The change for rpy2 fixes for my very own mistake.
What I had missing was obvious (well, once I found it... it kept me
wondering a little while):
a call to R_PreserveObject() to protect a newly created R "SEXP" (and this
when RPy vectors are created out of python sequences).
rpy2 and rpy differ a
That is good to hear. If I have a look at the change you made on rpy2, do
you think there is any hope i will be able to patch rpy1? I do not know much
about rpy yet, but if you think that is do-able this would give me a good
pretext to learn.
Laurent
2008/7/21 Laurent Gautier <[EMAIL PROTECTED]>:
I fixed a problem with rpy2 (the fix is in SVN)
It does seem to fit happily a lot of linear models without problems now.
For the moment rpy-1.x, calling R's gc() is the only known workaround, I think.
##---
#
# brutal and naive strategy for model selection:
# fit all possible two-variable mo
2008/7/18 Laurent Gautier <[EMAIL PROTECTED]>:
> 2008/7/17 laurent oget <[EMAIL PROTECTED]>:
>>
>>
>> 2008/7/17 Laurent Gautier <[EMAIL PROTECTED]>:
>>>
> It might not really be a race condition, but more an overzealous
> garbage collection.
> R does not have reference counting, and objects withou
2008/7/17 laurent oget <[EMAIL PROTECTED]>:
>
>
> 2008/7/17 Laurent Gautier <[EMAIL PROTECTED]>:
>>
>> I am a bit confused.
>>
>> Do you have data frames created and bound to the same Python variable
>> names ?
>>
>> Or do you have a set of vectors, and subsets put together in different
>> data fra
2008/7/17 Laurent Gautier <[EMAIL PROTECTED]>:
> I am a bit confused.
>
> Do you have data frames created and bound to the same Python variable names
> ?
> Or do you have a set of vectors, and subsets put together in different
> data frame ?
> (if you are iterating and building a lot of linear m
I am a bit confused.
Do you have data frames created and bound to the same Python variable names ?
Or do you have a set of vectors, and subsets put together in different
data frame ?
(if you are iterating and building a lot of linear model, you might be
trying to do some
sort of variable/model sel
I was thinking of another workaround and would love your opinion on that. My
intuition is that the problem occurs because I have variables who are in
several dataframes with the same name. Do you think mangling the names of
the variables before passing them to R might help? Would it be an
interesti
I have trouble narrowing down where exactly the problem is occurring
(it seems to keep moving each time I think that I am getting closer).
In the short term, and for your needs, I guess that it is good if calls to gc
prevents rpy from crashing.
To save on runtime, you can try a gc pooling strategy
Calling r.gc() before each creation seems to have solved the problem. We ran
a whole lot of things over the night without any segmentation fault. This is
however pretty expensive timewise.
Laurent
2008/7/14 laurent oget <[EMAIL PROTECTED]>:
> I am running things with a call to gc() before each r
I am running things with a call to gc() before each regression, in case what
happens is a race condition where the gc is called in the middle of the
constrction of a new dataframe...
Thanks for the prompt help!
Laurent
2008/7/14 Laurent Gautier <[EMAIL PROTECTED]>:
> 2008/7/15 laurent oget <[EM
2008/7/15 laurent oget <[EMAIL PROTECTED]>:
> can i get a quick hint on how i would go about calling --verbose through RPY
> ?
I suspect that the only way is to hack
line 93 of rpymodule.c
char *defaultargv[] = {"rpy", "-q", "--vanilla"};
and recompile/install rpy.
> I am pretty clueless about
can i get a quick hint on how i would go about calling --verbose through RPY
?
I am pretty clueless about the way R does the garbage collection. One thing
I know is there are columns that are shared between different linear
regressions, so it might be that the garbage collector cleans up a column
Someone else reported on this list a similar sounding problem not so long ago.
The problem might be caused by manipulating a stale pointer to an R object
(that is an object that was discarded during R's garbage collection),
and troubleshooting this will likely mean running things through a C debu
I am using rpy/R to perform linear regressions on a large number of
datasets, in one python run, and am encountering segmentation faults after a
large number of iteration, while handling cases which, taken on their own
run without a problem. My intuition is that the previous iterations somehow
corr
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