def test_zashitim_taigu_noconfirm(post_id, new_lib=False):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(post_id)
min_delay = cascade.get_minimum_delay()
#outcome = cascade.get_outcome(normalization_factor=min_delay)
outcome = cascade.get_outcome(normalization_factor=3600)
counters = casman.underlying_net.counters_meta
print(casman.get_underlying_path())
tmax = max(outcome.values())
tmax *= 1.5
if new_lib:
est.set_diffusion_data_newlib(casman.get_underlying_path(),
outcome=outcome,
observe_time=tmax)
print('Max: ' + str(
est.llmax_for_diffusion_noconfirm(
8.65381982e-04, 3.07426738e-08, 1.53208126e-07, new_lib=True)))
else:
est.set_diffusion_data(casman.get_underlying_path(),
counters=counters,
outcome=outcome)
print('Max: ' + str(
est.llmax_for_diffusion_noconfirm(1.41127581e-03,
1.07595601e-10,
2.65026813e-07,
observe_time=tmax)))
def test_zashitim_taigu_ICM_compare(post_id):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascades = []
tmaxs = []
cascade = casman.get_cascade_by_id(post_id)
outcome_old = cascade.get_outcome(normalization_factor=3600)
outcome = cascade.get_outcome_connected_only(
casman.underlying_net.network.nodes, normalization_factor=3600)
print("Outcome total size: " + str(len(outcome_old)))
print("Outcome giant size: " + str(len(outcome)))
print(outcome)
print(casman.get_underlying_path())
tmax = max(outcome.values())
tmax *= 1.5
est.set_diffusion_data_newlib(casman.get_underlying_path(),
outcome=outcome,
observe_time=tmax)
#est.set_alpha_for_node(-164443025, 30)
result1 = est.llmax_for_diffusion_SI_decay(1.49969075e-05, 1.34657003e-02)
#result1 = est.llmax_for_diffusion_SI_decay(1.49969075e-05, 0.03545553788287585, fixed_delta=True)
print(result1)
result2 = est.llmax_for_diffusion_ICM(0.001)
print(result2)
print("1-exp(theta/delta): " +
str(1. - math.exp(-result1['theta'] / result1['decay'])))
#print("1-exp(theta/delta): " + str(1. - math.exp(-result1['theta'] / 0.03545553788287585)))
print("ICM p: " + str(result2['theta']))
def test_zashitim_taigu_confirm_multiple(post_ids=None):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascades = []
outcomes = []
tmaxs = []
if post_ids is None:
cascades = casman.cascades
else:
for post_id in post_ids:
cascades.append(casman.get_cascade_by_id(post_id))
for cascade in cascades:
outcome = cascade.get_outcome(normalization_factor=3600)
print(len(outcome))
outcomes.append(outcome)
tmaxs.append(1.5 * max(outcome.values()))
return
print(casman.get_underlying_path())
est.set_diffusion_data_ensemble_newlib(casman.get_underlying_path(),
outcomes=outcomes,
observe_times=tmaxs)
thetas0 = [2.0334495598850714e-05 for i in range(len(outcomes))]
relics0 = [1.1620706275554255e-08 for i in range(len(outcomes))]
confirms0 = [0. for i in range(len(outcomes))]
#thetas0 = [6.640777840198647e-05, 2.0334495598850714e-05, 2.4494338855225353e-05]
#relics0 = [1.1620706275554255e-08, 1.8733700717464615e-08, 3.811750042715803e-08]
print('Max: ' + str(
est.llmax_for_diffusion_confirm_multiple_cases(
thetas0=thetas0,
decay0=0.03688237209138165,
relics0=relics0,
confirms0=confirms0)))
def simulate_diffusion_cascades_zashitim_taigu(n_cascades, theta, rho, delta):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(6846)
print(cascade.get_minimum_delay())
print(max(cascade.get_outcome(normalization_factor=3600).values()))
est.set_alpha_for_node(-164443025, 25)
outcome = Simulator.simulate_SI_decay_confirm_relicexp_hetero(
casman.underlying_net.network,
theta,
delta,
.0,
rho,
infected={-164443025},
tmax=2500.,
dt=30,
echo=True)
print(outcome)
def test_zashitim_taigu_confirm(post_id):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(post_id)
min_delay = cascade.get_minimum_delay()
outcome = cascade.get_outcome(normalization_factor=min_delay)
counters = casman.underlying_net.counters_meta
print(casman.get_underlying_path())
est.set_diffusion_data(casman.get_underlying_path(),
counters=counters,
outcome=outcome)
print('Max: ' + str(
est.llmax_for_diffusion(1.00019217e-6, 3.61779381e-01, 4.07895457e-03,
1.24224204e-07)))
def test_zashitim_taigu_noconfirm_converge(post_id, delta, steps=10):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(post_id)
outcome = cascade.get_outcome(normalization_factor=3600)
print(casman.get_underlying_path())
tmax = max(outcome.values())
tmax *= 1.5
delta_t = tmax / (20 * steps + 1)
thetas = []
rhos = []
lengths = []
thetasICM = []
rhosICM = []
print(outcome)
for i in range(steps):
t = delta_t * (i + 1)
new_outcome = {}
for k, v in outcome.items():
if v <= t:
new_outcome[k] = v
print("T: ", t)
print("Outcome length: ", len(new_outcome))
est.set_diffusion_data_newlib(casman.get_underlying_path(),
outcome=new_outcome,
observe_time=t)
#est.set_alpha_for_node(-164443025, 15)
result = est.llmax_for_diffusion_SI_relic(6.102733267373096e-05,
1.2196174044195352e-08,
delta=delta,
new_lib=True)
thetas.append(result['theta'])
rhos.append(result['relic'])
result2 = est.llmax_for_diffusion_ICM(0.0011, 0.0000011)
thetasICM.append(result2['theta'])
rhosICM.append(result2['relic'])
lengths.append(len(new_outcome))
print(thetas)
print(rhos)
print(lengths)
print(thetasICM)
print(rhosICM)
def test_zashitim_taigu_dlldthetas(post_id, theta, decay, relic):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(post_id)
min_delay = cascade.get_minimum_delay()
outcome = cascade.get_outcome(normalization_factor=min_delay)
counters = casman.underlying_net.counters_meta
print(casman.get_underlying_path())
est.set_diffusion_data(casman.get_underlying_path(),
counters=counters,
outcome=outcome)
dlldthetas = est.dlldthetas_noconfirm(theta, decay, relic, outcome.keys())
cascade.network.add_node_attribute('dth', 'dLL_dTheta', 'float')
for k, v in dlldthetas.items():
cascade.network.add_meta_for_node(k, {'dth': v})
cascade.network.export_gexf(
'D:/BigData/Charity/Cascades/Zashitim_taigu_-164443025_' +
str(post_id) + '_dlldth.gexf')
show_dlldthetas(dlldthetas)
def test_zashitim_taigu_noconfirm_theta_graph(post_id, decay, relic):
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(post_id)
min_delay = cascade.get_minimum_delay()
outcome = cascade.get_outcome(normalization_factor=min_delay)
counters = casman.underlying_net.counters_meta
print(casman.get_underlying_path())
est.set_diffusion_data(casman.get_underlying_path(),
counters=counters,
outcome=outcome)
print(outcome)
tmax = max(outcome.values())
tmax *= 1.5
thetas = np.arange(1e-9, 1e-6, 1e-8)
lls = np.zeros(len(thetas))
maxll = -999999999999999999.
for i in range(len(thetas)):
lls[i] = -est.loglikelyhood_noconfirm([thetas[i], decay, relic],
observe_time=tmax)
print(str(i) + ' - ' + str(lls[i]))
if (maxll < lls[i]):
maxll = lls[i]
maxll -= 300
for i in range(len(thetas)):
lls[i] = lls[i] - maxll
print(lls[i])
if lls[i] < 0:
lls[i] = 0
else:
lls[i] = math.exp(lls[i])
plt.plot(thetas, lls)
plt.show()
return self.libr.DADSLogLikelyhoodByNodeTheta(node_theta, node_id,
theta, confirm, decay,
relic, observe_time)
def thetas_derivatives(self, theta, confirm, decay, relic, ids,
observe_time):
self.libr.DADSCalculateDerivatives(theta, confirm, decay, relic,
observe_time)
derivatives = {}
for id in ids:
derivatives[id] = self.libr.DADSDLogLikelyhoodDthetaForId(id)
return derivatives
if __name__ == '__main__':
casman = CascadesManager(name='Zashitim_taigu_-164443025_8726_8021_6846',
base_dir='D:/BigData/Charity/Cascades/')
casman.load_from_file()
cascade = casman.get_cascade_by_id(8726)
min_delay = cascade.get_minimum_delay()
outcome = cascade.get_outcome(normalization_factor=min_delay)
counters = casman.underlying_net.counters_meta
estmtr = DiffusionEstimator(True)
estmtr.load_network(
'D:/BigData/Charity/Cascades/Zashitim_taigu_-164443025_8726_8021_6846u_network.dos',
counters, outcome)
for i in range(10):
ll = estmtr.loglikelyhood(0.0000001, 0.5, 0.00001, 0.00000001)
print(str(i) + ' ' + str(ll))
#150437823 28082048 519339743
# Created by Gubanov Alexander (aka Derzhiarbuz) at 05.06.2019 # Contacts: [email protected] from da_icascades_manager import CascadesManager from da_information_conductivity import InformationConductivity import numpy as np import matplotlib.pylab as pl casman = CascadesManager(name='WorldVita', base_dir='D:/BigData/Charity/Cascades/') casman.load_from_file() casman.update_cascades(uselikes=False, usehiddens=False) cond_net = InformationConductivity() cond_net.make_cascades_summary(casman.cascades, logweight=False) cond_net.make_underlying_summary([casman.underlying_net]) # print(cond_net.cascades_network.links) # print(cond_net.cascades_network.nodes) # print(cond_net.cascades_network.nodes_attributes) # print(len(cond_net.underlying.nodes)) # cond_net.cascades_network.export_gexf('D:/BigData/Charity/Cascades/PomogatLegko100Summary.gexf', drop_singletones=True) summary = cond_net.nodes_summary() print(summary['in']) print(len(summary['in'])) print(summary['out']) print(len(summary['out'])) print(summary['in_w']) print(len(summary['in_w'])) print(summary['out_w']) print(len(summary['out_w']))