Imbalanced Data Handling with mlr3

This use case compares different approaches to handle class imbalance for the optdigits binary classification data set.

Intro

This use case compares different approaches to handle class imbalance for the Optical Recognition of Handwritten Digits (optdigits) binary classification data set using the mlr3 package. We mainly focus on undersampling the majority class, oversampling the minority class, and the SMOTE imbalance correction (Chawla et al. 2002) that enriches the minority class with synthetic data. The use case requires prior knowledge in basic ML concepts (issues imbalanced data, hyperparameter tuning, nested cross-validation). The R packages mlr3, mlr3pipelines and mlr3tuning will be used. You can find most of the content here also in the mlr3book explained in a more detailed way.

These steps are performed:

• Retrieve data sets from OpenML
• Define imbalance correction pipeline Graphs (undersampling, oversampling and SMOTE) with mlr3pipelines
• Autotune the Graph together with a learner using mlr3tuning
• Benchmark the autotuned Graph and visualize the results using mlr3viz

Prerequisites

We load the most important packages for this post.

library(mlr3verse)
library(mlr3learners)
library(OpenML)

We initialize the random number generator with a fixed seed for reproducibility, and decrease the verbosity of the logger to keep the output clearly represented.

set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")

Retrieve data sets from OpenML

OpenML.org is an open machine learning platform, which allows users to share data, code and machine learning experiments. The OpenML R package can query available data sets using a filter-like approach by providing desired dataset characteristics like number.of.classes or number.of.features.

# get list of curated binary classification data sets (see https://arxiv.org/abs/1708.03731v2)
ds = listOMLDataSets(
  number.of.classes = 2,
  number.of.features = c(1, 100),
  number.of.instances = c(5000, 10000)
)
# select imbalanced data sets (without categorical features as SMOTE cannot handle them)
ds = subset(ds, minority.class.size / number.of.instances < 0.2 &
  number.of.symbolic.features == 1)
ds

# pick one data set from list above
d = getOMLDataSet(980)
d

After downloading the chosen data set, we create an mlr3 classification task:

# make sure target is a factor and create mlr3 tasks
data = as.data.frame(d)
data[[d$target.features]] = as.factor(data[[d$target.features]])
task = as_task_classif(data, id = d$desc$name, target = d$target.features)
task

Please note that the optdigits dataset is also included in the mlr3data package where you can get the preprocessed (integers properly encoded as such, etc.) data via:

data("optdigits", package = "mlr3data")
task = as_task_classif(optdigits, target = "binaryclass", positive = "P")

Quick overview of the data:

skimr::skim(task$data())

Table 1: Data summary

Name	task$data()
Number of rows	5620
Number of columns	65
Key	NULL
_______________________
Column type frequency:
factor	1
numeric	64
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
binaryclass	0	1	FALSE	2	N: 5048, P: 572

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
input1	0	1	0.00	0.00	0	0	0	0	0	▁▁▇▁▁
input10	0	1	1.97	3.10	0	0	0	3	16	▇▁▁▁▁
input11	0	1	10.51	5.43	0	6	12	15	16	▃▂▂▂▇
input12	0	1	11.80	4.00	0	9	13	15	16	▁▂▂▃▇
input13	0	1	10.51	4.79	0	7	11	15	16	▂▂▃▃▇
input14	0	1	8.26	5.97	0	2	9	14	16	▇▂▃▃▇
input15	0	1	2.09	3.92	0	0	0	2	16	▇▁▁▁▁
input16	0	1	0.14	0.94	0	0	0	0	15	▇▁▁▁▁
input17	0	1	0.00	0.10	0	0	0	0	5	▇▁▁▁▁
input18	0	1	2.60	3.49	0	0	1	4	16	▇▂▁▁▁
input19	0	1	9.68	5.83	0	4	12	15	16	▅▂▂▃▇
input2	0	1	0.30	0.88	0	0	0	0	8	▇▁▁▁▁
input20	0	1	6.82	5.88	0	1	6	12	16	▇▃▂▂▅
input21	0	1	7.16	6.15	0	1	6	13	16	▇▂▂▂▆
input22	0	1	7.97	6.26	0	0	9	14	16	▇▂▂▃▇
input23	0	1	1.96	3.48	0	0	0	3	16	▇▁▁▁▁
input24	0	1	0.05	0.44	0	0	0	0	8	▇▁▁▁▁
input25	0	1	0.00	0.03	0	0	0	0	1	▇▁▁▁▁
input26	0	1	2.38	3.11	0	0	1	4	16	▇▂▁▁▁
input27	0	1	9.19	6.15	0	3	11	15	16	▅▂▂▂▇
input28	0	1	9.03	5.90	0	4	10	15	16	▅▂▂▃▇
input29	0	1	9.75	6.24	0	3	12	16	16	▅▁▂▂▇
input3	0	1	5.39	4.67	0	1	5	9	16	▇▃▃▂▂
input30	0	1	7.77	5.96	0	1	8	14	16	▇▃▃▃▇
input31	0	1	2.33	3.64	0	0	0	4	16	▇▁▁▁▁
input32	0	1	0.00	0.06	0	0	0	0	2	▇▁▁▁▁
input33	0	1	0.00	0.03	0	0	0	0	1	▇▁▁▁▁
input34	0	1	2.14	3.30	0	0	0	4	15	▇▁▁▁▁
input35	0	1	7.66	6.28	0	0	8	14	16	▇▂▂▂▇
input36	0	1	9.18	6.22	0	3	11	16	16	▅▂▂▂▇
input37	0	1	10.33	5.92	0	6	13	16	16	▃▁▂▂▇
input38	0	1	9.05	5.88	0	4	10	15	16	▅▂▂▃▇
input39	0	1	2.91	3.50	0	0	1	6	14	▇▂▂▁▁
input4	0	1	11.82	4.26	0	10	13	15	16	▁▁▂▃▇
input40	0	1	0.00	0.00	0	0	0	0	0	▁▁▇▁▁
input41	0	1	0.02	0.27	0	0	0	0	7	▇▁▁▁▁
input42	0	1	1.46	2.95	0	0	0	2	16	▇▁▁▁▁
input43	0	1	6.59	6.52	0	0	4	14	16	▇▁▁▂▅
input44	0	1	7.20	6.46	0	0	7	14	16	▇▂▂▂▆
input45	0	1	7.84	6.30	0	1	8	14	16	▇▂▂▂▇
input46	0	1	8.53	5.77	0	3	9	14	16	▆▂▃▃▇
input47	0	1	3.49	4.36	0	0	1	7	16	▇▂▂▁▁
input48	0	1	0.02	0.25	0	0	0	0	6	▇▁▁▁▁
input49	0	1	0.01	0.25	0	0	0	0	10	▇▁▁▁▁
input5	0	1	11.58	4.46	0	9	13	15	16	▁▁▂▃▇
input50	0	1	0.78	1.93	0	0	0	0	16	▇▁▁▁▁
input51	0	1	7.75	5.66	0	2	8	13	16	▇▃▃▅▇
input52	0	1	9.77	5.17	0	6	10	15	16	▃▃▃▃▇
input53	0	1	9.65	5.31	0	5	10	15	16	▃▃▃▃▇
input54	0	1	9.12	5.97	0	3	11	15	16	▅▂▂▃▇
input55	0	1	3.74	4.91	0	0	1	7	16	▇▂▁▁▁
input56	0	1	0.17	0.84	0	0	0	0	13	▇▁▁▁▁
input57	0	1	0.00	0.02	0	0	0	0	1	▇▁▁▁▁
input58	0	1	0.28	0.93	0	0	0	0	10	▇▁▁▁▁
input59	0	1	5.76	5.02	0	1	5	10	16	▇▃▃▂▂
input6	0	1	5.59	5.63	0	0	4	10	16	▇▂▂▂▃
input60	0	1	11.99	4.35	0	10	13	15	16	▁▁▁▃▇
input61	0	1	11.57	4.98	0	9	13	16	16	▂▁▁▃▇
input62	0	1	6.72	5.82	0	0	6	12	16	▇▂▂▃▅
input63	0	1	2.09	4.05	0	0	0	2	16	▇▁▁▁▁
input64	0	1	0.25	1.42	0	0	0	0	16	▇▁▁▁▁
input7	0	1	1.38	3.36	0	0	0	0	16	▇▁▁▁▁
input8	0	1	0.14	1.05	0	0	0	0	16	▇▁▁▁▁
input9	0	1	0.00	0.09	0	0	0	0	5	▇▁▁▁▁

Imbalance correction

In mlr3pipelines , there is a class balancing and a smote pipe operator that can be combined with any learner. Below, we define the undersampling, oversampling and SMOTE PipeOps/Graph. All three imbalance correction methods have hyperparameters to control the degree of class imbalance. We apply the PipeOps/Graph to the current task with specific hyperparameter values to see how the class balance changes:

# check original class balance
table(task$truth())

   P    N 
 572 5048 

# undersample majority class (relative to majority class)
po_under = po("classbalancing",
  id = "undersample", adjust = "major",
  reference = "major", shuffle = FALSE, ratio = 1 / 6)
# reduce majority class by factor '1/ratio'
table(po_under$train(list(task))$output$truth())

  P   N 
572 841 

# oversample majority class (relative to majority class)
po_over = po("classbalancing",
  id = "oversample", adjust = "minor",
  reference = "minor", shuffle = FALSE, ratio = 6)
# enrich minority class by factor 'ratio'
table(po_over$train(list(task))$output$truth())

   P    N 
3432 5048 

Note that the original SMOTE algorithm only accepts numeric features (see smotefamily::SMOTE). To keep it simple, we therefore preprocess the data and coerce integers to numerics prior to running SMOTE and reverse this afterwards:

# SMOTE enriches the minority class with synthetic data
gr_smote =
  po("colapply", id = "int_to_num",
    applicator = as.numeric, affect_columns = selector_type("integer")) %>>%
  po("smote", dup_size = 6) %>>%
  po("colapply", id = "num_to_int",
    applicator = function(x) as.integer(round(x, 0L)), affect_columns = selector_type("numeric"))
# enrich minority class by factor (dup_size + 1)
table(gr_smote$train(task)[[1L]]$truth())

   P    N 
4004 5048 

Construct AutoTuner

We combine the PipeOps/Graph with a learner (here ranger) to make each pipeline graph behave like a learner:

# create random forest learner
learner = lrn("classif.ranger", num.trees = 50)

# combine learner with pipeline graph
learner_under = as_learner(po_under %>>% learner)
learner_under$id = "undersample.ranger"
learner_over = as_learner(po_over %>>% learner)
learner_over$id = "oversample.ranger"
learner_smote = as_learner(gr_smote %>>% learner)
learner_smote$id = "smote.ranger"

We define the search space in order to tune the hyperparameters of the class imbalance methods.

# define parameter search space for each method
search_space_under = ps(undersample.ratio = p_dbl(1 / 6, 1))
search_space_over = ps(oversample.ratio = p_dbl(1, 6))
search_space_smote = ps(
  smote.dup_size = p_int(1, 6),
  smote.K = p_int(1, 6),
  # makes sure we use numbers to the power of two to better explore the parameter space
  .extra_trafo = function(x, param_set) {
    x$smote.K = round(2^(x$smote.K))
    x
  }
)

We create an AutoTuner class from the learner to tune the graph (random forest learner + imbalance correction method) based on a 3-fold cross-validation using the F-beta Score as performance measure. To keep runtime low, we define the search space only for the imbalance correction method. However, one can also jointly tune the hyperparameter of the learner along with the imbalance correction method by extending the search space with the learner’s hyperparameters. Note that SMOTE has two hyperparameters K and dup_size. While K changes the behavior of the SMOTE algorithm, dup_size will affect oversampling rate. To focus on the effect of the oversampling rate on the performance, we will consider SMOTE with K = 2 as a different imbalance correction method as SMOTE with K = 4 (and so on). Hence, we use grid search with 5 different hyperparameter configurations for the undersampling method, the oversampling method and each SMOTE variant for tuning:

inner_cv3 = rsmp("cv", folds = 3)
measure = msr("classif.fbeta")

learns = list(
  auto_tuner(
    method = "grid_search",
    learner = learner_under,
    resampling = inner_cv3,
    measure = measure,
    search_space = search_space_under,
    resolution = 6
  ),
  auto_tuner(
    method = "grid_search",
    learner = learner_over,
    resampling = inner_cv3,
    measure = measure,
    search_space = search_space_over,
    resolution = 6
  ),
  auto_tuner(
    method = "grid_search",
    learner = learner_smote,
    resampling = inner_cv3,
    measure = measure,
    search_space = search_space_smote,
    resolution = 6
  )
)

Benchmark AutoTuner

The AutoTuner is a fully tuned graph that behaves like a usual learner. For the tuning a 3-fold CV is used. Now, we use the benchmark() function to compare the tuned class imbalance pipeline graphs based on a holdout for the outer evaluation:

outer_holdout = rsmp("holdout")
design = benchmark_grid(
  tasks = task,
  learners = learns,
  resamplings = outer_holdout
)
print(design)

                task         learner              resampling
1: <TaskClassif[48]> <AutoTuner[40]> <ResamplingHoldout[19]>
2: <TaskClassif[48]> <AutoTuner[40]> <ResamplingHoldout[19]>
3: <TaskClassif[48]> <AutoTuner[40]> <ResamplingHoldout[19]>

# NOTE: This code runs about 5 minutes
bmr = benchmark(design, store_models = TRUE)

Visualize benchmark results

bmr$aggregate(measure)

   nr      resample_result   task_id               learner_id resampling_id iters classif.fbeta
1:  1 <ResampleResult[20]> optdigits undersample.ranger.tuned       holdout     1     0.9576060
2:  2 <ResampleResult[20]> optdigits  oversample.ranger.tuned       holdout     1     0.9571788
3:  3 <ResampleResult[20]> optdigits       smote.ranger.tuned       holdout     1     0.9622166

# one value per boxplot since we used holdout as outer resampling
autoplot(bmr, measure = measure)

Visualize the tuning path

With store_models = TRUE we allow the benchmark() function to store each single model that was computed during tuning. Therefore, we can plot the tuning path of the best learner from the subsampling iterations:

library(ggplot2)
bmr_data_learners = as.data.table(bmr)$learner
utune_path = bmr_data_learners[[1]]$model$tuning_instance$archive$data
utune_gg = ggplot(utune_path, aes(x = undersample.ratio, y = classif.fbeta)) +
  geom_point(size = 3) +
  geom_line() + ylim(0.9, 1)

otune_path = bmr_data_learners[[2]]$model$tuning_instance$archive$data
otune_gg = ggplot(otune_path, aes(x = oversample.ratio, y = classif.fbeta)) +
  geom_point(size = 3) +
  geom_line() + ylim(0.9, 1)

stune_path = bmr_data_learners[[3]]$model$tuning_instance$archive$data
stune_gg = ggplot(stune_path, aes(
  x = smote.dup_size,
  y = classif.fbeta, col = factor(smote.K))) +
  geom_point(size = 3) +
  geom_line() + ylim(0.9, 1)

library(ggpubr)
ggarrange(utune_gg, otune_gg, stune_gg, common.legend = TRUE, nrow = 1)

The results show that oversampling the minority class (for simple oversampling as well as for SMOTE) and undersampling the majority class yield a better performance for this specific data set.

Conclusion

In this post, we tuned and compared 5 different settings of sampling ratios for the undersampling method, the oversampling method and different SMOTE variants (using different values of K nearest neighbors during the sampling process). If you want to know more, read the mlr3book and the documentation of the mentioned packages.

Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16 (June): 321–57. https://doi.org/10.1613/jair.953.