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PatrickRWright/caret_models_deps_types_as_df.ipynb

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caret_models_deps_types_as_df.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transfer info from `getModelInfo()` to a `data.frame`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# libs\n",
"library(caret)\n",
"library(stringr)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<caption>A data.frame: 6 × 3</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>model_name</th><th scope=col>lib_dep</th><th scope=col>method_type</th></tr>\n",
"\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>ada </td><td>ada,plyr </td><td>Classification</td></tr>\n",
"\t<tr><th scope=row>2</th><td>AdaBag </td><td>adabag,plyr </td><td>Classification</td></tr>\n",
"\t<tr><th scope=row>3</th><td>AdaBoost.M1</td><td>adabag,plyr </td><td>Classification</td></tr>\n",
"\t<tr><th scope=row>4</th><td>adaboost </td><td>fastAdaboost</td><td>Classification</td></tr>\n",
"\t<tr><th scope=row>5</th><td>amdai </td><td>adaptDA </td><td>Classification</td></tr>\n",
"\t<tr><th scope=row>6</th><td>ANFIS </td><td>frbs </td><td>Regression </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A data.frame: 6 × 3\n",
"\\begin{tabular}{r|lll}\n",
" & model\\_name & lib\\_dep & method\\_type\\\\\n",
" & <fct> & <fct> & <fct>\\\\\n",
"\\hline\n",
"\t1 & ada & ada,plyr & Classification\\\\\n",
"\t2 & AdaBag & adabag,plyr & Classification\\\\\n",
"\t3 & AdaBoost.M1 & adabag,plyr & Classification\\\\\n",
"\t4 & adaboost & fastAdaboost & Classification\\\\\n",
"\t5 & amdai & adaptDA & Classification\\\\\n",
"\t6 & ANFIS & frbs & Regression \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A data.frame: 6 × 3\n",
"\n",
"| <!--/--> | model_name &lt;fct&gt; | lib_dep &lt;fct&gt; | method_type &lt;fct&gt; |\n",
"|---|---|---|---|\n",
"| 1 | ada | ada,plyr | Classification |\n",
"| 2 | AdaBag | adabag,plyr | Classification |\n",
"| 3 | AdaBoost.M1 | adabag,plyr | Classification |\n",
"| 4 | adaboost | fastAdaboost | Classification |\n",
"| 5 | amdai | adaptDA | Classification |\n",
"| 6 | ANFIS | frbs | Regression |\n",
"\n"
],
"text/plain": [
" model_name lib_dep method_type \n",
"1 ada ada,plyr Classification\n",
"2 AdaBag adabag,plyr Classification\n",
"3 AdaBoost.M1 adabag,plyr Classification\n",
"4 adaboost fastAdaboost Classification\n",
"5 amdai adaptDA Classification\n",
"6 ANFIS frbs Regression "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# init\n",
"lib_dep <- c()\n",
"method_type <- c()\n",
"\n",
"# retrieve all data an model name, dependencies and method types\n",
"model_name <- names(getModelInfo())\n",
"for (i in getModelInfo()) { \n",
" lib_dep <- c(lib_dep, paste(i$library, collapse = \",\"))\n",
" method_type <- c(method_type, paste(i$type, collapse = \",\"))\n",
"}\n",
"# merge into a data.frame\n",
"all_caret <- as.data.frame(cbind(model_name, lib_dep, method_type))\n",
"head(all_caret)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
" Classification Classification,Regression Regression \n",
" 102 21 48 \n",
"Regression,Classification \n",
" 67 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# show amount of method types\n",
"table(all_caret$method_type)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<caption>A data.frame: 6 × 3</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>model_name</th><th scope=col>lib_dep</th><th scope=col>method_type</th></tr>\n",
"\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>6</th><td>ANFIS </td><td>frbs </td><td>Regression </td></tr>\n",
"\t<tr><th scope=row>7</th><td>avNNet </td><td>nnet </td><td>Classification,Regression</td></tr>\n",
"\t<tr><th scope=row>10</th><td>bag </td><td>caret</td><td>Regression,Classification</td></tr>\n",
"\t<tr><th scope=row>11</th><td>bagEarth </td><td>earth</td><td>Regression,Classification</td></tr>\n",
"\t<tr><th scope=row>12</th><td>bagEarthGCV</td><td>earth</td><td>Regression,Classification</td></tr>\n",
"\t<tr><th scope=row>15</th><td>bam </td><td>mgcv </td><td>Regression,Classification</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A data.frame: 6 × 3\n",
"\\begin{tabular}{r|lll}\n",
" & model\\_name & lib\\_dep & method\\_type\\\\\n",
" & <fct> & <fct> & <fct>\\\\\n",
"\\hline\n",
"\t6 & ANFIS & frbs & Regression \\\\\n",
"\t7 & avNNet & nnet & Classification,Regression\\\\\n",
"\t10 & bag & caret & Regression,Classification\\\\\n",
"\t11 & bagEarth & earth & Regression,Classification\\\\\n",
"\t12 & bagEarthGCV & earth & Regression,Classification\\\\\n",
"\t15 & bam & mgcv & Regression,Classification\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A data.frame: 6 × 3\n",
"\n",
"| <!--/--> | model_name &lt;fct&gt; | lib_dep &lt;fct&gt; | method_type &lt;fct&gt; |\n",
"|---|---|---|---|\n",
"| 6 | ANFIS | frbs | Regression |\n",
"| 7 | avNNet | nnet | Classification,Regression |\n",
"| 10 | bag | caret | Regression,Classification |\n",
"| 11 | bagEarth | earth | Regression,Classification |\n",
"| 12 | bagEarthGCV | earth | Regression,Classification |\n",
"| 15 | bam | mgcv | Regression,Classification |\n",
"\n"
],
"text/plain": [
" model_name lib_dep method_type \n",
"6 ANFIS frbs Regression \n",
"7 avNNet nnet Classification,Regression\n",
"10 bag caret Regression,Classification\n",
"11 bagEarth earth Regression,Classification\n",
"12 bagEarthGCV earth Regression,Classification\n",
"15 bam mgcv Regression,Classification"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# only regression\n",
"caret_regression <- all_caret[grep(x = all_caret$method_type,\n",
" pattern = \"Regression\"), ]\n",
"head(caret_regression)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<caption>A data.frame: 6 × 3</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>model_name</th><th scope=col>lib_dep</th><th scope=col>method_type</th></tr>\n",
"\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>ada </td><td>ada,plyr </td><td>Classification </td></tr>\n",
"\t<tr><th scope=row>2</th><td>AdaBag </td><td>adabag,plyr </td><td>Classification </td></tr>\n",
"\t<tr><th scope=row>3</th><td>AdaBoost.M1</td><td>adabag,plyr </td><td>Classification </td></tr>\n",
"\t<tr><th scope=row>4</th><td>adaboost </td><td>fastAdaboost</td><td>Classification </td></tr>\n",
"\t<tr><th scope=row>5</th><td>amdai </td><td>adaptDA </td><td>Classification </td></tr>\n",
"\t<tr><th scope=row>7</th><td>avNNet </td><td>nnet </td><td>Classification,Regression</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A data.frame: 6 × 3\n",
"\\begin{tabular}{r|lll}\n",
" & model\\_name & lib\\_dep & method\\_type\\\\\n",
" & <fct> & <fct> & <fct>\\\\\n",
"\\hline\n",
"\t1 & ada & ada,plyr & Classification \\\\\n",
"\t2 & AdaBag & adabag,plyr & Classification \\\\\n",
"\t3 & AdaBoost.M1 & adabag,plyr & Classification \\\\\n",
"\t4 & adaboost & fastAdaboost & Classification \\\\\n",
"\t5 & amdai & adaptDA & Classification \\\\\n",
"\t7 & avNNet & nnet & Classification,Regression\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A data.frame: 6 × 3\n",
"\n",
"| <!--/--> | model_name &lt;fct&gt; | lib_dep &lt;fct&gt; | method_type &lt;fct&gt; |\n",
"|---|---|---|---|\n",
"| 1 | ada | ada,plyr | Classification |\n",
"| 2 | AdaBag | adabag,plyr | Classification |\n",
"| 3 | AdaBoost.M1 | adabag,plyr | Classification |\n",
"| 4 | adaboost | fastAdaboost | Classification |\n",
"| 5 | amdai | adaptDA | Classification |\n",
"| 7 | avNNet | nnet | Classification,Regression |\n",
"\n"
],
"text/plain": [
" model_name lib_dep method_type \n",
"1 ada ada,plyr Classification \n",
"2 AdaBag adabag,plyr Classification \n",
"3 AdaBoost.M1 adabag,plyr Classification \n",
"4 adaboost fastAdaboost Classification \n",
"5 amdai adaptDA Classification \n",
"7 avNNet nnet Classification,Regression"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# only classification\n",
"caret_classification <- all_caret[grep(x = all_caret$method_type,\n",
" pattern = \"Classification\"), ]\n",
"head(caret_classification)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# install dependencies\n",
"all_libs <- unique(str_split(paste(all_caret$lib_dep, collapse = \",\"), pattern = \",\")[[1]])\n",
"install.packages(all_libs[1:43], dependencies = TRUE)\n",
"# Matrix needs to be installed separately\n",
"install.packages(all_libs[44], dependencies = TRUE)\n",
"install.packages(all_libs[44:116], dependencies = TRUE)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"R version 3.5.1 (2018-07-02)\n",
"Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
"Running under: Debian GNU/Linux 10 (buster)\n",
"\n",
"Matrix products: default\n",
"BLAS/LAPACK: /home/jupyterlab/conda/envs/r/lib/R/lib/libRlapack.so\n",
"\n",
"locale:\n",
" [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n",
" [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n",
" [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n",
"[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n",
"\n",
"attached base packages:\n",
"[1] stats graphics grDevices utils datasets methods base \n",
"\n",
"other attached packages:\n",
"[1] stringr_1.4.0 caret_6.0-86 ggplot2_3.3.0 lattice_0.20-41\n",
"\n",
"loaded via a namespace (and not attached):\n",
" [1] pbdZMQ_0.3-3 tidyselect_1.0.0 repr_1.1.0 \n",
" [4] purrr_0.3.4 reshape2_1.4.4 splines_3.5.1 \n",
" [7] colorspace_1.4-1 vctrs_0.2.4 generics_0.0.2 \n",
"[10] stats4_3.5.1 htmltools_0.4.0 base64enc_0.1-3 \n",
"[13] survival_3.1-12 prodlim_2019.11.13 rlang_0.4.5 \n",
"[16] ModelMetrics_1.2.2.2 pillar_1.4.3 glue_1.4.0 \n",
"[19] withr_2.2.0 uuid_0.1-4 foreach_1.5.0 \n",
"[22] lifecycle_0.2.0 plyr_1.8.6 lava_1.6.7 \n",
"[25] timeDate_3043.102 munsell_0.5.0 gtable_0.3.0 \n",
"[28] recipes_0.1.12 codetools_0.2-16 evaluate_0.14 \n",
"[31] class_7.3-17 IRdisplay_0.7.0 Rcpp_1.0.4.6 \n",
"[34] scales_1.1.0 IRkernel_0.8.12 ipred_0.9-9 \n",
"[37] jsonlite_1.6.1 digest_0.6.25 stringi_1.4.6 \n",
"[40] dplyr_0.8.5 grid_3.5.1 tools_3.5.1 \n",
"[43] magrittr_1.5 tibble_3.0.1 crayon_1.3.4 \n",
"[46] pkgconfig_2.0.3 ellipsis_0.3.0 MASS_7.3-51.6 \n",
"[49] Matrix_1.2-18 data.table_1.12.8 pROC_1.16.2 \n",
"[52] lubridate_1.7.8 gower_0.2.1 assertthat_0.2.1 \n",
"[55] iterators_1.0.12 R6_2.4.1 rpart_4.1-15 \n",
"[58] nnet_7.3-14 nlme_3.1-147 compiler_3.5.1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sessionInfo()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "conda-env-r-r"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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