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
# load graphs
gs, labels = load_graphs(dataset=args.graph)
# parallel
subdgms = gs2dgms_parallel(n_jobs=-1, fil=fil, fil_d='sub', norm=norm)
supdgms = gs2dgms_parallel(n_jobs=-1, fil=fil, fil_d='sup', norm=norm)
epddgms = gs2dgms_parallel(n_jobs=-1, fil=fil, one_hom=True, norm=norm)
# serial
# subdgms = gs2dgms(gs, fil=fil, fil_d='sub', norm=norm, one_hom=False) # step2 # TODO: need to add interface
# supdgms = gs2dgms(gs, fil=fil, fil_d='sup', norm=norm, one_hom=False) # step2 #
# epddgms = gs2dgms(gs, fil=fil, norm=norm, one_hom=True) # step2 # TODO
dgms = combine_dgms(subdgms, supdgms, epddgms, args)
dgms = permute_dgms(dgms, permute_flag=args.permute, permute_ratio=0.5)
dgms_summary(dgms)
# sw kernel
swdgms = dgms2swdgms(dgms)
kwargs = {'bw': args.bw, 'n_directions': 10, 'K': 1, 'p': 1}
sw_kernel, _ = sw_parallel(swdgms,
swdgms,
parallel_flag=True,
kernel_type='sw',
**kwargs)
print(sw_kernel.shape)
clf = classifier(labels,
labels,
method='svm',
n_cv=args.n_cv,
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
# node feat example
nodefeat_ = np.array(list(dict(nx.degree(g)).values())).reshape(
len(g), 1) # np.random.random((n_node, 1))
nodefeat_ = nodefeat_ / float(max(nodefeat_))
fil = fil_stradegy(g, fil='node', node_fil='sub', nodefeat=nodefeat_)
g = fil.build_fv()
x = graph2dgm(g)
diagram = x.get_diagram(g,
key='fv',
subflag='True',
one_homology_flag=False,
parallel_flag=False,
zigzag=False)
print(diagram)
# imdb
gs, labels = load_graphs(dataset='imdb_binary') # step 1
subdgms = gs2dgms_parallel(
n_jobs=1, fil='jaccard', fil_d='sub', one_hom=False,
debug_flag=False) # step2 # TODO: need to add interface
dgms_summary(subdgms)
debug(subdgms, 'subdgms')
g = nx.random_geometric_graph(100, 0.4)
print(edgefeat(g, fil='jaccard'))
np.random.seed(42)
n_node = 20
g = nx.random_geometric_graph(n_node, 0.5, seed=42)
diagram = node_fil_(g, fil='hop', norm=True, base=0)
print(diagram)
b'upperarm': 6,
b'upperleg': 7
}
return parts_dict
if __name__ == '__main__':
from Esme.dgms.stats import stat, dgms_summary
from itertools import chain
import matplotlib.pyplot as plt
num_pdpoints = []
for i in range(1, 5): #chain(range(1, 261), range(281,401)):
dgms = loaddgm(str(i), form='dionysus', print_flag=True)
print(get_cat(i))
_, stat = dgms_summary(dgms) # stat is (mean, std, min, max)
num_pdpoints.append(stat[0])
plt.plot(stat)
plt.show()
sys.exit()
res = face_idx(file='1')
print(res)
sys.exit()
cat_dict = prince_cat()
cat = 'Human'
for idx in chain(range(1, 261), range(281, 401)):
for k, v in cat_dict.items():
if k[0] <= idx and k[1] > idx:
cat = v