Package 'kerasnip'

Description

These methods allow butcher::butcher() to reduce the memory footprint of fitted kerasnip model objects. The Keras model itself (stored as raw bytes in ⁠$fit$keras_bytes⁠) is always preserved so that predict() continues to work after butchering.

The main saving comes from axe_data(), which removes the training history object (⁠$fit$history⁠). For long training runs this can be several MB.

Usage

## S3 method for class 'kerasnip_model_fit'
axe_data(x, verbose = FALSE, ...)

## S3 method for class 'kerasnip_model_fit'
axe_env(x, verbose = FALSE, ...)

## S3 method for class 'kerasnip_model_fit'
axe_call(x, verbose = FALSE, ...)

## S3 method for class 'kerasnip_model_fit'
axe_ctrl(x, verbose = FALSE, ...)

## S3 method for class 'kerasnip_model_fit'
axe_fitted(x, verbose = FALSE, ...)

Arguments

Value

An axed kerasnip_model_fit object with the butcher_kerasnip_model_fit class prepended.

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

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:

• Columns from the input grid.

• compiled_model: A list-column containing the compiled Keras model objects. If compilation failed, the element will be NULL.

• error: A list-column containing NA for successes or a character string with the error message for failures.

Examples

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

# 1. Define layer blocks
input_block <- function(model, input_shape) {
  keras_model_sequential(input_shape = input_shape)
}
hidden_block <- function(model, units = 32) {
  model |> layer_dense(units = units, activation = "relu")
}
output_block <- function(model, num_classes) {
  model |> layer_dense(units = num_classes, activation = "softmax")
}

# 2. Define a kerasnip model specification
create_keras_sequential_spec(
  model_name = "my_mlp_grid",
  layer_blocks = list(
    input = input_block,
    hidden = hidden_block,
    output = output_block
  ),
  mode = "classification"
)

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

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

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

# 5. 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)
remove_keras_spec("my_mlp_grid")

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

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

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 fit_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:

• 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.

Saving and Reloading Models

To save a fitted workflow and reload it in a new R session, use bundle::bundle() before saving — this is required to preserve the Keras model weights:

library(bundle)
bundled <- bundle(fitted_workflow)
saveRDS(bundled, "model.rds")

# New session:
library(kerasnip); library(bundle)
fitted_workflow <- unbundle(readRDS("model.rds"))
predict(fitted_workflow, new_data = test_data)  # works

Plain saveRDS() without bundle() does not preserve Keras weights, but predict() will still auto-register the parsnip model type from metadata stored on the spec.

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, fit_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)
  remove_keras_spec("my_resnet_spec")
  # tune::tunable(model_spec)
}

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

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.

Saving and Reloading Models

To save a fitted workflow and reload it in a new R session, use bundle::bundle() before saving — this is required to preserve the Keras model weights:

library(bundle)
bundled <- bundle(fitted_workflow)
saveRDS(bundled, "model.rds")

# New session:
library(kerasnip); library(bundle)
fitted_workflow <- unbundle(readRDS("model.rds"))
predict(fitted_workflow, new_data = test_data)  # works

Plain saveRDS() without bundle() does not preserve Keras weights, but predict() will still auto-register the parsnip model type from metadata stored on the spec.

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_seq_spec",
  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_seq_spec(
  num_hidden = 2,
  hidden_units = 64,
  fit_epochs = 10,
  learn_rate = 0.01
)

print(model_spec)
remove_keras_spec("my_mlp_seq_spec")
}

Description

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

Usage

extract_keras_history(object)

Arguments

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

Description

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

Usage

extract_keras_model(object)

Arguments

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

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

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

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

  # 1. Define layer blocks
  input_block <- function(model, input_shape) {
    keras_model_sequential(input_shape = input_shape)
  }
  hidden_block <- function(model, units = 32) {
    model |> layer_dense(units = units, activation = "relu")
  }
  output_block <- function(model, num_classes) {
    model |> layer_dense(units = num_classes, activation = "softmax")
  }

  # 2. Define a kerasnip model specification
  create_keras_sequential_spec(
    model_name = "my_mlp_grid_2",
    layer_blocks = list(
      input = input_block,
      hidden = hidden_block,
      output = output_block
    ),
    mode = "classification"
  )

  mlp_spec <- my_mlp_grid_2(
    hidden_units = tune(),
    compile_loss = "categorical_crossentropy",
    compile_optimizer = "adam"
  )

  # 3. Create a hyperparameter grid
  param_grid <- tibble::tibble(
    hidden_units = c(32, 64, -10)
  )

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

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

  # 6. Extract the valid grid
  valid_grid <- extract_valid_grid(compiled_grid)
  print(valid_grid)
  remove_keras_spec("my_mlp_grid_2")
}

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

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

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

  # 1. Define layer blocks
  input_block <- function(model, input_shape) {
    keras_model_sequential(input_shape = input_shape)
  }
  hidden_block <- function(model, units = 32) {
    model |> layer_dense(units = units, activation = "relu")
  }
  output_block <- function(model, num_classes) {
    model |> layer_dense(units = num_classes, activation = "softmax")
  }

  # 2. Define a kerasnip model specification
  create_keras_sequential_spec(
    model_name = "my_mlp_grid_3",
    layer_blocks = list(
      input = input_block,
      hidden = hidden_block,
      output = output_block
    ),
    mode = "classification"
  )

  mlp_spec <- my_mlp_grid_3(
    hidden_units = tune(),
    compile_loss = "categorical_crossentropy",
    compile_optimizer = "adam"
  )

  # 3. Create a hyperparameter grid
  param_grid <- tibble::tibble(
    hidden_units = c(32, 64, -10)
  )

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

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

  # 6. Inform about errors
  inform_errors(compiled_grid)
  remove_keras_spec("my_mlp_grid_3")
}

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

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, the names must match the argument names of block and each value must be the name of an upstream layer block whose output should be fed into that argument. This orientation matches the syntax (e.g., c(numeric = "processed_numerical")). This is used for blocks with multiple inputs, like a concatenation layer.

Note: Prior releases accepted the opposite orientation (c(processed_numerical = "numeric")). Existing code written in that style must flip the names/values when upgrading to this version.

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(input_a = "path_a", input_b = "path_b")
  ),
  output = inp_spec(output_block, "concatenated")
)

Description

This function provides an keras_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

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)) {
library(keras3)
library(parsnip)

# 1. Define layer blocks
input_block <- function(model, input_shape) {
  keras_model_sequential(input_shape = input_shape)
}
hidden_block <- function(model, units = 32) {
  model |> layer_dense(units = units, activation = "relu")
}
output_block <- function(model, num_classes) {
  model |> layer_dense(units = num_classes, activation = "softmax")
}

# 2. Define and fit a model ----
create_keras_sequential_spec(
  model_name = "my_mlp_tools",
  layer_blocks = list(
    input = input_block,
    hidden = hidden_block,
    output = output_block
  ),
  mode = "classification"
)

mlp_spec <- my_mlp_tools(
  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)

# 3. 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)

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

# 5. Extract the training history ----
history <- extract_keras_history(fitted_mlp)
plot(history)
remove_keras_spec("my_mlp_tools")
}

Description

workflows::add_tailor() cannot be used on a kerasnip multi-output or multistep workflow: tailor::fit() selects outcome/estimate via [[, which requires exactly one, flat, numeric column, and both a multi-output recipe (output_1 + output_2 ~ .) and a multistep model's nested .pred list-column violate that (see vignette("multi_output_postprocessing")). kerasnip_add_tailor() is a kerasnip-owned analogue that attaches a tailor post-processor to a single named output or forecast step, using kerasnip_output_view() or kerasnip_step_view() internally.

Usage

kerasnip_add_tailor(x, tailor, output = NULL, step = NULL, var = NULL)

Arguments

Details

At fit() time, the underlying model is trained as usual; the relevant view is then used to fit the tailor against that output's/step's predictions (on data_calibration if supplied, otherwise on data, mirroring workflows::add_tailor()'s data-usage convention). At predict() time, the full prediction is generated, the target output's/step's value(s) are replaced with the tailor-adjusted values, and everything else (other outputs; other steps in the same nested tibble) is left untouched.

Exactly one of output or step must be supplied: output for a multi-output model, step (and var, if more than one variable is forecast) for a multistep model.

Value

A kerasnip_tailored_workflow, to be trained with fit().

Examples

## Not run: 
tlr <- tailor::tailor() |> tailor::adjust_numeric_calibration()

# multi-output
tailored_wf <- kerasnip_add_tailor(wf, tlr, output = "output_1")

# multistep
tailored_wf <- kerasnip_add_tailor(wf, tlr, step = 2)

fit_obj <- fit(tailored_wf, data = train_data, data_calibration = cal_data)
predict(fit_obj, new_data = test_data)

## End(Not run)

Description

tailor and probably are built around models with a single outcome column and a single .pred/.pred_class prediction column. A kerasnip multi-output model (e.g. a recipe with output_1 + output_2 ~ .) instead produces .pred_output_1, .pred_output_2, ... columns from multiple truth columns in one fit — the standard parsnip::maybe_multivariate() shape, but one tailor::fit()/workflows::add_tailor() call cannot consume it (it selects outcome/estimate via [[, which requires exactly one column).

kerasnip_output_view() wraps a fitted multi-output workflow together with one output name, presenting it as if it were an ordinary single-output fit: predict() returns standard .pred / .pred_class / ⁠.pred_<level>⁠ columns for that output alone, letting you calibrate or post-process each output separately with the usual tailor/probably calls (see vignette("multi_output_postprocessing")).

Usage

kerasnip_output_view(x, output)

Arguments

Value

A kerasnip_output_view object.

Examples

## Not run: 
fit_obj <- fit(wf, data = train_data) # wf predicts output_1 and output_2
view_1 <- kerasnip_output_view(fit_obj, "output_1")
predict(view_1, new_data = test_data) # -> a single `.pred` column

## End(Not run)

Description

A multistep model's per-step outcome columns (e.g. lead_2_value) are engineered by step_lead() from a single raw column, so they are not present in a user's raw data the way genuine multi-output columns are. This re-bakes the fitted recipe on new_data to recover the actual future value at kerasnip_step_view()'s step, for calibration/interval use. Rows too close to the end of new_data for the lead to be computed return NA (dropped automatically by calibration routines that call sort()/stats::complete.cases() on the result).

Usage

kerasnip_step_truth(view, new_data)

Arguments

Value

A numeric vector, one truth value per row of new_data.

Description

A kerasnip multistep (vector-valued) regression model returns a nested .pred list-column: one inner tibble per row, with a .step column plus one prediction column per forecasted variable. tailor/probably expect a single flat numeric .pred column instead — tailor::check_variable_type() requires is.numeric() on the outcome/estimate columns, which a list-column fails outright.

kerasnip_step_view() wraps a fitted multistep workflow together with one forecast step (and, if more than one variable is forecast, which variable), presenting it as an ordinary single-output fit: predict() returns a flat .pred column for that step alone.

Usage

kerasnip_step_view(x, step, var = NULL)

Arguments

Details

Unlike kerasnip_output_view(), a multistep model's per-step outcome columns (e.g. lead_1_value) are recipe-engineered from a single raw column via step_lead() — they are not present in a user's raw data the way genuine multi-output columns are. kerasnip_step_truth() recovers the true future value at a given step by re-baking the fitted recipe on raw data, which is what int_conformal_split() uses internally for this class.

probably::int_conformal_full() is also supported (see int_conformal_full.kerasnip_step_view()), with a materially different design from kerasnip_output_view()'s: refitting for a candidate value at this step means substituting it into the single raw column step_lead() derives every step's truth from, which shifts every nearby row's target too. It is only supported when step_lead() and step_sequence() share a single source column, matching all of kerasnip's own multistep examples.

Value

A kerasnip_step_view object.

Examples

## Not run: 
fit_obj <- fit(wf, data = train_data) # a multistep forecasting workflow
step_2 <- kerasnip_step_view(fit_obj, step = 2)
predict(step_2, new_data = test_data) # -> a single `.pred` column

## End(Not run)

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

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()

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

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()

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

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")
}

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

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")
  !model_exists("my_temp_model")
}

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

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 (the selected column names), value (the name of the destination list-column), and id (the step identifier).

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)

Description

step_lead() creates a specification of a recipe step that will create one or more new columns of data that are leading (i.e. future) values of existing columns. This is the target-side companion to ⁠[recipes::step_lag()]⁠ (which only supports positive lag/past values, not lead/future values) and is intended for building multi-step-ahead forecasting targets, e.g. step_lead(y, lead = 1:6) produces the next six values of y as separate columns, one per row.

Usage

step_lead(
  recipe,
  ...,
  lead = 1,
  prefix = "lead_",
  default = NA,
  role = "outcome",
  trained = FALSE,
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = recipes::rand_id("lead")
)

Arguments

Details

Combine with ⁠[recipes::step_naomit()]⁠ (e.g. step_naomit(starts_with(prefix))) to drop the trailing rows that don't have a full future window, mirroring how ⁠[step_sequence()]⁠'s padding = "drop" removes rows lacking a full past window.

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 (the selected column names), value (the name of the resulting leading column), and id (the step identifier).

Examples

library(recipes)

dat <- data.frame(y = 1:10)

rec <- recipe(y ~ ., data = dat) %>%
  step_lead(y, lead = 1:2) %>%
  prep()

bake(rec, new_data = NULL)

Description

step_sequence() creates a specification of a recipe step that converts one or more ordered numeric predictor columns into a single list-column of ⁠(timesteps, features)⁠ matrices, one per row. This is the shape expected by recurrent layer blocks (e.g. keras3::layer_lstm(), keras3::layer_gru()) used with create_keras_functional_spec() or create_keras_sequential_spec().

Usage

step_sequence(
  recipe,
  ...,
  timesteps,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  new_col = "sequence_matrix",
  padding = c("drop", "zero"),
  skip = FALSE,
  id = recipes::rand_id("sequence")
)

Arguments

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 (the selected column names), value (the name of the destination list-column), timesteps, and id (the step identifier).

Examples

library(recipes)

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

rec <- recipe(y ~ ., data = dat) %>%
  step_sequence(x1, x2, timesteps = 3, new_col = "window") %>%
  prep()

bake(rec, new_data = NULL)