Last updated: 2019-09-05
Checks: 7 0
Knit directory: polymeRID/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a848def | goergen95 | 2019-09-05 | changed citation style |
| html | c26a428 | goergen95 | 2019-08-22 | Build site. |
| Rmd | 7e9eddd | goergen95 | 2019-08-22 | wflow_publish(files = c(“analysis/cnn_crossvalidation.Rmd”, “analysis/cnn_exploration.Rmd”, |
| html | f2ee83c | goergen95 | 2019-08-19 | Build site. |
| html | d960dc2 | goergen95 | 2019-08-19 | included calibration |
| html | b846f0b | goergen95 | 2019-08-19 | Build site. |
| Rmd | de84a71 | goergen95 | 2019-08-19 | large update for website |
| html | de84a71 | goergen95 | 2019-08-19 | large update for website |
In this section, the generalization potential of the CNNs with different parameter configurations is tested. Before, optimal data transformations and kernel sizes were explored (Tab. 1).
| X | types | kernel | loss | acc | val_loss | val_acc | |
|---|---|---|---|---|---|---|---|
| 163 | 163 | norm.d2 | 90 | 0.2898488 | 0.8961039 | 0.8323456 | 0.9142857 |
| 35 | 35 | sg | 70 | 0.2537513 | 0.9350649 | 1.3861195 | 0.9000000 |
| 135 | 135 | raw.d2 | 90 | 0.1529336 | 0.9350649 | 1.5874773 | 0.9000000 |
| 152 | 152 | norm.d1 | 150 | 0.1060385 | 0.9740260 | 0.8856056 | 0.9000000 |
We perform a 10-fold cross-validation (CV) which is repeated five times. The following code takes the different levels of the input data and applies the CV to each of the elements. To easily compare the results, the same folds are used for each data transformation.
data = read.csv(file = paste0(ref, "reference_database.csv"), header = TRUE)
kernels = c(90,70,90,150)
folds = 10
repeats = 5
p = 0.5
nOutcome = length(unique(data$class))
dataList = list()
normd2.data = preprocess(data[,1:ncol(data)-1], type = "norm.d2")
normd2.data$class = data$class
dataList[["norm.d2"]] = normd2.data
sg.data = preprocess(data[,1:ncol(data)-1], type = "norm")
sg.data$class = data$class
dataList[["sg"]] = sg.data
rawd2.data = preprocess(data[,1:ncol(data)-1], type = "raw.d2")
rawd2.data$class = data$class
dataList[["raw.d2"]] = rawd2.data
normd1.data = preprocess(data[,1:ncol(data)-1], type = "norm.d1")
normd1.data$class = data$class
dataList[["norm.d1"]] = nnormd1.data
for (i in 1:length(dataList)){
tmp = dataList[[i]]
# preparing data inputs
set.seed(42)
foldIndex = lapply(1:repeats, caret::createDataPartition, y=sg.tmp$class, times = folds, p=p)
foldIndex = do.call(c,foldIndex)
cvData = list()
for (rep in 1:repeats){
rep_Index = foldIndex[(rep*folds-folds+1):(rep*folds)] #always jump to the correct number of folds forward for each repeat
dataFold = lapply(1:folds,function(x){
training = tmp[unlist(rep_Index[x]), ]
validation = tmp[-unlist(rep_Index[x]), ]
foldtmp = list(training,validation)
names(foldtmp) = c("training","validation")
return(foldtmp)
})
cvData[[rep]] = dataFold
}
results = data.frame(repeats = rep(0,repeats*folds),
fold = rep(0,repeats*folds),
loss = rep(0,repeats*folds),
acc = rep(0,repeats*folds))
counter = 1
for (rep in 1:repeats){
#print(paste0("Starting repeat ",rep," out of ",repeats,"."))
for (fold in 1:folds){
variables = ncol(cvData[[rep]][[fold]][[1]])-1
x_train = cvData[[rep]][[fold]][["training"]][,1:variables]
y_train = unlist(cvData[[rep]][[fold]][["training"]][1+variables])
x_test = cvData[[rep]][[fold]][["validation"]][,1:variables]
y_test = unlist(cvData[[rep]][[fold]][["validation"]][1+variables])
# function to get keras array for dataframes
K <- keras::backend()
df_to_karray <- function(df){
tmp = as.matrix(df)
tmp = K$expand_dims(tmp, axis = 2L)
tmp = K$eval(tmp)
}
# coerce data to keras structure
x_train = df_to_karray(x_train)
x_test = df_to_karray(x_test)
y_train = keras::to_categorical(as.numeric(y_train)-1,nOutcome)
y_test = keras::to_categorical(as.numeric(y_test)-1,nOutcome)
# fitting the model
kernelMod = prepNNET(kernel, variables, nOutcome = nOutcome)
historyMod = keras::fit(kernelMod, x = x_train, y = y_train,
epochs=300,
batch_size = 10 )
evalK = keras::evaluate(kernelMod, x=x_test, y=y_test)
results$repeats[counter] = rep
results$fold[counter] = fold
results$loss[counter] = evalK$loss
results$acc[counter] = evalK$acc
print(results[counter,])
counter = counter + 1
write.csv(results, file = paste0(output,"nnet/cv/cvResults_K",kernel,".csv"))
}
}
}We can now retrieve information about the accurcies for the complete CV process by calculating averages accross the accuracy values.
results = data.frame(type = c("norm.d2", "sg", "raw.d2", "norm.d1"), accuracy = rep(0, 4))
results$accuracy[1] = round(mean(results.normd2$acc), 3)
results$accuracy[2] = round(mean(results.sg$acc), 3)
results$accuracy[3] = round(mean(results.rawd2$acc), 3)
results$accuracy[4] = round(mean(results.normd1$acc), 3)
results = results[order(-results$accuracy),]| type | accuracy | |
|---|---|---|
| 3 | raw.d2 | 0.868 |
| 1 | norm.d2 | 0.846 |
| 2 | sg | 0.839 |
| 4 | norm.d1 | 0.830 |
With an accuracy of approximately 0.87 the use of the second derivative of the raw data yielded to the highest accuracy value when calculated in a cross-validation approach. With 0.85 the second derivative of the normalized data yielded to the second highest accuracy.
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 19.1
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
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=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tensorflow_1.14.0 abind_1.4-5
[3] e1071_1.7-2 keras_2.2.4.1
[5] workflowr_1.4.0.9001 baseline_1.2-1
[7] gridExtra_2.3 stringr_1.4.0
[9] prospectr_0.1.3 RcppArmadillo_0.9.600.4.0
[11] openxlsx_4.1.0.1 magrittr_1.5
[13] ggplot2_3.2.0 reshape2_1.4.3
[15] dplyr_0.8.3
loaded via a namespace (and not attached):
[1] reticulate_1.13 tidyselect_0.2.5 xfun_0.8 purrr_0.3.2
[5] lattice_0.20-38 colorspace_1.4-1 generics_0.0.2 htmltools_0.3.6
[9] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 foreach_1.4.7 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.3.0 zip_2.0.3 codetools_0.2-16
[21] evaluate_0.14 knitr_1.24 SparseM_1.77 tfruns_1.4
[25] class_7.3-15 highr_0.8 Rcpp_1.0.2 scales_1.0.0
[29] backports_1.1.4 jsonlite_1.6 fs_1.3.1 digest_0.6.20
[33] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2 tools_3.6.1
[37] lazyeval_0.2.2 tibble_2.1.3 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1
[45] rmarkdown_1.14 iterators_1.0.12 R6_2.4.0 git2r_0.26.1
[49] compiler_3.6.1