Welcome to{tidyAML} which is a new Rpackage that makes it easy to use thetidymodels ecosystemto perform automated machine learning (AutoML). This package provides asimple and intuitive interface that allows users to quickly generatemachine learning models without worrying about the underlying details.It also includes a safety mechanism that ensures that the package willfail gracefully if any required extension packages are not installed onthe user’s machine. With{tidyAML}, users can easily buildhigh-quality machine learning models in just a few lines of code.Whether you are a beginner or an experienced machine learningpractitioner,{tidyAML} has something to offer.
Some ideas are that we should be able to generate regression modelson the fly without having to actually go through the process of buildingthe specification, especially if it is a non-tuning model, meaning weare not planing on tuning hyper-parameters likepenalty andcost.
The idea is not to re-write the excellent work thetidymodels team has done (because it’s not possible) butrather to try and make an enhanced easy to use set of functions that dowhat they say and can generate many models and predictions at once.
This is similar to the greath2o package, but,{tidyAML} does not require java to be setup properly likeh2o because{tidyAML} is built ontidymodels.
Thank youGarrickAden-Buie for the easy name change suggestion.
You can install{tidyAML} like so:
#install.packages("tidyAML")Or the development version from GitHub
# install.packages("devtools")#devtools::install_github("spsanderson/tidyAML")Part of the reason to use{tidyAML} is so that you cangenerate many models of your data set. One way of modeling a data set isusing regression for some numeric output. There is a convienent functionintidyAML that will generate a set of non-tuningmodels forfast regression. Let’s take a look below.
First let’s load the library
library(tidyAML)#> Loading required package: parsnip#>#> == Welcome to tidyAML ===========================================================================#> If you find this package useful, please leave a star:#> https://github.com/spsanderson/tidyAML'#>#> If you encounter a bug or want to request an enhancement please file an issue at:#> https://github.com/spsanderson/tidyAML/issues#>#> It is suggested that you run tidymodels::tidymodel_prefer() to set the defaults for your session.#>#> Thank you for using tidyAML!Now lets see the function in action.
fast_regression_parsnip_spec_tbl(.parsnip_fns ="linear_reg")#> # A tibble: 11 × 5#> .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec#> <int> <chr> <chr> <chr> <list>#> 1 1 lm regression linear_reg <spec[+]>#> 2 2 brulee regression linear_reg <spec[+]>#> 3 3 gee regression linear_reg <spec[+]>#> 4 4 glm regression linear_reg <spec[+]>#> 5 5 glmer regression linear_reg <spec[+]>#> 6 6 glmnet regression linear_reg <spec[+]>#> 7 7 gls regression linear_reg <spec[+]>#> 8 8 lme regression linear_reg <spec[+]>#> 9 9 lmer regression linear_reg <spec[+]>#> 10 10 stan regression linear_reg <spec[+]>#> 11 11 stan_glmer regression linear_reg <spec[+]>fast_regression_parsnip_spec_tbl(.parsnip_eng =c("lm","glm"))#> # A tibble: 3 × 5#> .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec#> <int> <chr> <chr> <chr> <list>#> 1 1 lm regression linear_reg <spec[+]>#> 2 2 glm regression linear_reg <spec[+]>#> 3 3 glm regression poisson_reg <spec[+]>fast_regression_parsnip_spec_tbl(.parsnip_eng =c("lm","glm","gee"),.parsnip_fns ="linear_reg")#> # A tibble: 3 × 5#> .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec#> <int> <chr> <chr> <chr> <list>#> 1 1 lm regression linear_reg <spec[+]>#> 2 2 gee regression linear_reg <spec[+]>#> 3 3 glm regression linear_reg <spec[+]>As shown we can easily select the models we want either by choosingthe supportedparsnip function likelinear_reg() or by choose the desiredengine,you can also use them both in conjunction with each other!
This function also does add a class to the output. Let’s see it.
class(fast_regression_parsnip_spec_tbl())#> [1] "tidyaml_mod_spec_tbl" "fst_reg_spec_tbl" "tidyaml_base_tbl"#> [4] "tbl_df" "tbl" "data.frame"We see that there are two added classes, firstfst_reg_spec_tbl because this creates a set of non-tuningregression models and thentidyaml_mod_spec_tbl becausethis is a model specification tibble built with{tidyAML}
Now, what if you want to create a non-tuning model spec without usingthefast_regression_parsnip_spec_tbl() function. Well, youcan. The function is calledcreate_model_spec().
create_model_spec(.parsnip_eng =list("lm","glm","glmnet","cubist"),.parsnip_fns =list("linear_reg","linear_reg","linear_reg","cubist_rules" ) )#> # A tibble: 4 × 4#> .parsnip_engine .parsnip_mode .parsnip_fns .model_spec#> <chr> <chr> <chr> <list>#> 1 lm regression linear_reg <spec[+]>#> 2 glm regression linear_reg <spec[+]>#> 3 glmnet regression linear_reg <spec[+]>#> 4 cubist regression cubist_rules <spec[+]>create_model_spec(.parsnip_eng =list("lm","glm","glmnet","cubist"),.parsnip_fns =list("linear_reg","linear_reg","linear_reg","cubist_rules" ),.return_tibble =FALSE )#> $.parsnip_engine#> $.parsnip_engine[[1]]#> [1] "lm"#>#> $.parsnip_engine[[2]]#> [1] "glm"#>#> $.parsnip_engine[[3]]#> [1] "glmnet"#>#> $.parsnip_engine[[4]]#> [1] "cubist"#>#>#> $.parsnip_mode#> $.parsnip_mode[[1]]#> [1] "regression"#>#>#> $.parsnip_fns#> $.parsnip_fns[[1]]#> [1] "linear_reg"#>#> $.parsnip_fns[[2]]#> [1] "linear_reg"#>#> $.parsnip_fns[[3]]#> [1] "linear_reg"#>#> $.parsnip_fns[[4]]#> [1] "cubist_rules"#>#>#> $.model_spec#> $.model_spec[[1]]#> Linear Regression Model Specification (regression)#>#> Computational engine: lm#>#>#> $.model_spec[[2]]#> Linear Regression Model Specification (regression)#>#> Computational engine: glm#>#>#> $.model_spec[[3]]#> Linear Regression Model Specification (regression)#>#> Computational engine: glmnet#>#>#> $.model_spec[[4]]#> Cubist Model Specification (regression)#>#> Computational engine: cubistNow the reason we are here. Let’s take a look at the first functionfor modeling with{tidyAML},fast_regression().
library(recipes)library(dplyr)rec_obj<-recipe(mpg~ .,data = mtcars)frt_tbl<-fast_regression(.data = mtcars,.rec_obj = rec_obj,.parsnip_eng =c("lm","glm","gee"),.parsnip_fns ="linear_reg",.drop_na =FALSE)glimpse(frt_tbl)#> Rows: 3#> Columns: 8#> $ .model_id <int> 1, 2, 3#> $ .parsnip_engine <chr> "lm", "gee", "glm"#> $ .parsnip_mode <chr> "regression", "regression", "regression"#> $ .parsnip_fns <chr> "linear_reg", "linear_reg", "linear_reg"#> $ model_spec <list> [~NULL, ~NULL, NULL, regression, TRUE, NULL, lm, TRUE]…#> $ wflw <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…#> $ fitted_wflw <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…#> $ pred_wflw <list> [<tbl_df[64 x 3]>], <NULL>, [<tbl_df[64 x 3]>]As we see above, one of the models has gracefully failed, thanks inpart to the functionpurrr::safely(), which was used tomake what I callsafe_make functions.
Let’s look at the fitted workflow predictions.
frt_tbl$pred_wflw#> [[1]]#> # A tibble: 64 × 3#> .data_category .data_type .value#> <chr> <chr> <dbl>#> 1 actual actual 22.8#> 2 actual actual 15#> 3 actual actual 17.8#> 4 actual actual 18.7#> 5 actual actual 30.4#> 6 actual actual 26#> 7 actual actual 14.3#> 8 actual actual 19.2#> 9 actual actual 14.7#> 10 actual actual 17.3#> # ℹ 54 more rows#>#> [[2]]#> NULL#>#> [[3]]#> # A tibble: 64 × 3#> .data_category .data_type .value#> <chr> <chr> <dbl>#> 1 actual actual 22.8#> 2 actual actual 15#> 3 actual actual 17.8#> 4 actual actual 18.7#> 5 actual actual 30.4#> 6 actual actual 26#> 7 actual actual 14.3#> 8 actual actual 19.2#> 9 actual actual 14.7#> 10 actual actual 17.3#> # ℹ 54 more rowsNow let’s load themultilevelmod library so that we canrun thegee linear regression.
library(multilevelmod)rec_obj<-recipe(mpg~ .,data = mtcars)frt_tbl<-fast_regression(.data = mtcars,.rec_obj = rec_obj,.parsnip_eng =c("lm","glm","gee"),.parsnip_fns ="linear_reg")extract_wflw_pred(frt_tbl,1:3)#> # A tibble: 192 × 4#> .model_type .data_category .data_type .value#> <chr> <chr> <chr> <dbl>#> 1 lm - linear_reg actual actual 15.5#> 2 lm - linear_reg actual actual 19.2#> 3 lm - linear_reg actual actual 21.5#> 4 lm - linear_reg actual actual 14.3#> 5 lm - linear_reg actual actual 21.4#> 6 lm - linear_reg actual actual 21#> 7 lm - linear_reg actual actual 13.3#> 8 lm - linear_reg actual actual 15.2#> 9 lm - linear_reg actual actual 24.4#> 10 lm - linear_reg actual actual 10.4#> # ℹ 182 more rowsGetting Regression Residuals
Getting residuals is easy with{tidyAML}. Let’s take alook.
extract_regression_residuals(frt_tbl)#> [[1]]#> # A tibble: 32 × 4#> .model_type .actual .predicted .resid#> <chr> <dbl> <dbl> <dbl>#> 1 lm - linear_reg 15.5 16.5 -0.988#> 2 lm - linear_reg 19.2 19.7 -0.488#> 3 lm - linear_reg 21.5 21.6 -0.127#> 4 lm - linear_reg 14.3 14.1 0.157#> 5 lm - linear_reg 21.4 24.6 -3.23#> 6 lm - linear_reg 21 21.1 -0.0800#> 7 lm - linear_reg 13.3 13.8 -0.482#> 8 lm - linear_reg 15.2 17.7 -2.52#> 9 lm - linear_reg 24.4 22.3 2.11#> 10 lm - linear_reg 10.4 11.5 -1.14#> # ℹ 22 more rows#>#> [[2]]#> # A tibble: 32 × 4#> .model_type .actual .predicted .resid#> <chr> <dbl> <dbl> <dbl>#> 1 gee - linear_reg 15.5 16.4 -0.896#> 2 gee - linear_reg 19.2 19.2 0.0385#> 3 gee - linear_reg 21.5 22.3 -0.797#> 4 gee - linear_reg 14.3 14.6 -0.250#> 5 gee - linear_reg 21.4 24.6 -3.24#> 6 gee - linear_reg 21 21.1 -0.135#> 7 gee - linear_reg 13.3 13.8 -0.505#> 8 gee - linear_reg 15.2 17.4 -2.16#> 9 gee - linear_reg 24.4 22.6 1.80#> 10 gee - linear_reg 10.4 11.8 -1.39#> # ℹ 22 more rows#>#> [[3]]#> # A tibble: 32 × 4#> .model_type .actual .predicted .resid#> <chr> <dbl> <dbl> <dbl>#> 1 glm - linear_reg 15.5 16.5 -0.988#> 2 glm - linear_reg 19.2 19.7 -0.488#> 3 glm - linear_reg 21.5 21.6 -0.127#> 4 glm - linear_reg 14.3 14.1 0.157#> 5 glm - linear_reg 21.4 24.6 -3.23#> 6 glm - linear_reg 21 21.1 -0.0800#> 7 glm - linear_reg 13.3 13.8 -0.482#> 8 glm - linear_reg 15.2 17.7 -2.52#> 9 glm - linear_reg 24.4 22.3 2.11#> 10 glm - linear_reg 10.4 11.5 -1.14#> # ℹ 22 more rowsYou can also pivot them into a long format making plotting easy withggplot2.
extract_regression_residuals(frt_tbl,.pivot_long =TRUE)#> [[1]]#> # A tibble: 96 × 3#> .model_type name value#> <chr> <chr> <dbl>#> 1 lm - linear_reg .actual 15.5#> 2 lm - linear_reg .predicted 16.5#> 3 lm - linear_reg .resid -0.988#> 4 lm - linear_reg .actual 19.2#> 5 lm - linear_reg .predicted 19.7#> 6 lm - linear_reg .resid -0.488#> 7 lm - linear_reg .actual 21.5#> 8 lm - linear_reg .predicted 21.6#> 9 lm - linear_reg .resid -0.127#> 10 lm - linear_reg .actual 14.3#> # ℹ 86 more rows#>#> [[2]]#> # A tibble: 96 × 3#> .model_type name value#> <chr> <chr> <dbl>#> 1 gee - linear_reg .actual 15.5#> 2 gee - linear_reg .predicted 16.4#> 3 gee - linear_reg .resid -0.896#> 4 gee - linear_reg .actual 19.2#> 5 gee - linear_reg .predicted 19.2#> 6 gee - linear_reg .resid 0.0385#> 7 gee - linear_reg .actual 21.5#> 8 gee - linear_reg .predicted 22.3#> 9 gee - linear_reg .resid -0.797#> 10 gee - linear_reg .actual 14.3#> # ℹ 86 more rows#>#> [[3]]#> # A tibble: 96 × 3#> .model_type name value#> <chr> <chr> <dbl>#> 1 glm - linear_reg .actual 15.5#> 2 glm - linear_reg .predicted 16.5#> 3 glm - linear_reg .resid -0.988#> 4 glm - linear_reg .actual 19.2#> 5 glm - linear_reg .predicted 19.7#> 6 glm - linear_reg .resid -0.488#> 7 glm - linear_reg .actual 21.5#> 8 glm - linear_reg .predicted 21.6#> 9 glm - linear_reg .resid -0.127#> 10 glm - linear_reg .actual 14.3#> # ℹ 86 more rows