def test_musae():
"""
Testing the MUSAE node embedding.
"""
graph = nx.newman_watts_strogatz_graph(50, 10, 0.2)
features = {i: random.sample(range(150), 50) for i in range(50)}
row = np.array([k for k, v in features.items() for val in v])
col = np.array([val for k, v in features.items() for val in v])
data = np.ones(50 * 50)
shape = (50, 150)
features = coo_matrix((data, (row, col)), shape=shape)
model = MUSAE()
model.fit(graph, features)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions * (1 + model.window_size)
assert type(embedding) == np.ndarray
#------------------------------------
# MUSAE example
#------------------------------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.2)
X = {i: random.sample(range(150),50) for i in range(100)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
data = np.ones(100*50)
shape = (100, 150)
X = coo_matrix((data, (row, col)), shape=shape)
model = MUSAE()
model.fit(g, X)
model.get_memberships()
#--------------
# SINE example
#--------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.2)
X = {i: random.sample(range(150),50) for i in range(100)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
#------------------------------------
# MUSAE example
#------------------------------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.2)
X = {i: random.sample(range(150),50) for i in range(100)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
data = np.ones(100*50)
shape = (100, 150)
X = coo_matrix((data, (row, col)), shape=shape)
model = MUSAE()
model.fit(g, X)
model.get_memberships()
#------------------------------------
# SINE example
#------------------------------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.2)
X = {i: random.sample(range(150),50) for i in range(100)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
"""MUSAE Example."""
import random
import numpy as np
import networkx as nx
from scipy.sparse import coo_matrix
from karateclub.node_embedding.attributed import MUSAE
g = nx.newman_watts_strogatz_graph(50, 10, 0.2)
X = {i: random.sample(range(150), 50) for i in range(50)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
data = np.ones(50 * 50)
shape = (50, 150)
X = coo_matrix((data, (row, col)), shape=shape)
model = MUSAE()
model.fit(g, X)