it is really a large number of experiments. I started my first
> > experiment about a week ago, and I am not even into grandparenting yet.
> > Is there a way to speed things up, e.g. by dropping some less
> > interesting variation in parameters? Or is there any support for
> > multiprocessing?
> > 
> > Some parameters are not really self-explanatory. Can you provide some
> > comments on grid.lisp? Which parameters are now supported in Pet? 
> looking at your log file, all seems to proceed as it should :-).  most
> of the time goes into parameter estimation, and there is little we can
> do about that (short of parallelizing experiments, which i would like
> to implement one day). 

As a quick workaround: would it work to just start 4 lisp processes with
4 different configuration files, say one for each level of

>  for each experiment, you get 18 grids:
>  :variance '(nil 1e4 1e2 1e-2 1e-4 1e-6)
>  :relative-tolerance '(1e-6 1e-8 1e-10))
> one grid takes between five minutes and one hour (for your 15,000 Eiche
> items), and by default each grid comprises two folds.  those hour-long
> runs appear to be ones with either no prior (`variance') or a very low
> relative tolerance; they often diverge.

That's really useful to know. 

>   maybe you could trim down the
> TADM parameter variation, e.g.
>  :variance '(1e4 1e2 1e-2 1e-4 1e-6)
>  :relative-tolerance '(1e-6 1e-8))
> assuming the default LOGON `grid.lisp, you should get 192 experiments
>  :grandparenting '(0 2 3 4)
>  :active-edges-p '(nil t)
>  :lexicalization-p nil
>  :constituent-weight '(1 2 0)

What does this feature do?

>  :ngram-size '(0 2 3 4) :ngram-back-off-p '(nil t)
> such that you are more than ten per cent done already :-).  so maybe my
> defaults are overly generous with cpu days!  if your main interest is a 
> model to use with PET, you can cut out all variation but grandparenting
> and active edges (aka partial configurations).  so maybe the following
>  :grandparenting '(0 2 3 4)
>  :active-edges-p '(nil t)
>  :lexicalization-p nil
>  :constituent-weight 0
>  :ngram-size 0 :ngram-back-off-p nil
> this would bring down the total to eight experiments, each of ten grids
> ... you will be done in no time!
> when talking to zhang yi recently, we (think we) worked out what would
> be needed for PET to also support those n-gram features (with selective
> unpacking that is; i personally believe it is not really worth adapting
> the non-selective universe for additional features).  but before making
> the time to implement such extensions, we should know how much we gain
> on top of the basic configurational features plus grandparenting.  from
> past experience, that could be relatively little.  to know for sure, we
> would have to complete more of those experiments in the above ...  but
> it might still make sense to narrow down estimation parameters first.

Sure. How can I inspect the result of the experiments? Do I have to
process the log files, or can I also inspect the tsdb profiles?
I would also like to do an error analysis. Is that possible with the
virtual profile setup, or will I end up creating this one large

Another question: what happens with partially disambiguated and/or
rejected parses. Is there a way to see how they contribute to the end
result? Are they ignored? 

Finally, there are two measures of accuracy reported in the log file:
eaccuracy and naccuracy. How doi they relate to each other?


>                                       i hope this helps!  cheers  -  oe
> +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> +++ Universitetet i Oslo (IFI); Boks 1080 Blindern; 0316 Oslo; (+47) 2284 0125
> +++     CSLI Stanford; Ventura Hall; Stanford, CA 94305; (+1 650) 723 0515
> +++       --- oe at csli.stanford.edu; oe at ifi.uio.no; stephan at oepen.net ---
> +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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