/
sg_wrappers.py
172 lines (125 loc) · 4.76 KB
/
sg_wrappers.py
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from ..imports import *
from ..data import SequenceDataset
# import stellargraph
try:
import stellargraph as sg
from stellargraph.mapper import node_mappers, link_mappers
except:
raise Exception(SG_ERRMSG)
if version.parse(sg.__version__) < version.parse('0.8'):
raise Exception(SG_ERRMSG)
class NodeSequenceWrapper(node_mappers.NodeSequence, SequenceDataset):
def __init__(self, node_seq):
if not isinstance(node_seq, node_mappers.NodeSequence):
raise ValueError('node_seq must by a stellargraph NodeSequence object')
self.node_seq = node_seq
self.targets = node_seq.targets
self.generator = node_seq.generator
self.ids = node_seq.ids
self.__len__ = node_seq.__len__
self.__getitem__ = node_seq.__getitem__
self.on_epoch_end = node_seq.on_epoch_end
self.indices = node_seq.indices
def __setattr__(self, name, value):
if name == 'batch_size':
self.generator.batch_size = value
elif name == 'data_size':
self.node_seq.data_size = value
elif name == 'shuffle':
self.node_seq.shuffle = value
elif name == 'head_node_types':
self.node_seq.head_node_types = value
elif name == '_sampling_schema':
self.node_seq._sample_schema = value
else:
self.__dict__[name] = value
return
def __getattr__(self, name):
if name == 'batch_size':
return self.generator.batch_size
elif name == 'data_size':
return self.node_seq.data_size
elif name == 'shuffle':
return self.node_seq.shuffle
elif name == 'head_node_types':
return self.node_seq.head_node_types
elif name == '_sampling_schema':
return self.node_seq._sampling_schema
elif name == 'reset':
# stellargraph did not implement reset for its generators
# return a zero-argument lambda that returns None
return lambda:None
elif name == 'graph':
return self.generator.graph
else:
try:
return self.__dict__[name]
except:
raise AttributeError
return
def nsamples(self):
return self.targets.shape[0]
def get_y(self):
return self.targets
def xshape(self):
return self[0][0][0].shape[1:] # returns 1st neighborhood only
def nclasses(self):
return self[0][1].shape[1]
class LinkSequenceWrapper(link_mappers.LinkSequence, SequenceDataset):
def __init__(self, link_seq):
if not isinstance(link_seq, link_mappers.LinkSequence):
raise ValueError('link_seq must by a stellargraph LinkSequence object')
self.link_seq = link_seq
self.targets = link_seq.targets
self.generator = link_seq.generator
self.ids = link_seq.ids
self.__len__ = link_seq.__len__
self.__getitem__ = link_seq.__getitem__
self.on_epoch_end = link_seq.on_epoch_end
self.indices = link_seq.indices
def __setattr__(self, name, value):
if name == 'batch_size':
self.generator.batch_size = value
elif name == 'data_size':
self.link_seq.data_size = value
elif name == 'shuffle':
self.link_seq.shuffle = value
elif name == 'head_node_types':
self.link_seq.head_node_types = value
elif name == '_sampling_schema':
self.link_seq._sample_schema = value
else:
self.__dict__[name] = value
return
def __getattr__(self, name):
if name == 'batch_size':
return self.generator.batch_size
elif name == 'data_size':
return self.link_seq.data_size
elif name == 'shuffle':
return self.link_seq.shuffle
elif name == 'head_node_types':
return self.link_seq.head_node_types
elif name == '_sampling_schema':
return self.link_seq._sampling_schema
elif name == 'reset':
# stellargraph did not implement reset for its generators
# return a zero-argument lambda that returns None
return lambda:None
elif name == 'graph':
return self.generator.graph
else:
try:
return self.__dict__[name]
except:
raise AttributeError
return
def nsamples(self):
return self.targets.shape[0]
def get_y(self):
return self.targets
def xshape(self):
return self[0][0][0].shape[1:] # returns 1st neighborhood only
def nclasses(self):
return 2
return self[0][1].shape[1]