diff --git a/NAMESPACE b/NAMESPACE
index 4bc035a24..95291f6ef 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -137,6 +137,7 @@ export(C5.0_train)
export(C5_rules)
export(add_rowindex)
export(augment)
+export(auto_ml)
export(autoplot)
export(bag_mars)
export(bag_tree)
diff --git a/R/auto_ml.R b/R/auto_ml.R
new file mode 100644
index 000000000..acad136b9
--- /dev/null
+++ b/R/auto_ml.R
@@ -0,0 +1,37 @@
+#' Automatic Machine Learning
+#'
+#' @description
+#'
+#' `auto_ml()` defines an automated searching and tuning process where
+#' many models of different families are trained and ranked given their
+#' performance on the training data.
+#'
+#' \Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("auto_ml")}
+#'
+#' More information on how \pkg{parsnip} is used for modeling is at
+#' \url{https://www.tidymodels.org/}.
+#'
+#' @param mode A single character string for the prediction outcome mode.
+#' Possible values for this model are "unknown", "regression", or
+#' "classification".
+#' @param engine A single character string specifying what computational engine
+#' to use for fitting.
+#'
+#' @template spec-details
+#'
+#' @template spec-references
+#'
+#' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("auto_ml")}
+#' @export
+auto_ml <- function(mode = "unknown", engine = "h2o") {
+ args <- list()
+ out <- list(args = args, eng_args = NULL,
+ mode = mode, method = NULL, engine = engine)
+ class(out) <- make_classes("auto_ml")
+ out
+}
+
+# ------------------------------------------------------------------------------
+set_new_model("auto_ml")
+set_model_mode("auto_ml", "regression")
+set_model_mode("auto_ml", "classification")
diff --git a/R/auto_ml_h2o.R b/R/auto_ml_h2o.R
new file mode 100644
index 000000000..3ac860244
--- /dev/null
+++ b/R/auto_ml_h2o.R
@@ -0,0 +1,12 @@
+#' Automatic machine learning via h2o
+#'
+#' [h2o::h2o.automl] defines an automated model training process and returns a
+#' leaderboard of models with best performances.
+#'
+#' @includeRmd man/rmd/auto_ml_h2o.md details
+#'
+#' @name details_auto_ml_h2o
+#' @keywords internal
+NULL
+
+# See inst/README-DOCS.md for a description of how these files are processed
diff --git a/R/print.R b/R/print.R
index 005ed0bfa..ff6236f97 100644
--- a/R/print.R
+++ b/R/print.R
@@ -32,6 +32,7 @@ get_model_desc <- function(cls) {
model_descs <- tibble::tribble(
~cls, ~desc,
+ "auto_ml", "Automatic Machine Learning",
"bag_mars", "Bagged MARS",
"bag_tree", "Bagged Decision Tree",
"bart", "BART",
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 648156d09..8e36a6d1d 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -33,6 +33,7 @@ figures:
reference:
- title: Models
contents:
+ - auto_ml
- bag_mars
- bag_tree
- bart
diff --git a/inst/models.tsv b/inst/models.tsv
index 402febbe4..e823ac58c 100644
--- a/inst/models.tsv
+++ b/inst/models.tsv
@@ -1,4 +1,6 @@
"model" "mode" "engine" "pkg"
+"auto_ml" "classification" "h2o" "agua"
+"auto_ml" "regression" "h2o" "agua"
"bag_mars" "classification" "earth" "baguette"
"bag_mars" "regression" "earth" "baguette"
"bag_tree" "censored regression" "rpart" "censored"
diff --git a/man/auto_ml.Rd b/man/auto_ml.Rd
new file mode 100644
index 000000000..e3380bbf9
--- /dev/null
+++ b/man/auto_ml.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/auto_ml.R
+\name{auto_ml}
+\alias{auto_ml}
+\title{Automatic Machine Learning}
+\usage{
+auto_ml(mode = "unknown", engine = "h2o")
+}
+\arguments{
+\item{mode}{A single character string for the prediction outcome mode.
+Possible values for this model are "unknown", "regression", or
+"classification".}
+
+\item{engine}{A single character string specifying what computational engine
+to use for fitting.}
+}
+\description{
+\code{auto_ml()} defines an automated searching and tuning process where
+many models of different families are trained and ranked given their
+performance on the training data.
+
+\Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("auto_ml")}
+
+More information on how \pkg{parsnip} is used for modeling is at
+\url{https://www.tidymodels.org/}.
+}
+\details{
+This function only defines what \emph{type} of model is being fit. Once an engine
+is specified, the \emph{method} to fit the model is also defined. See
+\code{\link[=set_engine]{set_engine()}} for more on setting the engine, including how to set engine
+arguments.
+
+The model is not trained or fit until the \code{\link[=fit.model_spec]{fit()}} function is used
+with the data.
+}
+\references{
+\url{https://www.tidymodels.org}, \href{https://www.tmwr.org/}{\emph{Tidy Modeling with R}}, \href{https://www.tidymodels.org/find/parsnip/}{searchable table of parsnip models}
+}
+\seealso{
+\Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("auto_ml")}
+}
diff --git a/man/details_auto_ml_h2o.Rd b/man/details_auto_ml_h2o.Rd
new file mode 100644
index 000000000..4c21dad53
--- /dev/null
+++ b/man/details_auto_ml_h2o.Rd
@@ -0,0 +1,96 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/auto_ml_h2o.R
+\name{details_auto_ml_h2o}
+\alias{details_auto_ml_h2o}
+\title{Automatic machine learning via h2o}
+\description{
+\link[h2o:h2o.automl]{h2o::h2o.automl} defines an automated model training process and returns a
+leaderboard of models with best performances.
+}
+\details{
+For this engine, there are multiple modes: classification and regression
+\subsection{Tuning Parameters}{
+
+This model has no tuning parameters.
+
+Engine arguments of interest
+\itemize{
+\item \code{max_runtime_secs} and \code{max_models}: controls the maximum running
+time and number of models to build in the automatic process.
+\item \code{exclude_algos} and \code{include_algos}: a character vector indicating
+the excluded or included algorithms during model building. To see a
+full list of supported models, see the details section in
+\code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}.
+\item \code{validation}: An integer between 0 and 1 specifying the \emph{proportion}
+of training data reserved as validation set. This is used by h2o for
+performance assessment and potential early stopping.
+}
+}
+
+\subsection{Translation from parsnip to the original package (regression)}{
+
+\code{\link[agua:h2o_train]{agua::h2o_train_auto()}} is a wrapper around
+\code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}.
+
+\if{html}{\out{}}\preformatted{auto_ml() \%>\%
+ set_engine("h2o") \%>\%
+ set_mode("regression") \%>\%
+ translate()
+}\if{html}{\out{
}}
+
+\if{html}{\out{}}\preformatted{## Automatic Machine Learning Model Specification (regression)
+##
+## Computational engine: h2o
+##
+## Model fit template:
+## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
+## validation_frame = missing_arg(), verbosity = NULL)
+}\if{html}{\out{
}}
+}
+
+\subsection{Translation from parsnip to the original package (classification)}{
+
+\if{html}{\out{}}\preformatted{auto_ml() \%>\%
+ set_engine("h2o") \%>\%
+ set_mode("classification") \%>\%
+ translate()
+}\if{html}{\out{
}}
+
+\if{html}{\out{}}\preformatted{## Automatic Machine Learning Model Specification (classification)
+##
+## Computational engine: h2o
+##
+## Model fit template:
+## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
+## validation_frame = missing_arg(), verbosity = NULL)
+}\if{html}{\out{
}}
+}
+
+\subsection{Preprocessing requirements}{
+
+Factor/categorical predictors need to be converted to numeric values
+(e.g., dummy or indicator variables) for this engine. When using the
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
+}
+
+\subsection{Initializing h2o}{
+
+To use the h2o engine with tidymodels, please run \code{h2o::h2o.init()}
+first. By default, This connects R to the local h2o server. This needs
+to be done in every new R session. You can also connect to a remote h2o
+server with an IP address, for more details see
+\code{\link[h2o:h2o.init]{h2o::h2o.init()}}.
+
+You can control the number of threads in the thread pool used by h2o
+with the \code{nthreads} argument. By default, it uses all CPUs on the host.
+This is different from the usual parallel processing mechanism in
+tidymodels for tuning, while tidymodels parallelizes over resamples, h2o
+parallelizes over hyperparameter combinations for a given resample.
+
+h2o will automatically shut down the local h2o instance started by R
+when R is terminated. To manually stop the h2o server, run
+\code{h2o::h2o.shutdown()}.
+}
+}
+\keyword{internal}
diff --git a/man/rmd/auto_ml_h2o.Rmd b/man/rmd/auto_ml_h2o.Rmd
new file mode 100644
index 000000000..cf987b668
--- /dev/null
+++ b/man/rmd/auto_ml_h2o.Rmd
@@ -0,0 +1,47 @@
+```{r, child = "aaa.Rmd", include = FALSE}
+```
+
+`r descr_models("auto_ml", "h2o")`
+
+## Tuning Parameters
+
+This model has no tuning parameters.
+
+Engine arguments of interest
+
+- `max_runtime_secs` and `max_models`: controls the maximum running time and number of models to build in the automatic process.
+
+- `exclude_algos` and `include_algos`: a character vector indicating the excluded or included algorithms during model building. To see a full list of supported models, see the details section in [h2o::h2o.automl()].
+
+- `validation`: An integer between 0 and 1 specifying the _proportion_ of training data reserved as validation set. This is used by h2o for performance assessment and potential early stopping.
+
+## Translation from parsnip to the original package (regression)
+
+[agua::h2o_train_auto()] is a wrapper around [h2o::h2o.automl()].
+
+```{r h2o-reg}
+auto_ml() %>%
+ set_engine("h2o") %>%
+ set_mode("regression") %>%
+ translate()
+```
+
+
+## Translation from parsnip to the original package (classification)
+
+```{r h2o-cls}
+auto_ml() %>%
+ set_engine("h2o") %>%
+ set_mode("classification") %>%
+ translate()
+```
+
+## Preprocessing requirements
+
+```{r child = "template-makes-dummies.Rmd"}
+```
+
+## Initializing h2o
+
+```{r child = "template-h2o-init.Rmd"}
+```
diff --git a/man/rmd/auto_ml_h2o.md b/man/rmd/auto_ml_h2o.md
new file mode 100644
index 000000000..2eae961b6
--- /dev/null
+++ b/man/rmd/auto_ml_h2o.md
@@ -0,0 +1,73 @@
+
+
+
+For this engine, there are multiple modes: classification and regression
+
+## Tuning Parameters
+
+This model has no tuning parameters.
+
+Engine arguments of interest
+
+- `max_runtime_secs` and `max_models`: controls the maximum running time and number of models to build in the automatic process.
+
+- `exclude_algos` and `include_algos`: a character vector indicating the excluded or included algorithms during model building. To see a full list of supported models, see the details section in [h2o::h2o.automl()].
+
+- `validation`: An integer between 0 and 1 specifying the _proportion_ of training data reserved as validation set. This is used by h2o for performance assessment and potential early stopping.
+
+## Translation from parsnip to the original package (regression)
+
+[agua::h2o_train_auto()] is a wrapper around [h2o::h2o.automl()].
+
+
+```r
+auto_ml() %>%
+ set_engine("h2o") %>%
+ set_mode("regression") %>%
+ translate()
+```
+
+```
+## Automatic Machine Learning Model Specification (regression)
+##
+## Computational engine: h2o
+##
+## Model fit template:
+## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
+## validation_frame = missing_arg(), verbosity = NULL)
+```
+
+
+## Translation from parsnip to the original package (classification)
+
+
+```r
+auto_ml() %>%
+ set_engine("h2o") %>%
+ set_mode("classification") %>%
+ translate()
+```
+
+```
+## Automatic Machine Learning Model Specification (classification)
+##
+## Computational engine: h2o
+##
+## Model fit template:
+## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
+## validation_frame = missing_arg(), verbosity = NULL)
+```
+
+## Preprocessing requirements
+
+
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
+
+## Initializing h2o
+
+
+To use the h2o engine with tidymodels, please run `h2o::h2o.init()` first. By default, This connects R to the local h2o server. This needs to be done in every new R session. You can also connect to a remote h2o server with an IP address, for more details see [h2o::h2o.init()].
+
+You can control the number of threads in the thread pool used by h2o with the `nthreads` argument. By default, it uses all CPUs on the host. This is different from the usual parallel processing mechanism in tidymodels for tuning, while tidymodels parallelizes over resamples, h2o parallelizes over hyperparameter combinations for a given resample.
+
+h2o will automatically shut down the local h2o instance started by R when R is terminated. To manually stop the h2o server, run `h2o::h2o.shutdown()`.