Performance Notes

Zaachary Charlop-Powers

2023-06-29

rCDK Performance

In September 2022, of this year, Stepehn Neumann created a benchmark for moecular weight calculation that he announced on twitter showing that rCDK had dismal performance relative to other tools in the R ecosystem. Something seemed a bit off so I looked into the code.

What I discovered is that the mass spec calculations were mediated by R classes instead of accessing the underlying Java code directly and if you write a function that does that you get a speedup, and if you avoid reflection by creating static calls then you get a really fast function.

This is a good example of how to think about using Java from within R where the use of the helper rJava functions, J and $ allow you to prototype code and benefit from reflection so you can code sort of like you would in R. Then, if you need performance, you can tighten down the code a bit by making the calls static. You can see that progression in the code below which is accompanied by the outputs from those benchmarks.

will give (2/3) runtime in ┬Ás:
   21 OrgMassSpecR 
  163 MetaboCoreUtils
  197 enviPat 
  545 Rdisop 
  645 CHNOSZ 
 4863 ChemmineR 
22510 rcdk

https://gist.github.com/sneumann/959a6d205ea4ac73eaf1393da0ec0673 ## Benchmark

# Bioconductor Packages. Use BiocManager::install()
#   Rdisop MetaboCoreUtils ChemmineR ChemmineOB enviPat

library(plyr)
library(CHNOSZ)
library(enviPat)
library(MetaboCoreUtils)
library(rcdk)
library(ChemmineR)
library(OrgMassSpecR)
library(Rdisop)
#library(ChemmineOB)

data(isotopes)

# original
# https://github.com/CDK-R/cdkr/blob/master/rcdk/R/formula.R
# get.formula <- function(mf, charge=0) {
#   
#   manipulator <- get("mfManipulator", envir = .rcdk.GlobalEnv)
#   if(!is.character(mf)) {
#     stop("Must supply a Formula string");
#   }else{
#     dcob <- .cdkFormula.createChemObject()
#     molecularformula <- .cdkFormula.createFormulaObject()
#     molecularFormula <- .jcall(manipulator,
#                                "Lorg/openscience/cdk/interfaces/IMolecularFormula;",
#                                "getMolecularFormula",
#                                mf,
#                                .jcast(molecularformula,.IMolecularFormula),
#                                TRUE);
#   }
#   
#   D <- new(J("java/lang/Integer"), as.integer(charge))
#   .jcall(molecularFormula,"V","setCharge",D);
#   object <- .cdkFormula.createObject(.jcast(molecularFormula,.IMolecularFormula));
#   return(object);
# }


mfManipulator    <- J("org/openscience/cdk/tools/manipulator/MolecularFormulaManipulator")
silentchemobject <- J("org.openscience.cdk.silent.SilentChemObjectBuilder")


#' Rewrite the formual object and directly access Java
#'
get.formula2 <- function(mf) {
  
  formula <- mfManipulator$getMolecularFormula(
    "C2H3", 
    silentchemobject$getInstance())
  
  mfManipulator$getMass(formula)
  
}

#' Add type hints
#'
get.formula3 <- function(mf) {
  builderinstance <- .jcall(
      silentchemobject,
     "Lorg/openscience/cdk/interfaces/IChemObjectBuilder;",
     "getInstance")
                       
  formula  <- .jcall(
      mfManipulator,
     "Lorg/openscience/cdk/interfaces/IMolecularFormula;",
     "getMolecularFormula",
      mf,
      builderinstance);

  mfManipulator$getMass(formula)
  
}


#' Add type hints
#'
get.formula4 <- function(mf) {
  builderinstance <- .jcall(
      silentchemobject,
      "Lorg/openscience/cdk/interfaces/IChemObjectBuilder;",
      "getInstance")

  formula  <- .jcall(
      mfManipulator,
     "Lorg/openscience/cdk/interfaces/IMolecularFormula;",
     "getMolecularFormula",
     mf,
     builderinstance);

  .jcall(
      mfManipulator,
      "D",
      "getMass",
     formula)
}



benchmark <- microbenchmark::microbenchmark(
  MetaboCoreUtils = MetaboCoreUtils::calculateMass("C2H6O"),
  rcdk = rcdk::get.formula("C2H6O", charge = 0)@mass,
  rcdk2 = get.formula2("C2H6O"),
  rcdk3 = get.formula3("C2H6O"),
  rcdk4 = get.formula4("C2H6O"),
  Rdisop = Rdisop::getMolecule("C2H6O")$exactmass,
  ChemmineR = ChemmineR::exactMassOB(ChemmineR::smiles2sdf("CCO")),
  OrgMassSpecR = OrgMassSpecR::MonoisotopicMass(formula = OrgMassSpecR::ListFormula("C2H6O)"), charge = 0),
  
  CHNOSZ = CHNOSZ::mass("C2H6O"),
  enviPat = enviPat::isopattern(isotopes, "C2H6O", charge=FALSE, verbose=FALSE)[[1]][1,1]
  , times=1000L)


masses <- c(
  MetaboCoreUtils=MetaboCoreUtils::calculateMass("C2H6O"),
  rcdk=rcdk::get.formula("C2H6O", charge = 0)@mass,
  Rdisop=Rdisop::getMolecule("C2H6O")$exactmass,
  #ChemmineR=ChemmineR::exactMassOB(ChemmineR::smiles2sdf("CCO")),
  OrgMassSpecR=OrgMassSpecR::MonoisotopicMass(formula = OrgMassSpecR::ListFormula("C2H6O)"), charge = 0),
  CHNOSZ=CHNOSZ::mass("C2H6O"),
  enviPat=enviPat::isopattern(isotopes, "C2H6O", charge=FALSE, verbose=FALSE)[[1]][1,1]
)

options(digits=10)
t(t(sort(masses)))
summary(benchmark)[order(summary(benchmark)[,"median"]) , ]
clipr::write_clip(as.data.frame(summary(benchmark)[order(summary(benchmark)[,"median"]) , ] ))

Results

              expr       min         lq         mean     median         uq
1  MetaboCoreUtils    69.479   122.8465   154.049427   139.6495   156.2700
10         enviPat    83.250   143.0935   170.429197   160.5360   179.6570
5            rcdk4   175.889   228.8605   324.182735   271.2955   327.7135
8     OrgMassSpecR   249.287   333.3135   392.479869   357.6665   401.5585
6           Rdisop   382.417   459.8790   538.068697   490.1505   557.9975
9           CHNOSZ   355.145   510.2910   588.186951   555.9165   632.2060
4            rcdk3   781.987  1004.7160  1294.507318  1133.3415  1339.4695
3            rcdk2  2078.465  2392.4950  2920.601088  2612.8025  2931.5465
7        ChemmineR  3227.320  3790.0455  4808.783873  4044.1410  4465.1000
2             rcdk 14823.815 16456.7715 19088.569430 17485.0800 19468.7195