Randomness in Laplacian Eigenmaps Embeddings
wendywangwwt opened this issue · comments
Hi! I'm using Laplacian Eigenmaps and noticed that the resulting embeddings are not always the same, even though I have explicitly set the seed:
model = LaplacianEigenmaps(dimensions=3,seed=0)
Running the same algorithm in the same python session for multiple times yields different embeddings each time. Here is a minimal reproducible example:
import networkx as nx
g_undirected = nx.newman_watts_strogatz_graph(1000, 20, 0.05, seed=1)
from karateclub.node_embedding.neighbourhood import LaplacianEigenmaps
import numpy as np
for _ in range(5):
model = LaplacianEigenmaps(dimensions=3,seed=0)
model.fit(g_undirected)
node_emb_le = model.get_embedding()
print(np.sum(node_emb_le))
It yields the following summed value of the embeddings for me:
31.647046936812927
-31.647046936812888
31.64704693681287
-31.690999529775908
-31.581837545720354
How can I control the randomness so that every time the resulting embeddings are exactly the same, even if I run the algorithm for arbitrary times in the same python session?
Can you also seed numpy?
Sorry, could you please reply @wendywangwwt ? Closing for now.