def SimExp(baseline, adjacency, decays, num_clusters, data):
hawkes = SimuHawkesExpKernels(adjacency=adjacency,
decays=decays,
baseline=baseline,
verbose=False)
#dt = 0.001 #millisecond granularity
#hawkes.track_intensity(dt) # turning this on will eat up memory
#need to compute and draw from the cluster length distrbution from the original data
cluster_lengths = ComputeClusterLengths(data)
multi = SimuHawkesMulti(hawkes, n_simulations=num_clusters)
multi.end_time = np.random.choice(cluster_lengths,
size=num_clusters,
replace=True)
multi.simulate()
sim_inner_timestamps = multi.timestamps
l = 0
for realisation in sim_inner_timestamps:
for series in realisation:
l += len(series)
print(f"Simulated {l} points")
return sim_inner_timestamps
def _sim_single_exposures(self):
if not self.sparse:
raise ValueError(
"'single_exposure' exposures can only be simulated"
" as sparse feature matrices")
if self.hawkes_exp_kernels is None:
np.random.seed(self.seed)
decays = .002 * np.ones((self.n_features, self.n_features))
baseline = 4 * np.random.random(self.n_features) / self.n_intervals
mult = np.random.random(self.n_features)
adjacency = mult * np.eye(self.n_features)
if self.n_correlations:
comb = list(permutations(range(self.n_features), 2))
if len(comb) > 1:
idx = itemgetter(
*np.random.choice(range(len(comb)),
size=self.n_correlations,
replace=False))
comb = idx(comb)
for i, j in comb:
adjacency[i, j] = np.random.random(1)
self._set(
'hawkes_exp_kernels',
SimuHawkesExpKernels(adjacency=adjacency,
decays=decays,
baseline=baseline,
verbose=False,
seed=self.seed))
self.hawkes_exp_kernels.adjust_spectral_radius(
.1) # TODO later: allow to change this parameter
hawkes = SimuHawkesMulti(self.hawkes_exp_kernels,
n_simulations=self.n_cases)
run_time = self.n_intervals
hawkes.end_time = [1 * run_time for _ in range(self.n_cases)]
dt = 1
self.hawkes_exp_kernels.track_intensity(dt)
hawkes.simulate()
self.hawkes_obj = hawkes
features = [[
np.min(np.floor(f)) if len(f) > 0 else -1 for f in patient_events
] for patient_events in hawkes.timestamps]
features = [
self.to_coo(feat, (run_time, self.n_features)) for feat in features
]
# Make sure patients have at least one exposure?
exposures_filter = itemgetter(
*[i for i, f in enumerate(features) if f.sum() > 0])
features = exposures_filter(features)
n_samples = len(features)
return features, n_samples
if __name__ == '__main__':
end_time = 50000
n_realizations = 1
decay = 3
baseline =np.ones(6) * .03
adjacency = np.zeros((6, 6))
adjacency[2:, 2:] = np.ones((4, 4)) * 0.1
adjacency[:3, :3] = np.ones((3, 3)) * 0.15
hawkes_exp_kernels = SimuHawkesExpKernels(adjacency=adjacency, decays=decay,
baseline=baseline, end_time=end_time,
verbose=False, seed=1039)
multi = SimuHawkesMulti(hawkes_exp_kernels, n_simulations=n_realizations)
multi.end_time = [(i + 1) / n_realizations * end_time for i in range(n_realizations)]
multi.simulate()
type_seq = []
time_seq = []
for i, d in enumerate(multi.timestamps[0]):
for j in d:
type_seq.append(i)
time_seq.append(j)
type_seq = np.array(type_seq)
time_seq = np.array(time_seq)
type_seq = type_seq[np.argsort(time_seq)]
time_seq = time_seq[np.argsort(time_seq)]
para = Parameters(decay=3, K=6)
