tidyAML

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Introduction

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.

Thanks

Thank you Garrick Aden-Buie for the easy name change suggestion.

Installation

You can install {tidyAML} like so:

#install.packages("tidyAML")

Or the development version from GitHub

# install.packages("devtools")
#devtools::install_github("spsanderson/tidyAML")

Examples

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
#> Warning: package 'parsnip' was built under R version 4.2.3
#> 
#> == 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 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(
      "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: cubist

Now 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",
  .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 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: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       18.1
#>  2 actual         actual       21  
#>  3 actual         actual       24.4
#>  4 actual         actual       21.4
#>  5 actual         actual       15.5
#>  6 actual         actual       13.3
#>  7 actual         actual       27.3
#>  8 actual         actual       14.7
#>  9 actual         actual       10.4
#> 10 actual         actual       15.2
#> # ℹ 54 more rows
#> 
#> [[2]]
#> NULL
#> 
#> [[3]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       18.1
#>  2 actual         actual       21  
#>  3 actual         actual       24.4
#>  4 actual         actual       21.4
#>  5 actual         actual       15.5
#>  6 actual         actual       13.3
#>  7 actual         actual       27.3
#>  8 actual         actual       14.7
#>  9 actual         actual       10.4
#> 10 actual         actual       15.2
#> # ℹ 54 more rows

Now let’s load the multilevelmod library so that we can run the gee 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)
#> [[1]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       26  
#>  2 actual         actual       10.4
#>  3 actual         actual       18.7
#>  4 actual         actual       14.7
#>  5 actual         actual       21.5
#>  6 actual         actual       15  
#>  7 actual         actual       27.3
#>  8 actual         actual       16.4
#>  9 actual         actual       15.5
#> 10 actual         actual       17.3
#> # ℹ 54 more rows
#> 
#> [[2]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       26  
#>  2 actual         actual       10.4
#>  3 actual         actual       18.7
#>  4 actual         actual       14.7
#>  5 actual         actual       21.5
#>  6 actual         actual       15  
#>  7 actual         actual       27.3
#>  8 actual         actual       16.4
#>  9 actual         actual       15.5
#> 10 actual         actual       17.3
#> # ℹ 54 more rows
#> 
#> [[3]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       26  
#>  2 actual         actual       10.4
#>  3 actual         actual       18.7
#>  4 actual         actual       14.7
#>  5 actual         actual       21.5
#>  6 actual         actual       15  
#>  7 actual         actual       27.3
#>  8 actual         actual       16.4
#>  9 actual         actual       15.5
#> 10 actual         actual       17.3
#> # ℹ 54 more rows

Getting Regression Residuals

Getting residuals is easy with {tidyAML}. Let’s take a look.

extract_regression_residuals(frt_tbl)
#> [[1]]
#> # A tibble: 32 × 4
#>    .model_type     .actual .predicted .resid
#>    <chr>             <dbl>      <dbl>  <dbl>
#>  1 lm - linear_reg    26         25.8  0.167
#>  2 lm - linear_reg    10.4       12.6 -2.20 
#>  3 lm - linear_reg    18.7       15.7  3.00 
#>  4 lm - linear_reg    14.7       12.9  1.84 
#>  5 lm - linear_reg    21.5       23.8 -2.32 
#>  6 lm - linear_reg    15         13.9  1.08 
#>  7 lm - linear_reg    27.3       28.8 -1.50 
#>  8 lm - linear_reg    16.4       15.6  0.789
#>  9 lm - linear_reg    15.5       15.8 -0.255
#> 10 lm - linear_reg    17.3       15.7  1.55 
#> # ℹ 22 more rows
#> 
#> [[2]]
#> # A tibble: 32 × 4
#>    .model_type      .actual .predicted .resid
#>    <chr>              <dbl>      <dbl>  <dbl>
#>  1 gee - linear_reg    26         25.5  0.466
#>  2 gee - linear_reg    10.4       12.4 -2.03 
#>  3 gee - linear_reg    18.7       15.7  2.98 
#>  4 gee - linear_reg    14.7       12.7  1.99 
#>  5 gee - linear_reg    21.5       23.4 -1.94 
#>  6 gee - linear_reg    15         13.9  1.13 
#>  7 gee - linear_reg    27.3       28.8 -1.48 
#>  8 gee - linear_reg    16.4       15.8  0.599
#>  9 gee - linear_reg    15.5       15.8 -0.295
#> 10 gee - linear_reg    17.3       15.9  1.36 
#> # ℹ 22 more rows
#> 
#> [[3]]
#> # A tibble: 32 × 4
#>    .model_type      .actual .predicted .resid
#>    <chr>              <dbl>      <dbl>  <dbl>
#>  1 glm - linear_reg    26         25.8  0.167
#>  2 glm - linear_reg    10.4       12.6 -2.20 
#>  3 glm - linear_reg    18.7       15.7  3.00 
#>  4 glm - linear_reg    14.7       12.9  1.84 
#>  5 glm - linear_reg    21.5       23.8 -2.32 
#>  6 glm - linear_reg    15         13.9  1.08 
#>  7 glm - linear_reg    27.3       28.8 -1.50 
#>  8 glm - linear_reg    16.4       15.6  0.789
#>  9 glm - linear_reg    15.5       15.8 -0.255
#> 10 glm - linear_reg    17.3       15.7  1.55 
#> # ℹ 22 more rows

You can also pivot them into a long format making plotting easy with ggplot2.

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    26    
#>  2 lm - linear_reg .predicted 25.8  
#>  3 lm - linear_reg .resid      0.167
#>  4 lm - linear_reg .actual    10.4  
#>  5 lm - linear_reg .predicted 12.6  
#>  6 lm - linear_reg .resid     -2.20 
#>  7 lm - linear_reg .actual    18.7  
#>  8 lm - linear_reg .predicted 15.7  
#>  9 lm - linear_reg .resid      3.00 
#> 10 lm - linear_reg .actual    14.7  
#> # ℹ 86 more rows
#> 
#> [[2]]
#> # A tibble: 96 × 3
#>    .model_type      name        value
#>    <chr>            <chr>       <dbl>
#>  1 gee - linear_reg .actual    26    
#>  2 gee - linear_reg .predicted 25.5  
#>  3 gee - linear_reg .resid      0.466
#>  4 gee - linear_reg .actual    10.4  
#>  5 gee - linear_reg .predicted 12.4  
#>  6 gee - linear_reg .resid     -2.03 
#>  7 gee - linear_reg .actual    18.7  
#>  8 gee - linear_reg .predicted 15.7  
#>  9 gee - linear_reg .resid      2.98 
#> 10 gee - linear_reg .actual    14.7  
#> # ℹ 86 more rows
#> 
#> [[3]]
#> # A tibble: 96 × 3
#>    .model_type      name        value
#>    <chr>            <chr>       <dbl>
#>  1 glm - linear_reg .actual    26    
#>  2 glm - linear_reg .predicted 25.8  
#>  3 glm - linear_reg .resid      0.167
#>  4 glm - linear_reg .actual    10.4  
#>  5 glm - linear_reg .predicted 12.6  
#>  6 glm - linear_reg .resid     -2.20 
#>  7 glm - linear_reg .actual    18.7  
#>  8 glm - linear_reg .predicted 15.7  
#>  9 glm - linear_reg .resid      3.00 
#> 10 glm - linear_reg .actual    14.7  
#> # ℹ 86 more rows