def yt_class_maker(accumulated_dir, df_name):
pd_pkl_list = os.listdir(accumulated_dir)
if 'YearTable_class' in pd_pkl_list:
pass
else:
os.mkdir(accumulated_dir + 'YearTable_class')
if 'YearTable_csv' in pd_pkl_list:
pass
else:
os.mkdir(accumulated_dir + 'YearTable_csv')
pd_pkl_list_raw = os.listdir(accumulated_dir + 'YearTable_csv')
if 'raw_csv' in pd_pkl_list_raw:
pass
else:
os.mkdir(accumulated_dir + 'YearTable_csv/raw_csv')
if df_name + '.pkl' in pd_pkl_list:
temp = pickle_read(accumulated_dir + df_name + '.pkl')
# making pickle object of YearTable class
pickle_write(Ityt.YearTable(np.array(temp), df_name),
accumulated_dir + 'YearTable_class/' + df_name + '.pkl')
# making CSV data from pandas DataFrame
temp.to_csv(accumulated_dir + 'YearTable_csv/raw_csv/' + df_name +
'.csv',
sep='\t',
index=False)
else:
pass
def csv_to_df_YTclass(accumulated_dir):
temp_list = os.listdir(accumulated_dir + 'YearTable_csv/raw_csv')
for i in temp_list:
df = pd.read_csv(accumulated_dir + 'YearTable_csv/raw_csv/' + i)
j = i.replace('.csv', '.pkl')
pickle_write(df, accumulated_dir + j)
pickle_write(Ityt.YearTable(np.array(df), df.name),
accumulated_dir + 'YearTable_class/' + j)
def obj_func_PSO(w1):
w1 = list(np.array(w1) * np.array(alpso_weight) + 1.)
f = Ityt.Uwg(ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).rorac(alpha) * -1.
g = [0.] * 2
g[0] = Ityt.Uwg(ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).xtvar(alpha) - Ityt.Uwg(
ITMC.MCbase(Premium_list, w_initial, cycle), ITMC.MCbase(Loss_list, w_initial, cycle),
ITMC.MCbase(Expense_list, w_initial, cycle)).xtvar(alpha)
g[1] = 0.9 * Ityt.Uwg(ITMC.MCbase(Premium_list, w_initial, cycle), ITMC.MCbase(Loss_list, w_initial, cycle),
ITMC.MCbase(Expense_list, w_initial, cycle)).xtvar(alpha) - Ityt.Uwg(
ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).xtvar(alpha)
fail = 0
print 'obj_value : ', f, ' variable : ', w1
print 'penalty(negative value is valid) : ', g[0], g[1]
return f, g, fail
def GA_const_JandH(w, C_JandH, alpha_JandH, ngen_count, feasible, adjuster):
xtvar_check = Ityt.Uwg(ITMC.MCbase(Premium_list, w_initial, cycle), ITMC.MCbase(Loss_list, w_initial, cycle),
ITMC.MCbase(Expense_list, w_initial, cycle)).xtvar(alpha) - Ityt.Uwg(
ITMC.MCbase(Premium_list, w, cycle), ITMC.MCbase(Loss_list, w, cycle),
ITMC.MCbase(Expense_list, w, cycle)).xtvar(alpha)
if abs(xtvar_check) < feasible:
feasible_ret = 0
else:
feasible_ret = (C_JandH * ngen_count) ** alpha_JandH * abs(xtvar_check)
return feasible_ret * adjuster
quicksum(
Aggregate(x[i], Premium_list_gurobi[i], realization_num) -
Aggregate(x[i], Loss_list_gurobi[i], realization_num) -
Aggregate(x[i], Expense_list_gurobi[i], realization_num)
for i in range(I)) / realization_num, GRB.MAXIMIZE)
model_TVaR.update()
model_TVaR.__data = x
model_TVaR.__data = VaR
return model_TVaR
default_TVaR = Ityt.Uwg(ITMC.MCbase(Premium_list, w0, cycle),
ITMC.MCbase(Loss_list, w0, cycle),
ITMC.MCbase(Expense_list, w0, cycle)).xtvar(alpha)
model_TVaR = markowitz_TVaR(I, cycle, default_TVaR, alpha, Premium_list_gurobi,
Loss_list_gurobi, Expense_list_gurobi)
model_TVaR.optimize()
status = model_TVaR.Status
if status == GRB.Status.UNBOUNDED or status == GRB.Status.INF_OR_UNBD:
model_TVaR.setObjective(0, GRB.MAXIMIZE)
model_TVaR.optimize()
status = model_TVaR.Status
if status == GRB.Status.OPTIMAL:
print 'UNbounded'
elif status == GRB.Status.INFEASIBLE:
print 'Infeasible'
def obj_func_ga_initial(w1):
return Ityt.Uwg(ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).rorac(alpha),
def obj_func_ga(w1, ngen_count):
return Ityt.Uwg(ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).rorac(alpha) - GA_const_JandH(w1, C_JandH,
alpha_JandH,
ngen_count, feasible,
adjuster),
def obj_func(w1):
return (Ityt.Uwg(ITMC.MCbase(Premium_list, w1, cycle), ITMC.MCbase(Loss_list, w1, cycle),
ITMC.MCbase(Expense_list, w1, cycle)).uwg_mean() - (Risk_charge * np.array(w1)).sum()) * -1. / total_capital
axes[1].xaxis.set_major_formatter(ptick.ScalarFormatter(useMathText=True))
axes[1].yaxis.set_major_formatter(ptick.ScalarFormatter(useMathText=True))
axes[1].ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
axes[1].tick_params(labelsize=9)
result_w = [] # result of allocation
result_uwg = [] # Uwg class list
result_uwg_mean = []
result_xtvar = []
for i in range(MC_cycle):
w = ITMC.weight_gen(w_lower, w_upper)
if ITMC.constraints(global_const, w):
result_w.append(w)
result_uwg.append(
Ityt.Uwg(ITMC.MCbase(Premium_list, w, cycle), ITMC.MCbase(Loss_list, w, cycle),
ITMC.MCbase(Expense_list, w, cycle)))
uwg_mean_temp = Ityt.Uwg(ITMC.MCbase(Premium_list, w, cycle), ITMC.MCbase(Loss_list, w, cycle), ITMC.MCbase(Expense_list, w, cycle)).uwg_mean()
xtvar_temp = Ityt.peril_allocation(Loss_list, w, cycle).xtvar(alpha)
xtvar_temp = Ityt.peril_allocation(Loss_list, w, cycle).xtvar(alpha)
result_uwg_mean.append(uwg_mean_temp)
result_xtvar.append(xtvar_temp)
print('%d th \t uwg_mean : %d \t\t XTVaR \t : %d' % (i + 1, int(uwg_mean_temp), int(xtvar_temp)))
axes[0].plot(uwg_mean_temp, xtvar_temp, 'o', ms=4, color=mklist[0])
else:
print('\n data is in the infeasible region . \n')
glabel = ITMC.clustering(result_uwg_mean, result_xtvar, num_clusters, result_w, global_opt_result)
for i in range(num_clusters):
axes[1].plot(np.array(result_uwg_mean)[glabel == i], np.array(result_xtvar)[glabel == i], 'o',
color=mklist[i + 1],