propflow 0.1.1

Highly configurable belief propagation simulator for factor graphs

Belief Propagation Simulator - PropFlow

PropFlow is a Python toolkit for building and experimenting with belief propagation and other distributed constraint optimization (DCOP) algorithms on factor graphs. It was designed for research and education, providing a flexible framework for implementing and testing new policies and engine variants.

for more comprehensive documentation, please click here

Key Features

• Belief Propagation Variants: Simulates a variety of belief propagation algorithms, including Min-Sum, Max-Sum, and Sum-Product.
• Search-Based DCOP Solvers: Implements local search algorithms like the Distributed Stochastic Algorithm (DSA) and Maximum Gain Message (MGM).
• Extensible Policy Framework: A modular system for applying policies like message damping, factor splitting, and cost reduction to alter the behavior of the core algorithms.
• Dynamic Graph Construction: Tools for programmatically creating factor graphs with different topologies (e.g., cycles, random graphs) and custom cost functions.
• Simulation and Analysis: A Simulator class for running multiple engine configurations in parallel and collecting results for comparison.
• Debugging and Visualization: Integrated logging and tools for visualizing factor graphs and analysis results.

Installation

Install from PyPI (Recommended)

PropFlow is now available on PyPI! Install it with pip:

pip install propflow

PyPI Package: https://pypi.org/project/propflow/

Install from Source (Development)

For development or to get the latest changes, clone the repository and install in editable mode:

git clone https://github.com/OrMullerHahitti/Belief-Propagation-Simulator.git
cd Belief-Propagation-Simulator
pip install -e .

Getting Started: A Complete Example

Here's how to create a simple factor graph, run a Min-Sum engine, and get the results.

import numpy as np
from propflow import (
    FactorGraph,
    VariableAgent,
    FactorAgent,
    BPEngine,
    MinSumComputator,
    create_random_int_table,
)

# 1. Define Variables and Factors
var1 = VariableAgent(name="x1", domain=2)
var2 = VariableAgent(name="x2", domain=2)

# Create a factor with a cost table that prefers different assignments
# The cost table is a 2x2 matrix for the two variables, each with a domain of 2.
factor = FactorAgent(
    name="f12",
    domain=2,
    ct_creation_func=create_random_int_table,
    param={"low": 1, "high": 100} # Params for the factory
)

# 2. Create the Factor Graph
# A factor graph connects variables to factors.
edges = {factor: [var1, var2]}
factor_graph = FactorGraph(
    variable_li=[var1, var2],
    factor_li=[factor],
    edges=edges
)

# 3. Initialize and Run the Engine
# The engine orchestrates the message-passing process.
engine = BPEngine(
    factor_graph=factor_graph,
    computator=MinSumComputator() # Use the Min-Sum algorithm
)
engine.run(max_iter=10)

# 4. View the Results
print(f"Final Assignments: {engine.assignments}")
print(f"Final Global Cost: {engine.calculate_global_cost()}")

Advanced Usage

Search-Based Algorithms

PropFlow also supports local search algorithms for DCOPs.

• DSA (Distributed Stochastic Algorithm): A simple and distributed algorithm where agents make independent, probabilistic decisions.
• MGM (Maximum Gain Message): A coordinated algorithm where only the agent with the maximum potential cost reduction is allowed to change its value.

from propflow.search import DSAEngine, DSAComputator

# Use the same factor_graph from the previous example
dsa_computator = DSAComputator(probability=0.8)
dsa_engine = DSAEngine(factor_graph=factor_graph, computator=dsa_computator)
results = dsa_engine.run(max_iter=50)

print(f"DSA Best Assignment: {results['best_assignment']}")
print(f"DSA Best Cost: {results['best_cost']}")

Policies

You can modify the behavior of an engine by applying different policies. Policies are implemented as specialized engine classes.

• Damping: Smooths messages over iterations to prevent oscillations. Use DampingEngine.
• Factor Splitting: Splits factors into two to alter message flow. Use SplitEngine.
• Cost Reduction: Applies a one-time discount to costs. Use CostReductionOnceEngine.

from propflow import DampingEngine, MinSumComputator

# Apply damping to the standard BP engine
damped_engine = DampingEngine(
    factor_graph=factor_graph,
    computator=MinSumComputator(),
    damping_factor=0.9
)
damped_engine.run(max_iter=20)
print(f"Damped Assignments: {damped_engine.assignments}")

Running Experiments

The Simulator class is designed to run experiments comparing multiple engine configurations across one or more graphs.

from propflow import (
    Simulator,
    FGBuilder,
    BPEngine,
    DampingEngine,
    SplitEngine,
    MinSumComputator,
    CTFactory,
)

# 1. Define Engine Configurations
engine_configs = {
    "Standard BP": {"class": BPEngine, "computator": MinSumComputator()},
    "Damped BP": {"class": DampingEngine, "computator": MinSumComputator(), "damping_factor": 0.5},
    "Split Factor BP": {"class": SplitEngine, "computator": MinSumComputator(), "split_factor": 0.6},
}

# 2. Create a list of graphs for the experiment
graphs = [
    FGBuilder.build_cycle_graph(
        num_vars=10,
        domain_size=3,
        ct_factory=CTFactory.random_int.fn,
        ct_params={"low": 0, "high": 100}
    ) for _ in range(5)
]

# 3. Run the simulations in parallel
simulator = Simulator(engine_configs)
results = simulator.run_simulations(graphs, max_iter=100)

# 4. Plot the average cost convergence
simulator.plot_results(verbose=True)

Preliminary Results

Results for 3 different variants of Min-Sum - regular, dampened, and using damping + splitting for 30 problems each, 90 simulations overall, with each one running 5000 steps (iterations). Using the following parameters: only binary constraints, domain size -10, density - 0.25, 50 Variable Nodes (approximately 306 Factor Nodes), and cost table generated from a uniform integer function with range [100, 200].