def name_estimation(graph, group, layer, graphreference, vectorizer, nameestimator, subgraphs):
if subgraphs:
map(remove_eden_annotation, subgraphs)
try:
data = vectorizer.transform(subgraphs)
except:
draw.graphlearn(subgraphs, contract= False)
clusterids = nameestimator.predict(data)
#for d, g in zip(data, subgraphs):
# g.graph['hash_title'] = hash_function(d)
#draw.graphlearn(subgraphs,size=2, title_key='hash_title', edge_label='label')
for sg, clid in zip(subgraphs, clusterids):
for n in sg.nodes():
graph.node[n][group] = '-' if clid == -1 else str(clid)
# doing the contraction...
graph = contraction([graph], contraction_attribute=group, modifiers=[],
nesting=False, dont_contract_attribute_symbol='-').next()
# write labels
def f(n, d):
d['label'] = graphreference.node[max(d['contracted'])]['label'] \
if d['label'] == '-' else "L%sC%s" % (layer, d['label'])
node_operation(graph, f)
return graph
def _transform_single(self, graph):
'''
Parameters
----------
score_attribute: string
name of the attribute used
group: string
annnotate in this field
Returns
-------
'''
graphcopy = graph.copy()
maxnodeid = max(graph.nodes())
# def f(n,d): d[score_attribute] = graph.degree(n)
# node_operation(graph,f)
tcc = ThresholdedConnectedComponents(attribute=self.score_attribute, more_than=False, shrink_graphs=True)
components = tcc._extract_ccomponents(graph, threshold=self.score_threshold, min_size=self.min_size,
max_size=self.max_size)
nodeset = {n for g in components for n in g.nodes()}
def f(n, d):
d[self.group_attribute] = '~' if n in nodeset else '-'
node_operation(graph, f)
# now we either contract what we have, or additionally rename the contracted nodes according to the group estimator
graph = name_estimation(graph, self.group_attribute, self.layer, graphcopy, self.vectorizer, self.nameestimator,
components)
#else:
# graph = contraction([graph],
# contraction_attribute=group,
# modifiers=[],
# nesting=False, dont_contract_attribute_symbol='-').next()
graph = nx.relabel_nodes(graph, dict(
zip(graph.nodes(), range(maxnodeid + 1, 1 + maxnodeid + graph.number_of_nodes()))), copy=False)
graph.graph['original']= graphcopy
graph.graph['layer']=self.layer
return graph
def fit(self, graphs_pos, graphs_neg=[]):
if self.trained:
return self
self.trained=True
map(utils.remove_eden_annotation,graphs_pos+graphs_neg)
map(lambda x: utils.node_operation(x, lambda n,d: d.pop('importance',None)), graphs_pos+graphs_neg)
map( lambda graph: graph.graph.pop('mass_annotate_mp_was_here',None) ,graphs_pos+graphs_neg)
if graphs_neg:
#print 'choosing to train binary esti'
self.estimator = SGDClassifier()
classes= [1]*len(graphs_pos)+[-1]*len(graphs_neg)
self.estimator.fit(self.vectorizer.transform(graphs_pos+graphs_neg),classes)
else:
self.estimator = ExperimentalOneClassEstimator()
self.estimator.fit(self.vectorizer.transform(graphs_pos))
return self