Iterative Bayesian optimization of a classification model

model tuning
Bayesian optimization
SVMs

Identify the best hyperparameters for a model using Bayesian optimization of iterative search.

Introduction

To use code in this article, you will need to install the following packages: kernlab, modeldata, themis, and tidymodels.

Many of the examples for model tuning focus on grid search. For that method, all the candidate tuning parameter combinations are defined prior to evaluation. Alternatively, iterative search can be used to analyze the existing tuning parameter results and then predict which tuning parameters to try next.

There are a variety of methods for iterative search and the focus in this article is on Bayesian optimization. For more information on this method, these resources might be helpful:

Cell segmenting revisited

To demonstrate this approach to tuning models, let’s return to the cell segmentation data from the Getting Started article on resampling:

library(tidymodels)
library(modeldata)

# Load data
data(cells)

set.seed(2369)
tr_te_split <- initial_split(cells %>% select(-case), prop = 3/4)
cell_train <- training(tr_te_split)
cell_test  <- testing(tr_te_split)

set.seed(1697)
folds <- vfold_cv(cell_train, v = 10)

The tuning scheme

Since the predictors are highly correlated, we can used a recipe to convert the original predictors to principal component scores. There is also slight class imbalance in these data; about 64% of the data are poorly segmented. To mitigate this, the data will be down-sampled at the end of the pre-processing so that the number of poorly and well segmented cells occur with equal frequency. We can use a recipe for all this pre-processing, but the number of principal components will need to be tuned so that we have enough (but not too many) representations of the data.

library(themis)

cell_pre_proc <-
  recipe(class ~ ., data = cell_train) %>%
  step_YeoJohnson(all_predictors()) %>%
  step_normalize(all_predictors()) %>%
  step_pca(all_predictors(), num_comp = tune()) %>%
  step_downsample(class)

In this analysis, we will use a support vector machine to model the data. Let’s use a radial basis function (RBF) kernel and tune its main parameter (\(\sigma\)). Additionally, the main SVM parameter, the cost value, also needs optimization.

svm_mod <-
  svm_rbf(mode = "classification", cost = tune(), rbf_sigma = tune()) %>%
  set_engine("kernlab")

These two objects (the recipe and model) will be combined into a single object via the workflow() function from the workflows package; this object will be used in the optimization process.

svm_wflow <-
  workflow() %>%
  add_model(svm_mod) %>%
  add_recipe(cell_pre_proc)

From this object, we can derive information about what parameters are slated to be tuned. A parameter set is derived by:

svm_set <- extract_parameter_set_dials(svm_wflow)
svm_set
#> Collection of 3 parameters for tuning
#> 
#>  identifier      type    object
#>        cost      cost nparam[+]
#>   rbf_sigma rbf_sigma nparam[+]
#>    num_comp  num_comp nparam[+]

The default range for the number of PCA components is rather small for this data set. A member of the parameter set can be modified using the update() function. Let’s constrain the search to one to twenty components by updating the num_comp parameter. Additionally, the lower bound of this parameter is set to zero which specifies that the original predictor set should also be evaluated (i.e., with no PCA step at all):

svm_set <- 
  svm_set %>% 
  update(num_comp = num_comp(c(0L, 20L)))

Sequential tuning

Bayesian optimization is a sequential method that uses a model to predict new candidate parameters for assessment. When scoring potential parameter value, the mean and variance of performance are predicted. The strategy used to define how these two statistical quantities are used is defined by an acquisition function.

For example, one approach for scoring new candidates is to use a confidence bound. Suppose accuracy is being optimized. For a metric that we want to maximize, a lower confidence bound can be used. The multiplier on the standard error (denoted as \(\kappa\)) is a value that can be used to make trade-offs between exploration and exploitation.

  • Exploration means that the search will consider candidates in untested space.

  • Exploitation focuses in areas where the previous best results occurred.

The variance predicted by the Bayesian model is mostly spatial variation; the value will be large for candidate values that are not close to values that have already been evaluated. If the standard error multiplier is high, the search process will be more likely to avoid areas without candidate values in the vicinity.

We’ll use another acquisition function, expected improvement, that determines which candidates are likely to be helpful relative to the current best results. This is the default acquisition function. More information on these functions can be found in the package vignette for acquisition functions.

set.seed(12)
search_res <-
  svm_wflow %>% 
  tune_bayes(
    resamples = folds,
    # To use non-default parameter ranges
    param_info = svm_set,
    # Generate five at semi-random to start
    initial = 5,
    iter = 50,
    # How to measure performance?
    metrics = metric_set(roc_auc),
    control = control_bayes(no_improve = 30, verbose = TRUE)
  )
#> 
#> ❯  Generating a set of 5 initial parameter results
#> ✓ Initialization complete
#> 
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> ✓ Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
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#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
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#> i Gaussian process model
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#> i Generating 5000 candidates
#> i Predicted candidates
#> i Estimating performance
#> ✓ Estimating performance
#> ! No improvement for 30 iterations; returning current results.

The resulting tibble is a stacked set of rows of the rsample object with an additional column for the iteration number:

search_res
#> # Tuning results
#> # 10-fold cross-validation 
#> # A tibble: 410 × 5
#>    splits             id     .metrics         .notes           .iter
#>    <list>             <chr>  <list>           <list>           <int>
#>  1 <split [1362/152]> Fold01 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  2 <split [1362/152]> Fold02 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  3 <split [1362/152]> Fold03 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  4 <split [1362/152]> Fold04 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  5 <split [1363/151]> Fold05 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  6 <split [1363/151]> Fold06 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  7 <split [1363/151]> Fold07 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  8 <split [1363/151]> Fold08 <tibble [5 × 7]> <tibble [0 × 3]>     0
#>  9 <split [1363/151]> Fold09 <tibble [5 × 7]> <tibble [0 × 3]>     0
#> 10 <split [1363/151]> Fold10 <tibble [5 × 7]> <tibble [0 × 3]>     0
#> # ℹ 400 more rows

As with grid search, we can summarize the results over resamples:

estimates <- 
  collect_metrics(search_res) %>% 
  arrange(.iter)

estimates
#> # A tibble: 45 × 10
#>        cost    rbf_sigma num_comp .metric .estimator  mean     n std_err .config
#>       <dbl>        <dbl>    <int> <chr>   <chr>      <dbl> <int>   <dbl> <chr>  
#>  1  0.233        6.26e-7        6 roc_auc binary     0.346    10  0.115  Prepro…
#>  2  2.74         3.06e-4       17 roc_auc binary     0.873    10  0.0128 Prepro…
#>  3  0.00140      3.26e-9       14 roc_auc binary     0.237    10  0.0586 Prepro…
#>  4  0.0185       4.40e-2        8 roc_auc binary     0.881    10  0.0119 Prepro…
#>  5  4.36         7.49e-5        1 roc_auc binary     0.773    10  0.0106 Prepro…
#>  6  0.114        3.39e-3        9 roc_auc binary     0.875    10  0.0122 Iter1  
#>  7 31.8          3.82e-2       15 roc_auc binary     0.855    10  0.0137 Iter2  
#>  8  0.00176      9.67e-1        5 roc_auc binary     0.872    10  0.0124 Iter3  
#>  9  0.00115      1.20e-3       19 roc_auc binary     0.347    10  0.115  Iter4  
#> 10  0.306        2.97e-1       13 roc_auc binary     0.868    10  0.0126 Iter5  
#> # ℹ 35 more rows
#> # ℹ 1 more variable: .iter <int>

The best performance of the initial set of candidate values was AUC = 0.8805091. The best results were achieved at iteration 10 with a corresponding AUC value of 0.8917785. The five best results are:

show_best(search_res, metric = "roc_auc")
#> # A tibble: 5 × 10
#>    cost rbf_sigma num_comp .metric .estimator  mean     n std_err .config .iter
#>   <dbl>     <dbl>    <int> <chr>   <chr>      <dbl> <int>   <dbl> <chr>   <int>
#> 1 0.935    0.0223       11 roc_auc binary     0.892    10  0.0103 Iter10     10
#> 2 1.23     0.0213       13 roc_auc binary     0.892    10  0.0103 Iter22     22
#> 3 0.806    0.0767       17 roc_auc binary     0.890    10  0.0101 Iter37     37
#> 4 0.301    0.0892       13 roc_auc binary     0.889    10  0.0110 Iter40     40
#> 5 0.141    0.0448        9 roc_auc binary     0.889    10  0.0112 Iter19     19

A plot of the search iterations can be created via:

autoplot(search_res, type = "performance")

There are many parameter combinations have roughly equivalent results.

How did the parameters change over iterations?

autoplot(search_res, type = "parameters") + 
  labs(x = "Iterations", y = NULL)

Session information

#> ─ Session info ─────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.3 (2024-02-29)
#>  os       macOS Sonoma 14.4.1
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Los_Angeles
#>  date     2024-03-26
#>  pandoc   2.17.1.1 @ /opt/homebrew/bin/ (via rmarkdown)
#> 
#> ─ Packages ─────────────────────────────────────────────────────────
#>  package    * version date (UTC) lib source
#>  broom      * 1.0.5   2023-06-09 [1] CRAN (R 4.3.0)
#>  dials      * 1.2.1   2024-02-22 [1] CRAN (R 4.3.1)
#>  dplyr      * 1.1.4   2023-11-17 [1] CRAN (R 4.3.1)
#>  ggplot2    * 3.5.0   2024-02-23 [1] CRAN (R 4.3.1)
#>  infer      * 1.0.7   2024-03-25 [1] CRAN (R 4.3.1)
#>  kernlab    * 0.9-32  2023-01-31 [1] CRAN (R 4.3.0)
#>  modeldata  * 1.3.0   2024-01-21 [1] CRAN (R 4.3.1)
#>  parsnip    * 1.2.1   2024-03-22 [1] CRAN (R 4.3.1)
#>  purrr      * 1.0.2   2023-08-10 [1] CRAN (R 4.3.0)
#>  recipes    * 1.0.10  2024-02-18 [1] CRAN (R 4.3.1)
#>  rlang      * 1.1.3   2024-01-10 [1] CRAN (R 4.3.1)
#>  rsample    * 1.2.1   2024-03-25 [1] CRAN (R 4.3.1)
#>  themis     * 1.0.2   2023-08-14 [1] CRAN (R 4.3.0)
#>  tibble     * 3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>  tidymodels * 1.2.0   2024-03-25 [1] CRAN (R 4.3.1)
#>  tune       * 1.2.0   2024-03-20 [1] CRAN (R 4.3.1)
#>  workflows  * 1.1.4   2024-02-19 [1] CRAN (R 4.3.1)
#>  yardstick  * 1.3.1   2024-03-21 [1] CRAN (R 4.3.1)
#> 
#>  [1] /Users/emilhvitfeldt/Library/R/arm64/4.3/library
#>  [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
#> 
#> ────────────────────────────────────────────────────────────────────
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