Welcome to {tidyAML} which is a new R package that makes it easy
to use the tidymodels ecosystem to perform automated machine learning
(AutoML). This package provides a simple and intuitive interface that
allows users to quickly generate machine learning models without
worrying about the underlying details. It also includes a safety
mechanism that ensures that the package will fail gracefully if any
required extension packages are not installed on the user’s machine.
With {tidyAML}, users can easily build high-quality machine learning
models in just a few lines of code. Whether you are a beginner or an
experienced machine learning practitioner, {tidyAML} has something to
offer.
Some ideas are that we should be able to generate regression models on
the fly without having to actually go through the process of building
the specification, especially if it is a non-tuning model, meaning we
are not planing on tuning hyper-parameters like penalty and cost.
The idea is not to re-write the excellent work the tidymodels team has
done (because it’s not possible) but rather to try and make an enhanced
easy to use set of functions that do what they say and can generate many
models and predictions at once.
This is similar to the great h2o package, but, {tidyAML} does not
require java to be setup properly like h2o because {tidyAML} is
built on tidymodels.
Thank you Garrick Aden-Buie for the easy name change suggestion.
You can install {tidyAML} like so:
install.packages("tidyAML")
#> Installing package into 'C:/Users/steve/AppData/Local/Temp/RtmpM1EnFK/temp_libpath77cdb3583e'
#> (as 'lib' is unspecified)
#> installing the source package 'tidyAML'Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("spsanderson/tidyAML")
#>
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> ✔ checking for file 'C:\Users\steve\AppData\Local\Temp\RtmpgxmfWe\remotes35203eea2395\spsanderson-tidyAML-e0a544d/DESCRIPTION'
#> ─ preparing 'tidyAML': (1.7s)
#> checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts
#> ─ checking for empty or unneeded directories
#> ─ building 'tidyAML_0.0.1.9000.tar.gz'
#>
#> Part of the reason to use {tidyAML} is so that you can generate many
models of your data set. One way of modeling a data set is using
regression for some numeric output. There is a convienent function in
tidyAML that will generate a set of non-tuning models for fast
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
#>
#> 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 choosing the
supported parsnip function like linear_reg() or by choose the
desired engine, 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, first fst_reg_spec_tbl
because this creates a set of non-tuning regression models and then
tidyaml_mod_spec_tbl because this is a model specification tibble
built with {tidyAML}
Now, what if you want to create a non-tuning model spec without using
the fast_regression_parsnip_spec_tbl() function. Well, you can. The
function is called create_model_spec().
create_model_spec(
.parsnip_eng = list("lm","glm","glmnet","cubist"),
.parsnip_fns = list(
rep(
"linear_reg", 3),
"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(
rep(
"linear_reg", 3),
"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]]
#> ! parsnip could not locate an implementation for `cubist_rules` regression
#> model specifications using the `cubist` engine.
#> Cubist Model Specification (regression)
#>
#> Computational engine: cubistNow the reason we are here. Let’s take a look at the first function for
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"
)
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[24 x 1]>], <NULL>, [<tbl_df[24 x 1]>]As we see above, one of the models has gracefully failed, thanks in part
to the function purrr::safely(), which was used to make what I call
safe_make functions.
Let’s look at the fitted workflow predictions.
frt_tbl$pred_wflw
#> [[1]]
#> # A tibble: 24 × 1
#> .pred
#> <dbl>
#> 1 14.6
#> 2 29.6
#> 3 23.3
#> 4 23.1
#> 5 13.3
#> 6 24.3
#> 7 21.4
#> 8 19.1
#> 9 16.2
#> 10 28.0
#> # … with 14 more rows
#>
#> [[2]]
#> NULL
#>
#> [[3]]
#> # A tibble: 24 × 1
#> .pred
#> <dbl>
#> 1 14.6
#> 2 29.6
#> 3 23.3
#> 4 23.1
#> 5 13.3
#> 6 24.3
#> 7 21.4
#> 8 19.1
#> 9 16.2
#> 10 28.0
#> # … with 14 more rows