Calling BFI from Python

Hassan Pazira

2024-11-08

Introduction

BFI is an R package that performs Bayesian Federated Inference (BFI) for linear, logistic, and survival regression models. Since there is no Python package for carrying out the BFI method so far, this vignette describes the usage of the R package BFI in the Python environment.

To move to the Python environment, we need to prepare our operating system. Although, we explain in the following how to prepare it in different systems (‘MacOS’, ‘Ubuntu’ and ‘Windows’), we recommend that to use ‘Google Colab’ to write and execute (the following) Python codes which can be done in any system through a browser without any preparations.

Google Colab

First go to Google Colab from https://colab.google or https://colab.research.google.com, and then click on New Notebook. Now, skip the two following sections and jump to the section Python Script.

Python Installation

The steps to install the latest version of Python on ‘MacOS’, ‘Ubuntu’ and ‘Windows’ are as follows:

On MacOS

Open a terminal window (cmd + space and search ‘Terminal’) and install the package manager ‘Homebrew’ by executing the following command:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

Install the latest version of Python (Python 3) with the following command:

brew install python@3

Verify the installation by checking the Python version:

python3 --version

It’s assumed that R is also installed and configured on your system. If not, the following can do it:

# Install XCode command-line tools
xcode-select --install

# Install R
brew install --cask r

On Ubuntu

Update package repositories and get latest package information by running the following command in a terminal window:

sudo apt-get update -y

Verify if Python is indeed installed on your system by the following command:

python3 --version

If you already have Python (Python 3) installed on your system, you only need to upgrade it to the latest version as follows:

sudo apt-get upgrade python3

In case you did not have Python installed in the first place, the latest version of Python can be installed using the following command:

sudo apt-get install python3

Verify the installation by checking the Python version:

python3 --version

If you don’t have R installed and configured, just run the following:

# Install R
sudo apt-get install r-base

On Windows

By default, Python is usually not installed on Windows. However, you can check if it exists on the system by running one line of command on the command prompt. Go to Start and enter cmd in the search bar, and then click Command Prompt. Enter the command python --version or python3 --version in the command prompt.

To download Python, go to the official Python download website for Windows: https://www.python.org. Find a stable Python 3 release, and download the executable file for your system from the appropriate link.

After the installer is downloaded, open/run the Python installer to install Python. Select the Add Python 3.x to PATH checkbox, which enables users to launch Python from the command line, and then select Install Now.

Once the installation is complete, you can verify whether the Python installation is successful through the command line. Enter the command python --version or python3 --version in the command prompt.

It’s assumed that R is also installed on your system. If you don’t have R installed, just visit CRAN downloads (https://cran.r-project.org) and get the last version.

Installation of Required Modules

Here the required modules are installed through PIP. PIP (a Python package manager) helps us to install and use various packages/modules in Python programming.

Install the latest version of PIP and required modules by running the following commands on different systems.

On MacOS

Install/Upgrade the latest version of PIP by running

# Installing PIP
python3 -m pip install --user --upgrade pip

Install required modules by running the following commands

# Installing the 'pandas' module
pip3 install pandas

# Installing the 'numpy' module
pip3 install numpy

# Installing the 'pickle' module
pip3 install pickle

# Installing the 'rpy2' module
pip3 install rpy2

On Ubuntu

To install PIP use the code

# Installing PIP
sudo apt-get install python3-pip

Verify you have successfully installed PIP by running

pip3 -V

Now, install required modules by typing the following commands

# Installing the 'pandas' module
sudo pip3 install pandas

# Installing the 'numpy' module
sudo pip3 install numpy

# Installing the 'pickle' module
sudo pip3 install pickle

# Installing the 'rpy2' module
sudo pip3 install rpy2

If the lines above for installation of the modules did not install them, use the following:

# Installing the 'pandas' module
sudo apt-get install python3-pandas

# Installing the 'numpy' module
sudo apt-get install python3-numpy

# Installing the 'pickle' module
sudo apt-get install python3-pickle

# Installing the 'rpy2' module
sudo apt-get install python3-rpy2

Finally, use the show command to verify whether the module is now part of your Python packages:

pip3 show pandas
pip3 show numpy
pip3 show pickle
pip3 show rpy2

On Windows

Usually, PIP is automatically installed if you are using Python downloaded from https://www.python.org. If you have followed the previous steps provided in this vignette, then you have PIP installed on your system. To check if PIP is already installed on Windows, you should open the command line again, type pip -V, and press Enter.

If PIP is not installed: First download get-pip.py to a folder on your computer. Then, open a command prompt and navigate to the folder containing the get-pip.py installer. Finally, run the following command:

python get-pip.py

PIP should now be installed successfully. If we receive a ‘file not found’ error, double check the directory path to the file. You can use the dir command to view the entire contents of a directory. More information can be found here: https://pip.pypa.io/en/stable/installation.

Now, install required modules by running the following commands:

# Installing the 'pandas' module
pip3 install pandas

# Installing the 'numpy' module
pip3 install numpy

# Installing the 'pickle' module
pip3 install pickle

# Installing the 'rpy2' module
pip3 install rpy2

Now, you can start coding in Python using Python’s command-line interpreter or IDLE application. Go to Start and enter python in the search bar. You can see Python 3.x and IDLE which can be used for coding. Open one of them and follow the following steps.

Python Script

It is assumed here that the required packages has been properly installed, which is the case in Google Colab. Now move on to work with the rpy2 package inside a Python script! If you are not in the Python script, first go to the Python Environment by typing the following command in the terminal window (you should skip this line if you are already in Google Colab):

python3

As of now, all the following codes should be run in the Python environment. If you want to copy all codes at once go to the last section.

Importing Python modules and functions

Use the following codes to import required Python modules:

# import 'pandas' package
import pandas as pd

# import 'numpy' package
import numpy as np                

# import 'pickle' package
import pickle

# import 'rpy2' package
import rpy2                  

and using the following codes imports the required functions such as importr() and data():

from rpy2.robjects.packages import importr, data
from rpy2.robjects.vectors import StrVector
from rpy2.robjects import numpy2ri, pandas2ri
# activation of the automatic conversion of 'numpy' and 'pandas' objects into rpy2 objects
numpy2ri.activate()   
pandas2ri.activate()

Installing R packages from CRAN and GitHub

First load the packages preinstalled with R using importr() as follows:

# import R's "utils" package
utils = importr('utils')

# import R's "base" package
base = importr('base')

Then install the R packages stats and BFI from CRAN by typing

utils.chooseCRANmirror(ind=1)  # which selects the first mirror in the list
package_names = ('stats', 'BFI')
utils.install_packages(StrVector(package_names))

Now load the installed packages using

stats = importr('stats')
BFI = importr('BFI')

Installing the BFI package from GitHub (if necessary)

If you want to install the BFI package from GitHub (instead of CRAN), it can be installed and loaded using the following lines:

utils.chooseCRANmirror(ind=1)
utils.install_packages('devtools')
devtools = importr('devtools')

# Installing the 'BFI' package
devtools.install_github("hassanpazira/BFI", force = True)

# Loading the package
BFI = importr('BFI')

Applying BFI method to the simulated data

Now we generate two datasets independently from Gaussian distribution, and then apply main functions in the BFI package to these datasets:

Data Simulation for two Local Centers

First generate 30 samples randomly from Gaussian distribution with 3 covariates:

# model parameters: 'theta' and 'p' are assumed to be the same for both centers:
theta = np.array([1, 2, 3, 4, 0.75])   # intercept is theta[0], sigma2 is theta[4]
p = 3     # number of regression parameters without intercept

# Center 1:
n1 = 30   # sample size of center 1
X1 = np.random.normal(size=(n1, p)) 
X1.shape   # dimension of X1
X1 = pd.DataFrame(X1, columns=['X1', 'X2', 'X3'])
mu1 = theta[0] + np.dot(X1, np.delete(theta, [0, 4]))  # for gaussian: \eta = \mu 
X1 = pandas2ri.py2rpy(X1)   # == base.as_data_frame(X1)
y1 = np.random.normal(loc=mu1, scale=np.sqrt(theta[4]))

Then generate randomly 50 samples from Gaussian distribution with 3 covariates:

# Center 2:
n2 = 50   # sample size of center 2
X2 = np.random.normal(size=(n2, p))
X2 = pd.DataFrame(X2, columns=['X1', 'X2', 'X3'])
mu2 = theta[0] + np.dot(X2, np.delete(theta, [0, 4]))
X2 = pandas2ri.py2rpy(X2)
y2 = np.random.normal(loc=mu2, scale=np.sqrt(theta[4]))

MAP Estimates at the Local Centers

The following compute the Maximum A Posterior (MAP) estimators of the parameters for center 1:

# Creating an inverse covariance matrix for a Gaussian prior for center 1
Lambda1 = BFI.inv_prior_cov(X1, 0.01, 'gaussian')
fit1 = BFI.MAP_estimation(y1, X1, 'gaussian', Lambda1)
print(fit1)
theta_hat1 = fit1.rx2("theta_hat") # MAP estimates of the intercept and coefficients
A_hat1 = fit1.rx2("A_hat")         # minus the curvature matrix
summary_1 = BFI.summary_bfi(fit1, cur_mat = True)

Obtaining the MAP estimators of the parameters for center 2 using the following:

# Creating an inverse covariance matrix for Gaussian prior for center 2
Lambda2 = BFI.inv_prior_cov(X2, 0.01, 'gaussian')
# MAP estimates
fit2 = BFI.MAP_estimation(y2, X2, 'gaussian', Lambda2)
theta_hat2 = fit2.rx2("theta_hat") # MAP estimates of the parameters
A_hat2 = fit2.rx2("A_hat")         # minus the curvature matrix around 'theta_hat2'
summary_2 = BFI.summary_bfi(fit2, cur_mat = True)

Sending the Required Files to Central Server

The required outputs for using the central server are fit1 for center 1 and fit2 for center 2. To send these outputs to the central server, you can save the list to a file in a common format (such as RDS, JSON, or CSV). This will allow the central server to open the file in either R or Python environments.

Central server with R

If the central server is using R, follow the instructions below. If it is using Python, go to the next subsection: Central server with Python. We will show how to do this for fit1 at center 1. The process is the same for center 2. Since fit1 is an R object and the central server is using R, you can save it as an RDS file, which is a file format native to R for single R objects.

import rpy2.robjects as ro

# Save fit1 as an RDS file
ro.r('saveRDS(fit1, file="fit1.rds")')

This file, fit1.rds should be sent to the central server, where it can be loaded as follows:

# Load the saved RDS file
fit1 <- readRDS("fit1.rds") # use the relative path to the file

Now, the variable fit1 is a list in R containing all information needed for further analysis:

theta_hat1 <- fit1$theta_hat
A_hat1 <- fit1$A_hat

To see how to proceed and use bfi() in R, see, for example, here.

Central server with Python

If the central server is using Python, follow the instructions below. We will show how to do this for fit1 at center 1. The process is the same for center 2. Since fit1 needs to be shared with the central server which is using Python, you can serialize it using the pickle module, which is a common way to save Python objects. At the center 1, use the following to create fit1.pkl:

# Save fit1 as a pickle file
with open('fit1.pkl', 'wb') as f:
    pickle.dump(fit1, f)

Now, the file fit1.pkl should be sent to the central server, where it can be loaded as follows:

# Load the pickle file
with open('fit1.pkl', 'rb') as f:  # use the relative path to the file
    fit1 = pickle.load(f)

The variable fit1 contains all information needed for further analysis in the central server. To see how to proceed and obtain the BFI estimates on the central server, follow the next section.

BFI at Central Server (using Python)

On the central server, first load the files saved to a proper address:

# Load the file form center 1
with open('fit1.pkl', 'rb') as f: # use the relative path to the file
    fit1 = pickle.load(f)

# Load the file form center 2, using the relative path to the file
with open('fit2.pkl', 'rb') as f:
    fit2 = pickle.load(f)

Now, The main function bfi() can be used to obtain the BFI estimates:

theta_hat1 = fit1.rx2("theta_hat")
A_hat1 = fit1.rx2("A_hat")
theta_hat2 = fit2.rx2("theta_hat")
A_hat2 = fit2.rx2("A_hat")
theta_hats = base.list(theta_hat1, theta_hat2)
A_hats = base.list(A_hat1, A_hat2)
Lambda1 = pd.DataFrame(Lambda1, index=fit1.rx2("names"), columns=fit1.rx2("names"))
Lambda2 = pd.DataFrame(Lambda2, index=fit2.rx2("names"), columns=fit2.rx2("names"))
Lambdas = base.list(Lambda1, Lambda2)
fit_bfi = BFI.bfi(theta_hats, A_hats, Lambdas)
print(fit_bfi)
summary_bfi = BFI.summary_bfi(fit_bfi, cur_mat = True)

Datasets included in the BFI package

To find a list of all datasets included in the package run:

print(utils.data(package = "BFI"))

In order to use the datasets available from the BFI package, use the following codes:

Nurses = data(BFI).fetch('Nurses')['Nurses']  # is equivalent to BFI::Nurses in R
print("Dimension of the 'Nurses' data: \n", base.dim(Nurses))
print("Colnames of the 'Nurses' data: \n", base.colnames(Nurses))

trauma = data(BFI).fetch('trauma')['trauma']  # is equivalent to BFI::trauma in R
print("Dimension of the 'trauma' data: \n", base.dim(trauma))
print("Colnames of the 'trauma' data: \n", base.colnames(trauma))

At the end, use the following to deactivate automatic conversion:

numpy2ri.deactivate()
pandas2ri.deactivate()

All codes together

If you need all the codes at once to copy and paste them for example into Google Colab, here’s the complete codes:

# import 'pandas' package
import pandas as pd

# import 'numpy' package
import numpy as np                

# import 'pickle' package
import pickle

# import 'rpy2' package
import rpy2                  

from rpy2.robjects.packages import importr, data
from rpy2.robjects.vectors import StrVector
from rpy2.robjects import numpy2ri, pandas2ri
# activation of the automatic conversion of 'numpy' and 'pandas' objects into rpy2 objects
numpy2ri.activate()   
pandas2ri.activate()

# import R's "utils" package
utils = importr('utils')

# import R's "base" package
base = importr('base')

utils.chooseCRANmirror(ind=1)  # which selects the first mirror in the list
package_names = ('stats', 'BFI')
utils.install_packages(StrVector(package_names))

# loading the installed packages
stats = importr('stats')
BFI = importr('BFI')

## Examples

# model parameters: 'theta' and 'p' are assumed to be the same for both centers:
theta = np.array([1, 2, 3, 4, 0.75])   # intercept is theta[0], sigma2 is theta[4]
p = 3     # number of regression parameters without intercept

# Data Simulation for center 1
n1 = 30   # sample size of center 1
X1 = np.random.normal(size=(n1, p)) 
X1.shape   # dimension of X1
X1 = pd.DataFrame(X1, columns=['X1', 'X2', 'X3'])
mu1 = theta[0] + np.dot(X1, np.delete(theta, [0, 4]))  # for gaussian: \eta = \mu 
X1 = pandas2ri.py2rpy(X1)   # == base.as_data_frame(X1)
y1 = np.random.normal(loc=mu1, scale=np.sqrt(theta[4]))

# Data Simulation for center 2
n2 = 50   # sample size of center 2
X2 = np.random.normal(size=(n2, p))
X2 = pd.DataFrame(X2, columns=['X1', 'X2', 'X3'])
mu2 = theta[0] + np.dot(X2, np.delete(theta, [0, 4]))
X2 = pandas2ri.py2rpy(X2)
y2 = np.random.normal(loc=mu2, scale=np.sqrt(theta[4]))

## MAP estimates at center 1
# Creating an inverse covariance matrix for a Gaussian prior for center 1
Lambda1 = BFI.inv_prior_cov(X1, 0.01, 'gaussian')
fit1 = BFI.MAP_estimation(y1, X1, 'gaussian', Lambda1)
print(fit1)
theta_hat1 = fit1.rx2("theta_hat") # MAP estimates of the intercept and coefficients
A_hat1 = fit1.rx2("A_hat")         # minus the curvature matrix
summary_1 = BFI.summary_bfi(fit1, cur_mat = True)
# Save fit1 as a pickle file for sending to the central server
with open('fit1.pkl', 'wb') as f:
    pickle.dump(fit1, f)

## MAP estimates at center 2
# Creating an inverse covariance matrix for Gaussian prior for center 2
Lambda2 = BFI.inv_prior_cov(X2, 0.01, 'gaussian')
# MAP estimates
fit2 = BFI.MAP_estimation(y2, X2, 'gaussian', Lambda2)
theta_hat2 = fit2.rx2("theta_hat") # MAP estimates of the parameters
A_hat2 = fit2.rx2("A_hat")         # minus the curvature matrix around 'theta_hat2'
summary_2 = BFI.summary_bfi(fit2, cur_mat = True)
# Save fit2 as a pickle file for sending to the central server
with open('fit2.pkl', 'wb') as f:
    pickle.dump(fit2, f)

## BFI at central server
# Load the pickle file using the relative path to the file
with open('fit1.pkl', 'rb') as f:  # use the relative path to the file
    fit1 = pickle.load(f)
theta_hat1 = fit1.rx2("theta_hat")
A_hat1 = fit1.rx2("A_hat")
# Load the pickle file using the relative path to the file
with open('fit2.pkl', 'rb') as f:
    fit2 = pickle.load(f)
theta_hat2 = fit2.rx2("theta_hat")
A_hat2 = fit2.rx2("A_hat")
theta_hats = base.list(theta_hat1, theta_hat2)
A_hats = base.list(A_hat1, A_hat2)
Lambda1 = pd.DataFrame(Lambda1, index=fit1.rx2("names"), columns=fit1.rx2("names"))
Lambda2 = pd.DataFrame(Lambda2, index=fit2.rx2("names"), columns=fit2.rx2("names"))
Lambdas = base.list(Lambda1, Lambda2)
fit_bfi = BFI.bfi(theta_hats, A_hats, Lambdas)
print(fit_bfi)
summary_bfi = BFI.summary_bfi(fit_bfi, cur_mat = True)

# To find a list of all datasets included in the package:
print(utils.data(package = "BFI"))

Nurses = data(BFI).fetch('Nurses')['Nurses']
print("Dimension of the 'Nurses' data: \n", base.dim(Nurses))
print("Colnames of the 'Nurses' data: \n", base.colnames(Nurses))

trauma = data(BFI).fetch('trauma')['trauma']
print("Dimension of the 'trauma' data: \n", base.dim(trauma))
print("Colnames of the 'trauma' data: \n", base.colnames(trauma))

Contact

If you find any errors, have any suggestions, or would like to request that something be added, please file an issue at issue report or send an email to: hassan.pazira@radboudumc.nl.