autoxgboostMC - Multiple-Criteria tuning and fitting of xgboost models.

Status

The software is still heavily under construction!

Examples on how to use the software be found in the vignette!

---

• Installing the development version

  # Install requirements
  install.packages("devtools")
  devtools::install_github("compstat-lmu/paper_2019_iml_measures")
  devtools::install_github("johnmyleswhite/log4r")
  devtools::install_github("mlr-org/mlrMBO")
  # Install package
  devtools::install_github("pfistfl/autoxgboostMC")

General overview

autoxgboost aims to find an optimal xgboost model automatically using the machine learning framework mlr and the bayesian optimization framework mlrMBO.

Work in progress!

AutoxgboostMC embraces R6 for a cleaner design. See the example code below for the new API.

First we split our data into train and test.

train = sample(c(TRUE, FALSE), getTaskSize(pid.task), replace = TRUE)
task_train = subsetTask(pid.task, subset = train)
task_test = subsetTask(pid.task, subset = !train)

Training and Testing

Then we start the AutoML process:

# Instantiate the object with a list of measures to optimize.
axgb = AutoxgboostMC$new(task_train, measures = list(auc, timepredict))
# Set hyperparameters (we want to work on two threads
axgb$nthread(2L)
# Fit for 5 seconds
axgb$fit(time_budget = 15L)

after searching and finding a good model, we can use the best model to predict.

p = axgb$predict(task_test)

Visualizing the Process

Several options are available for plotting:

axgb$plot_opt_path()
axgb$plot_opt_result()
axgb$plot_pareto_front()

Pipeline

AutoxgboostMC currently searches and optimizes the following Pipeline:

fix_factors %>% impact_encoding | dummy encoding %>% drop_constant_feats %>% learner %>% tune_threshold

To be added:

• Categorical Encoding using mixed models
• Imputation
• Fairness Post-Processing

Eventually:

• Ensemble Stacking
• Model Compression

autoxgboost - How to Cite

The Automatic Gradient Boosting framework was presented at the ICML/IJCAI-ECAI 2018 AutoML Workshop (poster). Please cite our ICML AutoML workshop paper on arxiv. You can get citation info via citation("autoxgboost") or copy the following BibTex entry:

@inproceedings{autoxgboost,
  title={Automatic Gradient Boosting},
  author={Thomas, Janek and Coors, Stefan and Bischl, Bernd},
  booktitle={International Workshop on Automatic Machine Learning at ICML},
  year={2018}
}