Title: A Bridge Between 'keras' and 'tidymodels'
Version: 0.0.1
Description: Provides a seamless bridge between 'keras' and the 'tidymodels' ecosystem. It allows for the dynamic creation of 'parsnip' model specifications for 'keras' models.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: abind, parsnip (≥ 1.0.0), rlang, keras3, tibble, purrr, dplyr, cli, recipes, reticulate
Suggests: testthat (≥ 3.0.0), modeldata, tidymodels, finetune, tune, dials, workflows, rsample, knitr, lme4, rmarkdown, future, ggplot2
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-08-23 14:37:26 UTC; david
Author: David Díaz [aut, cre]
Maintainer: David Díaz <daviddrsch@gmail.com>
Depends: R (≥ 4.1.0)
Repository: CRAN
Date/Publication: 2025-09-03 08:00:07 UTC

Compile Keras Models Over a Grid of Hyperparameters

Description

Pre-compiles Keras models for each hyperparameter combination in a grid.

This function is a powerful debugging tool to use before running a full tune::tune_grid(). It allows you to quickly validate multiple model architectures, ensuring they can be successfully built and compiled without the time-consuming process of actually fitting them. It helps catch common errors like incompatible layer shapes or invalid argument values early.

Usage

compile_keras_grid(spec, grid, x, y)

Arguments

spec

A parsnip model specification created by create_keras_sequential_spec() or create_keras_functional_spec().

grid

A tibble or data.frame containing the grid of hyperparameters to evaluate. Each row represents a unique model architecture to be compiled.

x

A data frame or matrix of predictors. This is used to infer the input_shape for the Keras model.

y

A vector or factor of outcomes. This is used to infer the output shape and the default loss function for the Keras model.

Details

Compile and Validate Keras Model Architectures

The function iterates through each row of the provided grid. For each hyperparameter combination, it attempts to build and compile the Keras model defined by the spec. The process is wrapped in a try-catch block to gracefully handle and report any errors that occur during model instantiation or compilation.

The output is a tibble that mirrors the input grid, with additional columns containing the compiled model object or the error message, making it easy to inspect which architectures are valid.

Value

A tibble with the following columns:

Examples


if (requireNamespace("keras3", quietly = TRUE)) {

# 1. Define a kerasnip model specification
create_keras_sequential_spec(
  model_name = "my_mlp",
  layer_blocks = list(
    input_block,
    hidden_block,
    output_block
  ),
  mode = "classification"
)

mlp_spec <- my_mlp(
  hidden_units = tune(),
  compile_loss = "categorical_crossentropy",
  compile_optimizer = "adam"
)

# 2. Create a hyperparameter grid
# Include an invalid value (-10) to demonstrate error handling
param_grid <- tibble::tibble(
  hidden_units = c(32, 64, -10)
)

# 3. Prepare dummy data
x_train <- matrix(rnorm(100 * 10), ncol = 10)
y_train <- factor(sample(0:1, 100, replace = TRUE))

# 4. Compile models over the grid
compiled_grid <- compile_keras_grid(
  spec = mlp_spec,
  grid = param_grid,
  x = x_train,
  y = y_train
)

print(compiled_grid)

# 5. Inspect the results
# The row with `hidden_units = -10` will show an error.
}


Create a Custom Keras Functional API 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 using the Functional API. This allows for creating complex, tunable architectures with non-linear topologies that integrate seamlessly with the tidymodels ecosystem.

Usage

create_keras_functional_spec(
  model_name,
  layer_blocks,
  mode = c("regression", "classification"),
  ...,
  env = parent.frame()
)

Arguments

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.

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.

mode

A character string, either "regression" or "classification".

...

Reserved for future use. Currently not used.

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.

Value

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.

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:

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.

See Also

remove_keras_spec(), parsnip::new_model_spec(), create_keras_sequential_spec()

Examples


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 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 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().

Usage

create_keras_sequential_spec(
  model_name,
  layer_blocks,
  mode = c("regression", "classification"),
  ...,
  env = parent.frame()
)

Arguments

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.

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.

mode

A character string, either "regression" or "classification".

...

Reserved for future use. Currently not used.

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 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.

Value

Invisibly returns NULL. Its primary side effect is to create a new 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.

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.

See Also

remove_keras_spec(), parsnip::new_model_spec(), create_keras_functional_spec()

Examples


if (requireNamespace("keras3", quietly = TRUE)) {
library(keras3)
library(parsnip)
library(dials)

# 1. Define layer blocks for a complete model.
# The first block must initialize the model. `input_shape` is passed automatically.
input_block <- function(model, input_shape) {
  keras_model_sequential(input_shape = input_shape)
}
# A block for hidden layers. `units` will become a tunable parameter.
hidden_block <- function(model, units = 32) {
  model |> layer_dense(units = units, activation = "relu")
}

# The output block. `num_classes` is passed automatically for classification.
output_block <- function(model, num_classes) {
  model |> layer_dense(units = num_classes, activation = "softmax")
}

# 2. Create the spec, providing blocks in the correct order.
create_keras_sequential_spec(
model_name = "my_mlp",
  layer_blocks = list(
    input = input_block,
    hidden = hidden_block,
    output = output_block
  ),
  mode = "classification"
)

# 3. Use the newly created specification function!
# Note the new arguments `num_hidden` and `hidden_units`.
model_spec <- my_mlp(
  num_hidden = 2,
  hidden_units = 64,
  epochs = 10,
  learn_rate = 0.01
)

print(model_spec)
}


Extract Keras Training History

Description

Extracts and returns the training history from a parsnip model_fit object created by kerasnip.

Usage

extract_keras_history(object)

Arguments

object

A model_fit object produced by a kerasnip specification.

Details

Extract Keras Training History

The history object contains the metrics recorded during model training, such as loss and accuracy, for each epoch. This is highly useful for visualizing the training process and diagnosing issues like overfitting. The returned object can be plotted directly.

Value

A keras_training_history object. You can call plot() on this object to visualize the learning curves.

See Also

keras_evaluate, extract_keras_model


Extract Keras Model from a Fitted Kerasnip Object

Description

Extracts and returns the underlying Keras model object from a parsnip model_fit object created by kerasnip.

Usage

extract_keras_model(object)

Arguments

object

A model_fit object produced by a kerasnip specification.

Details

Extract the Raw Keras Model from a Kerasnip Fit

This is useful when you need to work directly with the Keras model object for tasks like inspecting layer weights, creating custom plots, or passing it to other Keras-specific functions.

Value

The raw Keras model object (keras_model).

See Also

keras_evaluate, extract_keras_history


Extract Valid Grid from Compilation Results

Description

This helper function filters the results from compile_keras_grid() to return a new hyperparameter grid containing only the combinations that compiled successfully.

Usage

extract_valid_grid(compiled_grid)

Arguments

compiled_grid

A tibble, the result of a call to compile_keras_grid().

Details

Filter a Grid to Only Valid Hyperparameter Sets

After running compile_keras_grid(), you can use this function to remove problematic hyperparameter combinations before proceeding to the full tune::tune_grid().

Value

A tibble containing the subset of the original grid that resulted in a successful model compilation. The compiled_model and error columns are removed, leaving a clean grid ready for tuning.

Examples


# Continuing the example from `compile_keras_grid`:

# `compiled_grid` contains one row with an error.
valid_grid <- extract_valid_grid(compiled_grid)

# `valid_grid` now only contains the rows that compiled successfully.
print(valid_grid)

# This clean grid can now be passed to tune::tune_grid().


Internal Fitting Engine for Functional API Models

Description

This function serves as the internal engine for fitting kerasnip models that are based on the Keras functional API. It is not intended to be called directly by the user. The function is invoked by parsnip::fit() when a kerasnip functional model specification is used.

Usage

generic_functional_fit(formula, data, layer_blocks, ...)

Arguments

formula

A formula specifying the predictor and outcome variables, passed down from the parsnip::fit() call.

data

A data frame containing the training data, passed down from the parsnip::fit() call.

layer_blocks

A named list of layer block functions. This is passed internally from the parsnip model specification.

...

Additional arguments passed down from the model specification. These can include:

  • Layer Parameters: Arguments for the layer blocks, prefixed with the block name (e.g., dense_units = 64).

  • Architecture Parameters: Arguments to control the number of times a block is repeated, in the format ⁠num_{block_name}⁠ (e.g., num_dense = 2).

  • Compile Parameters: Arguments to customize model compilation, prefixed with compile_ (e.g., compile_loss = "mae", compile_optimizer = "sgd").

  • Fit Parameters: Arguments to customize model fitting, prefixed with fit_ (e.g., fit_callbacks = list(...), fit_class_weight = list(...)).

Details

Generic Fitting Function for Functional Keras Models

The function orchestrates the three main steps of the model fitting process:

  1. Build and Compile: It calls build_and_compile_functional_model() to construct the Keras model architecture based on the provided layer_blocks and hyperparameters.

  2. Process Data: It preprocesses the input (x) and output (y) data into the format expected by Keras.

  3. Fit Model: It calls keras3::fit() with the compiled model and processed data, passing along any fitting-specific arguments (e.g., epochs, batch_size, callbacks).

Value

A list containing the fitted model and other metadata. This list is stored in the fit slot of the parsnip model fit object. The list contains the following elements:

Examples

# This function is not called directly by users.
# It is called internally by `parsnip::fit()`.
# For example:

# create_keras_functional_spec(...) defines my_functional_model

spec <- my_functional_model(hidden_units = 128, fit_epochs = 10) |>
  set_engine("keras")

# This call to fit() would invoke generic_functional_fit() internally
fitted_model <- fit(spec, y ~ x, data = training_data)


Internal Fitting Engine for Sequential API Models

Description

This function serves as the internal engine for fitting kerasnip models that are based on the Keras sequential API. It is not intended to be called directly by the user. The function is invoked by parsnip::fit() when a kerasnip sequential model specification is used.

Usage

generic_sequential_fit(formula, data, layer_blocks, ...)

Arguments

formula

A formula specifying the predictor and outcome variables, passed down from the parsnip::fit() call.

data

A data frame containing the training data, passed down from the parsnip::fit() call.

layer_blocks

A named list of layer block functions. This is passed internally from the parsnip model specification.

...

Additional arguments passed down from the model specification. These can include:

  • Layer Parameters: Arguments for the layer blocks, prefixed with the block name (e.g., dense_units = 64).

  • Architecture Parameters: Arguments to control the number of times a block is repeated, in the format ⁠num_{block_name}⁠ (e.g., num_dense = 2).

  • Compile Parameters: Arguments to customize model compilation, prefixed with compile_ (e.g., compile_loss = "mae", compile_optimizer = "sgd").

  • Fit Parameters: Arguments to customize model fitting, prefixed with fit_ (e.g., fit_callbacks = list(...), fit_class_weight = list(...)).

Details

Generic Fitting Function for Sequential Keras Models

The function orchestrates the three main steps of the model fitting process:

  1. Build and Compile: It calls build_and_compile_sequential_model() to construct the Keras model architecture based on the provided layer_blocks and hyperparameters.

  2. Process Data: It preprocesses the input (x) and output (y) data into the format expected by Keras.

  3. Fit Model: It calls keras3::fit() with the compiled model and processed data, passing along any fitting-specific arguments (e.g., epochs, batch_size, callbacks).

Value

A list containing the fitted model and other metadata. This list is stored in the fit slot of the parsnip model fit object. The list contains the following elements:

Examples

# This function is not called directly by users.
# It is called internally by `parsnip::fit()`.
# For example:

# create_keras_sequential_spec(...) defines my_sequential_model

spec <- my_sequential_model(hidden_1_units = 128, fit_epochs = 10) |>
  set_engine("keras")

# This call to fit() would invoke generic_sequential_fit() internally
fitted_model <- fit(spec, y ~ x, data = training_data)


Get Parsnip's Model Environment

Description

This is an internal helper function to retrieve the environment where parsnip stores its model definitions. It is used to dynamically interact with the parsnip infrastructure.

Usage

get_model_env()

Value

The parsnip model environment.

Examples


model_env <- kerasnip::get_model_env()


Inform About Compilation Errors

Description

This helper function inspects the results from compile_keras_grid() and prints a formatted, easy-to-read summary of any compilation errors that occurred.

Usage

inform_errors(compiled_grid, n = 10)

Arguments

compiled_grid

A tibble, the result of a call to compile_keras_grid().

n

A single integer for the maximum number of distinct errors to display in detail.

Details

Display a Summary of Compilation Errors

This is most useful for interactive debugging of complex tuning grids where some hyperparameter combinations may lead to invalid Keras models.

Value

Invisibly returns the input compiled_grid. Called for its side effect of printing a summary to the console.

Examples


# Continuing the example from `compile_keras_grid`:

# `compiled_grid` contains one row with an error.
# This will print a formatted summary of that error.
inform_errors(compiled_grid)


Remap Layer Block Arguments for Model Specification

Description

Creates a wrapper function around a Keras layer block to rename its arguments. This is a powerful helper for defining the layer_blocks in create_keras_functional_spec() and create_keras_sequential_spec(), allowing you to connect reusable blocks into a model graph without writing verbose anonymous functions.

Usage

inp_spec(block, input_map)

Arguments

block

A function that defines a Keras layer or a set of layers. The first arguments should be the input tensor(s).

input_map

A single character string or a named character vector that specifies how to rename/remap the arguments of block.

Details

inp_spec() makes your model definitions cleaner and more readable. It handles the metaprogramming required to create a new function with the correct argument names, while preserving the original block's hyperparameters and their default values.

The function supports two modes of operation based on input_map:

  1. Single Input Renaming: If input_map is a single character string, the wrapper function renames the first argument of the block function to the provided string. This is the common case for blocks that take a single tensor input.

  2. Multiple Input Mapping: If input_map is a named character vector, it provides an explicit mapping from new argument names (the names of the vector) to the original argument names in the block function (the values of the vector). This is used for blocks with multiple inputs, like a concatenation layer.

Value

A new function (a closure) that wraps the block function with renamed arguments, ready to be used in a layer_blocks list.

Examples


# --- Example Blocks ---
# A standard dense block with one input tensor and one hyperparameter.
dense_block <- function(tensor, units = 16) {
  tensor |> keras3::layer_dense(units = units, activation = "relu")
}

# A block that takes two tensors as input.
concat_block <- function(input_a, input_b) {
  keras3::layer_concatenate(list(input_a, input_b))
}

# An output block with one input.
output_block <- function(tensor) {
  tensor |> keras3::layer_dense(units = 1)
}

# --- Usage ---
layer_blocks <- list(
  main_input = keras3::layer_input,
  path_a = inp_spec(dense_block, "main_input"),
  path_b = inp_spec(dense_block, "main_input"),
  concatenated = inp_spec(
    concat_block,
    c(path_a = "input_a", path_b = "input_b")
  ),
  output = inp_spec(output_block, "concatenated")
)


Evaluate a Kerasnip Model

Description

This function provides an kera_evaluate() method for model_fit objects created by kerasnip. It preprocesses the new data into the format expected by Keras and then calls keras3::evaluate() on the underlying model to compute the loss and any other metrics.

Usage

keras_evaluate(object, x, y = NULL, ...)

Arguments

object

A model_fit object produced by a kerasnip specification.

x

A data frame or matrix of new predictor data.

y

A vector or data frame of new outcome data corresponding to x.

...

Additional arguments passed on to keras3::evaluate() (e.g., batch_size).

Details

Evaluate a Fitted Kerasnip Model on New Data

Value

A named list containing the evaluation results (e.g., loss, accuracy). The names are determined by the metrics the model was compiled with.

Examples


if (requireNamespace("keras3", quietly = TRUE)) {

# 1. Define and fit a model ----
create_keras_sequential_spec(
  model_name = "my_mlp",
  layer_blocks = list(input_block, hidden_block, output_block),
  mode = "classification"
)

mlp_spec <- my_mlp(
  hidden_units = 32,
  compile_loss = "categorical_crossentropy",
  compile_optimizer = "adam",
  compile_metrics = "accuracy",
  fit_epochs = 5
) |> set_engine("keras")

x_train <- matrix(rnorm(100 * 10), ncol = 10)
y_train <- factor(sample(0:1, 100, replace = TRUE))
train_df <- data.frame(x = I(x_train), y = y_train)

fitted_mlp <- fit(mlp_spec, y ~ x, data = train_df)

# 2. Evaluate the model on new data ----
x_test <- matrix(rnorm(50 * 10), ncol = 10)
y_test <- factor(sample(0:1, 50, replace = TRUE))

eval_metrics <- keras_evaluate(fitted_mlp, x_test, y_test)
print(eval_metrics)

# 3. Extract the Keras model object ----
keras_model <- extract_keras_model(fitted_mlp)
summary(keras_model)

# 4. Extract the training history ----
history <- extract_keras_history(fitted_mlp)
plot(history)
}


Dynamically Discovered Keras Objects

Description

These exported vectors contain the names of optimizers, losses, and metrics discovered from the installed keras3 package when kerasnip is loaded. This ensures that kerasnip is always up-to-date with your Keras version.

Usage

keras_optimizers

keras_losses

keras_metrics

Format

An object of class character of length 12.

An object of class character of length 21.

An object of class character of length 32.

Details

These objects are primarily used to provide the default values for the dials parameter functions, optimizer_function() and loss_function_keras(). This allows for tab-completion in IDEs and validation of optimizer and loss names when tuning models.

The discovery process in .onLoad() scrapes the keras3 namespace for functions matching ⁠optimizer_*⁠, ⁠loss_*⁠, and ⁠metric_*⁠ patterns.


Dials Parameter for Keras Loss Functions

Description

Dials Parameter for Keras Loss Functions

Usage

loss_function_keras(values = NULL)

Arguments

values

A character vector of possible loss functions. Defaults to all known losses (keras defaults + custom registered).

Value

A dials parameter object for Keras loss.


Dials Parameter for Keras Optimizers

Description

Dials Parameter for Keras Optimizers

Usage

optimizer_function(values = NULL)

Arguments

values

A character vector of possible optimizers. Defaults to all known optimizers (keras defaults + custom registered).

Value

A dials parameter object for Keras optimizers.


Process Predictor Input for Keras (Functional API)

Description

Preprocesses predictor data (x) into a format suitable for Keras models built with the Functional API. Handles both tabular data and list-columns of arrays (e.g., for images), supporting multiple inputs.

Usage

process_x_functional(x)

Arguments

x

A data frame or matrix of predictors.

Value

A list containing:


Process Predictor Input for Keras

Description

Preprocesses predictor data (x) into a format suitable for Keras models. Handles both tabular data and list-columns of arrays (e.g., for images).

Usage

process_x_sequential(x)

Arguments

x

A data frame or matrix of predictors.

Value

A list containing:


Process Outcome Input for Keras (Functional API)

Description

Preprocesses outcome data (y) into a format suitable for Keras models built with the Functional API. Handles both regression (numeric) and classification (factor) outcomes, including one-hot encoding for classification, and supports multiple outputs.

Usage

process_y_functional(y, is_classification = NULL, class_levels = NULL)

Arguments

y

A vector or data frame of outcomes.

is_classification

Logical, optional. If TRUE, treats y as classification. If FALSE, treats as regression. If NULL (default), it's determined from is.factor(y).

class_levels

Character vector, optional. The factor levels for classification outcomes. If NULL (default), determined from levels(y).

Value

A list containing:


Process Outcome Input for Keras

Description

Preprocesses outcome data (y) into a format suitable for Keras models. Handles both regression (numeric) and classification (factor) outcomes, including one-hot encoding for classification.

Usage

process_y_sequential(y, is_classification = NULL, class_levels = NULL)

Arguments

y

A vector of outcomes.

is_classification

Logical, optional. If TRUE, treats y as classification. If FALSE, treats as regression. If NULL (default), it's determined from is.factor(y).

class_levels

Character vector, optional. The factor levels for classification outcomes. If NULL (default), determined from levels(y).

Value

A list containing:


Register a Custom Keras Loss

Description

Allows users to register a custom loss function so it can be used by name within kerasnip model specifications and tuned with dials.

Usage

register_keras_loss(name, loss_fn)

Arguments

name

The name to register the loss under (character).

loss_fn

The loss function.

Details

Registered losses are stored in an internal environment. When a model is compiled, kerasnip will first check this internal registry for a loss matching the provided name before checking the keras3 package.

Value

No return value, called for side effects.

See Also

register_keras_optimizer(), register_keras_metric()


Register a Custom Keras Metric

Description

Allows users to register a custom metric function so it can be used by name within kerasnip model specifications.

Usage

register_keras_metric(name, metric_fn)

Arguments

name

The name to register the metric under (character).

metric_fn

The metric function.

Details

Registered metrics are stored in an internal environment. When a model is compiled, kerasnip will first check this internal registry for a metric matching the provided name before checking the keras3 package.

Value

No return value, called for side effects.

See Also

register_keras_optimizer(), register_keras_loss()


Register a Custom Keras Optimizer

Description

Allows users to register a custom optimizer function so it can be used by name within kerasnip model specifications and tuned with dials.

Usage

register_keras_optimizer(name, optimizer_fn)

Arguments

name

The name to register the optimizer under (character).

optimizer_fn

The optimizer function. It should return a Keras optimizer object.

Details

Registered optimizers are stored in an internal environment. When a model is compiled, kerasnip will first check this internal registry for an optimizer matching the provided name before checking the keras3 package.

The optimizer_fn can be a simple function or a partially applied function using purrr::partial(). This is useful for creating versions of Keras optimizers with specific settings.

Value

No return value, called for side effects.

See Also

register_keras_loss(), register_keras_metric()

Examples

if (requireNamespace("keras3", quietly = TRUE)) {
  # Register a custom version of Adam with a different default beta_1
  my_adam <- purrr::partial(keras3::optimizer_adam, beta_1 = 0.8)
  register_keras_optimizer("my_adam", my_adam)

  # Now "my_adam" can be used as a string in a model spec, e.g.,
  # my_model_spec(compile_optimizer = "my_adam")
}

Remove a Keras Model Specification and its Registrations

Description

This function completely removes a model specification that was previously created by create_keras_sequential_spec() or create_keras_functional_spec(). It cleans up both the function in the user's environment and all associated registrations within the parsnip package.

Usage

remove_keras_spec(model_name, env = parent.frame())

Arguments

model_name

A character string giving the name of the model specification function to remove (e.g., "my_mlp").

env

The environment from which to remove the function and its update() method. Defaults to the calling environment (parent.frame()).

Details

This function is essential for cleanly unloading a dynamically created model. It performs three main actions:

  1. It removes the model specification function (e.g., my_mlp()) and its corresponding update() method from the specified environment.

  2. It searches parsnip's internal model environment for all objects whose names start with the model_name and removes them. This purges the fit methods, argument definitions, and other registrations.

  3. It removes the model's name from parsnip's master list of models.

This function uses the un-exported get_model_env() to perform the cleanup.

Value

Invisibly returns TRUE after attempting to remove the objects.

See Also

create_keras_sequential_spec(), create_keras_functional_spec()

Examples


if (requireNamespace("keras3", quietly = TRUE)) {
  # First, create a dummy spec
  input_block <- function(model, input_shape) {
    keras3::keras_model_sequential(input_shape = input_shape)
  }
  dense_block <- function(model, units = 16) {
    model |> keras3::layer_dense(units = units)
  }
  create_keras_sequential_spec(
    "my_temp_model",
    list(
      input = input_block,
      dense = dense_block
    ),
    "regression"
  )

  # Check it exists in the environment and in parsnip
  exists("my_temp_model")
  "my_temp_model" %in% parsnip::show_engines("my_temp_model")$model

  # Now remove it
  remove_keras_spec("my_temp_model")

  # Check it's gone
  !exists("my_temp_model")
  !"my_temp_model" %in% parsnip::show_engines(NULL)$model
}


Collapse Predictors into a single list-column

Description

step_collapse() creates a a specification of a recipe step that will convert a group of predictors into a single list-column. This is useful for custom models that need the predictors in a different format.

Usage

step_collapse(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  new_col = "predictor_matrix",
  skip = FALSE,
  id = recipes::rand_id("collapse")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See ⁠[selections()]⁠ for more details. For the tidy method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the new columns are used as predictors.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

columns

A character string of the selected variable names. This is NULL until the step is trained by ⁠[prep.recipe()]⁠.

new_col

A character string for the name of the new list-column. The default is "predictor_matrix".

skip

A logical. Should the step be skipped when the recipe is baked by ⁠[bake.recipe()]⁠? While all operations are baked when prep is run, skipping when bake is run may be other times when it is desirable to skip a processing step.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the columns that are affected and value which is the type of collapse.

Examples

library(recipes)

# 2 predictors
dat <- data.frame(
  x1 = 1:10,
  x2 = 11:20,
  y = 1:10
)

rec <- recipe(y ~ ., data = dat) %>%
  step_collapse(x1, x2, new_col = "pred") %>%
  prep()

bake(rec, new_data = NULL)