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BartPy

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Introduction

BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1].

Reasons to use BART

Reasons to use the library:

Trade offs:

How to use:

There are two main APIs for BaryPy:

  1. High level sklearn API
  2. Low level access for implementing custom conditions

If possible, it is recommended to use the sklearn API until you reach something that can't be implemented that way. The API is easier, shared with other models in the ecosystem, and allows simpler porting to other models.

Sklearn API

The high level API works as you would expect

from bartpy.sklearnmodel import SklearnModel
model = SklearnModel() # Use default parameters
model.fit(X, y) # Fit the model
predictions = model.predict() # Make predictions on the train set
out_of_sample_predictions = model.predict(X_test) # Make predictions on new data

The model object can be used in all of the standard sklearn tools, e.g. cross validation and grid search

from bartpy.sklearnmodel import SklearnModel
model = SklearnModel() # Use default parameters
cross_validate(model)
Extensions

BartPy offers a number of convenience extensions to base BART. The most prominent of these is using BART to predict the residuals of a base model. It is most natural to use a linear model as the base, but any sklearn compatible model can be used

from bartpy.extensions.baseestimator import ResidualBART
model = ResidualBART(base_estimator=LinearModel())
model.fit(X, y)

A nice feature of this is that we can combine the interpretability of a linear model with the power of a trees model

Lower level API

BartPy is designed to expose all of its internals, so that it can be extended and modifier. In particular, using the lower level API it is possible to:

Some care is recommended when working with these type of changes. Through time the process of changing them will become easier, but today they are somewhat complex

If all you want to customize are things like priors and number of trees, it is much easier to use the sklearn API

Alternative libraries

References

[1] https://arxiv.org/abs/0806.3286 [2] http://www.gatsby.ucl.ac.uk/~balaji/pgbart_aistats15.pdf [3] https://arxiv.org/ftp/arxiv/papers/1309/1309.1906.pdf [4] https://cran.r-project.org/web/packages/BART/vignettes/computing.pdf