alabebm 0.2.8

Implementation of event-based models for degenerative diseases.

EBM

This is the python package for implementing Event Based Models for Disease Progression.

Installation

pip install alabEBM

Generate Random Data

from alabEBM import generate, get_params_path
import os

# Get path to default parameters
params_file = get_params_path()

# Generate data using default parameters
S_ordering = [
    'HIP-FCI', 'PCC-FCI', 'AB', 'P-Tau', 'MMSE', 'ADAS',
    'HIP-GMI', 'AVLT-Sum', 'FUS-GMI', 'FUS-FCI'
]

generate(
    S_ordering=S_ordering,
    real_theta_phi_file=params_file,  # Use default parameters
    js = [50, 100], # Number of participants
    rs = [0.1, 0.5], # Percentage of non-diseased participants
    num_of_datasets_per_combination=2,
    output_dir='my_data'
)

Run MCMC Algorithms

from alabEBM import run_ebm
from alabEBM.data import get_sample_data_path
import os

print("Current Working Directory:", os.getcwd())

for algorithm in ['soft_kmeans', 'conjugate_priors', 'hard_kmeans']:
    results = run_ebm(
        data_file=get_sample_data_path('25|50_10.csv'),  # Use the path helper
        algorithm=algorithm,
        n_iter=2000,
        n_shuffle=2,
        burn_in=1000,
        thinning=20,
    )

Input data

The input data should have four columns:

• participant: int
• biomarker: str
• measurement: float
• diseased: bool

An example is https://raw.githubusercontent.com/hongtaoh/alabEBM/refs/heads/main/alabEBM/tests/my_data/10%7C100_0.csv

The data should be in a tidy format, i.e.,

• Each variable is a column.
• Each observation is a row.
• Each type of observational unit is a table.

Features

• Multiple MCMC algorithms:

  • Conjugate Priors
  • Hard K-means
  • Soft K-means

• Data generation utilities

• Extensive logging