Bayesian Additive Regression Trees For Python

Overview

BartPy

Build Status

Introduction

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

Reasons to use BART

  • Much less parameter optimization required that GBT
  • Provides confidence intervals in addition to point estimates
  • Extremely flexible through use of priors and embedding in bigger models

Reasons to use the library:

  • Can be plugged into existing sklearn workflows
  • Everything is done in pure python, allowing for easy inspection of model runs
  • Designed to be extremely easy to modify and extend

Trade offs:

  • Speed - BartPy is significantly slower than other BART libraries
  • Memory - BartPy uses a lot of caching compared to other approaches
  • Instability - the library is still under construction

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:

  • Customize the set of possible tree operations (prune and grow by default)
  • Control the order of sampling steps within a single Gibbs update
  • Extend the model to include additional sampling steps

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

A Python package to preprocess time series

Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate

Maximilian Christ 57 Dec 17, 2022
Machine Learning Course with Python:

A Machine Learning Course with Python Table of Contents Download Free Deep Learning Resource Guide Slack Group Introduction Motivation Machine Learnin

Instill AI 6.9k Jan 03, 2023
Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas.

Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas. Its objective is to ex

Taylor G Smith 54 Aug 20, 2022
A project based example of Data pipelines, ML workflow management, API endpoints and Monitoring.

MLOps template with examples for Data pipelines, ML workflow management, API development and Monitoring.

Utsav 33 Dec 03, 2022
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera

Intel Corporation 858 Dec 25, 2022
LightGBM + Optuna: no brainer

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Rishiraj Acharya 22 Dec 15, 2022
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Priyansh Sharma 7 Nov 09, 2022
This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing variance.

minvar_invest_portfolio This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing var

1 Jan 06, 2022
Machine-Learning with python (jupyter)

Machine-Learning with python (jupyter) 머신러닝 야학 작심 10일과 쥬피터 노트북 기반 데이터 사이언스 시작 들어가기전 https://nbviewer.org/ 페이지를 통해서 쥬피터 노트북 내용을 볼 수 있다. 위 페이지에서 현재 레포 기

HyeonWoo Jeong 1 Jan 23, 2022
Machine learning algorithms implementation

Machine learning algorithms implementation This repository consisits of implementation of various machine learning algorithms. The algorithms implemen

Karun Dawadi 1 Jan 03, 2022
Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations.

BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer

KTH Mechanics 8 Mar 31, 2022
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

2.3k Jan 04, 2023
A simple python program which predicts the success of a movie based on it's type, actor, actress and director

Movie-Success-Prediction A simple python program which predicts the success of a movie based on it's type, actor, actress and director. The program us

Mahalinga Prasad R N 1 Dec 17, 2021
ML Kaggle Titanic Problem using LogisticRegrission

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission here you will find the solution for the titanic problem on kaggle with comments and step by step c

Mahmoud Nasser Abdulhamed 3 Oct 23, 2022
Management of exclusive GPU access for distributed machine learning workloads

TensorHive is an open source tool for managing computing resources used by multiple users across distributed hosts. It focuses on granting

Paweł Rościszewski 131 Dec 12, 2022
Stacked Generalization (Ensemble Learning)

Stacking (stacked generalization) Overview ikki407/stacking - Simple and useful stacking library, written in Python. User can use models of scikit-lea

Ikki Tanaka 192 Dec 23, 2022
pymc-learn: Practical Probabilistic Machine Learning in Python

pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-

pymc-learn 196 Dec 07, 2022
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. Documenta

2.5k Jan 07, 2023