Last updated: 2019-08-19
Checks: 6 1
Knit directory: polymeRID/
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The overall aim of this project was to ease the process of classifying the spectra of particles found in environmental samples. To this end, we created a script which takes .txt files in the smp directory and classifies them based on the decision fusion explained here. The script expects on file for each sample. The names of the files need to be unique and will be used as an identifier in the output. Also, it is expected that the file consists of two columns. The first columns indicates the wavenumbers of the sample as an numeric, while the second column holds information about the reflectance value.
The head of the classification script contains some variables that might be changed. This is important if extensions were made to the data base and a new calibration was done. In this case, the directory containing the models can be specified with the MODEL variable. For now, however, the variables hints 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 could be selected. Finally, the FORMAT variables indicates the file extension of the sample data, since it would be also possible to provide spectral data in .csv format. At the beginning of each classification process, a directory is created in the smp directory which contains the current date and time as well as the MODEL type which was 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)Then, the data base and the sample data is read into R. The reflectance values of the sample data are resampled to the spectral resolution of the data base and then a baseline correction is applied, following the procedure of Primpke et al. (2018). Finally, all machine learning models are loaded.
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 preprocessed according to the expected input for the different models (see here) and the wavenumbers in the C02 window (2420 to 2200 1/cm) are set to 0. Then, each model is used to predict an output for the samples and the decision fusion takes places by combining the probability outputs from all models. Additionally, all non-synthetic polymer classes are merged to a broader class called 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))Finally, an individual plot with the three classes showing the highest probability is created for each sample. Additionally, a data frame called 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 “now 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. First we can take a look at the general classification results as it is written to the results.csv.
| 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, we included two exemplary plots, overlaying the spectrum of two samples with the mean spectrum of the prediction. Note that the firs plot corresponds to row 5 in the table above, the second plot to line 13.
Primpke, Sebastian, Marisa Wirth, Claudia Lorenz, and Gunnar Gerdts. 2018. “Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy.” Analytical and Bioanalytical Chemistry 410 (21). Analytical; Bioanalytical Chemistry: 5131–41. 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