Stochastic Gradient Descent (SGD)

This example demonstrates how to use the Stochastic Gradient Descent (SGD) layout algorithm.

Basic SGD Example

import networkx as nx
import egraph as eg
import matplotlib.pyplot as plt

# Create a graph from NetworkX
nx_graph = nx.les_miserables_graph()
graph = eg.Graph()
indices = {}
for u in nx_graph.nodes:
    indices[u] = graph.add_node(u)
for u, v in nx_graph.edges:
    graph.add_edge(indices[u], indices[v], (u, v))

# Create an initial drawing
drawing = eg.DrawingEuclidean2d.initial_placement(graph)

# Create a random number generator with a seed for reproducibility
rng = eg.Rng.seed_from(0)

# Create a SparseSgd instance using the builder pattern
sgd = eg.SparseSgd().h(50).build(graph, lambda _: 30, rng)

# Create a scheduler for the SGD algorithm
scheduler = sgd.scheduler(
    100,  # number of iterations
    0.1,  # eps: eta_min = eps * min d[i, j] ^ 2
)

# Define a step function for the scheduler
def step(eta):
    sgd.shuffle(rng)
    sgd.apply(drawing, eta)

# Run the scheduler
scheduler.run(step)

# Extract node positions
pos = {u: (drawing.x(i), drawing.y(i)) for u, i in indices.items()}

# Visualize with NetworkX
nx.draw(nx_graph, pos)

Using FullSgd

For smaller graphs, you can use FullSgd which computes all-pairs shortest path distances:

# Create a FullSgd instance using the builder pattern
sgd = eg.FullSgd().build(graph, lambda _: 30)

# The rest of the code is the same as the SparseSgd example
scheduler = sgd.scheduler(100, 0.1)

def step(eta):
    sgd.shuffle(rng)
    sgd.apply(drawing, eta)

scheduler.run(step)