def create_shot_list_tmp(original_shot,time_points,sigs=None):
shot_list_tmp = ShotList()
T = len(original_shot.ttd)
t_range = np.linspace(0,T-1,time_points,dtype=np.int)
for t in t_range:
new_shot = copy.copy(original_shot)
assert(new_shot.augmentation_fn == None)
new_shot.augmentation_fn = partial(hide_signal_data,t = t,sigs_to_hide=sigs)
#new_shot.number = original_shot.number
shot_list_tmp.append(new_shot)
return shot_list_tmp,t_range
def create_shot_list_tmp(original_shot,time_points,sigs=None):
shot_list_tmp = ShotList()
T = len(original_shot.ttd)
t_range = np.linspace(0,T-1,time_points,dtype=np.int)
for t in t_range:
new_shot = copy.copy(original_shot)
assert(new_shot.augmentation_fn == None)
new_shot.augmentation_fn = partial(hide_signal_data,t = t,sigs_to_hide=sigs)
#new_shot.number = original_shot.number
shot_list_tmp.append(new_shot)
return shot_list_tmp,t_range
def apply_bleed_in(conf,shot_list_train,shot_list_validate,shot_list_test):
np.random.seed(2)
num = conf['data']['bleed_in']
new_shots = []
if num > 0:
shot_list_bleed = ShotList()
print('applying bleed in with {} disruptive shots\n'.format(num))
num_total = len(shot_list_test)
num_d = shot_list_test.num_disruptive()
num_nd = num_total - num_d
assert(num_d >= num), "Not enough disruptive shots {} to cover bleed in {}".format(num_d,num)
num_sampled_d = 0
num_sampled_nd = 0
while num_sampled_d < num:
s = shot_list_test.sample_shot()
shot_list_bleed.append(s)
if conf['data']['bleed_in_remove_from_test']:
shot_list_test.remove(s)
if s.is_disruptive:
num_sampled_d += 1
else:
num_sampled_nd += 1
print("Sampled {} shots, {} disruptive, {} nondisruptive".format(num_sampled_nd+num_sampled_d,num_sampled_d,num_sampled_nd))
print("Before adding: training shots: {} validation shots: {}".format(len(shot_list_train),len(shot_list_validate)))
assert(num_sampled_d == num)
if conf['data']['bleed_in_equalize_sets']:#add bleed-in shots to training and validation set repeatedly
print("Applying equalized bleed in")
for shot_list_curr in [shot_list_train,shot_list_validate]:
for i in range(len(shot_list_curr)):
s = shot_list_bleed.sample_shot()
shot_list_curr.append(s)
elif conf['data']['bleed_in_repeat_fac'] > 1:
repeat_fac = conf['data']['bleed_in_repeat_fac']
print("Applying bleed in with repeat factor {}".format(repeat_fac))
num_to_sample = int(round(repeat_fac*len(shot_list_bleed)))
for i in range(num_to_sample):
s = shot_list_bleed.sample_shot()
shot_list_train.append(s)
shot_list_validate.append(s)
else: #add each shot only once
print("Applying bleed in without repetition")
for s in shot_list_bleed:
shot_list_train.append(s)
shot_list_validate.append(s)
print("After adding: training shots: {} validation shots: {}".format(len(shot_list_train),len(shot_list_validate)))
print("Added bleed in shots to training and validation sets")
# if num_d > 0:
# for i in range(num):
# s = shot_list_test.sample_single_class(True)
# shot_list_train.append(s)
# shot_list_validate.append(s)
# if conf['data']['bleed_in_remove_from_test']:
# shot_list_test.remove(s)
# else:
# print('No disruptive shots in test set, omitting bleed in')
# if num_nd > 0:
# for i in range(num):
# s = shot_list_test.sample_single_class(False)
# shot_list_train.append(s)
# shot_list_validate.append(s)
# if conf['data']['bleed_in_remove_from_test']:
# shot_list_test.remove(s)
# else:
# print('No nondisruptive shots in test set, omitting bleed in')
return shot_list_train,shot_list_validate,shot_list_test
def apply_bleed_in(conf, shot_list_train, shot_list_validate, shot_list_test):
np.random.seed(2)
num = conf['data']['bleed_in']
# new_shots = []
if num > 0:
shot_list_bleed = ShotList()
print('applying bleed in with {} disruptive shots\n'.format(num))
# num_total = len(shot_list_test)
num_d = shot_list_test.num_disruptive()
# num_nd = num_total - num_d
assert num_d >= num, (
"Not enough disruptive shots {} to cover bleed in {}".format(
num_d, num))
num_sampled_d = 0
num_sampled_nd = 0
while num_sampled_d < num:
s = shot_list_test.sample_shot()
shot_list_bleed.append(s)
if conf['data']['bleed_in_remove_from_test']:
shot_list_test.remove(s)
if s.is_disruptive:
num_sampled_d += 1
else:
num_sampled_nd += 1
print("Sampled {} shots, {} disruptive, {} nondisruptive".format(
num_sampled_nd + num_sampled_d, num_sampled_d, num_sampled_nd))
print("Before adding: training shots: {} validation shots: {}".format(
len(shot_list_train), len(shot_list_validate)))
assert (num_sampled_d == num)
# add bleed-in shots to training and validation set repeatedly
if conf['data']['bleed_in_equalize_sets']:
print("Applying equalized bleed in")
for shot_list_curr in [shot_list_train, shot_list_validate]:
for i in range(len(shot_list_curr)):
s = shot_list_bleed.sample_shot()
shot_list_curr.append(s)
elif conf['data']['bleed_in_repeat_fac'] > 1:
repeat_fac = conf['data']['bleed_in_repeat_fac']
print("Applying bleed in with repeat factor {}".format(repeat_fac))
num_to_sample = int(round(repeat_fac * len(shot_list_bleed)))
for i in range(num_to_sample):
s = shot_list_bleed.sample_shot()
shot_list_train.append(s)
shot_list_validate.append(s)
else: # add each shot only once
print("Applying bleed in without repetition")
for s in shot_list_bleed:
shot_list_train.append(s)
shot_list_validate.append(s)
print("After adding: training shots: {} validation shots: {}".format(
len(shot_list_train), len(shot_list_validate)))
print("Added bleed in shots to training and validation sets")
# if num_d > 0:
# for i in range(num):
# s = shot_list_test.sample_single_class(True)
# shot_list_train.append(s)
# shot_list_validate.append(s)
# if conf['data']['bleed_in_remove_from_test']:
# shot_list_test.remove(s)
# else:
# print('No disruptive shots in test set, omitting bleed in')
# if num_nd > 0:
# for i in range(num):
# s = shot_list_test.sample_single_class(False)
# shot_list_train.append(s)
# shot_list_validate.append(s)
# if conf['data']['bleed_in_remove_from_test']:
# shot_list_test.remove(s)
# else:
# print('No nondisruptive shots in test set, omitting bleed in')
return shot_list_train, shot_list_validate, shot_list_test
