openbt 0.0.4

the Python interface for Dr. Matthew Pratola's OpenBT project - allows the user to perform BART (Bayesian Additive Regression Trees) fits and predictions on datasets.

Openbt

This Python package is the Python interface for Dr. Matthew Pratola's OpenBT project. Currently, its only module is openbt, which contains the OPENBT class. This class allows the user to create fit objects in a scikit-learn style.

About:

OpenBT is a flexible and extensible C++ framework for implementing Bayesian regression tree models. Currently a number of models and inference tools are available for use in the released code with additional models/tools under development. The code makes use of MPI for parallel computing. Apart from this package, an R interface is provided via the ROpenbt package to demonstrate use of the software.

How to utilize this package (and its module and class):

1. Install the package from the command line by typing:
   $ python3 -m pip install openbt==[version number you want].
2. In Python3 (or a Python script), import the OPENBT class from the openbt module by typing:
   from openbt.openbt import OPENBT.
   This gives Python access to the OPENBT class. Typing
   from openbt.openbt import *
   or
   from openbt import openbt
   would also work, but for the former, the load() function is loaded unnecesarily (unless you wish to use that function, of course). For the latter, the class would be referred to as openbt.OPENBT, not simply OPENBT.
3. To utilize the OPENBT class/functions in Python 3 to conduct and interpret fits: create a fit object such as
   m = OPENBT(model = "bart", ...).
   The fit object is an instance of the class. Here's an example of running a functions from the class:
   fitp = m.predict(preds)
4. I attempted to upload example scripts (in the "PyScripts" folder), showing the usage of the OPENBT class on data, to this package. However, if these are difficult to access, you can also simply view them at the github "Homepage" .

Example:

See branin_ex.py in the PyScripts segment of this package for the script version of this walkthrough (or, see multiple example datasets and fits on the Github Homepage).

To start, let's create a test function. A popular one is the Branin function:

# Test Branin function, rescaled
def braninsc (xx):
    x1 = xx[0]
    x2 = xx[1]
    
    x1bar = 15 * x1 - 5
    x2bar = 15 * x2
    
    import math
    term1 = x2bar - 5.1*x1bar**2/(4*math.pi**2) + 5*x1bar/math.pi - 6
    term2 = (10 - 10/(8*math.pi)) * math.cos(x1bar)
    
    y = (term1**2 + term2 - 44.81) / 51.95
    return(y)


# Simulate branin data for testing
import numpy as np
np.random.seed(99)
n = 500
p = 2
x = np.random.uniform(size=n*p).reshape(n,p)
y = np.zeros(n)
for i in range(n):
    y[i] = braninsc(x[i,])

Note that the x and y data is a numpy array - this is the intended format. Now we can load the openbt package and fit a BART model. Here we set the model type as model="bart" which ensures we fit a homoscedastic BART model. The number of MPI threads to use is specified as tc=4. For a list of all optional parameters, see m._dict__ (after creating m) or help(OPENBT).

from openbt.openbt import OPENBT, load
m = OPENBT(model = "bart", tc = 4, modelname = "branin")
fit = m.fit(x, y)

Next we can construct predictions and make a simple plot comparing our predictions to the training data. Here, we are calculating the in-sample predictions since we passed the same x array to the predict() function.

# Calculate in-sample predictions
fitp = m.predict(x, tc = 4)

# Make a simple plot
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,9)); ax = fig.add_subplot(111)
ax.plot(y, fitp['mmean'], 'ro')
ax.set_xlabel("Observed"); ax.set_ylabel("Fitted")
ax.axline([0, 0], [1, 1])

To save the model, use OPENBT's save() function. Similarly, load the model using load(). Because the posterior can be large in sample-based models such as these, the fitted model is saved in a compressed file format with the extension .obt. Additionally, the estimator object can be saved and loaded (see below).

#--------------------------------------------------------------------------------------------
# Save fitted MODEL object (not the estimator object, m) as test.obt in the working directory
m.save(fit, "test", est = False)
# Load fitted model object (AKA fit object) to a new object
fit2 = load("test", est = False)

# We can also save/load the fit ESTIMATOR object by specifying est = True in save()/load().
# The estimator object has all our settings and properties, but not fit results. 
# This is similar to scikit-learn saving/loading its estimators.
m.save("test_fit_est", est = True)
m2 = load("test_fit_est", est = True)
# If you wish, you can see that m2 (the loaded estimator object) can perform fits:
# fit3 = m2.fit(x, y)
# m2 can perform predictions, too:
# fitp2 = m2.predict(x, tc = 4)
#--------------------------------------------------------------------------------------------

The standard variable activity information, calculated as the proportion of splitting rules involving each variable, can be computed using OPENBT's vartivity() function.

# Calculate variable activity information
fitv = m.vartivity()
print(fitv['mvdraws'])

A more accurate alternative is to calculate the Sobol indices.

# Calculate Sobol indices
fits = m.sobol(cmdopt = 'MPI', tc = 4)
print(fits['msi'])
print(fits['mtsi'])
print(fits['msij'])

Again, for more examples of using OpenBT, explore the PyScripts folder in the Github repo .

See Also:

Github "Homepage" for this package
PyPI Package Home
Zoltan Puha's class (the current class was built as a modification to this)
ROpenBT Project Home