Tuning Multiple Similar Parameters: Ensuring autoplot Uniqueness

# Tuning Multiple Similar Parameters: Ensuring `autoplot` Uniqueness

## Introduction

When using `kerasnip` to define and tune Keras models within the `tidymodels` framework, you might encounter situations where you want to tune multiple parameters that, by default, map to the same underlying `dials` parameter type. A common example is tuning the number of units in multiple `layer_dense` blocks within the same model.

While `kerasnip` intelligently maps these parameters (e.g., `dense1_units` and `dense2_units` both map to `dials::hidden_units()`), this can lead to ambiguity when visualizing tuning results with `ggplot2::autoplot()`. Without a way to distinguish between these otherwise identical parameter types, `autoplot()` may produce errors or misleading plots.

This vignette demonstrates how to explicitly provide unique identifiers to your tuned parameters, ensuring `autoplot()` can correctly visualize the results for each distinct parameter.

## The Problem (Implicit)

Consider a model with two dense layers, each with a `units` parameter. If you were to define them for tuning without unique `id`s, `autoplot()` would encounter an issue because it cannot distinguish between the two parameters.

For example, if you were to run `ggplot2::autoplot(tune_res)` without unique `id`s, you might encounter an error similar to this:

    #> Error in `dplyr::rename()`:
    #> ! Names must be unique.
    #> ✖ These names are duplicated:
    #>   * "# Hidden Units" at locations 1 and 2.

This error clearly indicates that `autoplot()` is trying to rename columns for plotting, but it finds duplicate names like “# Hidden Units” because both `dense1_units` and `dense2_units` are generically identified as `hidden_units` by `dials` without further distinction. This makes it impossible for `autoplot()` to differentiate their tuning results.

## The Solution: Using Unique `id`s with `tune()`

The solution is to provide a unique `id` argument to the `tune()` function for each parameter you wish to distinguish.

Let’s define a simple sequential Keras model with two dense layers:

    library(kerasnip)
    library(keras3)
    library(parsnip)
    #>
    #> Attaching package: 'parsnip'
    #> The following object is masked from 'package:kerasnip':
    #>
    #>     get_model_env
    library(dials)
    #> Loading required package: scales
    library(workflows)
    library(recipes)
    #> Loading required package: dplyr
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    #>
    #> Attaching package: 'recipes'
    #> The following object is masked from 'package:stats':
    #>
    #>     step
    library(rsample)
    library(tune)
    library(ggplot2)

    # Define a spec with multiple hidden unit parameters
    model_name <- "autoplot_unique_spec"
    # Clean up the spec if it already exists from a previous run
    if (exists(model_name, mode = "function")) {
      suppressMessages(remove_keras_spec(model_name))
    }

    input_block <- function(model, input_shape) {
      keras3::keras_model_sequential(input_shape = input_shape)
    }

    dense_block <- function(model, units = 10) {
      model |> keras3::layer_dense(units = units)
    }

    output_block <- function(model, num_classes) {
      model |>
        keras3::layer_dense(units = num_classes, activation = "softmax")
    }

    create_keras_sequential_spec(
      model_name = model_name,
      layer_blocks = list(
        input = input_block,
        dense1 = dense_block,
        dense2 = dense_block,
        output = output_block
      ),
      mode = "classification"
    )

    # Now, create the model specification and assign unique IDs for tuning
    tune_spec <- autoplot_unique_spec(
      dense1_units = tune(id = "dense_layer_one_units"),
      dense2_units = tune(id = "dense_layer_two_units")
    ) |>
      set_engine("keras")

    print(tune_spec)
    #> autoplot unique spec Model Specification (classification)
    #>
    #> Main Arguments:
    #>   num_input = structure(list(), class = "rlang_zap")
    #>   num_dense1 = structure(list(), class = "rlang_zap")
    #>   num_dense2 = structure(list(), class = "rlang_zap")
    #>   num_output = structure(list(), class = "rlang_zap")
    #>   dense1_units = tune(id = "dense_layer_one_units")
    #>   dense2_units = tune(id = "dense_layer_two_units")
    #>   learn_rate = structure(list(), class = "rlang_zap")
    #>   fit_batch_size = structure(list(), class = "rlang_zap")
    #>   fit_epochs = structure(list(), class = "rlang_zap")
    #>   fit_callbacks = structure(list(), class = "rlang_zap")
    #>   fit_validation_split = structure(list(), class = "rlang_zap")
    #>   fit_validation_data = structure(list(), class = "rlang_zap")
    #>   fit_shuffle = structure(list(), class = "rlang_zap")
    #>   fit_class_weight = structure(list(), class = "rlang_zap")
    #>   fit_sample_weight = structure(list(), class = "rlang_zap")
    #>   fit_initial_epoch = structure(list(), class = "rlang_zap")
    #>   fit_steps_per_epoch = structure(list(), class = "rlang_zap")
    #>   fit_validation_steps = structure(list(), class = "rlang_zap")
    #>   fit_validation_batch_size = structure(list(), class = "rlang_zap")
    #>   fit_validation_freq = structure(list(), class = "rlang_zap")
    #>   fit_verbose = structure(list(), class = "rlang_zap")
    #>   fit_view_metrics = structure(list(), class = "rlang_zap")
    #>   compile_optimizer = structure(list(), class = "rlang_zap")
    #>   compile_loss = structure(list(), class = "rlang_zap")
    #>   compile_metrics = structure(list(), class = "rlang_zap")
    #>   compile_loss_weights = structure(list(), class = "rlang_zap")
    #>   compile_weighted_metrics = structure(list(), class = "rlang_zap")
    #>   compile_run_eagerly = structure(list(), class = "rlang_zap")
    #>   compile_steps_per_execution = structure(list(), class = "rlang_zap")
    #>   compile_jit_compile = structure(list(), class = "rlang_zap")
    #>   compile_auto_scale_loss = structure(list(), class = "rlang_zap")
    #>
    #> Computational engine: keras

Notice how `dense1_units` and `dense2_units` are both passed to `tune()`, but each is given a distinct `id`. This `id` acts as a label that `autoplot()` can use to differentiate the parameters.

### Setting up the Tuning Workflow

Next, we’ll set up a `tidymodels` workflow, define the parameter ranges, and create a tuning grid.

    # Set up workflow and tuning grid
    rec <- recipes::recipe(Species ~ ., data = iris)
    tune_wf <- workflows::workflow(rec, tune_spec)

    params <- tune::extract_parameter_set_dials(tune_wf)

    # Update the parameter ranges using kerasnip::hidden_units
    # The `id`s provided in tune() are automatically detected and used here.
    params <- params |>
      update(
        dense_layer_one_units = hidden_units(range = c(4L, 8L)),
        dense_layer_two_units = hidden_units(range = c(4L, 8L))
      )

    grid <- dials::grid_regular(params, levels = 2)
    control <- tune::control_grid(save_pred = FALSE, verbose = FALSE)

    print(grid)
    #> # A tibble: 4 × 2
    #>   dense_layer_one_units dense_layer_two_units
    #>                   <int>                 <int>
    #> 1                     4                     4
    #> 2                     8                     4
    #> 3                     4                     8
    #> 4                     8                     8

### Running the Tuning Process

Now, we run `tune::tune_grid` to perform the actual tuning. For demonstration purposes, we’ll use a small number of resamples and a simple dataset.

    # Run tuning
    tune_res <- tune::tune_grid(
      tune_wf,
      resamples = rsample::vfold_cv(iris, v = 2),
      grid = grid,
      control = control
    )
    #> 3/3 - 0s - 79ms/step
    #> 3/3 - 0s - 29ms/step
    #> 3/3 - 0s - 67ms/step
    #> 3/3 - 0s - 24ms/step
    #> 3/3 - 0s - 60ms/step
    #> 3/3 - 0s - 24ms/step
    #> 3/3 - 0s - 94ms/step
    #> 3/3 - 0s - 26ms/step
    #> 3/3 - 0s - 75ms/step
    #> 3/3 - 0s - 36ms/step
    #> 3/3 - 0s - 90ms/step
    #> 3/3 - 0s - 34ms/step
    #> 3/3 - 0s - 73ms/step
    #> 3/3 - 0s - 22ms/step
    #> 3/3 - 0s - 85ms/step
    #> 3/3 - 0s - 33ms/step

    print(tune_res)
    #> # Tuning results
    #> # 2-fold cross-validation
    #> # A tibble: 2 × 4
    #>   splits          id    .metrics          .notes
    #>   <list>          <chr> <list>            <list>
    #> 1 <split [75/75]> Fold1 <tibble [12 × 6]> <tibble [0 × 4]>
    #> 2 <split [75/75]> Fold2 <tibble [12 × 6]> <tibble [0 × 4]>

### Visualizing Results with `autoplot()`

With the tuning complete, we can now use `ggplot2::autoplot()` to visualize the results. Because we provided unique `id`s, `autoplot()` can correctly generate separate plots for each tuned parameter.

    # Assert that autoplot works without error
    ggplot2::autoplot(tune_res)

plot of chunk autoplot_results

As you can see, `autoplot()` successfully generates a plot showing the performance across the different values for `dense_layer_one_units` and `dense_layer_two_units` independently.

## Why Unique `id`s are Necessary

Internally, `kerasnip` maps arguments like `units` from your `layer_blocks` functions to appropriate `dials` parameter objects (e.g., `dials::hidden_units()`). When multiple such arguments exist, they all point to the same type of `dials` parameter.

The `id` argument in `tune()` serves as a unique identifier that `tune` and `ggplot2::autoplot()` use to distinguish between different instances of these parameter types. Without it, `autoplot()` would see multiple parameters of type `hidden_units` and wouldn’t know how to plot them separately, leading to errors or combining them incorrectly.

## Best Practices

- Always use unique `id`s: When tuning multiple parameters that are conceptually similar (e.g., `units` in different layers, `rate` in different dropout layers), always provide a descriptive and unique `id` to the `tune()` function.
- Descriptive `id`s: Choose `id`s that clearly indicate which part of the model the parameter belongs to (e.g., `dense_layer_one_units`, `conv_filter_size`). This improves readability and understanding of your tuning results.

By following this practice, you ensure that your `kerasnip` models are robustly tunable and that their results can be clearly visualized using the `tidymodels` ecosystem.