def inference(dataset, decays):
adj, events, n = load(dataset)
tick_events = events_to_tick_events(events, n)
learner = HawkesExpKern(decays=decays, penalty="l1", solver="agd", C=1000, verbose=True)
learner.fit(tick_events)
influence_matrix = learner.adjacency
baseline = learner.baseline
print("score = {}".format(learner.score()))
with open("./model/" + dataset + ".pickle", "wb") as f:
pickle.dump([influence_matrix, baseline, decays, n], f)
return
def test_HawkesExpKern_score(self):
"""...Test HawkesExpKern score method
"""
n_nodes = 2
n_realizations = 3
train_events = [[
np.cumsum(np.random.rand(4 + i)) for i in range(n_nodes)
] for _ in range(n_realizations)]
test_events = [[
np.cumsum(np.random.rand(4 + i)) for i in range(n_nodes)
] for _ in range(n_realizations)]
learner = HawkesExpKern(self.decays)
msg = '^You must either call `fit` before `score` or provide events$'
with self.assertRaisesRegex(ValueError, msg):
learner.score()
given_baseline = np.random.rand(n_nodes)
given_adjacency = np.random.rand(n_nodes, n_nodes)
learner.fit(train_events)
train_score_current_coeffs = learner.score()
self.assertAlmostEqual(train_score_current_coeffs, 2.0855840)
train_score_given_coeffs = learner.score(baseline=given_baseline,
adjacency=given_adjacency)
self.assertAlmostEqual(train_score_given_coeffs, 0.59502417)
test_score_current_coeffs = learner.score(test_events)
self.assertAlmostEqual(test_score_current_coeffs, 1.6001762)
test_score_given_coeffs = learner.score(test_events,
baseline=given_baseline,
adjacency=given_adjacency)
self.assertAlmostEqual(test_score_given_coeffs, 0.89322199)