# Getting Started with tidyAML

## 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
    #>
    #> == 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]]
    #> ! parsnip could not locate an implementation for `cubist_rules` regression
    #>   model specifications using the `cubist` engine.
    #> 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"),
      .parsnip_fns = "linear_reg"
    )

    glimpse(frt_tbl)
    #> Rows: 2
    #> Columns: 8
    #> $ .model_id       <int> 1, 2
    #> $ .parsnip_engine <chr> "lm", "glm"
    #> $ .parsnip_mode   <chr> "regression", "regression"
    #> $ .parsnip_fns    <chr> "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]>], [<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       22.8
    #>  2 actual         actual       19.2
    #>  3 actual         actual       13.3
    #>  4 actual         actual       17.8
    #>  5 actual         actual       26
    #>  6 actual         actual       15
    #>  7 actual         actual       21.5
    #>  8 actual         actual       21
    #>  9 actual         actual       16.4
    #> 10 actual         actual       22.8
    #> # ℹ 54 more rows
    #>
    #> [[2]]
    #> # A tibble: 64 × 3
    #>    .data_category .data_type .value
    #>    <chr>          <chr>       <dbl>
    #>  1 actual         actual       22.8
    #>  2 actual         actual       19.2
    #>  3 actual         actual       13.3
    #>  4 actual         actual       17.8
    #>  5 actual         actual       26
    #>  6 actual         actual       15
    #>  7 actual         actual       21.5
    #>  8 actual         actual       21
    #>  9 actual         actual       16.4
    #> 10 actual         actual       22.8
    #> # ℹ 54 more rows