def test_gibbs_sbm(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print("Setting seed to ", seed)
np.random.seed(seed)
C = 2
K = 100
c = np.arange(C).repeat(np.ceil(K/float(C)))[:K]
T = 1000
dt = 1.0
B = 3
# Generate from a true model
true_p = np.random.rand(C,C) * 0.25
true_network = StochasticBlockModel(K, C, c=c, p=true_p, v=10.0)
true_model = \
DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K, dt=dt, B=B, network=true_network)
S,R = true_model.generate(T)
# Plot the true network
plt.ion()
true_im = true_model.plot_adjacency_matrix()
plt.pause(0.001)
# Make a new model for inference
test_network = StochasticBlockModel(K, C, beta=1./K)
test_model = \
DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K, dt=dt, B=B, network=test_network)
test_model.add_data(S)
# Gibbs sample
N_samples = 100
c_samples = []
lps = []
for itr in progprint_xrange(N_samples):
c_samples.append(test_network.c.copy())
lps.append(test_model.log_probability())
# Resample the network only
test_model.network.resample((true_model.weight_model.A,
true_model.weight_model.W))
c_samples = np.array(c_samples)
plt.ioff()
# Compute sample statistics for second half of samples
print("True c: ", true_model.network.c)
print("Test c: ", c_samples[-10:, :])
# Compute the adjusted mutual info score of the clusterings
amis = []
arss = []
for c in c_samples:
amis.append(adjusted_mutual_info_score(true_model.network.c, c))
arss.append(adjusted_rand_score(true_model.network.c, c))
plt.figure()
plt.plot(np.arange(N_samples), amis, '-r')
plt.plot(np.arange(N_samples), arss, '-b')
plt.xlabel("Iteration")
plt.ylabel("Clustering score")
plt.show()
def test_gibbs_sbm(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print("Setting seed to ", seed)
np.random.seed(seed)
C = 2
K = 100
c = np.arange(C).repeat(np.ceil(K / float(C)))[:K]
T = 1000
dt = 1.0
B = 3
# Generate from a true model
true_p = np.random.rand(C, C) * 0.25
true_network = StochasticBlockModel(K, C, c=c, p=true_p, v=10.0)
true_model = \
DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K, dt=dt, B=B, network=true_network)
S, R = true_model.generate(T)
# Plot the true network
plt.ion()
true_im = true_model.plot_adjacency_matrix()
plt.pause(0.001)
# Make a new model for inference
test_network = StochasticBlockModel(K, C, beta=1. / K)
test_model = \
DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K, dt=dt, B=B, network=test_network)
test_model.add_data(S)
# Gibbs sample
N_samples = 100
c_samples = []
lps = []
for itr in progprint_xrange(N_samples):
c_samples.append(test_network.c.copy())
lps.append(test_model.log_probability())
# Resample the network only
test_model.network.resample(
(true_model.weight_model.A, true_model.weight_model.W))
c_samples = np.array(c_samples)
plt.ioff()
# Compute sample statistics for second half of samples
print("True c: ", true_model.network.c)
print("Test c: ", c_samples[-10:, :])
# Compute the adjusted mutual info score of the clusterings
amis = []
arss = []
for c in c_samples:
amis.append(adjusted_mutual_info_score(true_model.network.c, c))
arss.append(adjusted_rand_score(true_model.network.c, c))
plt.figure()
plt.plot(np.arange(N_samples), amis, '-r')
plt.plot(np.arange(N_samples), arss, '-b')
plt.xlabel("Iteration")
plt.ylabel("Clustering score")
plt.show()
