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
interesting experiment?
Laurent
2008/7/16 Laurent Gautier <[EMAIL PROTECTED]>:
> 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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