def train(self):
self.death = self.load_dead()
self.recovered = self.load_recovered()
self.data = self.load_confirmed() - self.recovered - self.death
size = len(self.data)
bounds = [(1e-20, .2), (1e-20, .2), (5, size - 5), (1e-20, .2),
(1 / 120, 1.), (1e-20, .4), (1e-20, .4), (1e-20, .4),
(1e-20, .4), (1e-20, .4)]
maxiterations = 5500
f = self.create_lossOdeint()
de = DE(f, bounds, maxiters=maxiterations) #,popsize=100)
i = 0
with tqdm(total=maxiterations * 1750 * maxiterations / 3500) as pbar:
for step in de.geniterator():
idx = step.best_idx
norm_vector = step.population[idx]
best_params = de.denormalize([norm_vector])
pbar.update(i)
i += 1
p = best_params[0]
if self.under:
f = self.create_lossSub(p)
bnds = ((.1, 4), (.1, 4), (.1, 4))
x0 = [0.9, 0.9, 0.9]
minimizer_kwargs = {"method": "L-BFGS-B", "bounds": bnds}
optimal = basinhopping(f,
x0,
minimizer_kwargs=minimizer_kwargs,
disp=True,
niter=100)
p2 = optimal.x
else:
p2 = [1, 1, 1]
p = np.concatenate((p, p2))
beta0, beta01, startT, beta2, sigma, a, b, c, d, mu, sub, subRec, subDth = p
print("country {}".format(self.country))
print("under notifications cases {:.2f}".format(p2[0]))
print("under notifications recovered {:.2f}".format(p2[1]))
print("under notifications deaths {:.2f}".format(p2[2]))
today = datetime.today()
endDate = today + timedelta(days=self.deltaDay)
self.end_date = datetime.strftime(endDate, '%-m/%-d/%y')
self.death = self.load_dead()
self.recovered = self.load_recovered()
self.data = self.load_confirmed() - self.recovered - self.death
new_index, extended_actual, extended_death, extended_recovered, y0, y1, y2, y3, y4, y5 \
= self.predict(p)
#prepare dataframe to export
df = pd.DataFrame(
{
'Susceptible': y0,
'Exposed': y1,
'Asymptomatic': y2,
'Infected data': extended_actual,
'Infected': y3,
'Recovered': extended_recovered,
'Predicted Recovered': y4,
'Death data': extended_death,
'Predicted Deaths': y5
},
index=new_index)
if self.savedata:
#save simulation results for comparison and use in another codes/routines
df.to_pickle('./data/SEAIRDv5_Yabox_' + self.country + '.pkl')
df.to_csv('./results/data/SEAIRDv5_Yabox_' + self.country + '.csv',
sep=",")
del idx, norm_vector, best_params, df,\
new_index, extended_actual, extended_death, extended_recovered, y0, y1, y2, y3, y4, y5
return p
def train(self):
self.death = self.load_dead(self.country)
self.recovered = self.load_recovered(self.country)
self.data = self.load_confirmed(
self.country) - self.recovered - self.death
size = len(self.data)
bounds = [(1e-12, .2), (1e-12, .2), (5, size - 5), (1e-12, .2),
(1 / 160, 0.4), (1 / 160, .4), (1 / 160, .4), (1e-12, .4),
(1e-12, .4), (1e-12, .4), (1e-12, .4), (1e-12, .4), (1., 1.),
(1., 1.), (1., 1.)]
maxiterations = 8500
f=self.create_lossOdeint(self.data, \
self.death, self.recovered, self.s_0, self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.startNCases, \
self.weigthCases, self.weigthRecov)
de = DE(f, bounds, maxiters=maxiterations) #,popsize=100)
i = 0
with tqdm(total=maxiterations * 1750 * maxiterations / 3500) as pbar:
for step in de.geniterator():
idx = step.best_idx
norm_vector = step.population[idx]
best_params = de.denormalize([norm_vector])
pbar.update(i)
i += 1
p = best_params[0]
if self.under:
f=self.create_lossOdeint(self.data, \
self.death, self.recovered, self.s_0, self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.startNCases, \
self.weigthCases, self.weigthRecov)
bnds = ((p[0], p[0]), (p[1], p[1]), (p[2], p[2]), (p[3], p[3]),
(p[4], p[4]), (p[5], p[5]), (p[6], p[6]), (p[7], p[7]),
(p[8], p[8]), (p[9], p[9]), (p[10], p[10]), (p[11], p[11]),
(.5, 1.05), (.5, 1.05), (.5, 1.05))
x0 = [
p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9],
p[10], p[11], 0.9, 0.9, 0.9
]
minimizer_kwargs = {"method": "L-BFGS-B", "bounds": bnds}
optimal = basinhopping(f,
x0,
minimizer_kwargs=minimizer_kwargs,
disp=True)
#parameter list for optimization
beta0, beta01, startT, beta2, sigma, sigma2, sigma3, gamma, b, gamma2, d, mu, sub, subRec, subDth = optimal.x
print("country {}".format(self.country))
print("under notifications cases {:.2f}".format(sub))
print("under notifications recovered {:.2f}".format(subRec))
print("under notifications deaths {:.2f}".format(subDth))
p = optimal.x
else:
beta0, beta01, startT, beta2, sigma, sigma2, sigma3, gamma, b, gamma2, d, mu, sub, subRec, subDth = p
new_index, extended_actual, extended_death, extended_recovered, y0, y1, y2, y3, y4, y5 \
= self.predict(beta0, beta01, startT, beta2, sigma, sigma2, sigma3, gamma, b, gamma2, d, mu, sub, subRec, subDth, \
self.data, self.death, self.recovered, self.country, self.s_0, \
self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.predict_range)
#prepare dataframe to export
df = pd.DataFrame(
{
'Susceptible': y0,
'Exposed': y1,
'Asymptomatic': y2,
'Infected data': extended_actual,
'Infected': y3,
'Recovered': extended_recovered,
'Predicted Recovered': y4,
'Death data': extended_death,
'Predicted Deaths': y5
},
index=new_index)
if self.savedata:
#save simulation results for comparison and use in another codes/routines
df.to_pickle('./data/SEAIRDv5_Yabox_' + self.country + '.pkl')
df.to_csv('./results/data/SEAIRDv5_Yabox_' + self.country + '.csv',
sep=",")
del idx, norm_vector, best_params, df,\
new_index, extended_actual, extended_death, extended_recovered, y0, y1, y2, y3, y4, y5
return p