gbart 0.0.1

A python package to implement Variable grouping based on Bayesian additive regression tree

GBART

Introduction

GBART is a pure python package to implement our proposed algorithm Gbart, it is based on Bayesian additive regression tree(BART) framework of Chipman et al.

Through Gbart, We try to find potential grouping of variables in the sense that there is no interaction term between variables of different groups. For details please visit our recent paper.

This python package is build based on BartPy package, the pure python version of BART, for details please refer to the offical website of BartPy.

Installation

Using pip

Mannually installation

##Usage and examples ###The easiest way to run and obtain result

• Preparation

import copy
import numpy as np
import gbart.utilities as ut  # provide helper functions
import gbart.create_dataset as cd  # data generator
from gbart.groupbart import * 

• generate dataset

dataset = cd.create_friedman()
# the last column of dataset is dependent variable, the output Y.

• Build model and get accuracy by using BART.

acc_o = build_original_model(dataset)
# This function splits the whole dataset into training and testing (80% for training)
# This function returns accuracy in testing data.

• Get the grouping information

output_pair = get_pair(dataset)
# return the the grouping information. The first phase of Gbart algorithm.

• Build the gbart model

acc_g = build_group_wise_model(dataset, pair_list)
# take "output_pair" as pair_list 
# This function returns accuracy in testing data.

###To design a customization version and/or tune model parameters. Please consider the following.

• Write your own helper function instead of calling functions in groupbart.py, the only thing you may need in groupbart.py is get_pair(dataset), which will help you find the proper grouping information.

• Tune the parameters of both BART and GBART model. Here is an easy example.

import numpy as np
import gbart.utilities as ut
from gbart.modified_bartpy.sklearnmodel import SklearnModel

# Data preparation 
b = int(0.8 * np.shape(dataset)[0])  
Data_train = dataset[:b,:]
Data_predict = dataset[b:,:]
x_data = Data_train[:,:-1]
y_data = Data_train[:,-1]


# Building the model
model = SklearnModel(sublist=None,
                     n_trees=50,
                     n_chains=4,
                     n_samples=50,
                     n_burn=200,
                     thin=0.1,
                     n_jobs=1)
# This GBART model inherited BART model, a new feature named sublist is added. 
# sublist can either take "None" or list of groups of variables as input.
# when sublist is None, it will be the exact BART model.
# when sublist is a list, it will build GBART model.
# To tune other parameters, please refer to BartPy usage guide.

# fit and prediction 
model.fit(x_data, y_data)
y_pred = model.predict(Data_predict[:,:-1])
y_true = Data_predict[:,-1]
acc = ut.get_error_reg(y_pred, y_true)

Acknowledge

We truly thank Mr. Jake Coltman for his contribution to BartPy package.