Addin support for functional appi closses #2es #

NAMESPACE

@@ -1,7 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
-export(create_keras_spec)
-export(generic_keras_fit_impl)
+export(create_keras_functional_spec)
+export(create_keras_sequential_spec)
+export(generic_functional_fit)
+export(generic_sequential_fit)
+export(inp_spec)
 export(keras_losses)
 export(keras_metrics)
 export(keras_optimizers)

R/build_spec_function.R

@@ -0,0 +1,81 @@
+#' Build the Model Specification Function
+#'
+#' @description
+#' This internal helper uses metaprogramming to construct a complete R function
+#' that acts as a `parsnip` model specification (e.g., `my_mlp()`).
+#'
+#' @details
+#' The process involves three main steps:
+#' 1.  **Function Body Construction**: An expression for the function body is
+#'     created. This body uses `rlang::enquo()` and `rlang::enquos()` to
+#'     capture all user-provided arguments (both named and via `...`) into a
+#'     list of quosures. This list is then passed to `parsnip::new_model_spec()`.
+#' 2.  **Function Signature Construction**: A formal argument list is created
+#'     from `all_args`, and `...` is added to allow passthrough arguments.
+#'     `rlang::new_function()` combines the signature and body into a new
+#'     function object.
+#' 3.  **Documentation Attachment**: `generate_roxygen_docs()` creates a
+#'     comprehensive Roxygen comment block as a string, which is then attached
+#'     to the new function using `comment()`.
+#'
+#' @param model_name The name of the model specification function to create (e.g., "my_mlp").
+#' @param mode The model mode ("regression" or "classification").
+#' @param all_args A named list of formal arguments for the new function's
+#'   signature, as generated by `collect_spec_args()`. The values are typically
+#'   `rlang::missing_arg()` or `rlang::zap()`.
+#' @param parsnip_names A character vector of all argument names that should be
+#'   captured as quosures and passed to `parsnip::new_model_spec()`.
+#' @param layer_blocks The user-provided list of layer block functions. This is
+#'   passed directly to `generate_roxygen_docs()` to create documentation for
+#'   block-specific parameters.
+#' @param functional A logical indicating if the model is functional
+#'   (for `create_keras_functional_spec()`) or sequential. This is passed to
+#'   `generate_roxygen_docs()` to tailor the documentation.
+#' @return A new function object with attached Roxygen comments, ready to be
+#'   placed in the user's environment.
+#' @noRd
+build_spec_function <- function(
+  model_name,
+  mode,
+  all_args,
+  parsnip_names,
+  layer_blocks,
+  functional = FALSE
+) {
+  quos_exprs <- purrr::map(
+    parsnip_names,
+    ~ rlang::expr(rlang::enquo(!!rlang::sym(.x)))
+  )
+  names(quos_exprs) <- parsnip_names
+
+  body <- rlang::expr({
+    # Capture both explicit args and ... to pass to the fit impl
+    # Named arguments are captured into a list of quosures.
+    main_args <- rlang::list2(!!!quos_exprs)
+    # ... arguments are captured into a separate list of quosures.
+    dot_args <- rlang::enquos(...)
+    args <- c(main_args, dot_args)
+    parsnip::new_model_spec(
+      !!model_name,
+      args = args,
+      eng_args = NULL,
+      mode = !!mode,
+      method = NULL,
+      engine = NULL
+    )
+  })
+
+  # Add ... to the function signature to capture any other compile arguments
+  fn_args <- c(all_args, list(... = rlang::missing_arg()))
+
+  fn <- rlang::new_function(args = fn_args, body = body)
+
+  docs <- generate_roxygen_docs(
+    model_name,
+    layer_blocks,
+    all_args,
+    functional = functional
+  )
+  comment(fn) <- docs
+  fn
+}

R/create_keras_functional_spec.R

@@ -0,0 +1,141 @@
+#' Create a Custom Keras Functional API Model Specification for Tidymodels
+#'
+#' This function acts as a factory to generate a new `parsnip` model
+#' specification based on user-defined blocks of Keras layers using the
+#' Functional API. This allows for creating complex, tunable architectures
+#' with non-linear topologies that integrate seamlessly with the `tidymodels`
+#' ecosystem.
+#'
+#' @param model_name A character string for the name of the new model
+#'   specification function (e.g., "custom_resnet"). This should be a valid R
+#'   function name.
+#' @param layer_blocks A named list of functions where each function defines a
+#'   "block" (a node) in the model graph. The list names are crucial as they
+#'   define the names of the nodes. The arguments of each function define how
+#'   the nodes are connected. See the "Model Graph Connectivity" section for
+#'   details.
+#' @param mode A character string, either "regression" or "classification".
+#' @param ... Reserved for future use. Currently not used.
+#' @param env The environment in which to create the new model specification
+#'   function and its associated `update()` method. Defaults to the calling
+#'   environment (`parent.frame()`).
+#'
+#' @details
+#' This function generates all the boilerplate needed to create a custom,
+#' tunable `parsnip` model specification that uses the Keras Functional API.
+#' This is ideal for models with complex, non-linear topologies, such as
+#' networks with multiple inputs/outputs or residual connections.
+#'
+#' The function inspects the arguments of your `layer_blocks` functions and
+#' makes them available as tunable parameters in the generated model
+#' specification, prefixed with the block's name (e.g., `dense_units`).
+#' Common training parameters such as `epochs` and `learn_rate` are also added.
+#'
+#' @section Model Graph Connectivity:
+#' `kerasnip` builds the model's directed acyclic graph by inspecting the
+#' arguments of each function in the `layer_blocks` list. The connection logic
+#' is as follows:
+#'
+#' 1.  The **names of the elements** in the `layer_blocks` list define the names
+#'     of the nodes in your graph (e.g., `main_input`, `dense_path`, `output`).
+#' 2.  The **names of the arguments** in each block function specify its inputs.
+#'     A block function like `my_block <- function(input_a, input_b, ...)`
+#'     declares that it needs input from the nodes named `input_a` and `input_b`.
+#'     `kerasnip` will automatically supply the output tensors from those nodes
+#'     when calling `my_block`.
+#'
+#' There are two special requirements:
+#' *   **Input Block**: The first block in the list is treated as the input
+#'     node. Its function should not take other blocks as input, but it can have
+#'     an `input_shape` argument, which is supplied automatically during fitting.
+#' *   **Output Block**: Exactly one block must be named `"output"`. The tensor
+#'     returned by this block is used as the final output of the Keras model.
+#'
+#' A key feature is the automatic creation of `num_{block_name}` arguments
+#' (e.g., `num_dense_path`). This allows you to control how many times a block
+#' is repeated, making it easy to tune the depth of your network. A block can
+#' only be repeated if it has exactly one input from another block in the graph.
+#'
+#' The new model specification function and its `update()` method are created
+#' in the environment specified by the `env` argument.
+#'
+#' @importFrom rlang enquos dots_list arg_match env_poke
+#' @importFrom parsnip update_dot_check
+#'
+#' @return Invisibly returns `NULL`. Its primary side effect is to create a
+#'   new model specification function (e.g., `custom_resnet()`) in the
+#'   specified environment and register the model with `parsnip` so it can be
+#'   used within the `tidymodels` framework.
+#'
+#' @seealso [remove_keras_spec()], [parsnip::new_model_spec()],
+#'   [create_keras_sequential_spec()]
+#'
+#' @export
+#' @examples
+#' \dontrun{
+#' if (requireNamespace("keras3", quietly = TRUE)) {
+#'   library(keras3)
+#'   library(parsnip)
+#'
+#'   # 1. Define block functions. These are the building blocks of our model.
+#'   # An input block that receives the data's shape automatically.
+#'   input_block <- function(input_shape) layer_input(shape = input_shape)
+#'
+#'   # A dense block with a tunable `units` parameter.
+#'   dense_block <- function(tensor, units) {
+#'     tensor |> layer_dense(units = units, activation = "relu")
+#'   }
+#'
+#'   # A block that adds two tensors together (for the residual connection).
+#'   add_block <- function(input_a, input_b) layer_add(list(input_a, input_b))
+#'
+#'   # An output block for regression.
+#'   output_block_reg <- function(tensor) layer_dense(tensor, units = 1)
+#'
+#'   # 2. Create the spec. The `layer_blocks` list defines the graph.
+#'   create_keras_functional_spec(
+#'     model_name = "my_resnet_spec",
+#'     layer_blocks = list(
+#'       # The names of list elements are the node names.
+#'       main_input = input_block,
+#'
+#'       # The argument `main_input` connects this block to the input node.
+#'       dense_path = function(main_input, units = 32) dense_block(main_input, units),
+#'
+#'       # This block's arguments connect it to the original input AND the dense layer.
+#'       add_residual = function(main_input, dense_path) add_block(main_input, dense_path),
+#'
+#'       # This block must be named 'output'. It connects to the residual add layer.
+#'       output = function(add_residual) output_block_reg(add_residual)
+#'     ),
+#'     mode = "regression"
+#'   )
+#'
+#'   # 3. Use the newly created specification function!
+#'   # The `dense_path_units` argument was created automatically.
+#'   model_spec <- my_resnet_spec(dense_path_units = 64, epochs = 10)
+#'
+#'   # You could also tune the number of dense layers since it has a single input:
+#'   # model_spec <- my_resnet_spec(num_dense_path = 2, dense_path_units = 32)
+#'
+#'   print(model_spec)
+#'   # tune::tunable(model_spec)
+#' }
+#' }
+create_keras_functional_spec <- function(
+  model_name,
+  layer_blocks,
+  mode = c("regression", "classification"),
+  ...,
+  env = parent.frame()
+) {
+  mode <- rlang::arg_match(mode)
+  # 1. Argument Validation
+  create_keras_spec_impl(
+    model_name,
+    layer_blocks,
+    mode,
+    functional = TRUE,
+    env
+  )
+}

R/create_keras_spec.R → R/create_keras_sequential_spec.R

@@ -1,55 +1,63 @@
-#' Create a Custom Keras Model Specification for Tidymodels
+#' Create a Custom Keras Sequential Model Specification for Tidymodels
 #'
+#' @description
 #' This function acts as a factory to generate a new `parsnip` model
-#' specification based on user-defined blocks of Keras layers. This allows for
-#' creating complex, tunable architectures that integrate seamlessly with the
-#' `tidymodels` ecosystem.
+#' specification based on user-defined blocks of Keras layers using the
+#' Sequential API. This is the ideal choice for creating models that are a
+#' simple, linear stack of layers. For models with complex, non-linear
+#' topologies, see [create_keras_functional_spec()].
 #'
 #' @param model_name A character string for the name of the new model
 #'   specification function (e.g., "custom_cnn"). This should be a valid R
 #'   function name.
-#' @param layer_blocks A named list of functions. Each function defines a "block"
-#'   of Keras layers. The function must take a Keras model object as its first
-#'   argument and return the modified model. Other arguments to the function
-#'   will become tunable parameters in the final model specification.
+#' @param layer_blocks A named, ordered list of functions. Each function defines
+#'   a "block" of Keras layers. The function must take a Keras model object as
+#'   its first argument and return the modified model. Other arguments to the
+#'   function will become tunable parameters in the final model specification.
 #' @param mode A character string, either "regression" or "classification".
 #' @param ... Reserved for future use. Currently not used.
 #' @param env The environment in which to create the new model specification
 #'   function and its associated `update()` method. Defaults to the calling
 #'   environment (`parent.frame()`).
-#' @importFrom rlang enquos dots_list arg_match env_poke
-#' @importFrom parsnip update_dot_check
 #'
 #' @details
-#' The user is responsible for defining the entire model architecture by providing
-#' an ordered list of layer block functions.
-#' 1. The first block function must initialize the model (e.g., with
-#' \code{keras_model_sequential()}). It can accept an \code{input_shape} argument,
-#'  which will be provided automatically by the fitting engine.
-#' 2. Subsequent blocks add hidden layers.
-#' 3. The final block should add the output layer. For classification, it can
-#' accept a \code{num_classes} argument, which is provided automatically.
-#'
-#' The \code{create_keras_spec()} function will inspect the arguments of your
-#' \code{layer_blocks} functions (ignoring \code{input_shape} and \code{num_classes})
-#' and make them available as arguments in the generated model specification,
-#' prefixed with the block's name (e.g.,
-#' `dense_units`).
-#'
-#' It also automatically creates arguments like `num_dense` to control how many
-#' times each block is repeated. In addition, common training parameters such as
-#' `epochs`, `learn_rate`, `validation_split`, and `verbose` are added to the
-#' specification.
+#' This function generates all the boilerplate needed to create a custom,
+#' tunable `parsnip` model specification that uses the Keras Sequential API.
+#'
+#' The function inspects the arguments of your `layer_blocks` functions
+#' (ignoring special arguments like `input_shape` and `num_classes`)
+#' and makes them available as arguments in the generated model specification,
+#' prefixed with the block's name (e.g., `dense_units`).
 #'
 #' The new model specification function and its `update()` method are created in
 #' the environment specified by the `env` argument.
 #'
+#' @section Model Architecture (Sequential API):
+#' `kerasnip` builds the model by applying the functions in `layer_blocks` in
+#' the order they are provided. Each function receives the Keras model built by
+#' the previous function and returns a modified version.
+#'
+#' 1.  The **first block** must initialize the model (e.g., with
+#'     `keras_model_sequential()`). It can accept an `input_shape` argument,
+#'     which `kerasnip` will provide automatically during fitting.
+#' 2.  **Subsequent blocks** add layers to the model.
+#' 3.  The **final block** should add the output layer. For classification, it
+#'     can accept a `num_classes` argument, which is provided automatically.
+#'
+#' A key feature of this function is the automatic creation of `num_{block_name}`
+#' arguments (e.g., `num_hidden`). This allows you to control how many times
+#' each block is repeated, making it easy to tune the depth of your network.
+#'
+#' @importFrom rlang enquos dots_list arg_match env_poke
+#' @importFrom parsnip update_dot_check
+#'
 #' @return Invisibly returns `NULL`. Its primary side effect is to create a new
-#'   model specification function (e.g., `dynamic_mlp()`) in the specified
+#'   model specification function (e.g., `my_mlp()`) in the specified
 #'   environment and register the model with `parsnip` so it can be used within
 #'   the `tidymodels` framework.
 #'
-#' @seealso [remove_keras_spec()], [parsnip::new_model_spec()]
+#' @seealso [remove_keras_spec()], [parsnip::new_model_spec()],
+#'   [create_keras_functional_spec()]
 #'
 #' @export
 #' @examples
@@ -75,7 +83,7 @@
 #' }
 #'
 #' # 2. Create the spec, providing blocks in the correct order.
-#' create_keras_spec(
+#' create_keras_sequential_spec(
 #' model_name = "my_mlp",
 #'   layer_blocks = list(
 #'     input = input_block,
@@ -86,7 +94,7 @@
 #' )
 #'
 #' # 3. Use the newly created specification function!
-# Note the new arguments `num_hidden` and `hidden_units`.
+#' # Note the new arguments `num_hidden` and `hidden_units`.
 #' model_spec <- my_mlp(
 #'   num_hidden = 2,
 #'   hidden_units = 64,
@@ -97,28 +105,19 @@
 #' print(model_spec)
 #' }
 #' }
-create_keras_spec <- function(
+create_keras_sequential_spec <- function(
   model_name,
   layer_blocks,
   mode = c("regression", "classification"),
   ...,
   env = parent.frame()
 ) {
   mode <- arg_match(mode)
-  args_info <- collect_spec_args(layer_blocks)
-  spec_fun <- build_spec_function(
+  create_keras_spec_impl(
     model_name,
+    layer_blocks,
     mode,
-    args_info$all_args,
-    args_info$parsnip_names,
-    layer_blocks
+    functional = FALSE,
+    env
   )
-
-  register_core_model(model_name, mode)
-  register_model_args(model_name, args_info$parsnip_names)
-  register_fit_predict(model_name, mode, layer_blocks)
-  register_update_method(model_name, args_info$parsnip_names, env = env)
-
-  env_poke(env, model_name, spec_fun)
-  invisible(NULL)
 }