vignettes/mlr3automl.md

In a-hanf/mlr3automl: AutoML in mlr3

mlr3automl

Installation

devtools::install_github('https://github.com/mlr-org/mlr3extralearners')
devtools::install_github('https://github.com/a-hanf/mlr3automl', dependencies = TRUE)

Creating a model

mlr3automl creates classification and regression models on tabular data sets. The entry point is the AutoML function, which requires an mlr3::TaskClassif or mlr3::TaskRegr object. Creating a model on the iris data set works like this:

library(mlr3)
library(mlr3automl)

iris_task = tsk("iris")
iris_model = AutoML(iris_task)

This creates a stable Machine Learning pipeline with Logistic Regression, Random Forest and Gradient Boosting models. For supplying your own data sets, convert them into into an mlr3 Task.

Training, predictions and resampling

In the above code, our model has not been trained yet. Model training is very similar to training and predictions in mlr3. We use the train() method, which takes a vector of training indices as an additional argument.

train_indices = sample(1:iris_task$nrow, 2/3*iris_task$nrow)
iris_model$train(row_ids = train_indices)

To make predictions we use the predict() method. We can supply a held-out data set here, or alternatively use indices to specify observations that were not used during training.

predict_indices = setdiff(1:iris_task$nrow, train_indices)
predictions = iris_model$predict(row_ids = predict_indices)

In order to make this a bit more convenient, mlr3automl also provides a resampling endpoint. Instead of performing the train-test-split ourselves, we can let mlr3automl do this for us.

resampling_result = iris_model$resample()

We obtain an mlr3::ResampleResult. For more information on how to analyze this result, see the resampling section in the mlr3book.

Interpretable models

mlr3automl models can be easily interpreted using the popular iml or DALEX packages. Let’s interpret our model trained on the iris data set from the previous example:

dalex_explainer = iris_model$explain(iml_package = "DALEX")
iml_explainer = iris_model$explain(iml_package = "iml")

# compute and plot feature permutation importance using DALEX
dalex_importance = DALEX::model_parts(dalex_explainer)
plot(dalex_importance)

# partial dependency plot using iml package
iml_pdp = iml::FeatureEffect$new(iml_explainer, feature="Sepal.Width", method="pdp")
plot(iml_pdp)

Customisation

mlr3automl offers the following customization options in the AutoML function:

• custom runtime budgets: model = AutoML(task = iris_task, runtime = 180)
• custom measures: model = AutoML(task = iris_task, measure = msr("classif.logloss"))
• custom learners: model = AutoML(task = iris_task, learner_list = c("classif.svm", "classif.ranger"))
• custom resampling: model = AutoML(task = iris_task, resampling = rsmp("cv"))
• custom preprocessing - choose “none,” “stability” or “full” for predefined pipelines: model = AutoML(task = iris_task, learner_list = c("classif.ranger"), preprocessing = "none")
• custom preprocessing - supply your own Graph: see example #2 below.
• custom parameter spaces and parameter transformations - see example #3 below.

Examples

Example #1: we create a regression model with custom learners and a fixed time budget. Every hyperparameter evaluation is stopped after 10 seconds. After 300 seconds of training, tuning is stopped and the best result obtained so far is returned:

automl_model = AutoML(
  task=tsk("mtcars"),
  learner_list=c("regr.ranger", "regr.lm"),
  learner_timeout=10,
  runtime=300)

Example #2: we create a pipeline of preprocessing operators using mlr3pipelines. This pipeline replaces the preprocessing pipeline in mlr3automl. You can tune the choice of custom preprocessing operators and their associated hyperparameters by supplying additional parameters (see example #3).

library(mlr3pipelines)
imbalanced_preproc = po("imputemean") %>>%
  po("smote") %>>%
  po("classweights", minor_weight=2)

automl_model = AutoML(task=tsk("pima"),
  preprocessing = imbalanced_preproc) 

Example #3: we add a k-nearest-neighbors classifier to the learners, which has no pre-defined hyperparameter search space in mlr3automl. To perform hyperparameter tuning, we supply a parameter set using paradox. A parameter transformation is supplied in order to sample the hyperparameter on an exponential scale.

library(paradox)
new_params = ParamSet$new(list(
  ParamInt$new("classif.kknn.k",
    lower = 1, upper = 5, default = 3, tags = "kknn")))

my_trafo = function(x, param_set) {
  if ("classif.kknn.k" %in% names(x)) {
    x[["classif.kknn.k"]] = 2^x[["classif.kknn.k"]]
    }
    return(x)
}

automl_model = AutoML(
  task=tsk("iris"), 
  learner_list="classif.kknn",
  additional_params=new_params,
  custom_trafo=my_trafo)

Background

mlr3automl tackles the challenge of Automated Machine Learning from multiple angles.

Flexible preprocessing using mlr3pipelines

We tested mlr3automl on 39 challenging data sets in the AutoML Benchmark. By including up to 12 preprocessing steps, mlr3automl is stable in the presence of missing data, categorical and high cardinality features, huge data sets and constrained time budgets.

Strong and stable learning algorithms

We evaluated many learning algorithms and implementations in order to find the most stable and accurate learners for mlr3automl. We decided to use the following:

• Logistic Regression (Fan et al. 2008), which works best for small data sets or when the number of features is very high
• Random Forest (Wright and Ziegler 2017) for its high performance, fast and highly parallelised training
• Gradient Boosting (Chen and Guestrin 2016) for the same reasons as Random Forest

Static portfolio of known good pipelines

When selecting the best model for a data set, up to 8 predefined pipelines are evaluated first. These are our most robust, fast and accurate pipelines, which provide us with a strong baseline even on the most challenging tasks.

Hyperparameter Optimization with Hyperband

We use Hyperband (Li et al. 2017) to tune the hyperparameters of our machine learning pipeline. Hyperband speeds up random search through adaptive resource allocation and early-stopping. When tuning the hyperparameters in mlr3automl, at first many learners will be evaluated on small subsets of the dataset (this is quick). Later on, fewer models get trained on larger subsets or the full dataset (which is more expensive computationally). This allows us to find promising pipelines with little computational cost.

Performance and benchmarks

| Framework | avg. rank(binary tasks) | avg. rank(multi-class) | Failures | |:-----------------|:------------------------|:-----------------------|---------:| | AutoGluon | 2.32 | 2.09 | 0 | | autosklearn v0.8 | 3.34 | 3.15 | 2 | | autosklearn v2.0 | 3.57 | 4.06 | 9 | | H2O AutoML | 3.18 | 3.24 | 2 | | mlr3automl | 4.55 | 3.79 | 0 | | TPOT | 4.05 | 4.68 | 6 |

We benchmarked mlr3automl on the AutoML benchmark, which contains 39 challenging classification data sets. Under a restrictive time budget of at most 1 hour per task, mlr3automl successfully completed every single task.

References

Chen and Guestrin. 2016. “Xgboost: A Scalable Tree Boosting System.” *Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining*, 785–94.

Fan et al. 2008. “LIBLINEAR: A Library for Large Linear Classification.” *The Journal of Machine Learning Research* 9 (August): 1871–74.

Li et al. 2017. “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.” *The Journal of Machine Learning Research* 18 (1): 6765–6816.

Wright and Ziegler. 2017. “Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” *Journal of Statistical Software, Articles* 77 (1): 1–17. .

a-hanf/mlr3automl documentation built on Feb. 21, 2022, 1:06 a.m.