def g2_vola_heat_map():
file_name = du.data_dir + 'swo_gbp_g2pp_nn_adj_err_s0.5_0-264_mse_lr_1.0e-04_ex6_lay9_d20_bn_res_1_rlr_5.0e-01_rlrmin_5.0e-06_rlrpat_10_estop_41.p'
model = nn.read_model(file_name)
model_dict = inst.g2
swo = inst.get_swaptiongen(model_dict)
errs = swo.history_heatmap(model, dates=swo._dates[264:308])
def plot_history(file_name):
file_name = "../data_corr_mid_2014/" + file_name
model = nn.read_model(file_name)
data = np.array(model.history['history']['val_loss'])
data.shape = (data.shape[0], 1)
du.plot_data(None, data)
return data
def run(x, total = 499000., compare=False, epochs=500, prefix='SWO GBP ',
postfix='', dropout_first=None, dropout_middle=None,
dropout_last=None, dropout=0.2, earlyStopPatience=125, reduceLRPatience=40,
reduceLRFactor=0.5, reduceLRMinLR=0.000009, save=True, layers=4,
lr=0.001, exponent=6, load=False, file_name=None, model_dict=inst.g2,
residual_cells=1, train_file=None, do_transform=True, loss='mean_squared_error'):
assert residual_cells >= 0
lossid = "".join(map(lambda x: x[0], loss.split('_')))
postfix += "_" + lossid + '_lr_%.1e_ex%s_lay%s_d%s' % (lr, exponent, layers, int(dropout*100))
if residual_cells > 0:
postfix = postfix + '_bn_res_%s' % residual_cells
else:
postfix = postfix + '_simple'
postfix = postfix + '_rlr_%.1e_rlrmin_%.1e_rlrpat_%s_estop_%s' % (reduceLRFactor, reduceLRMinLR, reduceLRPatience, earlyStopPatience)
print('run ' + str(x) + ' ' + postfix)
if x < 1.0:
nn.total_size = x
else:
nn.total_size = x/total
nn.valid_size = nn.total_size*0.2
nn.test_size = 0.0
if load:
assert(file_name is not None)
model = nn.read_model(file_name)
else:
model = nn.hullwhite_fnn(exponent=exponent, layers=layers, lr=lr,
prefix=prefix, postfix=postfix,
dropout=dropout,
dropout_first=dropout_first,
dropout_middle=dropout_middle,
dropout_last=dropout_last,
earlyStopPatience=earlyStopPatience,
reduceLRPatience=reduceLRPatience,
reduceLRFactor=reduceLRFactor,
reduceLRMinLR=reduceLRMinLR,
model_dict=model_dict,
residual_cells=residual_cells,
train_file=train_file,
do_transform=do_transform,
activation="elu")
model.train(epochs)
if save:
nn.write_model(model)
if compare:
swo = inst.get_swaptiongen(model_dict)
(dates, values) = swo.compare_history(model)
file_name = du.data_dir + 'test' + postfix + '_'
np.save(file_name+'values', values)
np.save(file_name+'dates', dates)
np.save(file_name+'val_hist', model.history['history']['val_loss'])
np.save(file_name+'train_hist', model.history['history']['loss'])
return 0
def run(x,
total=239575.,
compare=False,
epochs=500,
prefix='SWO GBP ',
postfix='',
dropout_first=0.2,
dropout_middle=0.2,
dropout_last=0.2,
save=True,
layers=4,
lr=0.001,
exponent=6,
load=False,
file_name=None):
print 'run ' + str(x) + ' ' + postfix
nn.total_size = x / total
nn.valid_size = nn.total_size * 0.2
nn.test_size = 0.0
if load:
assert (file_name is not None)
model = nn.read_model(file_name)
else:
model = nn.hullwhite_elu(exponent=exponent,
layers=layers,
lr=lr,
prefix=prefix,
postfix=postfix,
dropout_first=dropout_first,
dropout_middle=dropout_middle,
dropout_last=dropout_last)
model.train(epochs)
if save:
nn.write_model(model)
if compare:
swo = inst.getSwaptionGen(inst.hullwhite_analytic)
(dates, values) = swo.compare_history(model)
file_name = du.data_dir + 'test' + postfix + '_fnn_l' \
+ str(layers) + '_e' + str(exponent) + '_epoch' + str(epochs) + '_'
np.save(file_name + 'values', values)
np.save(file_name + 'dates', dates)
np.save(file_name + 'val_hist', model.history['history']['val_loss'])
np.save(file_name + 'train_hist', model.history['history']['loss'])
return 0
def g2_objective_graph():
mark_read_1 = 'adj_err_s'
mark_read_2 = '_mse_lr_1.0e-04_ex6_lay9_d20_bn_res_1_rlr_5.0e-01_rlrmin_5.0e-06_rlrpat_10_estop_41'
mark_write = 'history_adj_err_4m'
data_labels = ('Simulated Annealing', 'Neural Network')
labels = ('0.5_0-264', '0.5_44-308', '0.5_88-352', '0.5_132-396',
'0.5_176-440', '0.5_220-484', '0.5_264-528', '0.5_308-572',
'0.5_352-616', '0.99_396-660', '0.99_440-704', '0.99_484-748',
'0.99_528-792', '0.99_572-836', '0.99_616-880')
labels = ('0.5_0-264', '0.5_88-352', '0.5_176-440', '0.5_264-528',
'0.5_352-616', '0.99_440-704', '0.99_528-792', '0.99_616-880')
model_dict = inst.g2
swo = inst.get_swaptiongen(model_dict)
max_rank = len(labels) - 1
prev = 0
npv = None
vola = None
for rank, label in enumerate(labels):
dates, values, _, _ = get_fnn(mark_read_1 + label + mark_read_2)
if npv is None:
npv = np.empty((dates.shape[0], len(data_labels)))
npv.fill(np.nan)
vola = np.empty((dates.shape[0], len(data_labels)))
vola.fill(np.nan)
out_of_sample = int(label.split('_')[1].split('-')[1])
file_name = du.data_dir + 'swo_gbp_g2pp_nn_' + mark_read_1 + label + mark_read_2 + '.p'
model = nn.read_model(file_name)
if rank < max_rank:
max_date = int(labels[rank + 1].split('_')[1].split('-')[1])
else:
max_date = -1
#Objective prior
npv[prev:max_date,
1], vola[prev:max_date,
1] = swo.objective_values(model, prev, max_date)
temp = values[prev:max_date, 4] #History
temp3 = values[prev:max_date, 3] #Default starting point
filt = temp3 < temp
temp[filt] = temp3[filt]
npv[prev:max_date, 0] = temp
temp_v = values[prev:max_date, 1] #History
temp3_v = values[prev:max_date, 0] #Default starting point
filt = temp3_v < temp_v
temp_v[filt] = temp3_v[filt]
vola[prev:max_date, 0] = temp_v
prev = max_date
vola *= 100
#colors = ('#66c2a5', '#fc8d62', '#8da0cb')
colors = ('#fc8d62', '#8da0cb')
du.plot_data(dates,
npv,
figsize=(21, 12),
labels=data_labels,
save=du.data_dir + mark_write + '_npv_error_fnn.eps',
colors=colors,
legend_fontsize=22,
legend_color='black',
xlabel_fontsize=22,
xlabel_color='black',
ylabel_fontsize=22,
ylabel_color='black',
xtick_fontsize=18,
xtick_color='black',
yticks_format='{:.2f}',
ytick_fontsize=18,
ytick_color='black',
title='NPV Mean Square Error',
title_fontsize=26,
out_of_sample=out_of_sample)
du.plot_data(dates,
vola,
figsize=(21, 12),
labels=data_labels,
save=du.data_dir + mark_write + '_vola_error_fnn.eps',
colors=colors,
legend_fontsize=22,
legend_color='black',
xlabel_fontsize=22,
xlabel_color='black',
ylabel_fontsize=22,
ylabel_color='black',
xtick_fontsize=18,
xtick_color='black',
yticks_format='{:.2f} %',
ytick_fontsize=18,
ytick_color='black',
title='Average Volatility Error',
title_fontsize=26,
out_of_sample=out_of_sample)
temp = vola[:, 1] - vola[:, 0]
temp = temp.reshape((temp.shape[0], 1))
du.plot_data(dates,
temp,
figsize=(21, 12),
labels=None,
save=du.data_dir + mark_write + '_vola_diff_error_fnn.eps',
colors=colors,
legend_fontsize=22,
legend_color='black',
xlabel_fontsize=22,
xlabel_color='black',
ylabel_fontsize=22,
ylabel_color='black',
xtick_fontsize=18,
xtick_color='black',
yticks_format='{:.2f} %',
ytick_fontsize=18,
ytick_color='black',
title='Difference in Average Volatility Error',
title_fontsize=26,
out_of_sample=out_of_sample)
return (npv, vola)
def run(x,
total=499000.,
compare=False,
epochs=500,
prefix='SWO GBP ',
postfix='',
dropout_first=None,
dropout_middle=None,
dropout_last=None,
dropout=0.2,
earlyStopPatience=125,
reduceLRPatience=40,
reduceLRFactor=0.5,
reduceLRMinLR=0.000009,
save=True,
layers=4,
lr=0.001,
exponent=6,
load=False,
file_name=None,
model_dict=inst.g2,
residual_cells=1,
train_file=None,
do_transform=True,
loss='mean_squared_error'):
assert residual_cells >= 0
lossid = "".join(map(lambda x: x[0], loss.split('_')))
postfix += "_" + lossid + '_lr_%.1e_ex%s_lay%s_d%s' % (
lr, exponent, layers, int(dropout * 100))
if residual_cells > 0:
postfix = postfix + '_bn_res_%s' % residual_cells
else:
postfix = postfix + '_simple'
postfix = postfix + '_rlr_%.1e_rlrmin_%.1e_rlrpat_%s_estop_%s' % (
reduceLRFactor, reduceLRMinLR, reduceLRPatience, earlyStopPatience)
print('run ' + str(x) + ' ' + postfix)
if x < 1.0:
nn.total_size = x
else:
nn.total_size = x / total
nn.valid_size = nn.total_size * 0.2
nn.test_size = 0.0
if load:
assert (file_name is not None)
model = nn.read_model(file_name)
else:
model = nn.hullwhite_fnn(exponent=exponent,
layers=layers,
lr=lr,
prefix=prefix,
postfix=postfix,
dropout=dropout,
dropout_first=dropout_first,
dropout_middle=dropout_middle,
dropout_last=dropout_last,
earlyStopPatience=earlyStopPatience,
reduceLRPatience=reduceLRPatience,
reduceLRFactor=reduceLRFactor,
reduceLRMinLR=reduceLRMinLR,
model_dict=model_dict,
residual_cells=residual_cells,
train_file=train_file,
do_transform=do_transform,
activation="elu")
model.train(epochs)
if save:
nn.write_model(model)
if compare:
swo = inst.get_swaptiongen(model_dict)
(dates, values) = swo.compare_history(model)
file_name = du.data_dir + 'test' + postfix + '_'
np.save(file_name + 'values', values)
np.save(file_name + 'dates', dates)
np.save(file_name + 'val_hist', model.history['history']['val_loss'])
np.save(file_name + 'train_hist', model.history['history']['loss'])
return 0