aGTBoost

Adaptive and automatic gradient tree boosting computations

aGTBoost is a lightning fast gradient boosting library designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations are in the thousands while mathematical and technical knowledge required on the user are minimized.

Note: Currently for academic purposes: Implementing and testing new innovations w.r.t. information theoretic choices of GTB-complexity. See below for to-do research list.

Installation

R: Finally on CRAN! Install the stable version with

install.packages("agtboost")

or install the development version from GitHub

devtools::install_github("Blunde1/agtboost/R-package")

Users experiencing errors after warnings during installlation, may be helped by the following command prior to installation:

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")

Example code and documentation

agtboost essentially has two functions, a train function gbt.train and a predict function predict. From the code below it should be clear how to train an aGTBoost model using a design matrix x and a response vector y, write ?gbt.train in the console for detailed documentation.

library(agtboost)

# -- Load data --
data(caravan.train, package = "agtboost")
data(caravan.test, package = "agtboost")
train <- caravan.train
test <- caravan.test

# -- Model building --
mod <- gbt.train(train$y, train$x, loss_function = "logloss", verbose=10)

# -- Predictions --
prob <- predict(mod, test$x) # Score after logistic transformation: Probabilities

agtboostalso contain functions for model inspection and validation.

• Feature importance: gbt.importance generates a typical feature importance plot. Techniques like inserting noise-features are redundant due to computations w.r.t. approximate generalization (test) loss.
• Convergence: gbt.convergence computes the loss over the path of boosting iterations. Check visually for convergence on test loss.
• Model validation: gbt.ksval transforms observations to standard uniformly distributed random variables, if the model is specified correctly. Perform a formal Kolmogorov-Smirnov test and plots transformed observations for visual inspection.

# -- Feature importance --
gbt.importance(feature_names=colnames(caravan.train$x), object=mod)

# -- Model validation --
gbt.ksval(object=mod, y=caravan.test$y, x=caravan.test$x)

The functions gbt.ksval and gbt.importance create the following plots:

Furthermore, an aGTBoost model is (see example code)

• highly robust to dimensions: Comparisons to (penalized) linear regression in (very) high dimensions
• has minimal worries of overfitting: Stock market classificatin
• and can train further given previous models: Boosting from a regularized linear model

Dependencies

• My research
• Eigen Linear algebra
• Rcpp for the R-package

Scheduled updates

• Adaptive and automatic deterministic frequentist gradient tree boosting.
• Information criterion for fast histogram algorithm (non-exact search) (Fall 2020, planned)
• Adaptive L2-penalized gradient tree boosting. (Fall 2020, planned)
• Automatic stochastic gradient tree boosting. (Fall 2020/Spring 2021, planned)

Hopeful updates

• Optimal stochastic gradient tree boosting.

References

• An information criterion for automatic gradient tree boosting
• agtboost: Adaptive and Automatic Gradient Tree Boosting Computations

Contribute

Any help on the following subjects are especially welcome:

• Utilizing sparsity (possibly Eigen sparsity).
• Paralellizatin (CPU and/or GPU).
• Distribution (Python, Java, Scala, ...),
• good ideas and coding best-practices in general.

Please note that the priority is to work on and push the above mentioned scheduled updates. Patience is a virtue. :)