jac=None,
hess=None,
hessp=None,
bounds=bounds,
constraints=(),
tol=None,
callback=None,
options=None)
elif method == 'DE':
ea = DE(simulator.objfunction,
bounds,
mutation=(0.5, 1.0),
maxiters=num)
p, f = ea.solve(show_progress=True)
elif method == 'GA':
ea = Evolutionary(simulator.objfunction,
lower,
upper,
popsize=15,
max_iter=num,
random_state=3)
xopt, gfit = ea.optimize(solver="cpso")
# the simulator object stores all the data itself
df_X = pd.DataFrame(
simulator.X, columns=['Solar (kW)', 'Wind (kW)', 'Battery (kWh)'])
df_Y = pd.DataFrame(
def train(self):
self.death = self.load_dead(self.country)
self.recovered = self.load_recovered(self.country)
if self.cleanRecovered:
zeroRecDeaths=0
else:
zeroRecDeaths=1
self.data = self.load_confirmed(self.country)-zeroRecDeaths*(self.recovered+self.death)
#optmizer solver setup and run
# bounds=[(1e-12, .4), (1e-12, .4), (1/300,0.2), (1/300,0.2), (1/300,0.2),\
# (1e-12, 0.4), (1e-12, 0.2), (1e-12, 0.2)]
# minimizer_kwargs = dict(method="L-BFGS-B", bounds=bounds, args=(self.data, self.recovered, \
# self.death, self.s_0, self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.startNCases, \
# self.weigthCases, self.weigthRecov))
# x0=[0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001]
# optimal = basinhopping(lossOdeint,x0,minimizer_kwargs=minimizer_kwargs,
# niter=200,disp=True)
out=[self.data, self.recovered, \
self.death, self.s_0, self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.startNCases, \
self.weigthCases, self.weigthRecov]
with open('./data/data.pkl','wb') as f:
pickle.dump(out,f)
bounds=[(1e-12, .4), (1e-12, .4), (1/300,0.2), (1/300,0.2), (1/300,0.2),\
(1e-12, 0.4), (1e-12, 0.2), (1e-12, 0.2)]
de = DE(lossOdeint, bounds, mutation=(0.5, 1.0), maxiters=20000)
p, f = de.solve(show_progress=True)
#parameter list for optimization
#beta, beta2, sigma, sigma2, sigma3, gamma, b, mu
print(p)
beta, beta2, sigma, sigma2, sigma3, gamma, b, mu = p
new_index, extended_actual, extended_recovered, extended_death, y0, y1, y2, y3, y4, y5 \
= self.predict(beta, beta2, sigma, sigma2, sigma3, gamma, b, mu, \
self.data, self.recovered, self.death, self.country, self.s_0, \
self.e_0, self.a_0, self.i_0, self.r_0, self.d_0)
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)
plt.rc('font', size=14)
fig, ax = plt.subplots(figsize=(15, 10))
ax.set_title("Global Opt - SEAIR-D Model for "+self.country)
ax.set_ylim((0, max(y0)*1.1))
df.plot(ax=ax) #,style=['-','-','-','o','-','x','-','s','-'])
print(f"country={self.country}, beta={beta:.8f}, beta2={beta2:.8f}, 1/sigma={1/sigma:.8f},"+\
f" 1/sigma2={1/sigma2:.8f},1/sigma3={1/sigma3:.8f}, gamma={gamma:.8f}, b={b:.8f},"+\
f" mu={mu:.8f}, r_0:{(beta/gamma):.8f}")
plt.annotate('Dr. Guilherme Araujo Lima da Silva, www.ats4i.com', fontsize=10,
xy=(1.04, 0.1), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
ha='right',rotation=90)
plt.annotate('Source: https://www.lewuathe.com/covid-19-dynamics-with-sir-model.html', fontsize=10,
xy=(1.06,0.1), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
ha='left',rotation=90)
plt.annotate('Original SEIR-D with delay model, São Paulo, Brazil', fontsize=10,
xy=(1.045,0.1), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
ha='left',rotation=90)
df.to_pickle('./data/SEAIRD_sigmaOpt_'+self.country+'.pkl')
country=self.country
strFile ="./results/modelSEAIRDOptGlobalOptimum"+country+".png"
savePlot(strFile)
plt.show()
plt.close()
plotX=new_index[range(0,self.predict_range)]
plotXt=new_index[range(0,len(extended_actual))]
fig, ax = plt.subplots(figsize=(15, 10))
ax.set_title("Zoom SEAIR-D Model for "+self.country)
plt.xticks(np.arange(0, self.predict_range, self.predict_range/8))
ax.set_ylim(0,max(y3)*1.1)
ax.plot(plotX,y3,'y-',label="Infected")
ax.plot(plotX,y4,'c-',label="Recovered")
ax.plot(plotX,y5,'m-',label="Deaths")
ax.plot(plotXt,extended_actual,'o',label="Infected data")
ax.plot(plotXt,extended_death,'x',label="Death data")
ax.plot(plotXt,extended_recovered,'s',label="Recovered data")
ax.legend()
plt.annotate('Dr. Guilherme A. L. da Silva, www.ats4i.com', fontsize=10,
xy=(1.04, 0.1), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
ha='right',rotation=90)
plt.annotate('Original SEAIR-D with delay model, São Paulo, Brazil', fontsize=10,
xy=(1.045,0.1), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
ha='left',rotation=90)
strFile ="./results/ZoomModelSEAIRDOpt"+country+".png"
savePlot(strFile)
plt.show()
plt.close()
print(self.country+" is done!")