alabebm 0.8.4

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

Change Log

• 2025-02-26 (V 0.3.4).
  • Modified the shuffle_order function to ensure full derangement, making convergence faster.
• 2025-03-06 (V 0.4.0)
  • use pyproject.toml instead
  • update conjuage_priors_algo.py, now without using the auxiliary variable of participant_stages. Kept the uncertainties just like in soft_kmeans_algo.py.
• 2025-03-07 (V 0.4.2)
  • Compute new_ln_likelihood_new_theta_phi based on new_theta_phi_estimates, which is based on stage_likelihoods_posteriors that is based on the newly proposed order and previous theta_phi_estimates.
  • Update theta_phi_estimates with new_theta_phi_estimates only if new order is accepted.
  • The fallback theta_phi_estimates is the previous parameters rather than theta_phi_default
  • all_accepted_orders.append(current_order_dict.copy()) to make sure the results are not mutated.
  • Previously I calculated the new_ln_likelihood and stage_likelihoods_posteriors based on the newly proposed order and previous theta_phi_estimates, and directly update theta_phi_estimates whether we accept the new order or not.
  • Previously, I excluded copy() in all_accepted_orders.append(current_order_dict.copy()), which is inaccurate.
• 2025-03-17 (V 0.4.3)
  • Added skip and title_detail parameter in save_traceplot function.
• 2025-03-18 (V 0.4.4)
  • Add optional horizontal bar indicating upper limit in trace plot.
• 2025-03-18 (V 0.4.7)
  • Allowed keeping all cols (keep_all_cols) in data generation.
• 2025-03-18 (V 0.4.9)
  • copy data_we_have and use data_we_have.loc[:, 'S_n'] in soft kmeans algo when preprocessing participant and biomarker data.
• 2025-03-20 (V 0.5.1)
  • In hard kmeans, updated delta = ln_likelihood - current_ln_likelihood, and in soft kmeans and conjugate priors, made sure I am using delta = new_ln_likelihood_new_theta_phi - current_ln_likelihood.
  • In each iteration, use theta_phi_estimates = theta_phi_default.copy() first. This means, stage_likelihoods_posteriors is based on the default theta_phi, not the previous iteration.
• 2025-03-21 (V 0.6.0)
  • Integrated all three algorithms to just one file algorithms/algorithm.py.
  • Changed the algorithm name of soft_kmeans to mle (maximum likelihood estimation)
  • Moved all helper functions from the algorithm script to utils/data_processing.py.
• 2025-03-22 (V 0.7.6)
  • Current state should include both the current accepted order and its associated theta/phi. When updating theta/phi at the start of each iteration, use the current state's theta/phi (1) in the calculation of stage likelihoods and (2) as the fallback if either of the biomarker's clusters is empty or has only one measurement; (3) as the prior mean and variance.
  • Set conjugate_priors as the default algorithm.
  • (Tried using cluster's mean and var as the prior but the results are not as good as using current state's theta/phi as the prior).
• 2025-03-24 (V 0.7.8)
  • In heatmap, reorder the biomarkers according to the most likely order.
  • In results.json reorder the biomarker according to their order rather than alphabetically ranked.
  • Modified obtain_most_likely_order_dic so that we assign stages for biomarkers that have the highest probabilities first.
  • In results.json, output the order associated with the highest total log likelihood. Also, calculate the kendall's tau and p values of it and the original order (if provided).
• 2025-03-25 (V 0.8.1)
  • In heatmap, reorder according to the order with highest log likelihood. Also, add the number just like (1).
  • Able to add title detail to heatmaps and traceplots.
  • Able to add fname_prefix in run_ebm().
• 2025-03-29 (V 0.8.4)
  • Added em algorithm.
  • Added Dirichlet-Multinomial Model to describe uncertainy of stage distribution (a multinomial disribution of all disease stages; because we cannot always assume all disease stages are equally likely).

Generate Random Data

from alabebm import generate, get_params_path, get_biomarker_order_path
import os
import json 

# Get path to default parameters
params_file = get_params_path()

# Get path to biomarker_order
biomarker_order_json = get_biomarker_order_path()

with open(biomarker_order_json, 'r') as file:
    biomarker_order = json.load(file)

generate(
    biomarker_order = biomarker_order,
    real_theta_phi_file=params_file,  # Use default parameters
    js = [50, 100],
    rs = [0.1, 0.5],
    num_of_datasets_per_combination=2,
    output_dir='my_data',
    seed = None,
    prefix = None,
    suffix = None,
    keep_all_cols = False
)

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,
        correct_ordering = None,
        plot_title_detail = "",
    )

Interpreting the results

After running the algorithm, you'll get the results in the folder of conjugate_priors, including

• heatmaps. This folder contains the heatmap. Note that the number following each biomarker, such as (1), indicates the order of this biomarker according to the order that is associated with the highest likelihood (You can see the folder of traceplots for the likelihood history.)
• records contains the logging information of the algorithm.
• traceplots contains the traceplots of log likelihood trajectory.
• results contains json files. Example of a result json:

{
    "n_iter": 200,
    "most_likely_order": {
        "HIP-FCI": 1,
        "PCC-FCI": 2,
        "FUS-GMI": 3,
        "P-Tau": 4,
        "AB": 5,
        "HIP-GMI": 6,
        "MMSE": 7,
        "ADAS": 8,
        "AVLT-Sum": 9,
        "FUS-FCI": 10
    },
    "kendalls_tau": 0.6,
    "p_value": 0.016666115520282188,
    "original_order": {
        "HIP-FCI": 1,
        "PCC-FCI": 2,
        "AB": 3,
        "P-Tau": 4,
        "MMSE": 5,
        "ADAS": 6,
        "HIP-GMI": 7,
        "AVLT-Sum": 8,
        "FUS-GMI": 9,
        "FUS-FCI": 10
    },
    "order_with_higest_ll": {
        "HIP-FCI": 1,
        "PCC-FCI": 2,
        "FUS-GMI": 3,
        "AB": 4,
        "P-Tau": 5,
        "HIP-GMI": 6,
        "MMSE": 7,
        "ADAS": 8,
        "AVLT-Sum": 9,
        "FUS-FCI": 10
    },
    "kendalls_tau2": 0.6444444444444444,
    "p_value2": 0.009148478835978836
}

n_iter means the number of iterations. most_likely_order is the most likely order if we consider all the iteration results, burn in, and thinning. kendalls_tau and p_value is the result of most likely order versus the original order (if provided). order_with_higest_ll is the order associated with the highest log likelihood. kendalls_tau2 and p_value2 is the result of most likely order versus the original order (if provided).

Input data

The input data should have at least 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
  • MLE

• Data generation utilities

• Extensive logging