machine-learning-with-graph 0.0.3

A comprehensive package for graph-based machine learning algorithms.

Machine Learning with Graphs Library

This Python library offers a comprehensive suite of graph-based machine learning algorithms, designed for ease of use and versatility.

Features

• Graph Neural Networks (GNNs): Includes GCNs, GATs, and more.
• Graph Clustering Algorithms: Features Spectral Clustering, Louvain method, and others.
• Graph Embedding Methods: Implements Node2Vec, DeepWalk, etc.
• Diverse Range of Algorithms: For various graph-based learning tasks.

Installation

pip install machine_learning_with_graph

Usage

Scripts in the examples folder demonstrate various algorithms' usage.

Example to integrate spectral clustering method

import networkx as nx
from networkx.generators.community import stochastic_block_model
from ml_wg.clustering.spectral import SpectralClustering
import numpy as np

# Create a Stochastic Block Model graph
sizes = [15, 15, 15]  # Sizes of each block
p_matrix = [[0.5, 0.1, 0.05],
            [0.1, 0.5, 0.1],
            [0.05, 0.1, 0.5]]  # Probability matrix
G = stochastic_block_model(sizes, p_matrix)

# Get the adjacency matrix
adj_matrix = nx.to_numpy_array(G)

# Apply our spectral clustering library
sc = SpectralClustering(n_clusters=3)
clusters = sc.fit_predict(adj_matrix)

# Create a color map based on cluster labels
color_map = ['red' if clusters[node] == 0 else 'blue' if clusters[node] == 1 else 'green' for node in G.nodes()]

# Draw the network
nx.draw(G, node_color=color_map, with_labels=True, node_size=500, font_size=10)
plt.title("Stochastic Block model Graph Visualization")
plt.show()

Output:

Testing

Run tests using pytest:

pytest

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Developer Guide

To contribute to the project, follow these steps to set up a local development environment:

1. Clone the Repository:

git clone https://github.com/susheelg1197/machine-learning-with-graphs-lib.git
cd machine-learning-with-graphs-lib

2. Create and Activate a Virtual Environment (optional but recommended):

python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate

3. Install Dependencies:

pip install -r requirements.txt

4. Make Changes:

• Implement new features or fix bugs.
• Write tests to ensure functionality.

5. Testing: Add test cases within testing folder

pytest

6. Commit Your Changes:

git add .
git commit -m "Your detailed description of changes"

7. Push to Your Fork and Create a Pull Request.

Please ensure your code adheres to the project's coding standards and include tests for new features.