R devel - Nov 2017 - Extreme bunching of random values from runif with Mersenne-Twister seed

Bill,

I have clarified this on SO, and I will copy that clarification in here:

"Sure, we tested them on other 8-digit numbers as well & we could not
replicate. However, these are honest-to-goodness numbers generated by a
non-adversarial system that has no conception of these numbers being used
for anything other than a unique key for an entity -- these are not a
specially constructed edge case. Would be good to know what seeds will and
will not work, and why."

These numbers are generated by an application that serves a form, and
associates form IDs in a sequence. The application calls our API depending
on the form values entered by users, which in turn calls our R code that
executes some code that needs an RNG. Since the API has to be stateless, to
be able to replicate the results for possible debugging, we need to draw
random numbers in a way that we can replicate the results of the API
response -- we use the form ID as seeds.

I repeat, there is no design or anything adversarial about the way that
these numbers were generated -- the system generating these numbers and the
users entering inputs have no conception of our use of an RNG -- this is
meant to just be a random sequence of form IDs. This issue was discovered
completely by chance when the output of the API was observed to be highly
non-random. It is possible that it is a 1/10^8 chance, but that is hard to
believe, given that the API hit depends on user input. Note also that the
issue goes away when we use a different RNG as mentioned below.

T

On Fri, Nov 3, 2017 at 9:58 PM, William Dunlap <wdunlap at tibco.com>
wrote:
> The random numbers in a stream initialized with one seed should have about
> the desired distribution.  You don't win by changing the seed all the
> time.  Your seeds caused the first numbers of a bunch of streams to be
> about the same, but the second and subsequent entries in each stream do
> look uniformly distributed.
>
> You didn't say what your 'upstream process' was, but it is easy
to come up
> with seeds that give about the same first value:
>
> > Filter(function(s){set.seed(s);runif(1,17,26)>25.99}, 1:10000)
>  [1]  514  532 1951 2631 3974 4068 4229 6092 6432 7264 9090
>
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Fri, Nov 3, 2017 at 12:49 AM, Tirthankar Chakravarty <
> tirthankar.lists at gmail.com> wrote:
>
>> This is cross-posted from SO (https://stackoverflow.com/q/4
>> 7079702/1414455),
>> but I now feel that this needs someone from R-Devel to help understand
why
>> this is happening.
>>
>> We are facing a weird situation in our code when using R's
[`runif`][1]
>> and
>> setting seed with `set.seed` with the `kind = NULL` option (which
>> resolves,
>> unless I am mistaken, to `kind = "default"`; the default
being
>> `"Mersenne-Twister"`).
>>
>> We set the seed using (8 digit) unique IDs generated by an upstream
>> system,
>> before calling `runif`:
>>
>>     seeds = c(
>>       "86548915", "86551615", "86566163",
"86577411", "86584144",
>>       "86584272", "86620568", "86724613",
"86756002", "86768593",
>> "86772411",
>>       "86781516", "86794389", "86805854",
"86814600", "86835092",
>> "86874179",
>>       "86876466", "86901193", "86987847",
"86988080")
>>
>>     random_values = sapply(seeds, function(x) {
>>       set.seed(x)
>>       y = runif(1, 17, 26)
>>       return(y)
>>     })
>>
>> This gives values that are **extremely** bunched together.
>>
>>     > summary(random_values)
>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>       25.13   25.36   25.66   25.58   25.83   25.94
>>
>> This behaviour of `runif` goes away when we use `kind >>
"Knuth-TAOCP-2002"`, and we get values that appear to be much more
evenly
>> spread out.
>>
>>     random_values = sapply(seeds, function(x) {
>>       set.seed(x, kind = "Knuth-TAOCP-2002")
>>       y = runif(1, 17, 26)
>>       return(y)
>>     })
>>
>> *Output omitted.*
>>
>> ---
>>
>> **The most interesting thing here is that this does not happen on
Windows
>> -- only happens on Ubuntu** (`sessionInfo` output for Ubuntu &
Windows
>> below).
>>
>> # Windows output: #
>>
>>     > seeds = c(
>>     +   "86548915", "86551615",
"86566163", "86577411", "86584144",
>>     +   "86584272", "86620568",
"86724613", "86756002", "86768593",
>> "86772411",
>>     +   "86781516", "86794389",
"86805854", "86814600", "86835092",
>> "86874179",
>>     +   "86876466", "86901193",
"86987847", "86988080")
>>     >
>>     > random_values = sapply(seeds, function(x) {
>>     +   set.seed(x)
>>     +   y = runif(1, 17, 26)
>>     +   return(y)
>>     + })
>>     >
>>     > summary(random_values)
>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>       17.32   20.14   23.00   22.17   24.07   25.90
>>
>> Can someone help understand what is going on?
>>
>> Ubuntu
>> ------
>>
>>     R version 3.4.0 (2017-04-21)
>>     Platform: x86_64-pc-linux-gnu (64-bit)
>>     Running under: Ubuntu 16.04.2 LTS
>>
>>     Matrix products: default
>>     BLAS: /usr/lib/libblas/libblas.so.3.6.0
>>     LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
>>
>>     locale:
>>     [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C
>>      [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8
>>      [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8
>>      [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8
>>      [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8
>>     [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
>>
>>     attached base packages:
>>     [1] parallel  stats     graphics  grDevices utils     datasets
>> methods   base
>>
>>     other attached packages:
>>     [1] RMySQL_0.10.8               DBI_0.6-1
>>      [3] jsonlite_1.4                tidyjson_0.2.2
>>      [5] optiRum_0.37.3              lubridate_1.6.0
>>      [7] httr_1.2.1                  gdata_2.18.0
>>      [9] XLConnect_0.2-12            XLConnectJars_0.2-12
>>     [11] data.table_1.10.4           stringr_1.2.0
>>     [13] readxl_1.0.0                xlsx_0.5.7
>>     [15] xlsxjars_0.6.1              rJava_0.9-8
>>     [17] sqldf_0.4-10                RSQLite_1.1-2
>>     [19] gsubfn_0.6-6                proto_1.0.0
>>     [21] dplyr_0.5.0                 purrr_0.2.4
>>     [23] readr_1.1.1                 tidyr_0.6.3
>>     [25] tibble_1.3.0                tidyverse_1.1.1
>>     [27] rBayesianOptimization_1.1.0 xgboost_0.6-4
>>     [29] MLmetrics_1.1.1             caret_6.0-76
>>     [31] ROCR_1.0-7                  gplots_3.0.1
>>     [33] effects_3.1-2               pROC_1.10.0
>>     [35] pscl_1.4.9                  lattice_0.20-35
>>     [37] MASS_7.3-47                 ggplot2_2.2.1
>>
>>     loaded via a namespace (and not attached):
>>     [1] splines_3.4.0      foreach_1.4.3      AUC_0.3.0
>> modelr_0.1.0
>>      [5] gtools_3.5.0       assertthat_0.2.0   stats4_3.4.0
>>  cellranger_1.1.0
>>      [9] quantreg_5.33      chron_2.3-50       digest_0.6.10
>> rvest_0.3.2
>>     [13] minqa_1.2.4        colorspace_1.3-2   Matrix_1.2-10
>> plyr_1.8.4
>>     [17] psych_1.7.3.21     XML_3.98-1.7       broom_0.4.2
>> SparseM_1.77
>>     [21] haven_1.0.0        scales_0.4.1       lme4_1.1-13
>> MatrixModels_0.4-1
>>     [25] mgcv_1.8-17        car_2.1-5          nnet_7.3-12
>> lazyeval_0.2.0
>>     [29] pbkrtest_0.4-7     mnormt_1.5-5       magrittr_1.5
>>  memoise_1.0.0
>>     [33] nlme_3.1-131       forcats_0.2.0      xml2_1.1.1
>>  foreign_0.8-69
>>     [37] tools_3.4.0        hms_0.3            munsell_0.4.3
>> compiler_3.4.0
>>     [41] caTools_1.17.1     rlang_0.1.1        grid_3.4.0
>>  nloptr_1.0.4
>>     [45] iterators_1.0.8    bitops_1.0-6       tcltk_3.4.0
>> gtable_0.2.0
>>     [49] ModelMetrics_1.1.0 codetools_0.2-15   reshape2_1.4.2    
R6_2.2.0
>>
>>     [53] knitr_1.15.1       KernSmooth_2.23-15 stringi_1.1.5
>> Rcpp_0.12.11
>>
>>
>>
>> Windows
>> -------
>>
>>     > sessionInfo()
>>     R version 3.3.2 (2016-10-31)
>>     Platform: x86_64-w64-mingw32/x64 (64-bit)
>>     Running under: Windows >= 8 x64 (build 9200)
>>
>>     locale:
>>     [1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252
>> LC_MONETARY=English_India.1252
>>     [4] LC_NUMERIC=C                   LC_TIME=English_India.1252
>>
>>     attached base packages:
>>     [1] graphics  grDevices utils     datasets  grid      stats
>>  methods   base
>>
>>     other attached packages:
>>      [1] bindrcpp_0.2         h2o_3.14.0.3         ggrepel_0.6.5
>> eulerr_1.1.0         VennDiagram_1.6.17
>>      [6] futile.logger_1.4.3  scales_0.4.1         FinCal_0.6.3
>>  xml2_1.0.0           httr_1.3.0
>>     [11] wesanderson_0.3.2    wordcloud_2.5        RColorBrewer_1.1-2
>>  htmltools_0.3.6      urltools_1.6.0
>>     [16] timevis_0.4          dtplyr_0.0.1         magrittr_1.5
>>  shiny_1.0.5          RODBC_1.3-14
>>     [21] zoo_1.8-0            sqldf_0.4-10         RSQLite_1.1-2
>> gsubfn_0.6-6         proto_1.0.0
>>     [26] gdata_2.17.0         stringr_1.2.0        XLConnect_0.2-12
>>  XLConnectJars_0.2-12 data.table_1.10.4
>>     [31] xlsx_0.5.7           xlsxjars_0.6.1       rJava_0.9-8
>> readxl_0.1.1         googlesheets_0.2.1
>>     [36] jsonlite_1.5         tidyjson_0.2.1       RMySQL_0.10.9
>> RPostgreSQL_0.4-1    DBI_0.5-1
>>     [41] dplyr_0.7.2          purrr_0.2.3          readr_1.1.1
>> tidyr_0.7.0          tibble_1.3.3
>>     [46] ggplot2_2.2.0        tidyverse_1.0.0      lubridate_1.6.0
>>
>>     loaded via a namespace (and not attached):
>>      [1] gtools_3.5.0         assertthat_0.2.0     triebeard_0.3.0
>> cellranger_1.1.0     yaml_2.1.14
>>      [6] slam_0.1-40          lattice_0.20-34      glue_1.1.1
>>  chron_2.3-48         digest_0.6.12.1
>>     [11] colorspace_1.3-1     httpuv_1.3.5         plyr_1.8.4
>>  pkgconfig_2.0.1      xtable_1.8-2
>>     [16] lazyeval_0.2.0       mime_0.5             memoise_1.0.0
>> tools_3.3.2          hms_0.3
>>     [21] munsell_0.4.3        lambda.r_1.1.9       rlang_0.1.1
>> RCurl_1.95-4.8       labeling_0.3
>>     [26] bitops_1.0-6         tcltk_3.3.2          gtable_0.2.0
>>  reshape2_1.4.2       R6_2.2.0
>>     [31] bindr_0.1            futile.options_1.0.0 stringi_1.1.2
>> Rcpp_0.12.12.1
>>
>>   [1]: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/Unif
>> orm.html
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-devel at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>
>
	[[alternative HTML version deleted]]

Another other generator is subject to the same problem with the same
probabilitiy.
> Filter(function(s){set.seed(s,
kind="Knuth-TAOCP-2002");runif(1,17,26)>25.99}, 1:10000)
 [1]  280  415  826 1372 2224 2544 3270 3594 3809 4116 4236 5018 5692 7043
7212 7364 7747 9256 9491 9568 9886



Bill Dunlap
TIBCO Software
wdunlap tibco.com

On Fri, Nov 3, 2017 at 10:31 AM, Tirthankar Chakravarty <
tirthankar.lists at gmail.com> wrote:
>
> Bill,
>
> I have clarified this on SO, and I will copy that clarification in here:
>
> "Sure, we tested them on other 8-digit numbers as well & we could
not
> replicate. However, these are honest-to-goodness numbers generated by a
> non-adversarial system that has no conception of these numbers being used
> for anything other than a unique key for an entity -- these are not a
> specially constructed edge case. Would be good to know what seeds will and
> will not work, and why."
>
> These numbers are generated by an application that serves a form, and
> associates form IDs in a sequence. The application calls our API depending
> on the form values entered by users, which in turn calls our R code that
> executes some code that needs an RNG. Since the API has to be stateless, to
> be able to replicate the results for possible debugging, we need to draw
> random numbers in a way that we can replicate the results of the API
> response -- we use the form ID as seeds.
>
> I repeat, there is no design or anything adversarial about the way that
> these numbers were generated -- the system generating these numbers and
> the users entering inputs have no conception of our use of an RNG -- this
> is meant to just be a random sequence of form IDs. This issue was
> discovered completely by chance when the output of the API was observed to
> be highly non-random. It is possible that it is a 1/10^8 chance, but that
> is hard to believe, given that the API hit depends on user input. Note also
> that the issue goes away when we use a different RNG as mentioned below.
>
> T
>
> On Fri, Nov 3, 2017 at 9:58 PM, William Dunlap <wdunlap at tibco.com>
wrote:
>
>> The random numbers in a stream initialized with one seed should have
>> about the desired distribution.  You don't win by changing the seed
all the
>> time.  Your seeds caused the first numbers of a bunch of streams to be
>> about the same, but the second and subsequent entries in each stream do
>> look uniformly distributed.
>>
>> You didn't say what your 'upstream process' was, but it is
easy to come
>> up with seeds that give about the same first value:
>>
>> > Filter(function(s){set.seed(s);runif(1,17,26)>25.99}, 1:10000)
>>  [1]  514  532 1951 2631 3974 4068 4229 6092 6432 7264 9090
>>
>>
>>
>> Bill Dunlap
>> TIBCO Software
>> wdunlap tibco.com
>>
>> On Fri, Nov 3, 2017 at 12:49 AM, Tirthankar Chakravarty <
>> tirthankar.lists at gmail.com> wrote:
>>
>>> This is cross-posted from SO (https://stackoverflow.com/q/4
>>> 7079702/1414455),
>>> but I now feel that this needs someone from R-Devel to help
understand
>>> why
>>> this is happening.
>>>
>>> We are facing a weird situation in our code when using R's
[`runif`][1]
>>> and
>>> setting seed with `set.seed` with the `kind = NULL` option (which
>>> resolves,
>>> unless I am mistaken, to `kind = "default"`; the default
being
>>> `"Mersenne-Twister"`).
>>>
>>> We set the seed using (8 digit) unique IDs generated by an upstream
>>> system,
>>> before calling `runif`:
>>>
>>>     seeds = c(
>>>       "86548915", "86551615",
"86566163", "86577411", "86584144",
>>>       "86584272", "86620568",
"86724613", "86756002", "86768593",
>>> "86772411",
>>>       "86781516", "86794389",
"86805854", "86814600", "86835092",
>>> "86874179",
>>>       "86876466", "86901193",
"86987847", "86988080")
>>>
>>>     random_values = sapply(seeds, function(x) {
>>>       set.seed(x)
>>>       y = runif(1, 17, 26)
>>>       return(y)
>>>     })
>>>
>>> This gives values that are **extremely** bunched together.
>>>
>>>     > summary(random_values)
>>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>>       25.13   25.36   25.66   25.58   25.83   25.94
>>>
>>> This behaviour of `runif` goes away when we use `kind >>>
"Knuth-TAOCP-2002"`, and we get values that appear to be much more
evenly
>>> spread out.
>>>
>>>     random_values = sapply(seeds, function(x) {
>>>       set.seed(x, kind = "Knuth-TAOCP-2002")
>>>       y = runif(1, 17, 26)
>>>       return(y)
>>>     })
>>>
>>> *Output omitted.*
>>>
>>> ---
>>>
>>> **The most interesting thing here is that this does not happen on
Windows
>>> -- only happens on Ubuntu** (`sessionInfo` output for Ubuntu &
Windows
>>> below).
>>>
>>> # Windows output: #
>>>
>>>     > seeds = c(
>>>     +   "86548915", "86551615",
"86566163", "86577411", "86584144",
>>>     +   "86584272", "86620568",
"86724613", "86756002", "86768593",
>>> "86772411",
>>>     +   "86781516", "86794389",
"86805854", "86814600", "86835092",
>>> "86874179",
>>>     +   "86876466", "86901193",
"86987847", "86988080")
>>>     >
>>>     > random_values = sapply(seeds, function(x) {
>>>     +   set.seed(x)
>>>     +   y = runif(1, 17, 26)
>>>     +   return(y)
>>>     + })
>>>     >
>>>     > summary(random_values)
>>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>>       17.32   20.14   23.00   22.17   24.07   25.90
>>>
>>> Can someone help understand what is going on?
>>>
>>> Ubuntu
>>> ------
>>>
>>>     R version 3.4.0 (2017-04-21)
>>>     Platform: x86_64-pc-linux-gnu (64-bit)
>>>     Running under: Ubuntu 16.04.2 LTS
>>>
>>>     Matrix products: default
>>>     BLAS: /usr/lib/libblas/libblas.so.3.6.0
>>>     LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
>>>
>>>     locale:
>>>     [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C
>>>      [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8
>>>      [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8
>>>      [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8
>>>      [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8
>>>     [11] LC_MEASUREMENT=en_US.UTF-8   
LC_IDENTIFICATION=en_US.UTF-8
>>>
>>>     attached base packages:
>>>     [1] parallel  stats     graphics  grDevices utils     datasets
>>> methods   base
>>>
>>>     other attached packages:
>>>     [1] RMySQL_0.10.8               DBI_0.6-1
>>>      [3] jsonlite_1.4                tidyjson_0.2.2
>>>      [5] optiRum_0.37.3              lubridate_1.6.0
>>>      [7] httr_1.2.1                  gdata_2.18.0
>>>      [9] XLConnect_0.2-12            XLConnectJars_0.2-12
>>>     [11] data.table_1.10.4           stringr_1.2.0
>>>     [13] readxl_1.0.0                xlsx_0.5.7
>>>     [15] xlsxjars_0.6.1              rJava_0.9-8
>>>     [17] sqldf_0.4-10                RSQLite_1.1-2
>>>     [19] gsubfn_0.6-6                proto_1.0.0
>>>     [21] dplyr_0.5.0                 purrr_0.2.4
>>>     [23] readr_1.1.1                 tidyr_0.6.3
>>>     [25] tibble_1.3.0                tidyverse_1.1.1
>>>     [27] rBayesianOptimization_1.1.0 xgboost_0.6-4
>>>     [29] MLmetrics_1.1.1             caret_6.0-76
>>>     [31] ROCR_1.0-7                  gplots_3.0.1
>>>     [33] effects_3.1-2               pROC_1.10.0
>>>     [35] pscl_1.4.9                  lattice_0.20-35
>>>     [37] MASS_7.3-47                 ggplot2_2.2.1
>>>
>>>     loaded via a namespace (and not attached):
>>>     [1] splines_3.4.0      foreach_1.4.3      AUC_0.3.0
>>> modelr_0.1.0
>>>      [5] gtools_3.5.0       assertthat_0.2.0   stats4_3.4.0
>>>  cellranger_1.1.0
>>>      [9] quantreg_5.33      chron_2.3-50       digest_0.6.10
>>> rvest_0.3.2
>>>     [13] minqa_1.2.4        colorspace_1.3-2   Matrix_1.2-10
>>> plyr_1.8.4
>>>     [17] psych_1.7.3.21     XML_3.98-1.7       broom_0.4.2
>>> SparseM_1.77
>>>     [21] haven_1.0.0        scales_0.4.1       lme4_1.1-13
>>> MatrixModels_0.4-1
>>>     [25] mgcv_1.8-17        car_2.1-5          nnet_7.3-12
>>> lazyeval_0.2.0
>>>     [29] pbkrtest_0.4-7     mnormt_1.5-5       magrittr_1.5
>>>  memoise_1.0.0
>>>     [33] nlme_3.1-131       forcats_0.2.0      xml2_1.1.1
>>>  foreign_0.8-69
>>>     [37] tools_3.4.0        hms_0.3            munsell_0.4.3
>>> compiler_3.4.0
>>>     [41] caTools_1.17.1     rlang_0.1.1        grid_3.4.0
>>>  nloptr_1.0.4
>>>     [45] iterators_1.0.8    bitops_1.0-6       tcltk_3.4.0
>>> gtable_0.2.0
>>>     [49] ModelMetrics_1.1.0 codetools_0.2-15   reshape2_1.4.2
>>>  R6_2.2.0
>>>
>>>     [53] knitr_1.15.1       KernSmooth_2.23-15 stringi_1.1.5
>>> Rcpp_0.12.11
>>>
>>>
>>>
>>> Windows
>>> -------
>>>
>>>     > sessionInfo()
>>>     R version 3.3.2 (2016-10-31)
>>>     Platform: x86_64-w64-mingw32/x64 (64-bit)
>>>     Running under: Windows >= 8 x64 (build 9200)
>>>
>>>     locale:
>>>     [1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252
>>> LC_MONETARY=English_India.1252
>>>     [4] LC_NUMERIC=C                   LC_TIME=English_India.1252
>>>
>>>     attached base packages:
>>>     [1] graphics  grDevices utils     datasets  grid      stats
>>>  methods   base
>>>
>>>     other attached packages:
>>>      [1] bindrcpp_0.2         h2o_3.14.0.3         ggrepel_0.6.5
>>> eulerr_1.1.0         VennDiagram_1.6.17
>>>      [6] futile.logger_1.4.3  scales_0.4.1         FinCal_0.6.3
>>>  xml2_1.0.0           httr_1.3.0
>>>     [11] wesanderson_0.3.2    wordcloud_2.5       
RColorBrewer_1.1-2
>>>  htmltools_0.3.6      urltools_1.6.0
>>>     [16] timevis_0.4          dtplyr_0.0.1         magrittr_1.5
>>>  shiny_1.0.5          RODBC_1.3-14
>>>     [21] zoo_1.8-0            sqldf_0.4-10         RSQLite_1.1-2
>>> gsubfn_0.6-6         proto_1.0.0
>>>     [26] gdata_2.17.0         stringr_1.2.0        XLConnect_0.2-12
>>>  XLConnectJars_0.2-12 data.table_1.10.4
>>>     [31] xlsx_0.5.7           xlsxjars_0.6.1       rJava_0.9-8
>>> readxl_0.1.1         googlesheets_0.2.1
>>>     [36] jsonlite_1.5         tidyjson_0.2.1       RMySQL_0.10.9
>>> RPostgreSQL_0.4-1    DBI_0.5-1
>>>     [41] dplyr_0.7.2          purrr_0.2.3          readr_1.1.1
>>> tidyr_0.7.0          tibble_1.3.3
>>>     [46] ggplot2_2.2.0        tidyverse_1.0.0      lubridate_1.6.0
>>>
>>>     loaded via a namespace (and not attached):
>>>      [1] gtools_3.5.0         assertthat_0.2.0     triebeard_0.3.0
>>> cellranger_1.1.0     yaml_2.1.14
>>>      [6] slam_0.1-40          lattice_0.20-34      glue_1.1.1
>>>  chron_2.3-48         digest_0.6.12.1
>>>     [11] colorspace_1.3-1     httpuv_1.3.5         plyr_1.8.4
>>>  pkgconfig_2.0.1      xtable_1.8-2
>>>     [16] lazyeval_0.2.0       mime_0.5             memoise_1.0.0
>>> tools_3.3.2          hms_0.3
>>>     [21] munsell_0.4.3        lambda.r_1.1.9       rlang_0.1.1
>>> RCurl_1.95-4.8       labeling_0.3
>>>     [26] bitops_1.0-6         tcltk_3.3.2          gtable_0.2.0
>>>  reshape2_1.4.2       R6_2.2.0
>>>     [31] bindr_0.1            futile.options_1.0.0 stringi_1.1.2
>>> Rcpp_0.12.12.1
>>>
>>>   [1]: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/Unif
>>> orm.html
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>
>>
>>
>
	[[alternative HTML version deleted]]

Bill,

Appreciate the point that both you and Serguei are making, but the sequence
in question is not a selected or filtered set. These are values as observed
in a sequence from a  mechanism described below. The probabilities required
to generate this exact sequence in the wild seem staggering to me.

T

On Fri, Nov 3, 2017 at 11:27 PM, William Dunlap <wdunlap at tibco.com>
wrote:
> Another other generator is subject to the same problem with the same
> probabilitiy.
>
> > Filter(function(s){set.seed(s,
kind="Knuth-TAOCP-2002");runif(1,17,26)>25.99},
> 1:10000)
>  [1]  280  415  826 1372 2224 2544 3270 3594 3809 4116 4236 5018 5692 7043
> 7212 7364 7747 9256 9491 9568 9886
>
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Fri, Nov 3, 2017 at 10:31 AM, Tirthankar Chakravarty <
> tirthankar.lists at gmail.com> wrote:
>
>>
>> Bill,
>>
>> I have clarified this on SO, and I will copy that clarification in
here:
>>
>> "Sure, we tested them on other 8-digit numbers as well & we
could not
>> replicate. However, these are honest-to-goodness numbers generated by a
>> non-adversarial system that has no conception of these numbers being
used
>> for anything other than a unique key for an entity -- these are not a
>> specially constructed edge case. Would be good to know what seeds will
and
>> will not work, and why."
>>
>> These numbers are generated by an application that serves a form, and
>> associates form IDs in a sequence. The application calls our API
depending
>> on the form values entered by users, which in turn calls our R code
that
>> executes some code that needs an RNG. Since the API has to be
stateless, to
>> be able to replicate the results for possible debugging, we need to
draw
>> random numbers in a way that we can replicate the results of the API
>> response -- we use the form ID as seeds.
>>
>> I repeat, there is no design or anything adversarial about the way that
>> these numbers were generated -- the system generating these numbers and
>> the users entering inputs have no conception of our use of an RNG --
this
>> is meant to just be a random sequence of form IDs. This issue was
>> discovered completely by chance when the output of the API was observed
to
>> be highly non-random. It is possible that it is a 1/10^8 chance, but
that
>> is hard to believe, given that the API hit depends on user input. Note
also
>> that the issue goes away when we use a different RNG as mentioned
below.
>>
>> T
>>
>> On Fri, Nov 3, 2017 at 9:58 PM, William Dunlap <wdunlap at
tibco.com> wrote:
>>
>>> The random numbers in a stream initialized with one seed should
have
>>> about the desired distribution.  You don't win by changing the
seed all the
>>> time.  Your seeds caused the first numbers of a bunch of streams to
be
>>> about the same, but the second and subsequent entries in each
stream do
>>> look uniformly distributed.
>>>
>>> You didn't say what your 'upstream process' was, but it
is easy to come
>>> up with seeds that give about the same first value:
>>>
>>> > Filter(function(s){set.seed(s);runif(1,17,26)>25.99},
1:10000)
>>>  [1]  514  532 1951 2631 3974 4068 4229 6092 6432 7264 9090
>>>
>>>
>>>
>>> Bill Dunlap
>>> TIBCO Software
>>> wdunlap tibco.com
>>>
>>> On Fri, Nov 3, 2017 at 12:49 AM, Tirthankar Chakravarty <
>>> tirthankar.lists at gmail.com> wrote:
>>>
>>>> This is cross-posted from SO (https://stackoverflow.com/q/4
>>>> 7079702/1414455),
>>>> but I now feel that this needs someone from R-Devel to help
understand
>>>> why
>>>> this is happening.
>>>>
>>>> We are facing a weird situation in our code when using R's
[`runif`][1]
>>>> and
>>>> setting seed with `set.seed` with the `kind = NULL` option
(which
>>>> resolves,
>>>> unless I am mistaken, to `kind = "default"`; the
default being
>>>> `"Mersenne-Twister"`).
>>>>
>>>> We set the seed using (8 digit) unique IDs generated by an
upstream
>>>> system,
>>>> before calling `runif`:
>>>>
>>>>     seeds = c(
>>>>       "86548915", "86551615",
"86566163", "86577411", "86584144",
>>>>       "86584272", "86620568",
"86724613", "86756002", "86768593",
>>>> "86772411",
>>>>       "86781516", "86794389",
"86805854", "86814600", "86835092",
>>>> "86874179",
>>>>       "86876466", "86901193",
"86987847", "86988080")
>>>>
>>>>     random_values = sapply(seeds, function(x) {
>>>>       set.seed(x)
>>>>       y = runif(1, 17, 26)
>>>>       return(y)
>>>>     })
>>>>
>>>> This gives values that are **extremely** bunched together.
>>>>
>>>>     > summary(random_values)
>>>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>>>       25.13   25.36   25.66   25.58   25.83   25.94
>>>>
>>>> This behaviour of `runif` goes away when we use `kind
>>>> "Knuth-TAOCP-2002"`, and we get values that appear to
be much more
>>>> evenly
>>>> spread out.
>>>>
>>>>     random_values = sapply(seeds, function(x) {
>>>>       set.seed(x, kind = "Knuth-TAOCP-2002")
>>>>       y = runif(1, 17, 26)
>>>>       return(y)
>>>>     })
>>>>
>>>> *Output omitted.*
>>>>
>>>> ---
>>>>
>>>> **The most interesting thing here is that this does not happen
on
>>>> Windows
>>>> -- only happens on Ubuntu** (`sessionInfo` output for Ubuntu
& Windows
>>>> below).
>>>>
>>>> # Windows output: #
>>>>
>>>>     > seeds = c(
>>>>     +   "86548915", "86551615",
"86566163", "86577411", "86584144",
>>>>     +   "86584272", "86620568",
"86724613", "86756002", "86768593",
>>>> "86772411",
>>>>     +   "86781516", "86794389",
"86805854", "86814600", "86835092",
>>>> "86874179",
>>>>     +   "86876466", "86901193",
"86987847", "86988080")
>>>>     >
>>>>     > random_values = sapply(seeds, function(x) {
>>>>     +   set.seed(x)
>>>>     +   y = runif(1, 17, 26)
>>>>     +   return(y)
>>>>     + })
>>>>     >
>>>>     > summary(random_values)
>>>>        Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>>>>       17.32   20.14   23.00   22.17   24.07   25.90
>>>>
>>>> Can someone help understand what is going on?
>>>>
>>>> Ubuntu
>>>> ------
>>>>
>>>>     R version 3.4.0 (2017-04-21)
>>>>     Platform: x86_64-pc-linux-gnu (64-bit)
>>>>     Running under: Ubuntu 16.04.2 LTS
>>>>
>>>>     Matrix products: default
>>>>     BLAS: /usr/lib/libblas/libblas.so.3.6.0
>>>>     LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
>>>>
>>>>     locale:
>>>>     [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C
>>>>      [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8
>>>>      [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8
>>>>      [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8
>>>>      [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8
>>>>     [11] LC_MEASUREMENT=en_US.UTF-8   
LC_IDENTIFICATION=en_US.UTF-8
>>>>
>>>>     attached base packages:
>>>>     [1] parallel  stats     graphics  grDevices utils    
datasets
>>>> methods   base
>>>>
>>>>     other attached packages:
>>>>     [1] RMySQL_0.10.8               DBI_0.6-1
>>>>      [3] jsonlite_1.4                tidyjson_0.2.2
>>>>      [5] optiRum_0.37.3              lubridate_1.6.0
>>>>      [7] httr_1.2.1                  gdata_2.18.0
>>>>      [9] XLConnect_0.2-12            XLConnectJars_0.2-12
>>>>     [11] data.table_1.10.4           stringr_1.2.0
>>>>     [13] readxl_1.0.0                xlsx_0.5.7
>>>>     [15] xlsxjars_0.6.1              rJava_0.9-8
>>>>     [17] sqldf_0.4-10                RSQLite_1.1-2
>>>>     [19] gsubfn_0.6-6                proto_1.0.0
>>>>     [21] dplyr_0.5.0                 purrr_0.2.4
>>>>     [23] readr_1.1.1                 tidyr_0.6.3
>>>>     [25] tibble_1.3.0                tidyverse_1.1.1
>>>>     [27] rBayesianOptimization_1.1.0 xgboost_0.6-4
>>>>     [29] MLmetrics_1.1.1             caret_6.0-76
>>>>     [31] ROCR_1.0-7                  gplots_3.0.1
>>>>     [33] effects_3.1-2               pROC_1.10.0
>>>>     [35] pscl_1.4.9                  lattice_0.20-35
>>>>     [37] MASS_7.3-47                 ggplot2_2.2.1
>>>>
>>>>     loaded via a namespace (and not attached):
>>>>     [1] splines_3.4.0      foreach_1.4.3      AUC_0.3.0
>>>> modelr_0.1.0
>>>>      [5] gtools_3.5.0       assertthat_0.2.0   stats4_3.4.0
>>>>  cellranger_1.1.0
>>>>      [9] quantreg_5.33      chron_2.3-50       digest_0.6.10
>>>> rvest_0.3.2
>>>>     [13] minqa_1.2.4        colorspace_1.3-2   Matrix_1.2-10
>>>> plyr_1.8.4
>>>>     [17] psych_1.7.3.21     XML_3.98-1.7       broom_0.4.2
>>>> SparseM_1.77
>>>>     [21] haven_1.0.0        scales_0.4.1       lme4_1.1-13
>>>> MatrixModels_0.4-1
>>>>     [25] mgcv_1.8-17        car_2.1-5          nnet_7.3-12
>>>> lazyeval_0.2.0
>>>>     [29] pbkrtest_0.4-7     mnormt_1.5-5       magrittr_1.5
>>>>  memoise_1.0.0
>>>>     [33] nlme_3.1-131       forcats_0.2.0      xml2_1.1.1
>>>>  foreign_0.8-69
>>>>     [37] tools_3.4.0        hms_0.3            munsell_0.4.3
>>>> compiler_3.4.0
>>>>     [41] caTools_1.17.1     rlang_0.1.1        grid_3.4.0
>>>>  nloptr_1.0.4
>>>>     [45] iterators_1.0.8    bitops_1.0-6       tcltk_3.4.0
>>>> gtable_0.2.0
>>>>     [49] ModelMetrics_1.1.0 codetools_0.2-15   reshape2_1.4.2
>>>>  R6_2.2.0
>>>>
>>>>     [53] knitr_1.15.1       KernSmooth_2.23-15 stringi_1.1.5
>>>> Rcpp_0.12.11
>>>>
>>>>
>>>>
>>>> Windows
>>>> -------
>>>>
>>>>     > sessionInfo()
>>>>     R version 3.3.2 (2016-10-31)
>>>>     Platform: x86_64-w64-mingw32/x64 (64-bit)
>>>>     Running under: Windows >= 8 x64 (build 9200)
>>>>
>>>>     locale:
>>>>     [1] LC_COLLATE=English_India.1252 
LC_CTYPE=English_India.1252
>>>> LC_MONETARY=English_India.1252
>>>>     [4] LC_NUMERIC=C                  
LC_TIME=English_India.1252
>>>>
>>>>     attached base packages:
>>>>     [1] graphics  grDevices utils     datasets  grid      stats
>>>>  methods   base
>>>>
>>>>     other attached packages:
>>>>      [1] bindrcpp_0.2         h2o_3.14.0.3        
ggrepel_0.6.5
>>>> eulerr_1.1.0         VennDiagram_1.6.17
>>>>      [6] futile.logger_1.4.3  scales_0.4.1         FinCal_0.6.3
>>>>  xml2_1.0.0           httr_1.3.0
>>>>     [11] wesanderson_0.3.2    wordcloud_2.5       
RColorBrewer_1.1-2
>>>>  htmltools_0.3.6      urltools_1.6.0
>>>>     [16] timevis_0.4          dtplyr_0.0.1         magrittr_1.5
>>>>  shiny_1.0.5          RODBC_1.3-14
>>>>     [21] zoo_1.8-0            sqldf_0.4-10        
RSQLite_1.1-2
>>>> gsubfn_0.6-6         proto_1.0.0
>>>>     [26] gdata_2.17.0         stringr_1.2.0       
XLConnect_0.2-12
>>>>  XLConnectJars_0.2-12 data.table_1.10.4
>>>>     [31] xlsx_0.5.7           xlsxjars_0.6.1       rJava_0.9-8
>>>> readxl_0.1.1         googlesheets_0.2.1
>>>>     [36] jsonlite_1.5         tidyjson_0.2.1      
RMySQL_0.10.9
>>>> RPostgreSQL_0.4-1    DBI_0.5-1
>>>>     [41] dplyr_0.7.2          purrr_0.2.3          readr_1.1.1
>>>> tidyr_0.7.0          tibble_1.3.3
>>>>     [46] ggplot2_2.2.0        tidyverse_1.0.0     
lubridate_1.6.0
>>>>
>>>>     loaded via a namespace (and not attached):
>>>>      [1] gtools_3.5.0         assertthat_0.2.0    
triebeard_0.3.0
>>>> cellranger_1.1.0     yaml_2.1.14
>>>>      [6] slam_0.1-40          lattice_0.20-34      glue_1.1.1
>>>>  chron_2.3-48         digest_0.6.12.1
>>>>     [11] colorspace_1.3-1     httpuv_1.3.5         plyr_1.8.4
>>>>  pkgconfig_2.0.1      xtable_1.8-2
>>>>     [16] lazyeval_0.2.0       mime_0.5            
memoise_1.0.0
>>>> tools_3.3.2          hms_0.3
>>>>     [21] munsell_0.4.3        lambda.r_1.1.9       rlang_0.1.1
>>>> RCurl_1.95-4.8       labeling_0.3
>>>>     [26] bitops_1.0-6         tcltk_3.3.2          gtable_0.2.0
>>>>  reshape2_1.4.2       R6_2.2.0
>>>>     [31] bindr_0.1            futile.options_1.0.0
stringi_1.1.2
>>>> Rcpp_0.12.12.1
>>>>
>>>>   [1]:
http://stat.ethz.ch/R-manual/R-devel/library/stats/html/Unif
>>>> orm.html
>>>>
>>>>         [[alternative HTML version deleted]]
>>>>
>>>> ______________________________________________
>>>> R-devel at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>>
>>>
>>>
>>
>
	[[alternative HTML version deleted]]