prediction

prediction.py

chosenmodel='Logistic'

if chosenmodel=="Logistic":
    print("Chosen Model: ", chosenmodel)
    reg_model = logistic_model
elif chosenmodel=='Gompertz':
    print("Chosen Model: ", chosenmodel)
    reg_model = gompertz_model


for country in countries_list:
  print("\n ***Country: "+str(country))
  x = np.array(list(df_dict[country]['DayCount'].values))
  print("Used data, Day Count: ", x[-1+leaveout])
  print("Used data, Last day date: ", datetime.strftime(df_dict[country].index[-1+leaveout], ' %d, %b %Y' ))
  y = np.array(list(df_dict[country]['Cases'].values))
  print("Used data, Number of cases: "+casestr+" in the last day: ", y[-1+leaveout])
  print("---")
  pred_days = 0
  day = x[-1]+pred_days
  pred_date = datetime.strftime(df_dict[country].index[-1] + timedelta(days=pred_days), ' %d, %b %Y' )
  print("Prediction for: ", pred_date)
  
  realdat = y[-1]


  a,b,c = (c_pars[(country,chosenmodel,par)] for par in ['a','b','c'])
  sigma_a,sigma_b,sigma_c = (c_pars[(country,chosenmodel,par)] for par in ['sga','sgb','sgc'])

  prediction = reg_model(day,a,b,c)
  pluspred = np.abs(reg_model(day,np.abs(a+sigma_a),np.abs(b+sigma_b),np.abs(c+sigma_c)))
  minuspred = np.abs(reg_model(day,np.abs(a-sigma_a),np.abs(b-sigma_b),np.abs(c-sigma_c)))

  print("Prediction from "+chosenmodel+" Model : ", frm(prediction), ' ( +'+frm(pluspred-prediction)+', -'+frm(prediction-minuspred)+')')

  print("Real data: ", frm(realdat))
  prediction_error_perc = 100*(prediction-realdat)/realdat
  print("Prediction Percentage error: ", '{:2f}'.format(prediction_error_perc), '%')