No, the 2nd call only happens when m > 2**31. Here's the code:
(RNG.c, lines 793ff)
double R_unif_index(double dn)
{
double cut = INT_MAX;
switch(RNG_kind) {
case KNUTH_TAOCP:
case USER_UNIF:
case KNUTH_TAOCP2:
cut = 33554431.0; /* 2^25 - 1 */
break;
default:
break;
}
double u = dn > cut ? ru() : unif_rand();
return floor(dn * u);
}
On Wed, Sep 19, 2018 at 9:20 AM Duncan Murdoch <murdoch.duncan at
gmail.com>
wrote:
> On 19/09/2018 12:09 PM, Philip B. Stark wrote:
> > The 53 bits only encode at most 2^{32} possible values, because the
> > source of the float is the output of a 32-bit PRNG (the obsolete
version
> > of MT). 53 bits isn't the relevant number here.
>
> No, two calls to unif_rand() are used. There are two 32 bit values, but
> some of the bits are thrown away.
>
> Duncan Murdoch
>
> >
> > The selection ratios can get close to 2. Computer scientists don't
do it
> > the way R does, for a reason.
> >
> > Regards,
> > Philip
> >
> > On Wed, Sep 19, 2018 at 9:05 AM Duncan Murdoch <murdoch.duncan at
gmail.com
> > <mailto:murdoch.duncan at gmail.com>> wrote:
> >
> > On 19/09/2018 9:09 AM, I?aki Ucar wrote:
> > > El mi?., 19 sept. 2018 a las 14:43, Duncan Murdoch
> > > (<murdoch.duncan at gmail.com <mailto:murdoch.duncan
at gmail.com>>)
> > escribi?:
> > >>
> > >> On 18/09/2018 5:46 PM, Carl Boettiger wrote:
> > >>> Dear list,
> > >>>
> > >>> It looks to me that R samples random integers using
an
> > intuitive but biased
> > >>> algorithm by going from a random number on [0,1)
from the PRNG
> > to a random
> > >>> integer, e.g.
> > >>>
> >
> https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808
> > >>>
> > >>> Many other languages use various rejection sampling
approaches
> > which
> > >>> provide an unbiased method for sampling, such as in
Go, python,
> > and others
> > >>> described here: https://arxiv.org/abs/1805.10941 (I
believe the
> > biased
> > >>> algorithm currently used in R is also described
there). I'm
> > not an expert
> > >>> in this area, but does it make sense for the R to
adopt one of
> > the unbiased
> > >>> random sample algorithms outlined there and used in
other
> > languages? Would
> > >>> a patch providing such an algorithm be welcome? What
concerns
> > would need to
> > >>> be addressed first?
> > >>>
> > >>> I believe this issue was also raised by Killie &
Philip in
> > >>>
http://r.789695.n4.nabble.com/Bug-in-sample-td4729483.html, and
> > more
> > >>> recently in
> > >>>
> > https://www.stat.berkeley.edu/~stark/Preprints/r-random-issues.pdf
> > <
> https://www.stat.berkeley.edu/%7Estark/Preprints/r-random-issues.pdf>,
> > >>> pointing to the python implementation for
comparison:
> > >>>
> >
>
https://github.com/statlab/cryptorandom/blob/master/cryptorandom/cryptorandom.py#L265
> > >>
> > >> I think the analyses are correct, but I doubt if a
change to the
> > default
> > >> is likely to be accepted as it would make it more
difficult to
> > reproduce
> > >> older results.
> > >>
> > >> On the other hand, a contribution of a new function like
> > sample() but
> > >> not suffering from the bias would be good. The normal
way to
> > make such
> > >> a contribution is in a user contributed package.
> > >>
> > >> By the way, R code illustrating the bias is probably not
very
> > hard to
> > >> put together. I believe the bias manifests itself in
sample()
> > producing
> > >> values with two different probabilities (instead of all
equal
> > >> probabilities). Those may differ by as much as one part
in
> > 2^32. It's
> > >
> > > According to Kellie and Philip, in the attachment of the
thread
> > > referenced by Carl, "The maximum ratio of selection
probabilities
> can
> > > get as large as 1.5 if n is just below 2^31".
> >
> > Sorry, I didn't write very well. I meant to say that the
difference
> in
> > probabilities would be 2^-32, not that the ratio of probabilities
> would
> > be 1 + 2^-32.
> >
> > By the way, I don't see the statement giving the ratio as 1.5,
but
> > maybe
> > I was looking in the wrong place. In Theorem 1 of the paper I was
> > looking in the ratio was "1 + m 2^{-w + 1}". In that
formula m is
> your
> > n. If it is near 2^31, R uses w = 57 random bits, so the ratio
> > would be
> > very, very small (one part in 2^25).
> >
> > The worst case for R would happen when m is just below 2^25,
where
> w
> > is at least 31 for the default generators. In that case the ratio
> > could
> > be about 1.03.
> >
> > Duncan Murdoch
> >
> >
> >
> > --
> > Philip B. Stark | Associate Dean, Mathematical and Physical Sciences |
> > Professor, Department of Statistics |
> > University of California
> > Berkeley, CA 94720-3860 | 510-394-5077 |
statistics.berkeley.edu/~stark
> > <http://statistics.berkeley.edu/%7Estark> |
> > @philipbstark
> >
>
>
--
Philip B. Stark | Associate Dean, Mathematical and Physical Sciences |
Professor, Department of Statistics |
University of California
Berkeley, CA 94720-3860 | 510-394-5077 | statistics.berkeley.edu/~stark |
@philipbstark
[[alternative HTML version deleted]]
On 19/09/2018 12:23 PM, Philip B. Stark wrote:> No, the 2nd call only happens when m > 2**31. Here's the code:Yes, you're right. Sorry! So the ratio really does come close to 2. However, the difference in probabilities between outcomes is still at most 2^-32 when m is less than that cutoff. That's not feasible to detect; the only detectable difference would happen if some event was constructed to hold an abundance of outcomes with especially low (or especially high) probability. As I said in my original post, it's probably not hard to construct such a thing, but as I've said more recently, it probably wouldn't happen by chance. Here's one attempt to do it: Call the values from unif_rand() "the unif_rand() outcomes". Call the values from sample() the sample outcomes. It would be easiest to see the error if half of the sample() outcomes used two unif_rand() outcomes, and half used just one. That would mean m should be (2/3) * 2^32, but that's too big and would trigger the other version. So how about half use 2 unif_rands(), and half use 3? That means m = (2/5) * 2^32 = 1717986918. A good guess is that sample() outcomes would alternate between the two possibilities, so our event could be even versus odd outcomes. Let's try it: > m <- (2/5)*2^32 > m > 2^31 [1] FALSE > x <- sample(m, 1000000, replace = TRUE) > table(x %% 2) 0 1 399850 600150 Since m is an even number, the true proportions of evens and odds should be exactly 0.5. That's some pretty strong evidence of the bug in the generator. (Note that the ratio of the observed probabilities is about 1.5, so I may not be the first person to have done this.) I'm still not convinced that there has ever been a simulation run with detectable bias compared to Monte Carlo error unless it (like this one) was designed specifically to show the problem. Duncan Murdoch> > (RNG.c, lines 793ff) > > double R_unif_index(double dn) > { > ? ? double cut = INT_MAX; > > ? ? switch(RNG_kind) { > ? ? case KNUTH_TAOCP: > ? ? case USER_UNIF: > ? ? case KNUTH_TAOCP2: > cut = 33554431.0; /* 2^25 - 1 */ > break; > ? ? default: > break; > ? ?} > > ? ? double u = dn > cut ? ru() : unif_rand(); > ? ? return floor(dn * u); > } > > On Wed, Sep 19, 2018 at 9:20 AM Duncan Murdoch <murdoch.duncan at gmail.com > <mailto:murdoch.duncan at gmail.com>> wrote: > > On 19/09/2018 12:09 PM, Philip B. Stark wrote: > > The 53 bits only encode at most 2^{32} possible values, because the > > source of the float is the output of a 32-bit PRNG (the obsolete > version > > of MT). 53 bits isn't the relevant number here. > > No, two calls to unif_rand() are used.? There are two 32 bit values, > but > some of the bits are thrown away. > > Duncan Murdoch > > > > > The selection ratios can get close to 2. Computer scientists > don't do it > > the way R does, for a reason. > > > > Regards, > > Philip > > > > On Wed, Sep 19, 2018 at 9:05 AM Duncan Murdoch > <murdoch.duncan at gmail.com <mailto:murdoch.duncan at gmail.com> > > <mailto:murdoch.duncan at gmail.com > <mailto:murdoch.duncan at gmail.com>>> wrote: > > > >? ? ?On 19/09/2018 9:09 AM, I?aki Ucar wrote: > >? ? ? > El mi?., 19 sept. 2018 a las 14:43, Duncan Murdoch > >? ? ? > (<murdoch.duncan at gmail.com > <mailto:murdoch.duncan at gmail.com> <mailto:murdoch.duncan at gmail.com > <mailto:murdoch.duncan at gmail.com>>>) > >? ? ?escribi?: > >? ? ? >> > >? ? ? >> On 18/09/2018 5:46 PM, Carl Boettiger wrote: > >? ? ? >>> Dear list, > >? ? ? >>> > >? ? ? >>> It looks to me that R samples random integers using an > >? ? ?intuitive but biased > >? ? ? >>> algorithm by going from a random number on [0,1) from > the PRNG > >? ? ?to a random > >? ? ? >>> integer, e.g. > >? ? ? >>> > > https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808 > >? ? ? >>> > >? ? ? >>> Many other languages use various rejection sampling > approaches > >? ? ?which > >? ? ? >>> provide an unbiased method for sampling, such as in Go, > python, > >? ? ?and others > >? ? ? >>> described here: https://arxiv.org/abs/1805.10941 (I > believe the > >? ? ?biased > >? ? ? >>> algorithm currently used in R is also described there).? I'm > >? ? ?not an expert > >? ? ? >>> in this area, but does it make sense for the R to adopt > one of > >? ? ?the unbiased > >? ? ? >>> random sample algorithms outlined there and used in other > >? ? ?languages?? Would > >? ? ? >>> a patch providing such an algorithm be welcome? What > concerns > >? ? ?would need to > >? ? ? >>> be addressed first? > >? ? ? >>> > >? ? ? >>> I believe this issue was also raised by Killie & Philip in > >? ? ? >>> > http://r.789695.n4.nabble.com/Bug-in-sample-td4729483.html, and > >? ? ?more > >? ? ? >>> recently in > >? ? ? >>> > > > https://www.stat.berkeley.edu/~stark/Preprints/r-random-issues.pdf > <https://www.stat.berkeley.edu/%7Estark/Preprints/r-random-issues.pdf> > > > ?<https://www.stat.berkeley.edu/%7Estark/Preprints/r-random-issues.pdf>, > >? ? ? >>> pointing to the python implementation for comparison: > >? ? ? >>> > > > https://github.com/statlab/cryptorandom/blob/master/cryptorandom/cryptorandom.py#L265 > >? ? ? >> > >? ? ? >> I think the analyses are correct, but I doubt if a change > to the > >? ? ?default > >? ? ? >> is likely to be accepted as it would make it more > difficult to > >? ? ?reproduce > >? ? ? >> older results. > >? ? ? >> > >? ? ? >> On the other hand, a contribution of a new function like > >? ? ?sample() but > >? ? ? >> not suffering from the bias would be good.? The normal way to > >? ? ?make such > >? ? ? >> a contribution is in a user contributed package. > >? ? ? >> > >? ? ? >> By the way, R code illustrating the bias is probably not very > >? ? ?hard to > >? ? ? >> put together.? I believe the bias manifests itself in > sample() > >? ? ?producing > >? ? ? >> values with two different probabilities (instead of all equal > >? ? ? >> probabilities).? Those may differ by as much as one part in > >? ? ?2^32.? It's > >? ? ? > > >? ? ? > According to Kellie and Philip, in the attachment of the > thread > >? ? ? > referenced by Carl, "The maximum ratio of selection > probabilities can > >? ? ? > get as large as 1.5 if n is just below 2^31". > > > >? ? ?Sorry, I didn't write very well.? I meant to say that the > difference in > >? ? ?probabilities would be 2^-32, not that the ratio of > probabilities would > >? ? ?be 1 + 2^-32. > > > >? ? ?By the way, I don't see the statement giving the ratio as > 1.5, but > >? ? ?maybe > >? ? ?I was looking in the wrong place.? In Theorem 1 of the paper > I was > >? ? ?looking in the ratio was "1 + m 2^{-w + 1}".? In that formula > m is your > >? ? ?n.? If it is near 2^31, R uses w = 57 random bits, so the ratio > >? ? ?would be > >? ? ?very, very small (one part in 2^25). > > > >? ? ?The worst case for R would happen when m? is just below > 2^25, where w > >? ? ?is at least 31 for the default generators.? In that case the > ratio > >? ? ?could > >? ? ?be about 1.03. > > > >? ? ?Duncan Murdoch > > > > > > > > -- > > Philip B. Stark | Associate Dean, Mathematical and Physical > Sciences | > > Professor, ?Department of Statistics | > > University of California > > Berkeley, CA 94720-3860 | 510-394-5077 | > statistics.berkeley.edu/~stark > <http://statistics.berkeley.edu/%7Estark> > > <http://statistics.berkeley.edu/%7Estark> | > > @philipbstark > > > > > > -- > Philip B. Stark | Associate Dean, Mathematical and Physical Sciences | > Professor, ?Department of Statistics | > University of California > Berkeley, CA 94720-3860 | 510-394-5077 | statistics.berkeley.edu/~stark > <http://statistics.berkeley.edu/%7Estark> | > @philipbstark >
Hi Duncan--
Nice simulation!
The absolute difference in probabilities is small, but the maximum relative
difference grows from something negligible to almost 2 as m approaches
2**31.
Because the L_1 distance between the uniform distribution on {1, ..., m}
and what you actually get is large, there have to be test functions whose
expectations are quite different under the two distributions. Whether those
correspond to commonly used statistics or not, I have no idea.
Regarding backwards compatibility: as a user, I'd rather the default
sample() do the best possible thing, and take an extra step to use
something like sample(..., legacy=TRUE) if I want to reproduce old results.
Regards,
Philip
On Wed, Sep 19, 2018 at 9:50 AM Duncan Murdoch <murdoch.duncan at
gmail.com>
wrote:
> On 19/09/2018 12:23 PM, Philip B. Stark wrote:
> > No, the 2nd call only happens when m > 2**31. Here's the code:
>
> Yes, you're right. Sorry!
>
> So the ratio really does come close to 2. However, the difference in
> probabilities between outcomes is still at most 2^-32 when m is less
> than that cutoff. That's not feasible to detect; the only detectable
> difference would happen if some event was constructed to hold an
> abundance of outcomes with especially low (or especially high) probability.
>
> As I said in my original post, it's probably not hard to construct such
> a thing, but as I've said more recently, it probably wouldn't
happen by
> chance. Here's one attempt to do it:
>
> Call the values from unif_rand() "the unif_rand() outcomes".
Call the
> values from sample() the sample outcomes.
>
> It would be easiest to see the error if half of the sample() outcomes
> used two unif_rand() outcomes, and half used just one. That would mean
> m should be (2/3) * 2^32, but that's too big and would trigger the
other
> version.
>
> So how about half use 2 unif_rands(), and half use 3? That means m >
(2/5) * 2^32 = 1717986918. A good guess is that sample() outcomes would
> alternate between the two possibilities, so our event could be even
> versus odd outcomes.
>
> Let's try it:
>
> > m <- (2/5)*2^32
> > m > 2^31
> [1] FALSE
> > x <- sample(m, 1000000, replace = TRUE)
> > table(x %% 2)
>
> 0 1
> 399850 600150
>
> Since m is an even number, the true proportions of evens and odds should
> be exactly 0.5. That's some pretty strong evidence of the bug in the
> generator. (Note that the ratio of the observed probabilities is about
> 1.5, so I may not be the first person to have done this.)
>
> I'm still not convinced that there has ever been a simulation run with
> detectable bias compared to Monte Carlo error unless it (like this one)
> was designed specifically to show the problem.
>
> Duncan Murdoch
>
> >
> > (RNG.c, lines 793ff)
> >
> > double R_unif_index(double dn)
> > {
> > double cut = INT_MAX;
> >
> > switch(RNG_kind) {
> > case KNUTH_TAOCP:
> > case USER_UNIF:
> > case KNUTH_TAOCP2:
> > cut = 33554431.0; /* 2^25 - 1 */
> > break;
> > default:
> > break;
> > }
> >
> > double u = dn > cut ? ru() : unif_rand();
> > return floor(dn * u);
> > }
> >
> > On Wed, Sep 19, 2018 at 9:20 AM Duncan Murdoch <murdoch.duncan at
gmail.com
> > <mailto:murdoch.duncan at gmail.com>> wrote:
> >
> > On 19/09/2018 12:09 PM, Philip B. Stark wrote:
> > > The 53 bits only encode at most 2^{32} possible values,
because
> the
> > > source of the float is the output of a 32-bit PRNG (the
obsolete
> > version
> > > of MT). 53 bits isn't the relevant number here.
> >
> > No, two calls to unif_rand() are used. There are two 32 bit
values,
> > but
> > some of the bits are thrown away.
> >
> > Duncan Murdoch
> >
> > >
> > > The selection ratios can get close to 2. Computer scientists
> > don't do it
> > > the way R does, for a reason.
> > >
> > > Regards,
> > > Philip
> > >
> > > On Wed, Sep 19, 2018 at 9:05 AM Duncan Murdoch
> > <murdoch.duncan at gmail.com <mailto:murdoch.duncan at
gmail.com>
> > > <mailto:murdoch.duncan at gmail.com
> > <mailto:murdoch.duncan at gmail.com>>> wrote:
> > >
> > > On 19/09/2018 9:09 AM, I?aki Ucar wrote:
> > > > El mi?., 19 sept. 2018 a las 14:43, Duncan Murdoch
> > > > (<murdoch.duncan at gmail.com
> > <mailto:murdoch.duncan at gmail.com>
<mailto:murdoch.duncan at gmail.com
> > <mailto:murdoch.duncan at gmail.com>>>)
> > > escribi?:
> > > >>
> > > >> On 18/09/2018 5:46 PM, Carl Boettiger wrote:
> > > >>> Dear list,
> > > >>>
> > > >>> It looks to me that R samples random
integers using an
> > > intuitive but biased
> > > >>> algorithm by going from a random number on
[0,1) from
> > the PRNG
> > > to a random
> > > >>> integer, e.g.
> > > >>>
> > >
> https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808
> > > >>>
> > > >>> Many other languages use various rejection
sampling
> > approaches
> > > which
> > > >>> provide an unbiased method for sampling,
such as in Go,
> > python,
> > > and others
> > > >>> described here:
https://arxiv.org/abs/1805.10941 (I
> > believe the
> > > biased
> > > >>> algorithm currently used in R is also
described there).
> I'm
> > > not an expert
> > > >>> in this area, but does it make sense for
the R to adopt
> > one of
> > > the unbiased
> > > >>> random sample algorithms outlined there
and used in other
> > > languages? Would
> > > >>> a patch providing such an algorithm be
welcome? What
> > concerns
> > > would need to
> > > >>> be addressed first?
> > > >>>
> > > >>> I believe this issue was also raised by
Killie & Philip
> in
> > > >>>
> > http://r.789695.n4.nabble.com/Bug-in-sample-td4729483.html, and
> > > more
> > > >>> recently in
> > > >>>
> > >
> > https://www.stat.berkeley.edu/~stark/Preprints/r-random-issues.pdf
> > <
> https://www.stat.berkeley.edu/%7Estark/Preprints/r-random-issues.pdf>
> > >
> > <
> https://www.stat.berkeley.edu/%7Estark/Preprints/r-random-issues.pdf>,
> > > >>> pointing to the python implementation for
comparison:
> > > >>>
> > >
> >
>
https://github.com/statlab/cryptorandom/blob/master/cryptorandom/cryptorandom.py#L265
> > > >>
> > > >> I think the analyses are correct, but I doubt
if a change
> > to the
> > > default
> > > >> is likely to be accepted as it would make it
more
> > difficult to
> > > reproduce
> > > >> older results.
> > > >>
> > > >> On the other hand, a contribution of a new
function like
> > > sample() but
> > > >> not suffering from the bias would be good.
The normal
> way to
> > > make such
> > > >> a contribution is in a user contributed
package.
> > > >>
> > > >> By the way, R code illustrating the bias is
probably not
> very
> > > hard to
> > > >> put together. I believe the bias manifests
itself in
> > sample()
> > > producing
> > > >> values with two different probabilities
(instead of all
> equal
> > > >> probabilities). Those may differ by as much
as one part
> in
> > > 2^32. It's
> > > >
> > > > According to Kellie and Philip, in the attachment
of the
> > thread
> > > > referenced by Carl, "The maximum ratio of
selection
> > probabilities can
> > > > get as large as 1.5 if n is just below 2^31".
> > >
> > > Sorry, I didn't write very well. I meant to say
that the
> > difference in
> > > probabilities would be 2^-32, not that the ratio of
> > probabilities would
> > > be 1 + 2^-32.
> > >
> > > By the way, I don't see the statement giving the
ratio as
> > 1.5, but
> > > maybe
> > > I was looking in the wrong place. In Theorem 1 of the
paper
> > I was
> > > looking in the ratio was "1 + m 2^{-w + 1}".
In that formula
> > m is your
> > > n. If it is near 2^31, R uses w = 57 random bits, so
the
> ratio
> > > would be
> > > very, very small (one part in 2^25).
> > >
> > > The worst case for R would happen when m is just below
> > 2^25, where w
> > > is at least 31 for the default generators. In that case
the
> > ratio
> > > could
> > > be about 1.03.
> > >
> > > Duncan Murdoch
> > >
> > >
> > >
> > > --
> > > Philip B. Stark | Associate Dean, Mathematical and Physical
> > Sciences |
> > > Professor, Department of Statistics |
> > > University of California
> > > Berkeley, CA 94720-3860 | 510-394-5077 |
> > statistics.berkeley.edu/~stark
> > <http://statistics.berkeley.edu/%7Estark>
> > > <http://statistics.berkeley.edu/%7Estark> |
> > > @philipbstark
> > >
> >
> >
> >
> > --
> > Philip B. Stark | Associate Dean, Mathematical and Physical Sciences |
> > Professor, Department of Statistics |
> > University of California
> > Berkeley, CA 94720-3860 | 510-394-5077 |
statistics.berkeley.edu/~stark
> > <http://statistics.berkeley.edu/%7Estark> |
> > @philipbstark
> >
>
>
--
Philip B. Stark | Associate Dean, Mathematical and Physical Sciences |
Professor, Department of Statistics |
University of California
Berkeley, CA 94720-3860 | 510-394-5077 | statistics.berkeley.edu/~stark |
@philipbstark
[[alternative HTML version deleted]]