Last updated: 2019-08-14
Checks: 6 1
Knit directory: polymeRID/
This reproducible R Markdown analysis was created with workflowr (version 1.4.0.9001). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190729) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rprofile
Ignored: .Rproj.user/
Ignored: analysis/library.bib
Ignored: docs/figure/
Ignored: fun/
Ignored: output/20190810_1538/
Ignored: output/20190810_1546/
Ignored: output/20190810_1609/
Ignored: output/20190813_1044/
Ignored: output/logs/
Ignored: output/natural/
Ignored: output/nnet/
Ignored: output/svm/
Ignored: output/testRunII/
Ignored: output/testRunIII/
Ignored: packrat/lib-R/
Ignored: packrat/lib-ext/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/BH/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/FactoMineR/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/IDPmisc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/KernSmooth/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/MASS/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Matrix/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/MatrixModels/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ModelMetrics/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/R6/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RColorBrewer/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rcpp/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppArmadillo/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppEigen/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppGSL/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppZiggurat/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rfast/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rgtsvm/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rmisc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/SQUAREM/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/SparseM/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/abind/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/askpass/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/assertthat/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/backports/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/base64enc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/baseline/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/bit/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/bit64/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/boot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/callr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/car/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/carData/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/caret/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cellranger/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/class/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cli/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/clipr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cluster/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/codetools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/colorspace/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/config/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cowplot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/crayon/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/crosstalk/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/curl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/data.table/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/dendextend/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/digest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/doParallel/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/dplyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/e1071/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ellipse/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ellipsis/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/evaluate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/factoextra/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/fansi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/flashClust/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/forcats/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/foreach/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/foreign/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/fs/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/generics/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/getPass/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggplot2/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggpubr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggrepel/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggsci/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggsignif/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/git2r/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/glue/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gower/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gridExtra/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gtable/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/haven/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/hexbin/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/highr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/hms/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/htmltools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/htmlwidgets/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/httpuv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/httr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ipred/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/iterators/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/jsonlite/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/keras/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/kerasR/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/knitr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/labeling/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/later/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lattice/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lava/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lazyeval/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/leaps/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lme4/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lubridate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/magrittr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/maptools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/markdown/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/mgcv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/mime/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/minqa/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/munsell/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nlme/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nloptr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nnet/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/numDeriv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/openssl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/openxlsx/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/packrat/tests/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pbkrtest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pillar/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pkgconfig/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plogr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plotly/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/polynom/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prettyunits/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/processx/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prodlim/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/progress/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/promises/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prospectr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ps/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/purrr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/quantreg/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/randomForest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/readr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/readxl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/recipes/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rematch/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/reshape2/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/reticulate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rio/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rlang/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rmarkdown/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rpart/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rprojroot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rstudioapi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/scales/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/scatterplot3d/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/shiny/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sourcetools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sp/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/stringi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/stringr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/survival/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sys/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tensorflow/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tfruns/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tibble/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tidyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tidyselect/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/timeDate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tinytex/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/utf8/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/vctrs/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/viridis/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/viridisLite/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/whisker/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/withr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/workflowr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/xfun/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/xtable/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/yaml/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/zeallot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/zip/
Ignored: packrat/src/
Ignored: polymeRID.Rproj
Ignored: smp/20190812_1723_NNET/files/
Ignored: smp/20190812_1723_NNET/plots/
Ignored: smp/20190812_1729_NNET/files/
Ignored: smp/20190812_1729_NNET/plots/
Ignored: smp/20190812_1731_NNET/files/
Ignored: smp/20190812_1731_NNET/plots/
Ignored: smp/20190812_1733_NNET/files/
Ignored: smp/20190812_1733_NNET/plots/
Ignored: website/analysis/
Ignored: website/code/
Ignored: website/docs/
Ignored: website/mod/
Ignored: website/output/
Ignored: website/run/
Ignored: website/smp/
Untracked files:
Untracked: website/ref/reference_HDPE.csv
Untracked: website/ref/reference_LDPE.csv
Untracked: website/ref/reference_Nylon.csv
Unstaged changes:
Modified: analysis/index.Rmd
Modified: analysis/rf_exploration.Rmd
Modified: website/ref/classes.txt
Modified: website/ref/reference_FUR.csv
Modified: website/ref/reference_PE.csv
Modified: website/ref/reference_database.csv
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 | 90bd244 | goergen95 | 2019-08-14 | forward on rf_exploration |
| html | 90bd244 | goergen95 | 2019-08-14 | forward on rf_exploration |
| Rmd | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
| html | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
Random Forest (RF) is a machine learning algorithm which is based on the traditional concept of decision trees. It’s popular implementation was developed by Breiman (2001). It represents an ensemble classifier which has been reported to be the primary choice between different ensemble classifiers due to its easy handling and high classification accuracies (Sagi and Rokach 2018). More elaborations on scientific background
Different levels of data reprocessing might emphasize different features of the patterns to be learned by an algorithm. To grasp this, different transformations of the data were presented to the RF algorithm. Additionally, the raw data signal was jittered to test which transformation might prove beneficial in delivering high classification accuracies even in the presence of noise. To test this, we define a function which adds noise to the raw data and returns a list with the number of elements equal to the levels of noise applied.
addNoise = function(data, levels = c(0), category="class"){
data.return = list()
index = which(names(data) == category)
for (n in levels){
tmp = as.matrix(data[ , -index])
if (n == 0){
tmp = data
}else{
tmp = as.data.frame(jitter(tmp, n))
tmp[category] = data[category]
}
data.return[[paste("noise", n, sep="")]] = tmp
}
return(data.return)
}
data = read.csv(file = paste0(ref, "reference_database.csv"), header = TRUE)
noisy_data = addNoise(data, levels = c(0,10,100,250,500), category = "class")
# indivitual elements can be selected by using [[ and refering to the index or the name
head(noisy_data[["noise100"]])[1:3,1:3] wvn3992.63003826141 wvn3990.70123147964 wvn3988.77242469788
1 -0.010328834 0.01829306 0.001603137
2 0.005237827 0.01683373 -0.013921331
3 -0.011751581 0.01958805 0.002650597Then, in another user defined function which uses the noisy_data objected as input specified data transformations are applied. These are normalization which centers and scales the input data, as well as different forms of the Savitkiy-Golay filter (Savitzky and Golay 1964) and first and second derivative of a raw spectrum. The functions iterates through the noise level elements in the noisy_data object and returns each specified transformation in a list element below the noise level. The exemplary function below applies the preprocessing for normalization, standard filtering and first derivative only. The implementation of the function used in the project can be found here.
createTrainingSet = function(data, category = "class",
SGpara = list(p=3,w=11), lag = 15){
data.return = list()
for (noise in names(data)){
tmp = as.data.frame(data[[noise]])
classes = tmp[,category]
tmp = tmp[!names(tmp) %in% category]
# original data
data.return[[noise]][["raw"]] = as.data.frame(data[[noise]])
# normalised data
data_norm = preprocess(tmp, type="norm")
data_norm[category] = classes
data.return[[noise]][["norm"]] = data_norm
# SG-filtered data
data_sg = preprocess(tmp, type="sg", SGpara = SGpara)
data_sg[category] = classes
data.return[[noise]][["sg"]] = data_sg
# first derivative of original data
data_rawd1 = preprocess(tmp, type="raw.d1", lag = lag)
data_rawd1[category] = classes
data.return[[noise]][["raw.d1"]] = data_rawd1
}
return(data.return)
}
# applying the function
test_dataset = createTrainingSet(noisy_data, category = "class")
# individual transformations at a certain noise level can be accessed with [[
head(test_dataset[["noise500"]][["raw.d1"]])[1:3,1:3] wvn3963.69793653488 wvn3961.76912975311 wvn3959.84032297134
1 0.08295627 0.02234302 0.17399975
2 0.04253842 0.07918397 0.03849980
3 -0.05579848 0.11670539 0.02503375The data base of (Primpke et al. 2018) currently shows 1863 variables for each observations. Most of these data points do not bear relevant information to distinguish between different types of particles. To shorten the computation time, one can use dimensionality reduction techniques such as principal component analysis (PCA). PCA also has been used to transform spectral data of micro-plastics in marine ecosystems before (Jung et al. 2018; Lorenzo-Navarro et al. 2018). PCA basically takes the input data for a given number of observation and by performing a orthogonal transformation to the data transforms these possible correlated variables to uncorrelated principal components. This way, both redundancies in the data as well as possible noise can be accounted for. PCA previously has been successfully applied to FTIR-spectrometer data [Hori and Sugiyama (2003);Nieuwoudt et al. (2004);Mueller et al. (2013);Ami, Mereghetti, and Maria (2013);Fu2014]. Simultaniously, the number of variables can be significantly reduced by applying PCA and thus speeding up the training process. Below we will apply a PCA to the raw data as an example.
library(factoextra)
tmp = test_dataset[["noise0"]][["raw"]]
pca = prcomp(tmp[ ,-1864]) # omitting class variable
var_info = factoextra::get_eigenvalue(pca)
# setting a threshold of 99% explained variance
threshold = 99
thresInd = which(var_info$cumulative.variance.percent>=threshold)[1]
pca_data = pca$x[,1:thresInd]We can use the index variable thresInd we just defined to take a look upon all the principal components which explain 99% of the variance in the data set.
| eigenvalue | variance.percent | cumulative.variance.percent | |
|---|---|---|---|
| Dim.1 | 1.5618496 | 57.7479523 | 57.74795 |
| Dim.2 | 0.3875503 | 14.3293173 | 72.07727 |
| Dim.3 | 0.2226548 | 8.2324559 | 80.30973 |
| Dim.4 | 0.1823539 | 6.7423696 | 87.05209 |
| Dim.5 | 0.0897978 | 3.3201919 | 90.37229 |
| Dim.6 | 0.0538395 | 1.9906665 | 92.36295 |
| Dim.7 | 0.0443676 | 1.6404525 | 94.00341 |
| Dim.8 | 0.0324243 | 1.1988595 | 95.20227 |
| Dim.9 | 0.0297937 | 1.1015939 | 96.30386 |
| Dim.10 | 0.0215566 | 0.7970366 | 97.10090 |
| Dim.11 | 0.0173647 | 0.6420450 | 97.74294 |
| Dim.12 | 0.0145994 | 0.5397977 | 98.28274 |
| Dim.13 | 0.0113917 | 0.4211964 | 98.70394 |
| Dim.14 | 0.0069264 | 0.2560972 | 98.96003 |
| Dim.15 | 0.0042735 | 0.1580104 | 99.11804 |
We effectivly reduced the number of variables from 1683 to 15 which still bear 99% of the variance we can find in the original data set. However, when it comes to machine learning, it is important to realise that this new dataset is not fit to be used in a training process. If we now randomly split the observations into training and test, we effectivly mix up these two sets because information of the test set has already influenced the outcome of the PCA. Therefor, the data set need to be split beforhand of the PCA. The analysis is done on the training data only and then the same orthogonal transformations will be applied to the test data. This way it can be ensured that the test set is truely independent from the training process. Here, we apply a 10-fold cross-validation which is repeated 5 times. The following code takes a complete data set as input, applies a splitting function from the caret package and then builds the PCA upon the the test set and finally applies the same transformation to the test set. We apply it for the raw data only. Also, we randomly split the data to 50% training and 50% test.
folds = 10
repeats = 5
split_percentage = 0.5
threshold = 99
tmp = test_dataset[["noise0"]][["raw"]]
set.seed(42) # ensure reproducibility
fold_index = lapply(1:repeats, caret::createDataPartition, y=tmp$class,
times = folds, p = split_percentage)
fold_index = do.call(c, fold_index)
pcaData = list()
for (rep in 1:repeats){
rep_index = fold_index[(rep*folds-folds+1):(rep*folds)] # jumps to the correct number of folds forward in each repeat
pcadata_fold = lapply(1:folds,function(x){
# splitting for current fold
training = tmp[unlist(rep_index[x]),]
validation = tmp[-unlist(rep_index[x]),]
# keep response
responseTrain = training$class
responseVal = validation$class
# apply PCA
pca = prcomp(training[,1:1863])
varInfo = factoextra::get_eigenvalue(pca)
thresInd = which(varInfo$cumulative.variance.percent >= threshold)[1]
pca_training = pca$x[ ,1:thresInd]
pca_validation = predict(pca, validation)[ ,1:thresInd]
training = as.data.frame(pca_training)
training$response = responseTrain
validation = as.data.frame(pca_validation)
validation$response = responseVal
foldtmp = list(training, validation)
names(foldtmp) = c("training","validation")
return(foldtmp)
})
names(pcadata_fold) = paste("fold", 1:folds, sep ="")
pcaData[[paste0("repeat",rep)]] = pcadata_fold
}We now have a list object with the number of elements equivalent to the repeats. Below each repeat element we can access the individual folds. There we find two elements which we can access by refering to "training" and "testing".
head(pcaData[["repeat5"]][["fold10"]][["training"]]) PC1 PC2 PC3 PC4 PC5 PC6
1 -0.4779164 -0.7786770 -0.02761266 -0.06607284 -0.1414115 -0.1534971
2 -0.4869471 -0.7181236 -0.02492981 -0.05903759 -0.1850104 -0.1409379
4 -0.4661699 -0.7463479 -0.11424932 -0.03956596 -0.1056265 -0.1399688
6 -0.5257137 -0.5042399 0.10500327 -0.05850722 -0.2114660 -0.1380447
8 -0.5117566 -0.5967677 0.04001548 -0.04896668 -0.2129714 -0.1515174
11 -0.5237685 -0.5894120 0.05200442 -0.05865827 -0.2263991 -0.1561229
PC7 PC8 PC9 PC10 PC11 PC12
1 0.2060466 -0.04470884 0.1514982 -0.04843538 0.21485350 -0.011536533
2 0.1918080 -0.04751573 0.1395856 -0.03745341 0.17256791 -0.002443176
4 0.1775101 -0.05680964 0.1531349 -0.04319456 0.21718253 -0.003368534
6 0.1901325 -0.03195793 0.1066313 -0.02132166 0.09589942 -0.006065318
8 0.2032426 -0.03895545 0.1157844 -0.03040473 0.13270558 -0.002097848
11 0.2032640 -0.04166006 0.1209914 -0.02936654 0.12332775 -0.002840695
PC13 response
1 0.04706193 FUR
2 0.01465483 FUR
4 0.02593991 FUR
6 0.03218995 FUR
8 0.02925628 FUR
11 0.03068548 FURtail(pcaData[["repeat5"]][["fold10"]][["validation"]]) PC1 PC2 PC3 PC4 PC5 PC6
138 -0.6244338 -0.2251354 -0.04125220 -0.07484999 -0.27266002 -0.026217908
139 -0.5832934 -0.5663093 -0.39824689 -0.09269745 -0.06750005 0.007607282
142 1.3643143 -0.4874876 -0.04759479 1.69655907 0.26013483 0.009538671
143 0.9617909 -0.4624777 -0.07106849 1.24167453 0.20000959 0.002779168
144 -0.8842106 1.0703276 -0.77433375 -0.02590177 0.34894910 0.001737278
147 -0.6888015 0.1002989 0.40046959 -0.12243199 -0.48345619 0.076636770
PC7 PC8 PC9 PC10 PC11
138 0.06659313 0.002040475 -0.017318487 0.025721276 -0.16978522
139 0.03009946 -0.013417290 -0.056429051 0.006177153 -0.11445905
142 -0.20273071 0.178143838 0.486645669 0.618606076 0.13437431
143 -0.17007004 0.168194290 0.401200526 0.556734081 0.07242852
144 0.01314587 -0.029058498 0.223687515 -0.056605387 0.01770163
147 -0.14184110 -0.032212557 0.002410302 -0.007140975 -0.16104385
PC12 PC13 response
138 -0.0006882923 -0.050474176 PA
139 0.0199881958 -0.003416057 PA
142 -0.2862799869 -0.260510805 PUR
143 -0.2309708768 -0.247415734 PUR
144 0.0369931976 0.014124356 HDPE
147 0.0171552493 -0.050210341 PSsummary(pcaData[["repeat5"]][["fold10"]][["training"]]$response)FIBRE FUR HDPE LDPE PA PE PES PET PP PS PUR WOOD
14 12 5 6 7 4 8 5 6 4 4 2 summary(pcaData[["repeat5"]][["fold10"]][["validation"]]$response)FIBRE FUR HDPE LDPE PA PE PES PET PP PS PUR WOOD
13 11 5 5 7 4 7 4 6 3 3 2 This process of splitting the dataset into training and test was automised by putting the above code in a function which can be found here. Finally, we can apply this function to the different levels of preprocssing which were discussed before.
source("code/functions.R")
wavenumbers = readRDS(paste0(ref,"wavenumbers.rds"))
# add noise to data
noisyData = addNoise(data,levels = c(0,10,100,250,500), category = "class")
# preprocessing
testDataset = createTrainingSet(noisyData, category = "class",
SGpara = list(p=3, w=11), lag=15,
type = c("raw", "norm", "sg", "sg.d1", "sg.d2",
"sg.norm", "sg.norm.d1", "sg.norm.d2",
"raw.d1", "raw.d2", "norm.d1", "norm.d2"))
types = names(testDataset[[1]])
levels = lapply(names(testDataset), function(x){
rep(x, length(types))
})
levels = unlist(levels)
results = data.frame(level=levels,type = types, kappa = rep(0,length(levels)))
for (level in unique(levels)){
for (type in types){
print(paste0("Level: ",level," Type: ",type))
tmpData = testDataset[[level]][[type]]
tmpData[which(wavenumbers<=2420 & wavenumbers>=2200)] = 0 # setting C02 window to 0
tmpModel = pcaCV(tmpData, folds = 10, repeats = 5, threshold = 99, metric = "Kappa", p=0.5, method="rf")
saveRDS(tmpModel,file = paste0(output,"rf/model_",level,"_",type,"_",round(tmpModel[[1]],2),".rds"))
results[which(results$level==level & results$type==type),"kappa"] = as.numeric(tmpModel[[1]])
print(results)
}
}We can now take a look at the kappa scores for the algorithm achived during training with different representations of the data and noise levels.
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutAmi, Diletta, Paolo Mereghetti, and Silvia Maria. 2013. “Multivariate Analysis for Fourier Transform Infrared Spectra of Complex Biological Systems and Processes.” Multivariate Analysis in Management, Engineering and the Sciences. https://doi.org/10.5772/53850.
Breiman, L. 2001. “Random forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Hori, Ritsuko, and Junji Sugiyama. 2003. “A combined FT-IR microscopy and principal component analysis on softwood cell walls.” Carbohydrate Polymers 52 (4): 449–53. https://doi.org/10.1016/S0144-8617(03)00013-4.
Jung, Melissa R., F. David Horgen, Sara V. Orski, Viviana Rodriguez C., Kathryn L. Beers, George H. Balazs, T. Todd Jones, et al. 2018. “Validation of ATR FT-IR to identify polymers of plastic marine debris, including those ingested by marine organisms.” Marine Pollution Bulletin 127 (December 2017). Elsevier: 704–16. https://doi.org/10.1016/j.marpolbul.2017.12.061.
Mueller, Daniela, Marco Flôres Ferrão, Luciano Marder, Adilson Ben da Costa, and Rosana de Cássia de Souza Schneider. 2013. “Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.” Sensors (Switzerland) 13 (4): 4258–71. https://doi.org/10.3390/s130404258.
Nieuwoudt, Helene H., Bernard A. Prior, Isak S. Pretorius, Marena Manley, and Florian F. Bauer. 2004. “Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples.” Journal of Agricultural and Food Chemistry 52 (12): 3726–35. https://doi.org/10.1021/jf035431q.
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.
Sagi, Omer, and Lior Rokach. 2018. “Ensemble learning: A survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (4): 1–18. https://doi.org/10.1002/widm.1249.
Savitzky, Abraham, and Marcel J.E. Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures.” Analytical Chemistry 36 (8): 1627–39. https://doi.org/10.1021/ac60214a047.
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 knitr_1.24
[3] factoextra_1.0.5 tensorflow_1.14.0
[5] abind_1.4-5 e1071_1.7-2
[7] keras_2.2.4.1 workflowr_1.4.0.9001
[9] baseline_1.2-1 gridExtra_2.3
[11] stringr_1.4.0 prospectr_0.1.3
[13] RcppArmadillo_0.9.600.4.0 openxlsx_4.1.0.1
[15] magrittr_1.5 ggplot2_3.2.0
[17] reshape2_1.4.3 dplyr_0.8.3
loaded via a namespace (and not attached):
[1] httr_1.4.1 tidyr_0.8.3 viridisLite_0.3.0
[4] jsonlite_1.6 splines_3.6.1 foreach_1.4.7
[7] prodlim_2018.04.18 shiny_1.3.2 assertthat_0.2.1
[10] stats4_3.6.1 highr_0.8 yaml_2.2.0
[13] ggrepel_0.8.1 ipred_0.9-9 pillar_1.4.2
[16] backports_1.1.4 lattice_0.20-38 glue_1.3.1
[19] reticulate_1.13 digest_0.6.20 promises_1.0.1
[22] colorspace_1.4-1 recipes_0.1.6 httpuv_1.5.1
[25] htmltools_0.3.6 Matrix_1.2-17 plyr_1.8.4
[28] timeDate_3043.102 pkgconfig_2.0.2 SparseM_1.77
[31] caret_6.0-84 xtable_1.8-4 purrr_0.3.2
[34] scales_1.0.0 whisker_0.3-2 later_0.8.0
[37] gower_0.2.1 lava_1.6.5 git2r_0.26.1
[40] tibble_2.1.3 generics_0.0.2 withr_2.1.2
[43] nnet_7.3-12 lazyeval_0.2.2 mime_0.7
[46] survival_2.44-1.1 crayon_1.3.4 evaluate_0.14
[49] fs_1.3.1 nlme_3.1-140 MASS_7.3-51.4
[52] class_7.3-15 tools_3.6.1 data.table_1.12.2
[55] munsell_0.5.0 zip_2.0.3 compiler_3.6.1
[58] rlang_0.4.0 grid_3.6.1 iterators_1.0.12
[61] htmlwidgets_1.3 crosstalk_1.0.0 labeling_0.3
[64] base64enc_0.1-3 rmarkdown_1.14 gtable_0.3.0
[67] ModelMetrics_1.2.2 codetools_0.2-16 R6_2.4.0
[70] tfruns_1.4 lubridate_1.7.4 zeallot_0.1.0
[73] rprojroot_1.3-2 stringi_1.4.3 Rcpp_1.0.2
[76] rpart_4.1-15 tidyselect_0.2.5 xfun_0.8