Last updated: 2019-08-19
Checks: 7 0
Knit directory: polymeRID/
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Here, we test for the generalization potential of the CNNs trained with the parameters yielding to the highest accuracy results. To this end, we perform a 10-fold cross-validation which is repeated 5 times.
| 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 |
The following code takes the different levels of the input data and applies the same cross-validation to each of the elements. The same folds are used, so that the results can be compared easily.
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 retrive information about the accurcies for the complete cross-validation 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 approximatley 0.87 the use of the second derivative of the raw data yielded to the highes accuracy value when calculated in a cross-validation approach. With 0.85 the second derivateve 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