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 (call gc every N
iterations).
2008/7/15 laurent oget <[EMAIL PROTECTED]>:
> 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 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 <[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 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
>>> > from iteration n-1 and breaks column n in the process.
>>>
>>>
>>>
>>> > Is there a way to
>>> > call the garbage collection explicitely before I create a new dataframe
>>> > to
>>> > test this hypothesis?
>>>
>>> gc()
>>> #possibly gc(verbose = TRUE)
>>>
>>>
>>> I have made a toy example to see if I could reproduce it here (with rpy2
>>> -
>>> but there are similarities in the way R objects pointed at from Python
>>> objects
>>> are protected from R's garbage collection)... and it seem that there
>>> is something going on with garbage collection (depending on how the
>>> python implementation, it either crashes after 10-50 iterations... or can
>>> go through 1000 iterations.
>>>
>>>
>>> > Thanks,
>>> >
>>> > Laurent
>>> >
>>> > 2008/7/14 Laurent Gautier <[EMAIL PROTECTED]>:
>>> >>
>>> >> 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
>>> >> debugger.
>>> >> You could try starting up you embedded R process with '--verbose' and
>>> >> see
>>> >> if the problem happens right after garbage collection.
>>> >> Without having further details on the exact code ran, it is difficult
>>> >> to say more.
>>> >>
>>> >>
>>> >>
>>> >>
>>> >>
>>> >> 2008/7/14 laurent oget <[EMAIL PROTECTED]>:
>>> >> > 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
>>> >> > corrupted the heap (or the stack). The code breaks in different
>>> >> > places.
>>> >> > One
>>> >> > sample stack trace:
>>> >> >
>>> >> >
>>> >> > *** caught segfault ***
>>> >> > address 0x8, cause 'memory not mapped'
>>> >> >
>>> >> > Traceback:
>>> >> > 1: unique.default(unlist(lapply(answer, length)))
>>> >> > 2: unique(unlist(lapply(answer, length)))
>>> >> > 3: sapply(xlev, is.null)
>>> >> > 4: .getXlevels(mt, mf)
>>> >> > 5: function (formula, data, subset, weights, na.action, method =
>>> >> > "qr",
>>> >> > model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
>>> >> > contrasts = NULL, offset, ...) { ret.x <- x ret.y <- y cl
>>> >> > <-
>>> >> > match.call() mf <- match.call(expand.dots = FALSE) m <-
>>> >> > match(c("formula", "data", "subset", "weights", "na.action",
>>> >> > "offset"), names(mf), 0L) mf <- mf[c(1L, m)]
>>> >> > mf$drop.unused.levels
>>> >> > <-
>>> >> > TRUE mf[[1L]] <- as.name("model.frame") mf <- eval(mf,
>>> >> > parent.frame()) if (method == "model.frame") return(mf)
>>> >> > else
>>> >> > if (method != "qr") warning(gettextf("method = '%s' is not
>>> >> > supported. Using 'qr'", method), domain = NA) mt <-
>>> >> > attr(mf,
>>> >> > "terms") y <- model.response(mf, "numeric") w <-
>>> >> > as.vector(model.weights(mf)) if (!is.null(w) && !is.numeric(w))
>>> >> > stop("'weights' must be a numeric vector") offset <-
>>> >> > as.vector(model.offset(mf)) if (!is.null(offset)) { if
>>> >> > (length(offset) == 1) offset <- rep(offset, NROW(y))
>>> >> > else
>>> >> > if (length(offset) != NROW(y)) stop(gettextf("number of
>>> >> > offsets
>>> >> > is %d, should equal %d (number of observations)",
>>> >> > length(offset), NROW(y)), domain = NA) } if
>>> >> > (is.empty.model(mt))
>>> >> > { x <- NULL z <- list(coefficients = if (is.matrix(y))
>>> >> > matrix(, 0, 3) else numeric(0), residuals = y,
>>> >> > fitted.values
>>> >> > = 0
>>> >> > * y, weights = w, rank = 0L, df.residual = if
>>> >> > (is.matrix(y))
>>> >> > nrow(y) else length(y)) if (!is.null(offset)) {
>>> >> > z$fitted.values <- offset z$residuals <- y - offset
>>> >> > }
>>> >> > } else { x <- model.matrix(mt, mf, contrasts) z <-
>>> >> > if
>>> >> > (is.null(w)) lm.fit(x, y, offset = offset, singular.ok =
>>> >> > singular.ok, ...) else lm.wfit(x, y, w,
>>> >> > offset =
>>> >> > offset, singular.ok = singular.ok, ...) } class(z)
>>> >> > <-
>>> >> > c(if
>>> >> > (is.matrix(y)) "mlm", "lm") z$na.action <- attr(mf, "na.action")
>>> >> > z$offset <- offset z$contrasts <- attr(x, "contrasts")
>>> >> > z$xlevels
>>> >> > <-
>>> >> > .getXlevels(mt, mf) z$call <- cl z$terms <- mt if (model)
>>> >> > z$model <- mf if (ret.x) z$x <- x if (ret.y)
>>> >> > z$y
>>> >> > <-
>>> >> > y if (!qr) z$qr <- NULL
>>> >> > z}("sls_1133745~dp_1133745+dp_3124901", list(dp_3124901 = c(0, 0, 0,
>>> >> > 0,
>>> >> > 0,
>>> >> > -0.328504066972036, 0, 0, 0, 0, 0, 0, 0, -0.328504066972036, 0, 0,
>>> >> > 0, 0,
>>> >> > 0,
>>> >> > -0.328504066972036, -0.328504066972036, 0, 0, 0, 0, 0, 0, 0, 0, 0,
>>> >> > 0,
>>> >> > -0.693147180559945, 0, -0.693147180559945, 0, 0, 0,
>>> >> > -0.328504066972036,
>>> >> > 0,
>>> >> > 0, 0, 0, 0, 0, 0, 0, 0, -0.328504066972036, -0.328504066972036, 0,
>>> >> > 0, 0,
>>> >> > 0,
>>> >> > 0, -0.693147180559945, 0, 0, 0, 0, -0.693147180559945, 0,
>>> >> > -0.693147180559945, -0.693147180559945, 0, 0, 0, 0, 0, 0, 0,
>>> >> > -0.248461359298500, 0, -0.248461359298500, 0, 0, 0, 0, 0, 0, 0, 0,
>>> >> > 0, 0,
>>> >> > 0,
>>> >> > -0.693147180559945, 0, 0, 0, -0.248461359298500, 0, 0,
>>> >> > -0.248461359298500,
>>> >> > 0, 0, -0.693147180559945, 0, 0, 0, 0, 0, 0), sls_1133745 =
>>> >> > c(7.3487612610094, 7.30613534736707, 8.00046685932928,
>>> >> > 7.39499697533915,
>>> >> > 7.34321275228777, 7.88963522981753, 7.22973732372956,
>>> >> > 8.18020882367827,
>>> >> > 7.91558910409137, 7.26812538738378, 7.25818677308703,
>>> >> > 7.3498801314529,
>>> >> > 7.31767047076391, 7.65018297965786, 7.90898624450853,
>>> >> > 8.19434808229588,
>>> >> > 8.21457885218454, 7.3641669547084, 7.35953128995355,
>>> >> > 7.7040815977251,
>>> >> > 7.2031227430099, 7.31216649861154, 7.19473710631952,
>>> >> > 7.29547045158008,
>>> >> > 7.35154326140946, 7.17917838793797, 8.36984796851258,
>>> >> > 7.1754285028756,
>>> >> > 8.12093952739025, 7.687240693355, 7.2225733180656, 7.36405924490386,
>>> >> > 7.3452615426351, 7.25804588634565, 7.23995411189234,
>>> >> > 7.21269261931628,
>>> >> > 7.3210695020337, 7.8386981421886, 7.373405696737, 7.17968137538913,
>>> >> > 7.82070844738252, 7.43464714668485, 7.25244367037469,
>>> >> > 7.23292820385588,
>>> >> > 7.33945977029367, 7.2810277192681, 7.34753163260778,
>>> >> > 7.94492823905035,
>>> >> > 7.87310661464411, 7.14571650431343, 7.1568316491227,
>>> >> > 8.04067559226017,
>>> >> > 7.16502195742237, 7.27351582356697, 7.28802564845575,
>>> >> > 8.77370188389706,
>>> >> > 7.3224047480958, 6.4802899305943, 7.2810277192681, 7.26784638120198,
>>> >> > 7.35638204246829, 7.20306320059336, 7.15358357480953,
>>> >> > 8.01940553608164,
>>> >> > 8.78195611526034, 7.24723014605046, 7.319056656224,
>>> >> > 7.24884540813453,
>>> >> > 7.25487774291456, 7.211054762329, 8.21131456124779,
>>> >> > 7.19920773459773,
>>> >> > 7.9786879985435, 7.28814186671376, 7.27840117311202,
>>> >> > 7.2972806890663,
>>> >> > 8.14356390282749, 7.34424090367824, 7.32067255061534,
>>> >> > 7.34136070803461,
>>> >> > 7.76719625525884, 7.20539015753155, 7.19294925869672,
>>> >> > 8.12521670148863,
>>> >> > 7.3678032648606, 7.32193563210652, 7.16022459567817,
>>> >> > 7.34345210203388,
>>> >> > 8.11895788519276, 7.81886461055585, 7.76421532379024,
>>> >> > 7.93634917049156,
>>> >> > 7.20526391225165, 7.2842720651344, 7.28607526956675,
>>> >> > 7.29555188093676,
>>> >> > 7.19784694602772, 7.18371895847972, 7.21964936074816,
>>> >> > 7.87895077602558,
>>> >> > 7.26955407354936), dp_1133745 = c(-0.223143551314210, 0,
>>> >> > -0.287682072451781,
>>> >> > 0, 0, -0.116533816255952, 0, -0.223143551314210, -0.116533816255952,
>>> >> > 0,
>>> >> > 0,
>>> >> > 0, 0, -0.116533816255952, -0.116533816255952, -0.223143551314210,
>>> >> > -0.287682072451781, 0, 0, -0.116533816255952, 0, 0, 0, 0, 0, 0,
>>> >> > -0.287682072451781, 0, -0.287682072451781, -0.116533816255952, 0, 0,
>>> >> > 0,
>>> >> > 0,
>>> >> > 0, 0, 0, -0.116533816255952, 0, 0, -0.248461359298500, 0, 0, 0, 0,
>>> >> > 0, 0,
>>> >> > -0.116533816255952, -0.116533816255952, 0, 0, -0.287682072451781, 0,
>>> >> > 0,
>>> >> > 0,
>>> >> > -0.400477566597125, 0, 0, 0, 0, 0, 0, 0, -0.248461359298500,
>>> >> > -0.400477566597125, 0, 0, 0, 0, 0, -0.287682072451781, 0,
>>> >> > -0.287682072451781, 0, 0, 0, -0.223143551314210, 0, 0, 0,
>>> >> > -0.116533816255952, 0, 0, -0.223143551314210, 0, 0, 0, 0,
>>> >> > -0.287682072451781, -0.248461359298500, -0.116533816255952,
>>> >> > -0.287682072451781, 0, 0, 0, 0, 0, 0, 0, -0.116533816255952, 0)))
>>> >> >
>>> >> >
>>> >> > would anybody have a hint on how to troubleshoot this?
>>> >> >
>>> >> > Thanks,
>>> >> >
>>> >> > Laurent
>>> >> >
>>> >> >
>>> >> >
>>> >> >
>>> >> > -------------------------------------------------------------------------
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>>> >> > _______________________________________________
>>> >> > rpy-list mailing list
>>> >> > rpy-list@lists.sourceforge.net
>>> >> > https://lists.sourceforge.net/lists/listinfo/rpy-list
>>> >> >
>>> >> >
>>> >
>>> >
>>
>
>
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