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