class Test(unittest.TestCase):
def setUp(self):
np.random.seed(238924)
decays = np.array([1., 3.])
self.n_nodes = 2
baseline = np.ones(self.n_nodes)
adjacency = np.random.rand(self.n_nodes, self.n_nodes, len(decays))
self.hawkes_simu = SimuHawkesSumExpKernels(decays=decays,
baseline=baseline,
adjacency=adjacency,
max_jumps=100,
verbose=False,
seed=32098)
self.hawkes_simu.simulate()
def test_plot_hawkes_kernels(self):
"""...Test plot_hawkes_history rendering given a fitted Hawkes
learner
"""
decays = np.array([1.5, 3.5])
hawkes_sumexp = HawkesSumExpKern(decays, max_iter=0)
# We set some specific coeffs to be free from any future learner
# modifications
# With 0 iteration and coeffs as start point it should remain there
coeffs = np.array(
[0.99, 0.99, 0.55, 0.37, 0.39, 0.16, 0.63, 0.49, 0.49, 0.30])
hawkes_sumexp.fit(self.hawkes_simu.timestamps, start=coeffs)
n_points = 10
for support in [None, 4]:
fig = plot_hawkes_kernels(hawkes_sumexp,
hawkes=self.hawkes_simu,
show=False,
n_points=n_points,
support=support)
if support is None:
max_support = hawkes_sumexp.get_kernel_supports().max() * 1.2
else:
max_support = support
for i, j in itertools.product(range(self.n_nodes), repeat=2):
index = i * self.n_nodes + j
ax = fig.axes[index]
ax_t_axis, ax_estimated_kernel = ax.lines[0].get_xydata().T
t_axis = np.linspace(0, max_support, n_points)
np.testing.assert_array_equal(ax_t_axis, t_axis)
estimated_kernel = hawkes_sumexp.get_kernel_values(
i, j, t_axis)
np.testing.assert_array_equal(ax_estimated_kernel,
estimated_kernel)
_, ax_true_kernel = ax.lines[1].get_xydata().T
true_kernel = self.hawkes_simu.kernels[i, j].get_values(t_axis)
np.testing.assert_array_equal(ax_true_kernel, true_kernel)
def setUp(self):
np.random.seed(238924)
decays = np.array([1., 3.])
self.n_nodes = 2
baseline = np.ones(self.n_nodes)
adjacency = np.random.rand(self.n_nodes, self.n_nodes, len(decays))
self.hawkes_simu = SimuHawkesSumExpKernels(decays=decays,
baseline=baseline,
adjacency=adjacency,
max_jumps=100,
verbose=False,
seed=32098)
self.hawkes_simu.simulate()
def setUp(self):
np.random.seed(23982)
self.n_nodes = 3
self.n_decays = 4
self.baseline = np.random.rand(self.n_nodes)
self.adjacency = np.random.rand(self.n_nodes, self.n_nodes,
self.n_decays) / 10
self.decays = np.random.rand(self.n_decays)
self.adjacency[0, 0, :] = 0
self.adjacency[-1, -1, :] = 0
self.hawkes = SimuHawkesSumExpKernels(self.adjacency,
self.decays,
baseline=self.baseline,
seed=203,
verbose=False)
def fit_exp_hawkes_and_simulate(train_times, decays, end_time):
learner = HawkesSumExpKern(decays,
penalty='elasticnet',
elastic_net_ratio=0.8)
learner.fit(train_times)
score = learner.score()
print(f'obtained {score}\n\n')
# simulation
simulation = SimuHawkesSumExpKernels(adjacency=learner.adjacency,
decays=decays,
baseline=learner.baseline,
end_time=end_time,
verbose=False,
seed=42)
simulation.simulate()
return learner, simulation
def test_hawkes_sumexp_constructor_errors(self):
bad_adjacency = np.random.rand(self.n_nodes, self.n_nodes,
self.n_decays + 1)
msg = "^adjacency matrix shape should be \(3, 3, 4\) but its shape " \
"is \(3, 3, 5\)$"
with self.assertRaisesRegex(ValueError, msg):
SimuHawkesSumExpKernels(bad_adjacency,
self.decays,
baseline=self.baseline)
def simulate_sparse_realization(self):
"""Simulate realization in which some nodes are sometimes empty
"""
baseline = np.array([0.3, 0.001])
adjacency = np.array([[[0.5, 0.3], [0.8, 0.2]], [[0., 0.], [1.3,
0.9]]])
sim = SimuHawkesSumExpKernels(adjacency=adjacency,
decays=self.decays,
baseline=baseline,
verbose=False,
seed=13487,
end_time=500)
sim.adjust_spectral_radius(0.8)
multi = SimuHawkesMulti(sim, n_simulations=100)
adjacency = sim.adjacency
multi.simulate()
# Check that some but not all realizations are empty
self.assertGreater(max(map(lambda r: len(r[1]), multi.timestamps)), 1)
self.assertEqual(min(map(lambda r: len(r[1]), multi.timestamps)), 0)
return baseline, adjacency, multi.timestamps
def get_train_data(decays, n_nodes=3, n_decays=2):
np.random.seed(130947)
baseline = np.random.rand(n_nodes)
adjacency = np.random.rand(n_nodes, n_nodes, n_decays)
sim = SimuHawkesSumExpKernels(adjacency=adjacency,
decays=decays,
baseline=baseline,
verbose=False,
seed=13487,
end_time=3000)
sim.adjust_spectral_radius(0.8)
adjacency = sim.adjacency
sim.simulate()
return sim.timestamps, baseline, adjacency
class Test(unittest.TestCase):
def setUp(self):
np.random.seed(23982)
self.n_nodes = 3
self.n_decays = 4
self.baseline = np.random.rand(self.n_nodes)
self.adjacency = np.random.rand(self.n_nodes, self.n_nodes,
self.n_decays) / 10
self.decays = np.random.rand(self.n_decays)
self.adjacency[0, 0, :] = 0
self.adjacency[-1, -1, :] = 0
self.hawkes = SimuHawkesSumExpKernels(self.adjacency,
self.decays,
baseline=self.baseline,
seed=203,
verbose=False)
def test_hawkes_exponential_kernels(self):
"""...Test creation of a Hawkes Process with exponential kernels
"""
kernel_0 = None
for i, j in product(range(self.n_nodes), range(self.n_nodes)):
kernel_ij = self.hawkes.kernels[i, j]
if np.linalg.norm(self.adjacency[i, j, :]) == 0:
self.assertEqual(kernel_ij.__class__, HawkesKernel0)
# We check that all 0 adjacency share the same kernel 0
# This might save lots of memory with very large,
# very sparse adjacency matrices
if kernel_0 is None:
kernel_0 = kernel_ij
else:
self.assertEqual(kernel_0, kernel_ij)
else:
self.assertEqual(kernel_ij.__class__, HawkesKernelSumExp)
np.testing.assert_array_equal(kernel_ij.decays, self.decays)
np.testing.assert_array_equal(kernel_ij.intensities,
self.adjacency[i, j, :])
np.testing.assert_array_equal(self.baseline, self.hawkes.baseline)
def test_hawkes_spectral_radius_exp_kernel(self):
"""...Hawkes Process spectral radius and adjust spectral radius
methods
"""
self.assertAlmostEqual(self.hawkes.spectral_radius(),
0.5202743505580953)
self.hawkes.adjust_spectral_radius(0.6)
self.assertAlmostEqual(self.hawkes.spectral_radius(), 0.6)
def test_hawkes_mean_intensity(self):
"""...Test that Hawkes obtained mean intensity is consistent
"""
self.assertLess(self.hawkes.spectral_radius(), 1)
self.hawkes.end_time = 1000
self.hawkes.track_intensity(0.01)
self.hawkes.simulate()
mean_intensity = self.hawkes.mean_intensity()
for i in range(self.hawkes.n_nodes):
self.assertAlmostEqual(np.mean(self.hawkes.tracked_intensity[i]),
mean_intensity[i],
delta=0.1)
def test_hawkes_sumexp_constructor_errors(self):
bad_adjacency = np.random.rand(self.n_nodes, self.n_nodes,
self.n_decays + 1)
msg = "^adjacency matrix shape should be \(3, 3, 4\) but its shape " \
"is \(3, 3, 5\)$"
with self.assertRaisesRegex(ValueError, msg):
SimuHawkesSumExpKernels(bad_adjacency,
self.decays,
baseline=self.baseline)
"""
import matplotlib.pyplot as plt
from tick.hawkes import SimuHawkesSumExpKernels, HawkesSumExpKern
from tick.plot import plot_point_process
end_time = 1000
decays = [0.1, 0.5, 1.]
baseline = [0.12, 0.07]
adjacency = [[[0, .1, .4], [.2, 0., .2]], [[0, 0, 0], [.6, .3, 0]]]
hawkes_exp_kernels = SimuHawkesSumExpKernels(adjacency=adjacency,
decays=decays,
baseline=baseline,
end_time=end_time,
verbose=False,
seed=1039)
hawkes_exp_kernels.track_intensity(0.1)
hawkes_exp_kernels.simulate()
learner = HawkesSumExpKern(decays, penalty='elasticnet', elastic_net_ratio=0.8)
learner.fit(hawkes_exp_kernels.timestamps)
t_min = 100
t_max = 200
fig, ax_list = plt.subplots(2, 1, figsize=(10, 6))
learner.plot_estimated_intensity(hawkes_exp_kernels.timestamps,
t_min=t_min,
t_max=t_max,
import matplotlib.pyplot as plt
from tick.plot import plot_hawkes_kernels
from tick.hawkes import SimuHawkesSumExpKernels, SimuHawkesMulti, \
HawkesSumExpKern
end_time = 1000
n_realizations = 10
decays = [.5, 2., 6.]
baseline = [0.12, 0.07]
adjacency = [[[0, .1, .4], [.2, 0., .2]],
[[0, 0, 0], [.6, .3, 0]]]
hawkes_exp_kernels = SimuHawkesSumExpKernels(
adjacency=adjacency, decays=decays, baseline=baseline,
end_time=end_time, verbose=False, seed=1039)
multi = SimuHawkesMulti(hawkes_exp_kernels, n_simulations=n_realizations)
multi.end_time = [(i + 1) / 10 * end_time for i in range(n_realizations)]
multi.simulate()
learner = HawkesSumExpKern(decays, penalty='elasticnet',
elastic_net_ratio=0.8)
learner.fit(multi.timestamps)
fig = plot_hawkes_kernels(learner, hawkes=hawkes_exp_kernels, show=False)
for ax in fig.axes:
ax.set_ylim([0., 1.])
from tick.plot import plot_hawkes_baseline_and_kernels
from tick.hawkes import (
SimuHawkesSumExpKernels, SimuHawkesMulti, HawkesSumExpKern
)
period_length = 300
baselines = [[0.3, 0.5, 0.6, 0.4, 0.2, 0],
[0.8, 0.5, 0.2, 0.3, 0.3, 0.4]]
n_baselines = len(baselines[0])
decays = [.5, 2., 6.]
adjacency = [[[0, .1, .4], [.2, 0., .2]],
[[0, 0, 0], [.6, .3, 0]]]
# simulation
hawkes = SimuHawkesSumExpKernels(baseline=baselines,
period_length=period_length,
decays=decays, adjacency=adjacency,
seed=2093, verbose=False)
hawkes.end_time = 1000
hawkes.adjust_spectral_radius(0.5)
multi = SimuHawkesMulti(hawkes, n_simulations=4)
multi.simulate()
# estimation
learner = HawkesSumExpKern(decays=decays,
n_baselines=n_baselines,
period_length=period_length)
learner.fit(multi.timestamps)
# plot
def _corresponding_simu(self):
return SimuHawkesSumExpKernels(
adjacency=self.adjacency,
decays=self.decays,
baseline=self.baseline,
period_length=self._model_obj.period_length)