def test_hawkes_cumulants(self):
"""...Test that estimated cumulants are coorect
"""
timestamps, baseline, adjacency = Test.get_train_data(decay=3.)
expected_L = [2.149652, 2.799746, 4.463995]
expected_C = [[15.685827, 16.980316, 30.232248],
[16.980316, 23.765304, 36.597161],
[30.232248, 36.597161, 66.271089]]
expected_K = [[49.179092, -959.246309, -563.529052],
[-353.706952, -1888.600201, -1839.608349],
[-208.913969, -2103.952235, -150.937999]]
learner = HawkesCumulantMatching(100.)
learner._set_data(timestamps)
self.assertFalse(learner._cumulant_computer.cumulants_ready)
learner.compute_cumulants()
self.assertTrue(learner._cumulant_computer.cumulants_ready)
np.testing.assert_array_almost_equal(learner.mean_intensity,
expected_L)
np.testing.assert_array_almost_equal(learner.covariance,
expected_C)
np.testing.assert_array_almost_equal(learner.skewness, expected_K)
self.assertAlmostEqual(learner.approximate_optimal_cs_ratio(),
0.999197628503)
learner._set_data(timestamps)
self.assertTrue(learner._cumulant_computer.cumulants_ready)
def test_hawkes_cumulants_unfit(self):
"""...Test that HawkesCumulantMatching raises an error if no data is
given
"""
learner = HawkesCumulantMatching(100., cs_ratio=0.9, max_iter=299,
print_every=30, step=1e-2,
solver='adam')
msg = '^Cannot compute cumulants if no realization has been provided$'
with self.assertRaisesRegex(RuntimeError, msg):
learner.compute_cumulants()
def run_cumulants(data):
def objective(h):
model = HawkesCumulantMatching(h, max_iter=300)
model.fit(data)
return model.objective(model.adjacency)
best = fmin(objective,
space=hp.uniform('h', 1, 100),
algo=tpe.suggest,
max_evals=20)
half_width = best['h']
model = HawkesCumulantMatching(half_width, max_iter=300, verbose=True)
model.fit(data)
return model
def get_model(args, n_types):
if args.model == "ERPP":
model = ExplainableRecurrentPointProcess(n_types=n_types, **vars(args))
elif args.model == "RPPN":
model = RecurrentPointProcessNet(n_types=n_types, **vars(args))
elif args.model == "HExp":
from tick.hawkes import HawkesExpKern
model = HawkesExpKern(args.decay, C=args.penalty, verbose=args.verbose)
elif args.model == "HSG":
from tick.hawkes import HawkesSumGaussians
model = HawkesSumGaussians(
args.max_mean,
n_gaussians=args.n_gaussians,
C=args.penalty,
n_threads=args.n_threads,
verbose=args.verbose,
)
elif args.model == "NPHC":
from tick.hawkes import HawkesCumulantMatching
model = HawkesCumulantMatching(
integration_support=args.integration_support,
C=args.penalty,
verbose=args.verbose,
)
else:
raise ValueError(f"Unsupported model={args.model}")
return model
def test_hawkes_cumulants_solve_l2(self):
"""...Test that hawkes cumulant reached expected value with l2
penalization
"""
timestamps, baseline, adjacency = Test.get_train_data(decay=3.)
learner = HawkesCumulantMatching(
100., cs_ratio=0.9, max_iter=299, print_every=30, step=1e-2,
solver='adam', penalty='l2', C=0.1, tol=1e-5)
learner.fit(timestamps)
expected_R_pred = [[0.516135, -0.484529,
-0.323191], [-0.265853, 0.291741, -0.35285],
[0.482819, 0.331344, 1.591535]]
np.testing.assert_array_almost_equal(learner.solution,
expected_R_pred)
expected_baseline = [17.066997, 17.79795, -6.07811]
np.testing.assert_array_almost_equal(learner.baseline,
expected_baseline)
expected_adjacency = [[-1.310854, -2.640152, -1.054596],
[-1.004887, -2.886297,
-1.065671], [0.910245, 1.610029, 0.913469]]
np.testing.assert_array_almost_equal(learner.adjacency,
expected_adjacency)
np.testing.assert_array_almost_equal(
learner.objective(learner.adjacency), 149232.94041039888)
np.testing.assert_array_almost_equal(
learner.objective(R=learner.solution), 149232.94041039888)
def test_hawkes_cumulants_solve_l1(self):
"""...Test that hawkes cumulant reached expected value with l1
penalization
"""
timestamps, baseline, adjacency = Test.get_train_data(decay=3.)
learner = HawkesCumulantMatching(
100., cs_ratio=0.9, max_iter=299, print_every=30, step=1e-2,
solver='adam', penalty='l1', C=1, tol=1e-5)
learner.fit(timestamps)
expected_R_pred = [[0.434197, -0.552021,
-0.308883], [-0.299366, 0.272764, -0.347764],
[0.48448, 0.331059, 1.591587]]
np.testing.assert_array_almost_equal(learner.solution,
expected_R_pred)
expected_baseline = [32.788801, 29.324684, -13.275885]
np.testing.assert_array_almost_equal(learner.baseline,
expected_baseline)
expected_adjacency = [[-2.925945, -5.54899, -1.97438],
[-2.201373, -5.009153,
-1.740234], [1.652958, 2.939054, 1.334677]]
np.testing.assert_array_almost_equal(learner.adjacency,
expected_adjacency)
np.testing.assert_array_almost_equal(
learner.objective(learner.adjacency), 149061.5590630687)
np.testing.assert_array_almost_equal(
learner.objective(R=learner.solution), 149061.5590630687)
def objective(h):
model = HawkesCumulantMatching(h, max_iter=300)
model.fit(data)
return model.objective(model.adjacency)
np.random.seed(7168)
n_nodes = 3
baselines = 0.3 * np.ones(n_nodes)
decays = 0.5 + np.random.rand(n_nodes, n_nodes)
adjacency = np.array([
[1, 1, -0.5],
[0, 1, 0],
[0, 0, 2],
], dtype=float)
adjacency /= 4
end_time = 1e5
integration_support = 5
n_realizations = 5
simu_hawkes = SimuHawkesExpKernels(
baseline=baselines, adjacency=adjacency, decays=decays,
end_time=end_time, verbose=False, seed=7168)
simu_hawkes.threshold_negative_intensity(True)
multi = SimuHawkesMulti(simu_hawkes, n_simulations=n_realizations, n_threads=-1)
multi.simulate()
nphc = HawkesCumulantMatching(integration_support, cs_ratio=.15, tol=1e-10,
step=0.3)
nphc.fit(multi.timestamps)
plot_hawkes_kernel_norms(nphc)
def test_hawkes_cumulants_solve(self):
"""...Test that hawkes cumulant reached expected value
"""
timestamps, baseline, adjacency = Test.get_train_data(decay=3.)
learner = HawkesCumulantMatching(100.,
cs_ratio=0.9,
max_iter=300,
print_every=30,
step=1e-2,
solver='adam',
C=1e-3,
tol=1e-5)
learner.fit(timestamps)
expected_R_pred = [[0.423305, -0.559607, -0.307212],
[-0.30411, 0.27066, -0.347162],
[0.484648, 0.331057, 1.591584]]
np.testing.assert_array_almost_equal(learner.solution,
expected_R_pred)
expected_baseline = [36.808583, 32.304106, -15.123118]
np.testing.assert_array_almost_equal(learner.baseline,
expected_baseline)
expected_adjacency = [[-3.34742247, -6.28527387, -2.21012092],
[-2.51556256, -5.55341413, -1.91501755],
[1.84706793, 3.2770494, 1.44302449]]
np.testing.assert_array_almost_equal(learner.adjacency,
expected_adjacency)
np.testing.assert_array_almost_equal(
learner.objective(learner.adjacency), 149029.4540306161)
np.testing.assert_array_almost_equal(
learner.objective(R=learner.solution), 149029.4540306161)
# Ensure learner can be fit again
timestamps_2, baseline, adjacency = Test.get_train_data(decay=2.)
learner.step = 1e-1
learner.penalty = 'l2'
learner.fit(timestamps_2)
expected_adjacency_2 = [[-0.021966, -0.178811, -0.107636],
[0.775206, 0.384494, 0.613925],
[0.800584, 0.581281, 0.60177]]
np.testing.assert_array_almost_equal(learner.adjacency,
expected_adjacency_2)
learner_2 = HawkesCumulantMatching(100.,
cs_ratio=0.9,
max_iter=299,
print_every=30,
step=1e-1,
solver='adam',
penalty='l2',
C=1e-3,
tol=1e-5)
learner_2.fit(timestamps_2)
np.testing.assert_array_almost_equal(learner.adjacency,
expected_adjacency_2)
# Check cumulants are not computed again
learner_2.step = 1e-2
learner_2.fit(timestamps_2)
