README.md

Vantage6 algorithm for logistic regression

This algorithm was designed for the vantage6 architecture.

Input data

The algorithm expects each data node to hold data that adheres to the same standard.

Using the algorithm

Below you can see an example of how to run the algorithm:

import time
from vantage6.client import Client

# Initialise the client
client = Client('http://127.0.0.1', 5000, '/api')
client.authenticate('username', 'password')
client.setup_encryption(None)

# Define algorithm input
input_ = {
    'method': 'master',
    'master': True,
    'kwargs': {
        'org_ids': [2, 3],          # organisations to run algorithm
        'predictors': ['c1', 'c2'], # columns to be used as predictors
        'outcome': 'outcome',       # column to be used as outcome
        'classes': [0, 1],          # classes to be predicted
        'max_iter': 15,             # maximum number of iterations to perform
        'delta': 0.01,              # threshold loss difference for convergence
    }
}

# Send the task to the central server
task = client.task.create(
    collaboration=1,
    organizations=[2, 3],
    name='v6-logistic-regression-py',
    image='ghcr.io/maastrichtu-cds/v6-logistic-regression-py:latest',
    description='run logistic regression',
    input=input_,
    data_format='json'
)

# Retrieve the results
task_info = client.task.get(task['id'], include_results=True)
while not task_info.get('complete'):
    task_info = client.task.get(task['id'], include_results=True)
    time.sleep(1)
result_info = client.result.list(task=task_info['id'])
results = result_info['data'][0]['result']

Testing locally

If you wish to test the algorithm locally, you can create a Python virtual environment, using your favourite method, and do the following:

source .venv/bin/activate
pip install -e .
python v6_kmeans_py/example.py

The algorithm was developed and tested with Python 3.7.