def _links(db, node):
"""Gets the links to and from a node"""
links = set(node.links)
for link_id in db.index("links_index", "get_ids", node.id, model.account_key(node.owner)):
links.update(link_id)
return links
def map(self, db, obj_id, obj):
if 'links' in obj and 'type' in obj and obj['type'] == 'node':
for link_uri in obj.links:
link_hash = scarecrow.ident(model.node_key(link_uri))
link_obj = self.model[link_hash]
if link_obj == None: continue
link_owner = scarecrow.ident(model.account_key(link_obj.owner))
db.execute("INSERT INTO " + self.name + " VALUES (%s, %s, %s)", obj_id, link_hash, link_owner)
def delete_to(self, uri):
hash = scarecrow.ident(model.node_key(uri))
results = False
#Iterate through all the linked nodes and delete the link if it still
#exists
for node in self.db.index('links_index', 'get', hash, model.account_key(self.current_user)):
if not uri in node.links:
continue
results = True
del node.links[uri]
self.db[model.node_key(node.id)] = node
#If no changes were made, the node might not exist; throw a not found
#if it doesn't
if not results and not node in self.db:
raise web.HTTPError(404, 'could not find node')
def candidates(node_store, root, max_visit):
"""
Returns a set of candidates that could be used by recommendation algorithms
for a given node. It is a list of sub-lists, where each sub-list contains
the uri and resized weight.
"""
owner = scarecrow.ident(model.account_key(root.owner))
candidates = {}
# Store a list of already visited links so we don't revisit them
visited_links = set([uri for uri in root.links])
visited_links.add(root.id)
# Store a list of already visited nodes so we don't revisit them
visited_nodes = set(root.id)
# A queue of nodes to process
queue = [[uri, root.links[uri].weight, 1] for uri in root.links]
# Keep processing all the items in the queue until we reach max_visit to
# ensure that the recommendations are returned quickly enough if there are
# a lot of candidates
while max_visit > 0:
next_queue = []
next_visited_links = set([])
# Process all nodes in the current queue
for uri, weight, count in queue:
if max_visit <= 0:
break
if uri in visited_nodes:
continue
hash = model.node_key(uri)
node = node_store[hash]
# Visit each outbound link in the currently processed node
for link_uri in node.links:
link_weight = node.links[link_uri].weight
_visit(candidates, visited_links, next_visited_links, next_queue, link_uri, weight, link_weight, count)
# Visit each inbound link to the currently processed node
for link_node in node_store.db.index("links_index", "get", hash, owner):
if uri in link_node.links:
link_uri = link_node.id
link_weight = weight + link_node.links[uri].weight
_visit(
candidates, visited_links, next_visited_links, next_queue, link_uri, weight, link_weight, count
)
max_visit -= 1
visited_nodes.add(uri)
# Skip any further logic if we've processed the maximum number of nodes
if max_visit <= 0 or len(queue) == 0:
break
queue = next_queue
visited_links.update(next_visited_links)
# Each node has been potentially visited multiple times. Average out the
# scores to create an overall weight
for uri in candidates:
weight, count = candidates[uri]
candidates[uri] = weight / count
# Store the results in the cache
root._cache.candidates = candidates
node_store.db[model.node_key(root.id)] = root
return candidates