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| Rmd | 53a6b62 | pat-s | 2020-02-28 | save feature-importance state |
| Rmd | 518d0cb | pat-s | 2019-09-01 | style files using tidyverse style |
| Rmd | d7df860 | pat-s | 2019-07-10 | add feat imp plan |
| Rmd | 3999cd9 | pat-s | 2019-06-30 | add feature importance report |
Preview the ordered feature importance results for datasets “HR” and “VI”.
# A tibble: 122 x 5
feature importance wavelength numeric_id rank
<chr> <dbl> <dbl> <dbl> <int>
1 B69 1.57 724 69 1
2 B70 1.28 729. 70 2
3 B126 1.26 995. 126 3
4 B115 1.11 942. 115 4
5 B68 1.01 719. 68 5
6 B7 0.953 430. 7 6
7 B71 0.951 734. 71 7
8 B67 0.874 714. 67 8
9 B113 0.837 933 113 9
10 B124 0.773 985. 124 10
# … with 112 more rows# A tibble: 89 x 3
feature importance rank
<chr> <dbl> <int>
1 Vogelmann2 1.79 1
2 Vogelmann4 1.47 2
3 Vogelmann 0.914 3
4 NPCI 0.717 4
5 Vogelmann3 0.707 5
6 D2 0.657 6
7 Datt3 0.637 7
8 PWI 0.545 8
9 SR7 0.526 9
10 SRPI 0.509 10
# … with 79 more rowsPROSAIL is a algorithm simulating Spectral Signature (mean)s of vegetation, see ?hsdar::PROSAIL. Reflectance is scaled to 0-10 to be able to plot it in the same plot as the feature importance rankings -> the axis limits for the y and z axis needs to match.
PROSAIL returns a Spectral Signature (mean) from 400 nm to 2500 nm -> we take the values only and subset to 400 nm - 1000 nm. Because we order from 1 - 10 with 1 being the best rank, we have to reverse the scaling of the reflectance values.
Next we bind the simulated data with the feature importance rankings. To join both data.frames we need to round the reflectance centers of the bands to integers to match with the reflectance values created by PROSAIL.
To label only a subset of the data, a custom data.frame is created.
| Version | Author | Date |
|---|---|---|
| ac809cf | pat-s | 2020-03-03 |
Vogelmann2 \((R_{734}-R_{747})/(R_{715}+R_{726})\) Vogelmann et al. (1993)
Vogelmann4 \((R_{734}-R_{747})/(R_{715}+R_{720})\) Vogelmann et al. (1993)
Vogelmann3 \(D_{715}/D_{705}\) Vogelmann et al. (1993)
Vogelmann \(R_{740}/R_{720}\) Vogelmann et al. (1993)
NPCI \((R_{680}-R_{430})/(R_{680}+R_{430})\)
D2 \(D_{705}/D_{722}\)
Datt3 \(D_{754}/D_{704}\)
PWI \(R_{900}/R_{970}\)
SR7 \(R_{440}/R_{690}\)
SRPI \(R_{430}/R_{680}\)
Dxxx: First derivation of reflectance values at wavelength ‘xxx’. Rxxx: Reflectance at wavelength ‘xxx’.
Reference: ?hsdar::vegindex()
ALE plots via package {iml}
Top ten HR features from permutation Vimp
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Top ten HR features from permutation Vimp
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| Version | Author | Date |
|---|---|---|
| 0268923 | pat-s | 2020-04-20 |
| e9f4589 | pat-s | 2020-04-19 |
| ae6228a | pat-s | 2020-04-19 |
| a0b0e88 | pat-s | 2020-04-19 |
| 4e59d28 | pat-s | 2020-04-19 |
| 1c8753c | pat-s | 2020-04-18 |
| 1e06eb1 | pat-s | 2020-04-18 |
| 869a536 | pat-s | 2020-04-18 |
| 049f6e9 | pat-s | 2020-04-18 |
| f374d46 | pat-s | 2020-03-22 |
| 72b9228 | pat-s | 2020-03-17 |
| e252447 | pat-s | 2020-03-17 |
Top ten VI features from permutation Vimp
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Top ten HR features from permutation Vimp
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will replace the existing scale.
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R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so
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=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyselect_0.2.5 iml_0.10.0 patchwork_1.0.0 ggpmisc_0.3.3
[5] ggpubr_0.1.6 magrittr_1.5 dplyr_0.8.3 hsdar_0.5.2
[9] caret_6.0-81 ggplot2_3.3.0 lattice_0.20-38 signal_0.7-6
[13] rootSolve_1.7 rgdal_1.4-8 raster_3.0-12 sp_1.3-1
[17] drake_7.12.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-0 smoof_1.5.1 class_7.3-15
[4] rprojroot_1.3-2 fs_1.4.1 listenv_0.8.0
[7] farver_2.0.3 ParamHelpers_1.12 ggrepel_0.8.0
[10] prodlim_2018.04.18 fansi_0.4.1 lubridate_1.7.4
[13] codetools_0.2-16 splines_3.6.1 knitr_1.23
[16] mco_1.0-15.1 jsonlite_1.6 workflowr_1.6.1
[19] Metrics_0.1.4 kernlab_0.9-27 mlrMBO_1.1.2
[22] compiler_3.6.1 httr_1.4.0 backports_1.1.5
[25] assertthat_0.2.1 Matrix_1.2-15 lazyeval_0.2.1
[28] cli_2.0.2 later_1.0.0 htmltools_0.3.6
[31] prettyunits_1.0.2 tools_3.6.1 igraph_1.2.4.1
[34] misc3d_0.8-4 gtable_0.3.0 glue_1.4.0
[37] reshape2_1.4.3 fastmatch_1.1-0 Rcpp_1.0.3
[40] parallelMap_1.4 vctrs_0.2.4 RJSONIO_1.3-1.1
[43] nlme_3.1-142 iterators_1.0.10 timeDate_3043.102
[46] gower_0.1.2 xfun_0.5 mlr_2.17.0.9001
[49] stringr_1.4.0 globals_0.12.5 lifecycle_0.1.0
[52] future_1.16.0 MASS_7.3-51.4 scales_1.1.0
[55] ipred_0.9-8 hms_0.5.3 promises_1.0.1
[58] parallel_3.6.1 plot3D_1.1.1 RColorBrewer_1.1-2
[61] BBmisc_1.11 yaml_2.2.0 gridExtra_2.3
[64] DiceKriging_1.5.6 rpart_4.1-13 stringi_1.3.1
[67] foreach_1.4.4 checkmate_2.0.0 lhs_1.0.1
[70] filelock_1.0.2 lava_1.6.5 storr_1.2.1
[73] rlang_0.4.5 pkgconfig_2.0.3 evaluate_0.13
[76] purrr_0.3.4 prediction_0.3.14 labeling_0.3
[79] recipes_0.1.4 htmlwidgets_1.3 plyr_1.8.4
[82] R6_2.4.1 generics_0.0.2 base64url_1.4
[85] txtq_0.1.4 pillar_1.4.3 whisker_0.3-2
[88] withr_2.1.2 survival_2.43-3 nnet_7.3-12
[91] future.apply_1.4.0 tibble_2.1.3 crayon_1.3.4
[94] utf8_1.1.4 plotly_4.8.0 rmarkdown_1.13
[97] progress_1.2.0 grid_3.6.1 data.table_1.12.8
[100] git2r_0.26.1 ModelMetrics_1.2.2 digest_0.6.25
[103] tidyr_1.0.0 httpuv_1.4.5.1 stats4_3.6.1
[106] munsell_0.5.0 viridisLite_0.3.0