def set_up_collection(name):
"""
Set up a collection of edges for a named graph
This will ensure that the fields edge, source and target exists, that edges are unique and there's an index on source
"""
client = mongodb.client()
db = client['edges']
db.create_index('edge', unique=True)
db.create_index('source')
return db
import mongodb
client = mongodb.client()
db = client.euchr_test
edge_col = db['edges']
def set_up_collection(name):
"""
Set up a collection of edges for a named graph
This will ensure that the fields edge, source and target exists, that edges are unique and there's an index on source
"""
client = mongodb.client()
db = client['edges']
db.create_index('edge', unique=True)
db.create_index('source')
return db
def get_common_neighbors(x,y,G):
try
u = set(scoped_neighborhood(x,x,G))
v = set(scoped_neighborhood(y,x,G))
return u.intersection(v)
def get_precalculated(candidate_edges, field):
pre_calculated = list(edge_col.find({'edge': {'$in': candidate_edges}}, {'source': True, 'target': True, field: True}))
results = [(record['source'], record['target'], {'score': record[field]}) for record in pre_calculated]
candidate_edges = list(set(candidate_edges) - set([(record['source'], record['target']) for record in pre_calculated]))
return results, validation_set
def save_precalculated(records, field):
records = [{'edge': (x,y), field: data['score']}]
db.insert_many(records)