Last updated: 2019-08-13
Checks: 6 1
Knit directory: polymeRID/
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Modified: analysis/preparation.Rmd
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Modified: code/setup_website.R
Modified: ref/wavenumbers.rds
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.
There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
The idea of the exploration stage is to find an optimal configuration between the hyper-parameters of the machine learning algorithms and different representations of the input data. On the one hand, algorithms such as support-vector-machines (SVM), random forest (RF) and convolutional neural networks (CNN), need proper fine-tuning of their parameters to effectively learn the patterns we are interested in. On the other hand, it is possible to present the data in different forms to the algorithms, each representing different “ways” to look at the samples. The main goal of such an exploration is to optimize the outcome of the training process, bearing in mind that such a process is basically a function of desired accuracy, computational costs, and data availability.
Here, all three previously mentioned machine learning algorithms, SVM, RF, and, CNN were tested for their ability to classify plastic polymers based on their spectral reflectance data measured by a FTIR spectrometer. Consequently, the workflow and the results are presented individually for the different algorithms.
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] reticulate_1.13 tidyselect_0.2.5 xfun_0.8
[4] purrr_0.3.2 lattice_0.20-38 colorspace_1.4-1
[7] generics_0.0.2 viridisLite_0.3.0 htmltools_0.3.6
[10] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0
[13] pillar_1.4.2 glue_1.3.1 withr_2.1.2
[16] foreach_1.4.7 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.3.0 zip_2.0.3 htmlwidgets_1.3
[22] codetools_0.2-16 evaluate_0.14 knitr_1.24
[25] SparseM_1.77 tfruns_1.4 class_7.3-15
[28] Rcpp_1.0.2 scales_1.0.0 backports_1.1.4
[31] jsonlite_1.6 fs_1.3.1 digest_0.6.20
[34] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2
[37] tools_3.6.1 lazyeval_0.2.2 tibble_2.1.3
[40] tidyr_0.8.3 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 zeallot_0.1.0 Matrix_1.2-17
[46] data.table_1.12.2 httr_1.4.1 assertthat_0.2.1
[49] rmarkdown_1.14 iterators_1.0.12 R6_2.4.0
[52] git2r_0.26.1 compiler_3.6.1