asymptote and metrics

asymp.py

y_pred = logistic_model(x,ai,bi,ci)
MSLE=sklm.mean_squared_log_error(y,y_pred)
print("Mean squared log error (MSLE): ", '{:.3f}'.format(MSLE))
print("Exp of RMSLE: ", '{:.3f}'.format(np.exp(np.sqrt(MSLE))))
print("R2 score: ", '{:.3f}'.format(sklm.r2_score(y,y_pred)))

perc_flat = 0.98
sol = int(fsolve(lambda x : logistic_model(x,ai,bi,ci) - perc_flat*int(ci), bi))
print('Day of flattening of the infection curve')
datesol = datetime.strftime(df_dict[country].index[0] + timedelta(days=sol), ' %d, %b %Y' )
print('-->'+datesol)
c_pars[(country,'Logistic','c_time')] = sol