From ec574cff822a1238138c0aa69b3d1459bdc3dfa8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 19 Jun 2015 17:46:11 +0200 Subject: Use odeintr instead of ccSolve for compiling models --- vignettes/FOCUS_D.Rmd | 2 +- vignettes/FOCUS_D.html | 16 +++++------ vignettes/FOCUS_Z.pdf | Bin 215014 -> 223035 bytes vignettes/compiled_models.Rmd | 35 +++++++++-------------- vignettes/compiled_models.html | 63 ++++++++++++++++++----------------------- 5 files changed, 51 insertions(+), 65 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_D.Rmd b/vignettes/FOCUS_D.Rmd index 902d3d24..f3dd6661 100644 --- a/vignettes/FOCUS_D.Rmd +++ b/vignettes/FOCUS_D.Rmd @@ -27,7 +27,7 @@ kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector `$diffs` of the `mkinmod` object. If -the `ccSolve` package is installed and functional, the differential equation model +a compiler (g++) is installed and functional, the differential equation model will be compiled from auto-generated C code. diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 6573cc7a..9522e881 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -276,13 +276,13 @@ kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics

The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector $diffs of the mkinmod object. If -the ccSolve package is installed and functional, the differential equation model +a compiler (g++) is installed and functional, the differential equation model will be compiled from auto-generated C code.

SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
-
## Compiling differential equation model from auto-generated C code...
+
## Compiling differential equation model from auto-generated C++ code...
 

 
 
print(SFO_SFO$diffs)
@@ -312,7 +312,7 @@ using the plot method for mkinfit objects.

 
mkinparplot(fit)
 

 
-
plot of chunk unnamed-chunk-6 

+
plot of chunk unnamed-chunk-6 

 
 
A comprehensive report of the results is obtained using the summary method for mkinfit
 objects.

@@ -322,16 +322,16 @@ objects.

 
 
## mkin version:    0.9.36 
 ## R version:       3.2.0 
-## Date of fit:     Fri Jun  5 14:20:31 2015 
-## Date of summary: Fri Jun  5 14:20:31 2015 
+## Date of fit:     Fri Jun 19 16:21:21 2015 
+## Date of summary: Fri Jun 19 16:21:21 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
 ## d_m1 = + k_parent_m1 * parent - k_m1_sink * m1
 ## 
-## Model predictions using solution type deSolve 
+## Model predictions using solution type odeintr 
 ## 
-## Fitted with method Port using 153 model solutions performed in 0.621 s
+## Fitted with method Port using 153 model solutions performed in 0.562 s
 ## 
 ## Weighting: none
 ## 
@@ -370,7 +370,7 @@ objects.

 ## parent_0           1.00000            0.6075        -0.06625       -0.1701
 ## log_k_parent_sink  0.60752            1.0000        -0.08740       -0.6253
 ## log_k_parent_m1   -0.06625           -0.0874         1.00000        0.4716
-## log_k_m1_sink     -0.17006           -0.6253         0.47163        1.0000
+## log_k_m1_sink     -0.17006           -0.6253         0.47164        1.0000
 ## 
 ## Residual standard error: 3.211 on 36 degrees of freedom
 ## 
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index 3174a23a..e2a4baa9 100644
Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ
diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index bac284c5..b6d54710 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -15,22 +15,20 @@ output:
 ```{r, include = FALSE}
 library(knitr)
 opts_chunk$set(tidy = FALSE, cache = TRUE)
-if (!require("ccSolve")) 
-  message("Please install the ccSolve package for this vignette to produce sensible output")
-
 ```
 
 # Benchmark for a model that can also be solved with Eigenvalues
 
 This evaluation is taken from the example section of mkinfit. When using an mkin version
-greater than 0.9-36 and the ccSolve package is installed and functional, you will get a
-message that the model is being compiled when defining a model using mkinmod.
+greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows,
+install Rtools), you will get a message that the model is being compiled when
+defining a model using mkinmod.
 
 ```{r create_SFO_SFO}
 library("mkin")
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))
+  m1 = list(type = "SFO"), odeintr_compile = "yes")
 ```
 
 We can compare the performance of the Eigenvalue based solution against the
@@ -39,28 +37,23 @@ the microbenchmark package.
 
 
 ```{r benchmark_SFO_SFO, echo=-(1:2)}
-# Redefining the model, in order not to confuse the knitr cache which leads to segfaults
-suppressMessages(SFO_SFO <- mkinmod(
-  parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO")))
 library("microbenchmark")
 mb.1 <- microbenchmark(
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 
-          quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
   mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.1 <- summary(mb.1)[-1]
-rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
+rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled")
 print(smb.1)
 ```
 
-We see that using the compiled model is almost a factor of 8 faster than using the R version
+We see that using the compiled model is more than a factor of 7 faster than using the R version
 with the default ode solver, and it is even faster than the Eigenvalue based solution implemented
 in R which does not need iterative solution of the ODEs:
 
 ```{r}
-smb.1["median"]/smb.1["deSolve, compiled", "median"]
+smb.1["median"]/smb.1["odeintr, compiled", "median"]
 ```
 
 # Benchmark for a model that can not be solved with Eigenvalues
@@ -73,15 +66,15 @@ FOMC_SFO <- mkinmod(
   m1 = list(type = "SFO"))
 
 mb.2 <- microbenchmark(
-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.2 <- summary(mb.2)[-1]
-rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")
+rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled")
 print(smb.2)
-smb.2["median"]/smb.2["deSolve, compiled", "median"]
+smb.2["median"]/smb.2["odeintr, compiled", "median"]
 
 ```
 
 Here we get a performance benefit of more than a factor of 8 using the version
-of the differential equation model compiled from C code using the ccSolve package!
+of the differential equation model compiled from C++ code using the odeintr package!
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index 2f2a6edb..efdbe20d 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -77,37 +77,30 @@ img {
 -->
 

 
Benchmark for a model that can also be solved with Eigenvalues

-
This evaluation is taken from the example section of mkinfit. When using an mkin version greater than 0.9-36 and the ccSolve package is installed and functional, you will get a message that the model is being compiled when defining a model using mkinmod.

+
This evaluation is taken from the example section of mkinfit. When using an mkin version greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows, install Rtools), you will get a message that the model is being compiled when defining a model using mkinmod.

 
library("mkin")
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))

-
## Compiling differential equation model from auto-generated C code...

+  m1 = list(type = "SFO"), odeintr_compile = "yes")

+
## Compiling differential equation model from auto-generated C++ code...

 
We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the microbenchmark package.

-
library("microbenchmark")
-mb.1 <- microbenchmark(
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 
-          quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
-  times = 3, control = list(warmup = 1))
-smb.1 <- summary(mb.1)[-1]
-rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
+
smb.1 <- summary(mb.1)[-1]
+rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled")
 print(smb.1)

 
##                             min        lq      mean    median        uq
-## deSolve, not compiled 6192.0125 6195.3470 6211.0309 6198.6816 6220.5401
-## Eigenvalue based       956.7604 1008.7224 1026.2572 1060.6844 1061.0055
-## deSolve, compiled      869.6880  871.9315  883.4929  874.1751  890.3953
+## deSolve, not compiled 5254.1030 5261.3501 5277.1074 5268.5973 5288.6096
+## Eigenvalue based       897.1575  921.6935  930.9546  946.2296  947.8531
+## odeintr, compiled      693.6001  709.0719  719.5530  724.5438  732.5295
 ##                             max neval
-## deSolve, not compiled 6242.3986     3
-## Eigenvalue based      1061.3266     3
-## deSolve, compiled      906.6155     3

-
We see that using the compiled model is almost a factor of 8 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:

-
smb.1["median"]/smb.1["deSolve, compiled", "median"]

+## deSolve, not compiled 5308.6218     3
+## Eigenvalue based       949.4766     3
+## odeintr, compiled      740.5151     3

+
We see that using the compiled model is more than a factor of 7 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:

+
smb.1["median"]/smb.1["odeintr, compiled", "median"]

 
##                         median
-## deSolve, not compiled 7.120877
-## Eigenvalue based      1.205328
-## deSolve, compiled     1.000000

+## deSolve, not compiled 7.290796
+## Eigenvalue based      1.370242
+## odeintr, compiled     1.000000

 
 

 
Benchmark for a model that can not be solved with Eigenvalues

@@ -115,25 +108,25 @@ print(smb.1)
FOMC_SFO <- mkinmod(
   parent = list(type = "FOMC", to = "m1", sink = TRUE),
   m1 = list(type = "SFO"))
-
## Compiling differential equation model from auto-generated C code...
+
## Compiling differential equation model from auto-generated C++ code...
mb.2 <- microbenchmark(
-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.2 <- summary(mb.2)[-1]
-rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")
+rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled")
 print(smb.2)
##                             min        lq      mean    median        uq
-## deSolve, not compiled 13.297283 13.427702 13.481155 13.558121 13.573092
-## deSolve, compiled      1.486926  1.526887  1.546851  1.566848  1.576813
+## deSolve, not compiled 11.243675 11.324875 11.382415 11.406074 11.451785
+## odeintr, compiled      1.207114  1.209908  1.239989  1.212703  1.256426
 ##                             max neval
-## deSolve, not compiled 13.588063     3
-## deSolve, compiled      1.586778     3
-
smb.2["median"]/smb.2["deSolve, compiled", "median"]
+## deSolve, not compiled 11.497496 3 +## odeintr, compiled 1.300149 3 +
smb.2["median"]/smb.2["odeintr, compiled", "median"]
##                         median
-## deSolve, not compiled 8.653119
-## deSolve, compiled     1.000000
-

Here we get a performance benefit of more than a factor of 8 using the version of the differential equation model compiled from C code using the ccSolve package!

+## deSolve, not compiled 9.405494 +## odeintr, compiled 1.000000 +

Here we get a performance benefit of more than a factor of 8 using the version of the differential equation model compiled from C++ code using the odeintr package!

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