Last updated: 2019-09-19
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 |
|---|---|---|---|---|
| html | 75bc270 | goergen95 | 2019-09-05 | Build site. |
| 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. |
| Rmd | b6fd75e | goergen95 | 2019-08-19 | added classification side |
| html | b6fd75e | goergen95 | 2019-08-19 | added classification side |
The overall aim of this project was to ease the process of classifying the spectra of particles found in environmental samples. A script was created which takes .txt files in the smp directory and classifies them based on the decision fusion explained here. The script expects one file for each sample. The names of the files need to be unique and will be used as an identifier in the output. It is also expected that the file consists of two columns. The first column indicates the wavenumbers of the sample as a numeric, while the second column contains information about the reflectance value.
The head of the classification script contains some variables that can be changed. This is important if extensions were made to the database or a new calibration was to be done. In this case, the directory containing the models can be specified with the MODEL variable. For now, however, the variables hint towards the BASE models which were created during the first calibration. Also, the type of the classification can be specified in the TYPE variable. Currently, the fusion of all four models is chosen, but any single model can be selected. Finally, the FORMAT variable indicates the file extension of the sample data, since it is also possible to provide spectral data in .csv format. At the beginning of each classification process, an entry is created in the smp directory named with the current timestamp as well as the MODEL type which is going to be used.
MODEL = "BASE"
TYPE = "FUSION"
FORMAT = ".txt"
TIME = format(Sys.time(),"%Y%m%d_%H%M")
root = paste0(smp,TIME,"_",TYPE)
plots = paste0(root,"/plots")
raw = paste0(root,"/files")
dir.create(root)
dir.create(plots)
dir.create(raw)
model = paste0(mod,MODEL)The database and the sample data is read into R. The reflectance values of the sample data are resampled to the spectral resolution of the database and then a baseline correction is applied, using the procedure of Primpke et al. (2018). Finally, all machine-learning models are loaded from the mod directory.
classes = readLines(paste0(ref,"classes.txt"))
data = lapply(classes,function(x){
print(x)
specs = read.csv(list.files(ref,full.names=T)[grep(paste("_",x,".csv",sep=""),list.files(ref))],header=T)
return(specs)
})
data = do.call("rbind",data)
wavenumbers = readRDS(paste0(model,"/wavenumbers.rds"))
wvn = as.numeric(str_remove(names(data)[-ncol(data)],"wvn"))
index = which(wvn %in% wavenumbers)
data = data[,c(index,ncol(data))]
sampleList = list.files(smp,pattern=FORMAT,full.names = TRUE)
if (length(sampleList)==0){
cat("No samples present in sample directory")
#quit(status = 1)
}
Nsamples = length(sampleList)
prepSMP = function(x,wvn){
tmp = read.table(x)
names(tmp) = c("wavenumbers","reflectance")
tmp = prospectr::resample2(tmp$reflectance,tmp$wavenumbers,wvn)
return(tmp)
}
samples = lapply(sampleList,prepSMP,wavenumbers)
samples = as.data.frame(do.call("rbind",samples))
names(samples) = names(data)[-ncol(data)]
dummy = as.matrix(samples)
baselineDummy = baseline(dummy,method="rfbaseline",span=NULL,NoXP=64,maxit=c(10))
#baselineDummy = baseline(dummy, method="rollingBall", wm = 500, ws = 500 )
spectra = getCorrected(baselineDummy)
samples = as.data.frame(spectra)
files = list.files(model, full.names = TRUE)
rfModRaw = readRDS(files[grep("rfModRaw.rds", files)])
rfModSG = readRDS(files[grep("rfModSG.rds", files)])
pcaRaw = readRDS(files[grep("rfModRawPCA.rds", files)])
pcaSG = readRDS(files[grep("rfModSGPCA.rds", files)])
cnnD2 = keras::load_model_hdf5(files[grep("cnnD2",files)])
cnnND2 = keras::load_model_hdf5(files[grep("cnnND2", files)])For the decision fusion, the samples are pre-processed according to the expected input for the different models (see here) and the wavenumbers in the C02 window (2200 to 2420 1/cm) are set to 0. Each model is then used to predict an output for the samples. The decision fusion takes places by combining the probability outputs from each model. In addition, all non-synthetic polymer classes are merged to a broader class named OTHER.
# predicting
classRFRaw = as.character(stats::predict(rfModRaw, pcaRAW))
propRFRaw = stats::predict(rfModRaw, pcaRAW, type = "prob")
classRFSG = as.character(stats::predict(rfModSG, pcaSG))
propRFSG = stats::predict(rfModSG, pcaSG, type = "prob")
classCNND2 = as.character(classes[keras::predict_classes(cnnD2, x_sampleD2)+1])
propCNND2 = keras::predict_proba(cnnD2, x_sampleD2)
classCNNND2 = as.character(classes[keras::predict_classes(cnnND2, x_sampleND2)+1])
propCNNND2 = keras::predict_proba(cnnND2, x_sampleND2)
# restructuring results
probs = (propRFRaw + propRFSG + propCNND2 + propCNNND2) / 4
pred = lapply(1:nrow(probs), function(x){
which.max(probs[x,])
})
predVals = lapply(1:nrow(probs), function(x){
probs[x,unlist(pred)[x]]
})
hits = lapply(1:nrow(probs), function(x){
hits = sort(probs[x, ], decreasing = T)[1:3]
})
predVals = unlist(predVals)
pred = names(unlist(pred))
pred[which(pred %in% c("FIBRE","FUR","WOOD"))] = "OTHER"
results = data.frame(id = ids, class = pred, prob = predVals, level = rep(0,Nsamples))In conclusion, several outputs are created for the user to assess the classification results. Individual plots with the three classes showing the highest probability are created for each sample. Furthermore, a data frame named results containing information on the level of agreement for the class with the highest probability is written to disk to allow a quick assessment of the classification process. The level of agreement is based on the fused classification probability, labeling probabilities below 0.5 as “no agreement” and increasing the agreement level every 10% up to >0.90 labeled as “very high agreement”. The plots are saved to a plot directory in the current classification directory and can be used to manually assess the classification.
ids = list.files(smp,pattern = FORMAT)
ids = str_remove(ids, FORMAT)
for (id in 1:length(ids)){
hit = hits[[id]]
classes = names(hit)
values = as.numeric(hit)
sample = as.data.frame(t(samples[id,]))
sample$wavenumbers = wavenumbers
sample[which(wavenumbers<=2420 & wavenumbers>=2200),] = 0
names(sample) = c( "reflectance", "wavenumbers")
if(values[1] < .5) level = "no agreement"
if(values[1] >= .5 & values[1] < .6) level = "very low agreement"
if(values[1] >= .6 & values[1] < .7) level = "low agreement"
if(values[1] >= .7 & values[1] < .8) level = "medium agreement"
if(values[1] >= .8 & values[1] < .9) level = "high agreement"
if(values[1] >= .9) level = "very high agreement"
results$level[id] = level
annotation = paste0(level,"\n",
classes[1], ": ", round(values[1], 3), "\n",
classes[2], ": ", round(values[2], 3), "\n",
classes[3], ": ", round(values[3], 3))
class1 = samplePlot(data = data, sample = sample, class = classes[1], prob = annotation, name = ids[id])
class2 = samplePlot(data = data, sample = sample, class = classes[2])
class3 = samplePlot(data = data, sample = sample, class = classes[3])
multiclass = gridExtra::grid.arrange(class1,class2,class3)
ggsave(plot=multiclass,file=paste0(plots,"/",ids[id],"_probClasses.png"),dpi=300,device="png",units="cm",width=50,height=30)
}
write.csv(results, paste0(root, "/results_",TIME,".csv"))Here, we evaluate the decision fusion models by some environmental samples which were provided by Sarah Brüning and Frauke von den Driesch. The overall classification results, as they are written to the results.csv, are presented below.
| X | id | class | prob | level |
|---|---|---|---|---|
| 1 | 120619_W2_1000_1 | OTHER | 0.3020575 | no agreement |
| 2 | 120619_W2_1000_2 | PES | 0.3153827 | no agreement |
| 3 | 120619_W2_300_1 | PES | 0.2533208 | no agreement |
| 4 | 120619_W2_300_2 | PUR | 0.2301582 | no agreement |
| 5 | 120619_W2_300_3 | HDPE | 0.8341086 | high agreement |
| 6 | 120619_W2_300_4 | HDPE | 0.5052317 | very low agreement |
| 7 | 120619_W2_300_5 | HDPE | 0.6929572 | low agreement |
| 8 | 120619_W2_500_1 | PS | 0.3061395 | no agreement |
| 9 | 120619_W2_500_2 | PES | 0.2373230 | no agreement |
| 10 | 120619_W2_500_3 | PES | 0.2901537 | no agreement |
| 11 | 120619_W2_500_4 | HDPE | 0.3477586 | no agreement |
| 12 | 120619_W2_500_5 | PES | 0.2785373 | no agreement |
| 13 | 120619_W2_500_6 | PA | 0.4990000 | no agreement |
| 14 | 120619_W2_500_7 | PES | 0.2935073 | no agreement |
Additionally, two exemplary plots overlaying the spectra of two samples with mean spectra of the classified polymer in the database are shown. Note that the first plot corresponds to row 5 in the table above, the second plot to line 13.
Primpke, S., Wirth, M., Lorenz, C., Gerdts, G., 2018. Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy. Analytical and Bioanalytical Chemistry 410, 5131–5141. https://doi.org/10.1007/s00216-018-1156-x
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] plotly_4.9.0 tensorflow_1.14.0
[3] abind_1.4-5 e1071_1.7-2
[5] keras_2.2.4.1 workflowr_1.4.0.9001
[7] baseline_1.2-1 gridExtra_2.3
[9] stringr_1.4.0 prospectr_0.1.3
[11] RcppArmadillo_0.9.600.4.0 openxlsx_4.1.0.1
[13] magrittr_1.5 ggplot2_3.2.0
[15] reshape2_1.4.3 dplyr_0.8.3
loaded via a namespace (and not attached):
[1] httr_1.4.1 tidyr_0.8.3 jsonlite_1.6
[4] viridisLite_0.3.0 foreach_1.4.7 shiny_1.3.2
[7] assertthat_0.2.1 highr_0.8 yaml_2.2.0
[10] pillar_1.4.2 backports_1.1.4 lattice_0.20-38
[13] glue_1.3.1 reticulate_1.13 digest_0.6.20
[16] promises_1.0.1 colorspace_1.4-1 htmltools_0.3.6
[19] httpuv_1.5.1 Matrix_1.2-17 plyr_1.8.4
[22] pkgconfig_2.0.2 SparseM_1.77 xtable_1.8-4
[25] purrr_0.3.2 scales_1.0.0 whisker_0.3-2
[28] later_0.8.0 git2r_0.26.1 tibble_2.1.3
[31] generics_0.0.2 withr_2.1.2 lazyeval_0.2.2
[34] mime_0.7 crayon_1.3.4 IDPmisc_1.1.19
[37] evaluate_0.14 fs_1.3.1 class_7.3-15
[40] RcppZiggurat_0.1.5 tools_3.6.1 data.table_1.12.2
[43] munsell_0.5.0 zip_2.0.3 Rfast_1.9.5
[46] compiler_3.6.1 rlang_0.4.0 grid_3.6.1
[49] iterators_1.0.12 Rmisc_1.5 htmlwidgets_1.3
[52] crosstalk_1.0.0 base64enc_0.1-3 labeling_0.3
[55] rmarkdown_1.14 gtable_0.3.0 codetools_0.2-16
[58] R6_2.4.0 tfruns_1.4 knitr_1.24
[61] zeallot_0.1.0 rprojroot_1.3-2 stringi_1.4.3
[64] parallel_3.6.1 Rcpp_1.0.2 tidyselect_0.2.5
[67] xfun_0.8