Author: B. Cloez & I. Sanchez

# How to perform a kfino outlier detection on multiple individuals

#### B. Cloez & I. Sanchez

#### octobre 07, 2022

    library(kfino)
    library(dplyr)
    library(foreach)
    library(parallel)
    library(doParallel)
    #> Le chargement a nécessité le package : iterators

This vignette shows how to use parallelization on a data set containing a set of animals weighted over time with the walk-over-weighing system. The `lambs` data set is included in the kfino package and can be loaded using the `data()` function.

We use the parallel and doParallel libraries to accelerate the computing time.

    data(lambs)
    myIDE<-unique(lambs$IDE)

    print(myIDE)
    #> [1] "250017033503030" "250017033503074" "250017033503092" "250017033503096"

# 1 Without parallelization

    param=list(m0=NULL,
                 mm=NULL,
                 pp=NULL,
                 aa=0.001,
                 expertMin=10,
                 expertMax=45,
                 sigma2_m0=1,
                 sigma2_mm=0.05,
                 sigma2_pp=5,
                 K=15,
                 seqp=seq(0.4,0.7,0.1))

    t0 <- Sys.time()
    resu1<-list()

    for (i in seq_along(myIDE)){
      print(myIDE[i])
      tp.test<-filter(lambs,IDE == myIDE[i])
      print(dim(tp.test))
      resu1[[i]]<-kfino_fit(datain=tp.test,
                  Tvar="dateNum",Yvar="Poids",
                  param=param,
                  doOptim=TRUE)
    }
    #> [1] "250017033503030"
    #> [1] 101   5
    #> [1] "250017033503074"
    #> [1] 416   5
    #> [1] "250017033503092"
    #> [1] 213   5
    #> [1] "250017033503096"
    #> [1] 566   5
    Sys.time() - t0
    #> Time difference of 30.07354 secs

    print(length(resu1))
    #> [1] 4

# 2 Parallel execution

An example improving the computation time of a run on a complete dataset by parallelizing the call.

    param=list(m0=NULL,
                 mm=NULL,
                 pp=NULL,
                 aa=0.001,
                 expertMin=10,
                 expertMax=45,
                 sigma2_m0=1,
                 sigma2_mm=0.05,
                 sigma2_pp=5,
                 K=15,
                 seqp=seq(0.4,0.7,0.1))

    t0<-Sys.time()

    simpleCall<-function(datain,Index,Tvar,Yvar,param){
      datain<-as.data.frame(datain)
      ici<-unique(datain[,"IDE"])
      tp.data<-datain[ datain[,"IDE"] == ici[Index],]

      tp.resu<-kfino::kfino_fit(datain=tp.data,
                  Tvar=Tvar,Yvar=Yvar,
                  param=param,
                  doOptim=TRUE)
      return(tp.resu)
    }

    ncores<-parallel::detectCores()
    myCluster<-parallel::makeCluster(ncores - 1)
    doParallel::registerDoParallel(myCluster)

    resu2<-foreach(i=seq_along(myIDE), .packages="kfino") %dopar%
                simpleCall(datain=lambs,
                           Index=i,
                           Tvar="dateNum",
                           Yvar="Poids",
                           param=param)

    parallel::stopCluster(myCluster)
    Sys.time() - t0
    #> Time difference of 16.21493 secs

    print(length(resu2))
    #> [1] 4

    identical(resu1,resu2)
    #> [1] TRUE

# 3 References

1. E.González-García et. al. (2018) A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. vol 153: 226-238. https://doi.org/10.1016/j.compag.2018.08.022
2. Corporation M, Weston S (2022). doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.17, https://CRAN.R-project.org/package=doParallel.

# 4 session info

    sessionInfo()
    #> R version 4.2.1 (2022-06-23 ucrt)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows 10 x64 (build 19044)
    #>
    #> Matrix products: default
    #>
    #> locale:
    #> [1] LC_COLLATE=C                   LC_CTYPE=French_France.utf8
    #> [3] LC_MONETARY=French_France.utf8 LC_NUMERIC=C
    #> [5] LC_TIME=French_France.utf8
    #>
    #> attached base packages:
    #> [1] parallel  stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #> [1] doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2     ggplot2_3.3.6
    #> [5] dplyr_1.0.9       kfino_1.0.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] highr_0.9        pillar_1.8.1     bslib_0.4.0      compiler_4.2.1
    #>  [5] jquerylib_0.1.4  tools_4.2.1      digest_0.6.29    jsonlite_1.8.0
    #>  [9] evaluate_0.16    lifecycle_1.0.1  tibble_3.1.8     gtable_0.3.0
    #> [13] pkgconfig_2.0.3  rlang_1.0.4      cli_3.4.0        DBI_1.1.3
    #> [17] rstudioapi_0.13  yaml_2.3.5       xfun_0.30        fastmap_1.1.0
    #> [21] withr_2.5.0      stringr_1.4.1    knitr_1.39       generics_0.1.3
    #> [25] vctrs_0.4.1      sass_0.4.2       grid_4.2.1       tidyselect_1.1.2
    #> [29] glue_1.6.2       R6_2.5.1         fansi_1.0.3      rmarkdown_2.15
    #> [33] farver_2.1.1     purrr_0.3.4      magrittr_2.0.3   codetools_0.2-18
    #> [37] ellipsis_0.3.2   scales_1.2.1     htmltools_0.5.2  assertthat_0.2.1
    #> [41] colorspace_2.0-3 labeling_0.4.2   utf8_1.2.2       stringi_1.7.8
    #> [45] munsell_0.5.0    cachem_1.0.6     crayon_1.5.1