parser.add_argument("--fil", default='deg', type=str, help='graph')
parser.add_argument('--s1', default=42, type=int, help='random seed')
parser.add_argument('--s2', default=41, type=int, help='random seed2')
parser.add_argument('--doublefake',
action='store_true',
help='viz two fake dgms')
parser.add_argument('--viz', action='store_true', help='viz or not')
if __name__ == '__main__':
args = parser.parse_args()
graph = args.graph
norm = True
# seed_flag = True # bad. Even double fake has good accuracy.
seed_flag = True
fil = args.fil
gs, labels = load_tugraphs(graph)
subdgms = gs2dgms_parallel(gs,
fil=fil,
fil_d='sub',
norm=norm,
graph=graph,
ntda=False,
debug_flag=False)
supdgms = gs2dgms_parallel(gs,
fil=fil,
fil_d='sup',
norm=norm,
graph=graph,
ntda=False,
debug_flag=False)
epddgms = gs2dgms_parallel(gs,
def main(graph, fil, norm, permute, ss, epd, n_cv, flip, feat, feat_kwargs,
ntda):
"""
All hyperprameter goes here.
:param graph: graph dataset
:param fil: filtration function
:param norm: normalize or not
:param permute: whether permute dgm
:param ss: both sublevel and superlevel or not
:param epd: include extended persistence or not
:param n_cv: number of cross validation
:return:
"""
global gs
print('feat kwargs', feat_kwargs)
db = get_tda_db()
params = {
'graph': graph,
'fil': fil,
'norm': norm,
'permute': permute,
'ss': ss,
'epd': epd,
'n_cv': n_cv,
'flip': flip,
'feat': feat,
'ntda': ntda,
'feat_kwargs': feat_kwargs
}
if check_duplicate(db, params): return
label_flag = dgms_dir_test(fil=fil, fil_d='sub', norm=norm, graph=graph)[1]
# gs, labels = load_graphs(dataset=graph, labels_only=label_flag) # step 1
gs, labels = load_tugraphs(
graph, labels_only=False
) # labels_only true means gs is None. Turned on for high speed
# parallel
# subdgms = gs2dgms(gs, n_jobs=-1, fil=fil, fil_d='sub', norm=norm, graph = graph, ntda = ntda, debug_flag=True)
subdgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
fil_d='sub',
norm=norm,
graph=graph,
ntda=ntda)
supdgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
fil_d='sup',
norm=norm,
graph=graph,
ntda=ntda)
epddgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
one_hom=True,
norm=norm,
graph=graph,
ntda=ntda)
dgms = combine_dgms(subdgms, supdgms, epddgms, ss=ss, epd=epd, flip=flip)
dgms = permute_dgms(dgms, permute_flag=permute) # old way
dgms_summary(dgms)
swdgms = dgms2swdgms(dgms)
if feat == 'sw':
print(feat_kwargs)
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='sw',
**feat_kwargs)
print(k.shape)
cmargs = {'print_flag': 'off'} # confusion matrix
clf = classifier(labels,
labels,
method='svm',
n_cv=n_cv,
kernel=k,
**cmargs)
clf.svm_kernel_(n_splits=10)
elif feat == 'pi': # vector
params = {
'bandwidth': 1.0,
'weight': (1, 1),
'im_range': [0, 1, 0, 1],
'resolution': [5, 5]
}
images = merge_dgms(subdgms,
supdgms,
epddgms,
vectype='pi',
ss=ss,
epd=epd,
**params)
clf = classifier(images, labels, method='svm', n_cv=n_cv)
clf.svm(n_splits=10)
elif feat == 'pss':
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='pss',
**feat_kwargs)
# print(k.shape, k, np.max(k))
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
elif feat == 'wg':
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='wg',
**feat_kwargs)
print(k.shape)
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
elif feat == 'pervec':
cmargs = {'print_flag': 'on'} # confusion matrix
pd_vector = dgms2vec(dgms, vectype='pervec', **feat_kwargs)
clf = classifier(pd_vector, labels, method='svm', n_cv=n_cv, **cmargs)
clf.svm(n_splits=10)
elif feat == 'pf':
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=False,
kernel_type='pf',
**feat_kwargs)
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
else:
raise Exception('No such feat %s' % feat)
print(clf.stat)
print_line()
return clf.stat
def main(graph, fil, norm, permute, ss, epd, n_cv, flip, feat, feat_kwargs):
"""
All hyperprameter goes here.
:param graph: graph dataset
:param fil: filtration function
:param norm: normalize or not
:param permute: whether permute dgm
:param ss: both sublevel and superlevel or not
:param epd: include extended persistence or not
:param n_cv: number of cross validation
:return:
"""
global gs
print('kwargs', feat_kwargs)
label_flag = dgms_dir_test(fil=fil, fil_d='sub', norm=norm, graph=graph)[1]
# gs, labels = load_graphs(dataset=graph, labels_only=label_flag) # step 1
gs, labels = load_tugraphs(graph, labels_only=True)
# parallel
subdgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
fil_d='sub',
norm=norm,
graph=graph)
supdgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
fil_d='sup',
norm=norm,
graph=graph)
epddgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
one_hom=True,
norm=norm,
graph=graph)
dgms = combine_dgms(subdgms, supdgms, epddgms, ss=ss, epd=epd, flip=flip)
dgms = permute_dgms(dgms, permute_flag=permute, permute_ratio=0.5)
dgms_summary(dgms)
swdgms = dgms2swdgms(dgms)
if feat == 'sw':
print(feat_kwargs)
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='sw',
**feat_kwargs)
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
print(clf.stat)
return clf.stat
elif feat == 'pi':
params = {
'bandwidth': 1.0,
'weight': (1, 1),
'im_range': [0, 1, 0, 1],
'resolution': [5, 5]
}
images = merge_dgms(subdgms,
supdgms,
epddgms,
vectype='pi',
ss=ss,
epd=epd,
**params)
clf = classifier(images, labels, method='svm', n_cv=n_cv)
clf.svm(n_splits=10)
return clf.stat
elif feat == 'pss':
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=False,
kernel_type='pss',
**feat_kwargs)
print(k.shape, k, np.max(k))
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
print(clf.stat)
return clf.stat
elif feat == 'wg':
k, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='wg',
**feat_kwargs)
print(k.shape)
clf = classifier(labels, labels, method='svm', n_cv=n_cv, kernel=k)
clf.svm_kernel_(n_splits=10)
print(clf.stat)
return clf.stat
elif feat == 'pdvector':
pass