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Introduction
I have been doing a lot of work on a new package called {tidyAML}. {tidyAML} 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}.
This package is not yet release, so you can only install from GitHub with the following:
# install.packages("devtools")
devtools::install_github("spsanderson/tidyAML")
Example
library(tidyAML) 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!
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]] 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)
library(purrr)
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[24 x 1]>], [<tbl_df[24 x 1]>]
Now lets take a look at a few different things in the frt_tbl.
names(frt_tbl)
[1] ".model_id" ".parsnip_engine" ".parsnip_mode" ".parsnip_fns" [5] "model_spec" "wflw" "fitted_wflw" "pred_wflw"
Let’s look at a single model spec.
frt_tbl %>% slice(1) %>% select(model_spec) %>% pull() %>% pluck(1)
Linear Regression Model Specification (regression) Computational engine: lm
Now the wflw column.
frt_tbl %>% slice(1) %>% select(wflw) %>% pull() %>% pluck(1)
══ Workflow ════════════════════════════════════════════════════════════════════ Preprocessor: Recipe Model: linear_reg() ── Preprocessor ──────────────────────────────────────────────────────────────── 0 Recipe Steps ── Model ─────────────────────────────────────────────────────────────────────── Linear Regression Model Specification (regression) Computational engine: lm
The fitted wflw object.
frt_tbl %>% slice(1) %>% select(fitted_wflw) %>% pull() %>% pluck(1)
══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
0 Recipe Steps
── Model ───────────────────────────────────────────────────────────────────────
Call:
stats::lm(formula = ..y ~ ., data = data)
Coefficients:
(Intercept) cyl disp hp drat wt
11.77621 0.59296 0.01626 -0.03191 -0.55350 -5.30785
qsec vs am gear carb
0.97840 2.64023 1.68549 0.87059 0.58785
frt_tbl %>% slice(1) %>% select(fitted_wflw) %>% pull() %>% pluck(1) %>% broom::glance() %>% glimpse()
Rows: 1 Columns: 12 $ r.squared <dbl> 0.9085669 $ adj.r.squared <dbl> 0.8382337 $ sigma <dbl> 2.337527 $ statistic <dbl> 12.91804 $ p.value <dbl> 3.367361e-05 $ df <dbl> 10 $ logLik <dbl> -47.07551 $ AIC <dbl> 118.151 $ BIC <dbl> 132.2877 $ deviance <dbl> 71.03241 $ df.residual <int> 13 $ nobs <int> 24
And finally the predictions (this one I am probably going to change up).
frt_tbl %>% slice(1) %>% select(pred_wflw) %>% pull() %>% pluck(1)
# A tibble: 24 × 1 .pred <dbl> 1 17.4 2 28.4 3 17.2 4 10.7 5 13.4 6 17.0 7 22.8 8 14.3 9 22.4 10 15.5 # … with 14 more rows
Voila!
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