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
class Annotator():
def __init__(self, multiprocess=True, score_attribute='importance'):
self.score_attribute=score_attribute
self.vectorizer=Vectorizer()
self.multi_process=multiprocess
self.trained=False
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
def fit_transform(self,graphs_p, graphs_n=[]):
self.fit(graphs_p,graphs_n)
return self.transform(graphs_p),self.transform(graphs_n)
def transform(self,graphs):
return self.annotate(graphs)
def annotate(self,graphs,neg=False):
if not graphs:
return []
return mass_annotate_mp(graphs,self.vectorizer,score_attribute=self.score_attribute,estimator=self.estimator,
multi_process=self.multi_process, invert_score=neg)