Last updated: 2023-02-02
Checks: 7 0
Knit directory: multiclass_AUC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 413c810 | Ross Gayler | 2023-01-28 | end 2023-01-28 |
| html | b13919c | Ross Gayler | 2023-01-26 | Add notebook 04_score_dependencies |
| Rmd | 0cdb34a | Ross Gayler | 2023-01-24 | Initial commit |
| html | 0cdb34a | Ross Gayler | 2023-01-24 | Initial commit |
Read the saved example data.
d_scores <- readRDS(file = here::here("output", "d_scores.RDS")) |>
# convert case, class_id and score_id to integer factors for safety & better label order
dplyr::mutate(
case = forcats::as_factor(case),
class_id = forcats::as_factor(class_id),
score_id = forcats::as_factor(score_id)
)Check the ranges of the variables in the example data. This is a matter of standard practice for me. I never analyse data without first getting a general appreciation for the data.
Show compact summaries of the variables, grouped by dataset and model.
d_scores |>
dplyr::group_by(dataset, model) |>
skimr::skim()| Name | dplyr::group_by(d_scores,… |
| Number of rows | 17800 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| factor | 3 |
| numeric | 1 |
| ________________________ | |
| Group variables | dataset, model |
Variable type: factor
| skim_variable | dataset | model | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|---|---|
| case | UCR_14 | HDC_MINIROCKET | 0 | 1 | FALSE | 1380 | 1: 4, 2: 4, 3: 4, 4: 4 |
| case | UCR_14 | MINIROCKET | 0 | 1 | FALSE | 1380 | 1: 4, 2: 4, 3: 4, 4: 4 |
| case | UCR_48 | HDC_MINIROCKET | 0 | 1 | FALSE | 130 | 1: 26, 2: 26, 3: 26, 4: 26 |
| case | UCR_48 | MINIROCKET | 0 | 1 | FALSE | 130 | 1: 26, 2: 26, 3: 26, 4: 26 |
| class_id | UCR_14 | HDC_MINIROCKET | 0 | 1 | FALSE | 4 | 0: 1400, 2: 1380, 1: 1372, 3: 1368 |
| class_id | UCR_14 | MINIROCKET | 0 | 1 | FALSE | 4 | 0: 1400, 2: 1380, 1: 1372, 3: 1368 |
| class_id | UCR_48 | HDC_MINIROCKET | 0 | 1 | FALSE | 26 | 0: 130, 1: 130, 2: 130, 3: 130 |
| class_id | UCR_48 | MINIROCKET | 0 | 1 | FALSE | 26 | 0: 130, 1: 130, 2: 130, 3: 130 |
| score_id | UCR_14 | HDC_MINIROCKET | 0 | 1 | FALSE | 4 | 0: 1380, 1: 1380, 2: 1380, 3: 1380 |
| score_id | UCR_14 | MINIROCKET | 0 | 1 | FALSE | 4 | 0: 1380, 1: 1380, 2: 1380, 3: 1380 |
| score_id | UCR_48 | HDC_MINIROCKET | 0 | 1 | FALSE | 26 | 0: 130, 1: 130, 2: 130, 3: 130 |
| score_id | UCR_48 | MINIROCKET | 0 | 1 | FALSE | 26 | 0: 130, 1: 130, 2: 130, 3: 130 |
Variable type: numeric
| skim_variable | dataset | model | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| score_val | UCR_14 | HDC_MINIROCKET | 0 | 1 | -0.50 | 0.73 | -2.32 | -1.02 | -0.80 | -0.12 | 1.47 | ▁▇▅▂▂ |
| score_val | UCR_14 | MINIROCKET | 0 | 1 | -0.50 | 0.68 | -2.24 | -0.99 | -0.73 | -0.13 | 1.44 | ▁▇▅▂▂ |
| score_val | UCR_48 | HDC_MINIROCKET | 0 | 1 | -0.92 | 0.18 | -1.57 | -1.03 | -0.96 | -0.86 | 1.15 | ▃▇▁▁▁ |
| score_val | UCR_48 | MINIROCKET | 0 | 1 | -0.92 | 0.20 | -1.82 | -1.04 | -0.96 | -0.85 | 1.18 | ▁▇▁▁▁ |
That looks as expected.
sessionInfo()R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] skimr_2.1.5 forcats_0.5.2 dplyr_1.0.10 here_1.0.1
[5] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 bslib_0.4.2 compiler_4.2.2 pillar_1.8.1
[5] later_1.3.0 git2r_0.30.1 jquerylib_0.1.4 base64enc_0.1-3
[9] tools_4.2.2 getPass_0.2-2 digest_0.6.31 jsonlite_1.8.4
[13] evaluate_0.20 lifecycle_1.0.3 tibble_3.1.8 pkgconfig_2.0.3
[17] rlang_1.0.6 cli_3.6.0 rstudioapi_0.14 yaml_2.3.7
[21] xfun_0.36 fastmap_1.1.0 withr_2.5.0 repr_1.1.5
[25] httr_1.4.4 stringr_1.5.0 knitr_1.42 generics_0.1.3
[29] sass_0.4.5 fs_1.6.0 vctrs_0.5.2 tidyselect_1.2.0
[33] rprojroot_2.0.3 glue_1.6.2 R6_2.5.1 processx_3.8.0
[37] fansi_1.0.4 rmarkdown_2.20 tidyr_1.3.0 purrr_1.0.1
[41] callr_3.7.3 magrittr_2.0.3 whisker_0.4.1 ellipsis_0.3.2
[45] ps_1.7.2 promises_1.2.0.1 htmltools_0.5.4 renv_0.16.0
[49] httpuv_1.6.8 utf8_1.2.2 stringi_1.7.12 cachem_1.0.6