def query(self,
topic,
max_depth=3,
dont_follow=['enrichment', 'classification'],
config=None):
"""
:param topic: a graph to return the context of. At least one node ID in topic \
must be in full graph g to return any context.
:param max_depth: The maximum distance from the topic to search
:param config: The titanDB configuration to use if not using the one configured with the plugin
:param dont_follow: A list of attribute types to not follow
:return: subgraph in networkx format
"""
if config is None:
config = self.neo4j_config
neo_graph = py2neoGraph(config)
sg = nx.MultiDiGraph()
# Get IDs of topic nodes in graph (if they exist). Also add topics to subgraph
topic_ids = set()
for t, data in topic.nodes(data=True):
cypher = ("MATCH (topic: {0} {1}) "
"RETURN collect(topic) as topics").format(
data['class'], "{key:{KEY}, value:{VALUE}}")
props = {"KEY": data['key'], "VALUE": data['value']}
records = neo_graph.cypher.execute(cypher, props)
#print cypher, props # DEBUG
#print type(records)
for record in records:
#print record # DEBUG
for tnode in record.topics:
attr = dict(tnode.properties)
uri = u'class={0}&key={1}&value={2}'.format(
attr['class'], attr['key'], attr['value'])
sg.add_node(uri, attr)
topic_ids.add(int(tnode.ref.split("/")[-1]))
# Add nodes at depth 1 (done separately as it doesn't include the intermediary
nodes = dict()
if max_depth > 0 and len(
topic_ids) > 0: # no depth or no topicID means no subgraph
cypher = self.build_query(max_depth)
attr = {"TOPICS": list(topic_ids), "DONT_FOLLOW": dont_follow}
#print cypher, attr # DEBUG
# for record in neo_graph.cypher.stream(cypher, attr): # Prefer streaming to execute, if it works
for record in neo_graph.cypher.execute(cypher, attr):
#print record # DEBUG
for node in record.nodes:
attr = dict(node.properties)
uri = 'class={0}&key={1}&value={2}'.format(
attr['class'], attr['key'], attr['value'])
sg.add_node(uri, attr)
nodes[node.ref.split("/")[-1]] = uri
for rel in record.rels:
# print type(rel) # DEBUG
# add edges SRC node
# src_attr = dict(rel.start_node.properties)
# src_uri = u"class={0}&key={1}&value={2}".format(src_attr['class'], src_attr['key'], src_attr['value'])
# sg.add_node(src_uri, src_attr)
src_uri = nodes[rel.start_node.ref.split("/")
[-1]] # src node uri from neo4j ID
# Add edge DST node
# dst_attr = dict(rel.end_node.properties)
# dst_uri = u"class={0}&key={1}&value={2}".format(dst_attr['class'], dst_attr['key'], dst_attr['value'])
# sg.add_node(dst_uri, dst_attr)
dst_uri = nodes[rel.end_node.ref.split("/")
[-1]] # dst node uri from neo4j ID
# add edge
edge_attr = dict(rel.properties)
edge_attr['relationship'] = rel.type
source_hash = uuid.uuid3(uuid.NAMESPACE_URL, src_uri)
dest_hash = uuid.uuid3(uuid.NAMESPACE_URL, dst_uri)
edge_uri = u"source={0}&destionation={1}".format(
str(source_hash), str(dest_hash))
rel_chain = u"relationship"
while rel_chain in edge_attr:
edge_uri = edge_uri + u"&{0}={1}".format(
rel_chain, edge_attr[rel_chain])
rel_chain = edge_attr[rel_chain]
if "origin" in edge_attr:
edge_uri += u"&{0}={1}".format(u"origin",
edge_attr["origin"])
edge_attr["uri"] = edge_uri
sg.add_edge(src_uri, dst_uri, edge_uri, edge_attr)
# Set the topic distances
distances = self.get_topic_distance(sg.to_undirected(), topic)
nx.set_node_attributes(sg, u'topic_distance', distances)
# TODO: Handle duplicate edges (may dedup but leave in for now)
# Take edges into dataframe
# group by combine on features to be deduplicated. Return edge_id's in each group. Combine those edge_ids using a combine algorithm
# Could do the same with dedup algo, but just return the dedup edge_ids and delete them from the graph
return sg
def enrich(self, g): # Neo4j
"""
:param g: networkx graph to be merged
:param neo4j: bulbs neo4j config
:return: Nonetype
Note: Neo4j operates differently from the current titan import. The neo4j import does not aggregate edges which
means they must be handled at query time. The current titan algorithm aggregates edges based on time on
merge.
"""
#neo4j_graph = NEO_Graph(neo4j) # Bulbs
neo_graph = py2neoGraph(self.neo4j_config)
nodes = set()
node_map = dict()
edges = set()
settled = set()
# Merge all nodes first
tx = neo_graph.cypher.begin()
cypher = ("MERGE (node: {0} {1}) "
"ON CREATE SET node = {2} "
"RETURN collect(node) as nodes")
# create transaction for all nodes
for node, data in g.nodes(data=True):
query = cypher.format(data['class'], "{key:{KEY}, value:{VALUE}}",
"{MAP}")
props = {"KEY": data['key'], "VALUE": data['value'], "MAP": data}
# TODO: set "start_time" and "finish_time" to dummy variables in attr.
# TODO: Add nodes to graph, and cyper/gremlin query to compare to node start_time & end_time to dummy
# TODO: variable update if node start > dummy start & node finish < dummy finish, and delete dummy
# TODO: variables.
tx.append(query, props)
# commit transaction and create mapping of returned nodes to URIs for edge creation
for record_list in tx.commit():
for record in record_list:
# print record, record.nodes[0]._Node__id, len(record.nodes)
for n in record.nodes:
# print n._Node__id
attr = n.properties
uri = "class={0}&key={1}&value={2}".format(
attr['class'], attr['key'], attr['value'])
node_map[uri] = int(n.ref.split("/")[1])
# node_map[uri] = n._Node__id
# print node_map # DEBUG
# Create edges
cypher = ("MATCH (src: {0}), (dst: {1}) "
"WHERE id(src) = {2} AND id(dst) = {3} "
"CREATE (src)-[rel: {4} {5}]->(dst) ")
tx = neo_graph.cypher.begin()
for edge in g.edges(data=True):
try:
if 'relationship' in edge[2]:
relationship = edge[2].pop('relationship')
else:
# default to 'described_by'
relationship = 'describedBy'
query = cypher.format(g.node[edge[0]]['class'],
g.node[edge[1]]['class'], "{SRC_ID}",
"{DST_ID}", relationship, "{MAP}")
props = {
"SRC_ID": node_map[edge[0]],
"DST_ID": node_map[edge[1]],
"MAP": edge[2]
}
# create the edge
# NOTE: No attempt is made to deduplicate edges between the graph to be merged and the destination graph.
# The query scripts should handle this.
# print edge, query, props # DEBUG
tx.append(query, props)
# rel = py2neoRelationship(node_map[src_uri], relationship, node_map[dst_uri])
# rel.properties.update(edge[2])
# neo_graph.create(rel) # Debug
# edges.add(rel)
except:
print edge
print node_map
raise
# create edges all at once
#print edges # Debug
# neo_graph.create(*edges)
tx.commit()
def enrich(self, g): # Neo4j
"""
:param g: networkx graph to be merged
:param neo4j: bulbs neo4j config
:return: Nonetype
Note: Neo4j operates differently from the current titan import. The neo4j import does not aggregate edges which
means they must be handled at query time. The current titan algorithm aggregates edges based on time on
merge.
"""
#neo4j_graph = NEO_Graph(neo4j) # Bulbs
neo_graph = py2neoGraph(self.neo4j_config)
nodes = set()
node_map = dict()
edges = set()
settled = set()
# Merge all nodes first
tx = neo_graph.cypher.begin()
cypher = ("MERGE (node: {0} {1}) "
"ON CREATE SET node = {2} "
"RETURN collect(node) as nodes"
)
# create transaction for all nodes
for node, data in g.nodes(data=True):
query = cypher.format(data['class'], "{key:{KEY}, value:{VALUE}}", "{MAP}")
props = {"KEY": data['key'], "VALUE":data['value'], "MAP": data}
# TODO: set "start_time" and "finish_time" to dummy variables in attr.
# TODO: Add nodes to graph, and cyper/gremlin query to compare to node start_time & end_time to dummy
# TODO: variable update if node start > dummy start & node finish < dummy finish, and delete dummy
# TODO: variables.
tx.append(query, props)
# commit transaction and create mapping of returned nodes to URIs for edge creation
for record_list in tx.commit():
for record in record_list:
# print record, record.nodes[0]._Node__id, len(record.nodes)
for n in record.nodes:
# print n._Node__id
attr = n.properties
uri = "class={0}&key={1}&value={2}".format(attr['class'], attr['key'], attr['value'])
node_map[uri] = int(n.ref.split("/")[1])
# node_map[uri] = n._Node__id
# print node_map # DEBUG
# Create edges
cypher = ("MATCH (src: {0}), (dst: {1}) "
"WHERE id(src) = {2} AND id(dst) = {3} "
"CREATE (src)-[rel: {4} {5}]->(dst) "
)
tx = neo_graph.cypher.begin()
for edge in g.edges(data=True):
try:
if 'relationship' in edge[2]:
relationship = edge[2].pop('relationship')
else:
# default to 'described_by'
relationship = 'describedBy'
query = cypher.format(g.node[edge[0]]['class'],
g.node[edge[1]]['class'],
"{SRC_ID}",
"{DST_ID}",
relationship,
"{MAP}"
)
props = {
"SRC_ID": node_map[edge[0]],
"DST_ID": node_map[edge[1]],
"MAP": edge[2]
}
# create the edge
# NOTE: No attempt is made to deduplicate edges between the graph to be merged and the destination graph.
# The query scripts should handle this.
# print edge, query, props # DEBUG
tx.append(query, props)
# rel = py2neoRelationship(node_map[src_uri], relationship, node_map[dst_uri])
# rel.properties.update(edge[2])
# neo_graph.create(rel) # Debug
# edges.add(rel)
except:
print edge
print node_map
raise
# create edges all at once
#print edges # Debug
# neo_graph.create(*edges)
tx.commit()
def query(self, topic, max_depth=3, dont_follow=['enrichment', 'classification'], config=None):
"""
:param topic: a graph to return the context of. At least one node ID in topic \
must be in full graph g to return any context.
:param max_depth: The maximum distance from the topic to search
:param config: The titanDB configuration to use if not using the one configured with the plugin
:param dont_follow: A list of attribute types to not follow
:return: subgraph in networkx format
"""
if config is None:
config = self.neo4j_config
neo_graph = py2neoGraph(config)
sg = nx.MultiDiGraph()
# Get IDs of topic nodes in graph (if they exist). Also add topics to subgraph
topic_ids = set()
for t, data in topic.nodes(data=True):
cypher = ("MATCH (topic: {0} {1}) "
"RETURN collect(topic) as topics").format(data['class'], "{key:{KEY}, value:{VALUE}}")
props = {"KEY":data['key'], "VALUE":data['value']}
records = neo_graph.cypher.execute(cypher, props)
#print cypher, props # DEBUG
#print type(records)
for record in records:
#print record # DEBUG
for tnode in record.topics:
attr = dict(tnode.properties)
uri = u'class={0}&key={1}&value={2}'.format(attr['class'],attr['key'], attr['value'])
sg.add_node(uri, attr)
topic_ids.add(int(tnode.ref.split("/")[-1]))
# Add nodes at depth 1 (done separately as it doesn't include the intermediary
nodes = dict()
if max_depth > 0 and len(topic_ids) > 0: # no depth or no topicID means no subgraph
cypher = self.build_query(max_depth)
attr = {"TOPICS": list(topic_ids),
"DONT_FOLLOW": dont_follow}
#print cypher, attr # DEBUG
# for record in neo_graph.cypher.stream(cypher, attr): # Prefer streaming to execute, if it works
for record in neo_graph.cypher.execute(cypher, attr):
#print record # DEBUG
for node in record.nodes:
attr = dict(node.properties)
uri = 'class={0}&key={1}&value={2}'.format(attr['class'],attr['key'], attr['value'])
sg.add_node(uri, attr)
nodes[node.ref.split("/")[-1]] = uri
for rel in record.rels:
# print type(rel) # DEBUG
# add edges SRC node
# src_attr = dict(rel.start_node.properties)
# src_uri = u"class={0}&key={1}&value={2}".format(src_attr['class'], src_attr['key'], src_attr['value'])
# sg.add_node(src_uri, src_attr)
src_uri = nodes[rel.start_node.ref.split("/")[-1]] # src node uri from neo4j ID
# Add edge DST node
# dst_attr = dict(rel.end_node.properties)
# dst_uri = u"class={0}&key={1}&value={2}".format(dst_attr['class'], dst_attr['key'], dst_attr['value'])
# sg.add_node(dst_uri, dst_attr)
dst_uri = nodes[rel.end_node.ref.split("/")[-1]] # dst node uri from neo4j ID
# add edge
edge_attr = dict(rel.properties)
edge_attr['relationship'] = rel.type
source_hash = uuid.uuid3(uuid.NAMESPACE_URL, src_uri)
dest_hash = uuid.uuid3(uuid.NAMESPACE_URL, dst_uri)
edge_uri = u"source={0}&destionation={1}".format(str(source_hash), str(dest_hash))
rel_chain = u"relationship"
while rel_chain in edge_attr:
edge_uri = edge_uri + u"&{0}={1}".format(rel_chain,edge_attr[rel_chain])
rel_chain = edge_attr[rel_chain]
if "origin" in edge_attr:
edge_uri += u"&{0}={1}".format(u"origin", edge_attr["origin"])
edge_attr["uri"] = edge_uri
sg.add_edge(src_uri, dst_uri, edge_uri, edge_attr)
# Set the topic distances
distances = self.get_topic_distance(sg.to_undirected(), topic)
nx.set_node_attributes(sg, u'topic_distance', distances)
# TODO: Handle duplicate edges (may dedup but leave in for now)
# Take edges into dataframe
# group by combine on features to be deduplicated. Return edge_id's in each group. Combine those edge_ids using a combine algorithm
# Could do the same with dedup algo, but just return the dedup edge_ids and delete them from the graph
return sg
def minion(self, *args, **xargs):
self.shutdown = False
# Get graph
neo_graph = py2neoGraph(self.neo4j_config)
random_cypher = ''' MATCH (a)-[:describedBy]->()
RETURN a, rand() as r
ORDER BY r
LIMIT 1
'''
# pick a random node
records = neo_graph.cypher.execute(random_cypher)
node = records[0][0]
logging.info(
"first node to consolidate edges for is class: {0}, key: {1}, value: {2}"
.format(node.properties['class'], node.properties['key'],
node.properties['value']))
print "first node to consolidate edges for is class: {0}, key: {1}, value: {2}".format(
node.properties['class'], node.properties['key'],
node.properties['value']) # DEBUG
while not self.shutdown:
edges = defaultdict(set)
destinations = set()
# get edges starting with the node
for rel in node.match_outgoing():
if 'uri' in rel.properties:
edge_uri = rel.properties['uri']
else:
# SRC URI
if 'uri' in rel.start_node.properties:
source_uri = rel.start_node.properties['uri']
else:
source_uri = "class={0}&key={1}&value={2}".format(
rel.start_node.properties['attribute'],
rel.start_node.properties['key'],
rel.start_node.properties['value'])
# DST URI
if 'uri' in rel.end_node.properties:
dest_uri = rel.end_node.properties['uri']
else:
dest_uri = "class={0}&key={1}&value={2}".format(
rel.end_node.properties['attribute'],
rel.end_node.properties['key'],
rel.end_node.properties['value'])
# Remove non-ascii as it gumms up uuid.
# NOTE: This shouldn't effect anything as it's just for the key in the edges dictionary
source_uri = self.Verum.removeNonAscii(source_uri)
dest_uri = self.Verum.removeNonAscii(dest_uri)
# Edge URI
source_hash = uuid.uuid3(uuid.NAMESPACE_URL, source_uri)
dest_hash = uuid.uuid3(uuid.NAMESPACE_URL, dest_uri)
edge_uri = "source={0}&destionation={1}".format(
str(source_hash), str(dest_hash))
rel_chain = "relationship"
while rel_chain in rel.properties:
edge_uri = edge_uri + "&{0}={1}".format(
rel_chain, rel.properties[rel_chain])
rel_chain = rel.properties[rel_chain]
if "origin" in rel.properties:
edge_uri += "&{0}={1}".format("origin",
rel.properties["origin"])
# aggregate edges by dst, and uri
edges[edge_uri].add(
rel
) # WARNING: The use of URI here is vulnerable to values being out of order in the URI and edges not being removed.
# collect destinations to pick next node
destinations.add(rel.end_node)
time = datetime.utcnow()
# SRC URI
if 'uri' in node.properties:
source_uri = node.properties['uri']
else:
source_uri = "class={0}&key={1}&value={2}".format(
node.properties['attribute'], node.properties['key'],
node.properties['value'])
for edge_uri in edges:
edge_list = list(edges[edge_uri])
# DST URI
if 'uri' in edge_list[0].end_node.properties:
dest_uri = edge_list[0].end_node.properties['uri']
else:
dest_uri = "class={0}&key={1}&value={2}".format(
edge_list[0].end_node.properties['attribute'],
edge_list[0].end_node.properties['key'],
edge_list[0].end_node.properties['value'])
logging.debug(
"Removing {0} edges from node {1} to {2}.".format(
len(edge_list[1:]), source_uri, dest_uri))
#print "Removing {0} edges from node {1} to {2}.".format(len(edge_list[1:]), source_uri, dest_uri) # DEBUG
for edge in edge_list[1:]:
# keep earliest time as start
try:
edge_time = datetime.strptime(
edge.properties['start_time'],
"%Y-%m-%dT%H:%M:%SZ")
if 'start_time' in edge.properties and time > edge_time:
time = edge_time
except ValueError: # The time on the node wasn't legit
pass
try: # sometimes the edge is no longer there. Better to pass than fail.
# remove all but one node of each group
edge.delete()
except:
pass
# Update time on remaining node
try:
edge_time = datetime.strptime(
edge_list[0].properties['start_time'],
"%Y-%m-%dT%H:%M:%SZ")
except:
edge_time = datetime.utcnow()
if 'start_time' not in edge_list[
0].properties or time < edge_time:
edge_list[0].properties['start_time'] = time.strftime(
"%Y-%m-%dT%H:%M:%SZ")
edge_list[0].push()
logging.debug(
"Keeping edge {0} from node {1} to node {2}.".format(
edge_list[0].uri, source_uri, dest_uri))
#print "Keeping edge {0} from node {1} to node {2}.".format(edge_list[0].uri, source_uri, dest_uri) # DEBUG
# Sleep to slow it down
sleep(self.sleep_time)
jump = random.random()
# do the random walk
if len(destinations) == 0 or jump <= self.jump:
# pick a random node
records = neo_graph.cypher.execute(random_cypher)
node = records[0][0]
logging.debug("Edge consolidation random walk jumped.")
else:
node = random.choice(destinations)
logging.debug("Edge consolidation random walk didn't jumped.")
logging.info(
"Next node to consolidate edges for is class: {0}, key: {1}, value: {2}"
.format(node.properties['class'], node.properties['key'],
node.properties['value']))
