Last updated: 2019-08-21
Checks: 6 1
Knit directory: polymeRID/
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Unstaged changes:
Modified: analysis/exploration.Rmd
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 | f2ee83c | goergen95 | 2019-08-19 | Build site. |
| html | d960dc2 | goergen95 | 2019-08-19 | included calibration |
| html | b846f0b | goergen95 | 2019-08-19 | Build site. |
| Rmd | de84a71 | goergen95 | 2019-08-19 | large update for website |
| html | de84a71 | goergen95 | 2019-08-19 | large update for website |
| html | 2385fbc | goergen95 | 2019-08-14 | republish for layout change |
| Rmd | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
| html | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
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 measured by an FTIR spectrometer. Consequently, the workflow and the results are presented individually for each individual algorithm.
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 Rcpp_1.0.2 scales_1.0.0 backports_1.1.4
[29] jsonlite_1.6 fs_1.3.1 digest_0.6.20 stringi_1.4.3
[33] grid_3.6.1 rprojroot_1.3-2 tools_3.6.1 lazyeval_0.2.2
[37] tibble_2.1.3 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[41] zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1 rmarkdown_1.14
[45] iterators_1.0.12 R6_2.4.0 git2r_0.26.1 compiler_3.6.1