def load_graphs_synthetic():
"""Load the SYNTHETIC graph dataset for graph classification from:
Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., & Borgwardt, K. M. (2013)
Scalable kernels for graphs with continuous attributes.
In Neural Information Processing Systems (NIPS) 2013 (pp. 216–224).
Retrieved from http://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/SYNTHETICnew.labels'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/SYNTHETICnew.gspan'
#input_target_url='datasets/ENZYMES.labels'
#input_data_url='datasets/ENZYMES.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url) #url
#return Bunch(data=flat_data,
# target=target.astype(np.int),
# target_names=np.arange(10),
# images=images,
# DESCR=descr)
g = [i for i in g_it]
for i in g:
for n in i.nodes():
i.node[n]['label'] = str(i.degree(n))
print 'Loaded SYNTHETIC graph dataset for graph classification from:'
print 'Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., & Borgwardt, K. M. (2013)'
print 'Scalable kernels for graphs with continuous attributes.'
print 'In Neural Information Processing Systems (NIPS) 2013 (pp. 216–224).'
return Bunch(graphs=g, target=_target, labels=True, veclabels=True)
def load_graphs_PROTEINS_full():
"""Load the PROTEINS_full graph dataset for graph classification from:
Neumann, M., Garnett R., Bauckhage Ch., Kersting K.: Propagation Kernels: Efficient Graph
Kernels from Propagated Information. Under review at MLJ.
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/PROTEINS_full_graph_labels.txt'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/PROTEINS_full.gspan'
#input_target_url='datasets/ENZYMES.labels'
#input_data_url='datasets/ENZYMES.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url) #url
#return Bunch(data=flat_data,
# target=target.astype(np.int),
# target_names=np.arange(10),
# images=images,
# DESCR=descr)
print 'Loaded PROTEINS_full graph dataset for graph classification from:'
print 'Neumann, M., Garnett R., Bauckhage Ch., Kersting K.: Propagation Kernels: Efficient Graph'
print 'Kernels from Propagated Information. MLJ 2015.'
return Bunch(graphs=[i for i in g_it],
target=_target,
labels=True,
veclabels=True)
def load_graphs_proteins():
"""Load the PROTEINS graph dataset for graph classification from:
Dobson, P. D., & Doig, A. J. (2003)
Distinguishing enzyme structures from non-enzymes without alignments.
Journal of Molecular Biology, 330, 771–783. doi:10.1016/S0022-2836(03)00628-4
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/PROTEINS.labels'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/PROTEINS.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url) #url
print 'Loaded PROTEINS graph dataset for graph classification from:'
print 'Dobson, P. D., & Doig, A. J. (2003)'
print 'Distinguishing enzyme structures from non-enzymes without alignments.'
print 'Journal of Molecular Biology, 330, 771–783. doi:10.1016/S0022-2836(03)00628-4'
return Bunch(graphs=[i for i in g_it],
target=_target,
labels=True,
veclabels=True)
def load_graphs_enzymes():
"""Load the ENZYMES graph dataset for (multiclass) graph classification from:
Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., & Schomburg, D. (2004).
BRENDA, the enzyme database: updates and major new developments.
Nucleic Acids Research, 32, D431–D433. doi:10.1093/nar/gkh081
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/ENZYMES.labels'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/ENZYMES.gspan'
#input_target_url='datasets/ENZYMES.labels'
#input_data_url='datasets/ENZYMES.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url) #url
#return Bunch(data=flat_data,
# target=target.astype(np.int),
# target_names=np.arange(10),
# images=images,
# DESCR=descr)
print 'Loaded ENZYMES graph dataset for (multiclass) graph classification from:'
print 'Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., & Schomburg, D. (2004).'
print 'BRENDA, the enzyme database: updates and major new developments.'
print 'Nucleic Acids Research, 32, D431–D433. doi:10.1093/nar/gkh081'
return Bunch(graphs=[i for i in g_it],
target=_target,
labels=True,
veclabels=True)
def load_graphs_GDD():
"""Load the GDD graph dataset for graph classification..
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/GDD/GDD_labels.txt'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/GDD/graphs.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url)
gra = [i for i in g_it]
print 'Loaded GDD graph dataset for graph classification.'
print len(gra), 'graphs.'
return Bunch(graphs=gra, target=_target, labels=True, veclabels=False)
def load_graphs_NCI_AIDS():
"""Load the NCI antiHIV graph dataset for graph classification..
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/NCI_AIDS/AIDO99SD_numeric.labels'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/NCI_AIDS/AIDO99SD.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url)
print 'Loaded NCI antiHIV dataset graph dataset for graph classification.'
return Bunch(graphs=[i for i in g_it],
target=_target,
labels=True,
veclabels=False)
def load_graphs_bursi():
"""Load the Bursi graph dataset for graph classification..
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.bioinf.uni-freiburg.de/~costa/bursi.target'
input_data_url = 'http://www.bioinf.uni-freiburg.de/~costa/bursi.gspan'
_target = load_target(input_target_url)
g_it = instance_to_graph(input=input_data_url)
print 'Loaded Bursi graph dataset for graph classification.'
return Bunch(graphs=[i for i in g_it],
target=_target,
labels=True,
veclabels=False)
def load_graphs_LMdata():
"""Load the LMdata graph dataset for graph classification..
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/LMdata/labels.txt.standardized'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets//LMdata/graphs.gspan.standardized'
_target = load_target(input_target_url)
label_dict = {}
counter = [1]
g_it = instance_to_graph(input_data_url, label_dict, counter)
print 'Loaded LMdata graph dataset for graph classification.'
return Bunch(graphs=[i for i in g_it],
label_dict=label_dict,
target=_target,
labels=True,
veclabels=False)
def load_graphs_NCI1():
"""Load the NCI1 graph dataset for graph classification..
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'graphs', the graphs in the dataset in Networkx format, 'target', the classification labels for each
sample.
"""
input_target_url = 'http://www.math.unipd.it/~nnavarin/datasets/NCI1/NCI1_labels.txt'
input_data_url = 'http://www.math.unipd.it/~nnavarin/datasets/NCI1/NCI1_graphs.gspan'
_target = load_target(input_target_url)
label_dict = {}
g_it = instance_to_graph(input=input_data_url)
#g_it=instance_to_graph(input = input_data_url,label_dict=label_dict)
print 'Loaded NCI1 graph dataset for graph classification.'
return Bunch(
graphs=[i for i in g_it],
target=_target,
#label_dict=label_dict,
labels=True,
veclabels=False)
