def __init__(self, neptune_endpoint, elasticache_endpoint):
GremlinUtils.init_statics(globals())
gremlin_utils = GremlinUtils(
Endpoints(neptune_endpoint=neptune_endpoint))
self.vertext_metrics = VertexMetrics(elasticache_endpoint)
self.neptune_connection = gremlin_utils.remote_connection()
self.g = gremlin_utils.traversal_source(
connection=self.neptune_connection)
def handle_records(self, stream_log):
params = json.loads(os.environ['AdditionalParams'])
neptune_endpoint = params['neptune_cluster_endpoint']
neptune_port = params['neptune_port']
GremlinUtils.init_statics(globals())
endpoints = Endpoints(neptune_endpoint=neptune_endpoint,
neptune_port=neptune_port)
gremlin_utils = GremlinUtils(endpoints)
conn = gremlin_utils.remote_connection()
g = gremlin_utils.traversal_source(connection=conn)
records = stream_log[RECORDS_STR]
last_op_num = None
last_commit_num = None
count = 0
try:
for record in records:
# Process record
op = record[OPERATION_STR]
data = record[DATA_STR]
type = data['type']
id = data['id']
if op == ADD_OPERATION:
if type == 'vl':
logger.info(g.V(id).valueMap(True).toList())
if type == 'e':
logger.info(g.E(id).valueMap(True).toList())
# Update local checkpoint info
last_op_num = record[EVENT_ID_STR][OP_NUM_STR]
last_commit_num = record[EVENT_ID_STR][COMMIT_NUM_STR]
count += 1
except Exception as e:
logger.error('Error occurred - {}'.format(str(e)))
raise e
finally:
try:
conn.close()
yield HandlerResponse(last_op_num, last_commit_num, count)
except Exception as e:
logger.error('Error occurred - {}'.format(str(e)))
raise e
finally:
conn.close()
class GlueGremlinClient:
def __init__(self, endpoints):
self.gremlin_utils = GremlinUtils(endpoints)
GremlinUtils.init_statics(globals())
def add_vertices(self, label):
"""Adds a vertex with the supplied label for each row in a DataFrame partition.
If the DataFrame contains an '~id' column, the values in this column will be treated as user-supplied IDs for the new vertices.
If the DataFrame does not have an '~id' column, Neptune will autogenerate a UUID for each vertex.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.add_vertices('Product'))
"""
def add_vertices_for_label(rows):
try:
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
for row in rows:
entries = row.asDict()
traversal = g.addV(label)
for key, value in entries.items():
key = key.split(':')[0]
if key == '~id':
traversal.property(id, value)
elif key == '~label':
pass
else:
traversal.property(key, value)
traversal.next()
conn.close()
except GremlinServerError as err:
print("Neptune error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return add_vertices_for_label
def upsert_vertices(self, label):
"""Conditionally adds vertices for the rows in a DataFrame partition using the Gremlin coalesce() idiom.
The DataFrame must contain an '~id' column.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.upsert_vertices('Product'))
"""
def upsert_vertices_for_label(rows):
try:
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
for row in rows:
entries = row.asDict()
create_traversal = __.addV(label)
for key, value in entries.items():
key = key.split(':')[0]
if key == '~id':
create_traversal.property(id, value)
elif key == '~label':
pass
else:
create_traversal.property(key, value)
g.V(entries['~id']).fold().coalesce(
__.unfold(), create_traversal).next()
conn.close()
except GremlinServerError as err:
print("Neptune error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return upsert_vertices_for_label
def add_edges(self, label):
"""Adds an edge with the supplied label for each row in a DataFrame partition.
If the DataFrame contains an '~id' column, the values in this column will be treated as user-supplied IDs for the new edges.
If the DataFrame does not have an '~id' column, Neptune will autogenerate a UUID for each edge.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.add_edges('ORDER_DETAIL'))
"""
def add_edges_for_label(rows):
try:
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
for row in rows:
entries = row.asDict()
traversal = g.V(row['~from']).addE(label).to(V(
row['~to'])).property(id, row['~id'])
for key, value in entries.items():
key = key.split(':')[0]
if key not in ['~id', '~from', '~to', '~label']:
traversal.property(key, value)
traversal.next()
conn.close()
except GremlinServerError as err:
print("Neptune error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return add_edges_for_label
def upsert_edges(self, label):
"""Conditionally adds edges for the rows in a DataFrame partition using the Gremlin coalesce() idiom.
The DataFrame must contain '~id', '~from', '~to' and '~label' columns.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.upsert_edges('ORDER_DETAIL'))
"""
def add_edges_for_label(rows):
try:
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
for row in rows:
entries = row.asDict()
create_traversal = __.V(row['~from']).addE(label).to(
V(row['~to'])).property(id, row['~id'])
for key, value in entries.items():
key = key.split(':')[0]
if key not in ['~id', '~from', '~to', '~label']:
create_traversal.property(key, value)
g.E(entries['~id']).fold().coalesce(
__.unfold(), create_traversal).next()
conn.close()
except GremlinServerError as err:
print("Neptune error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
return add_edges_for_label
class BatchUtils:
def __init__(self,
endpoints,
job_name=None,
to_dict=lambda x: x,
pool_size=1,
**kwargs):
self.gremlin_utils = GremlinUtils(endpoints)
self.conn = None
self.g = None
self.region = endpoints.region
self.job_name = job_name
self.to_dict = to_dict
self.pool_size = pool_size
self.kwargs = kwargs
def close(self):
try:
self.gremlin_utils.close()
except:
pass
def __execute_batch_internal(self, rows, operations, **kwargs):
@backoff.on_exception(backoff.constant,
tuple(retriable_errors),
max_tries=5,
giveup=is_non_retriable_error,
on_backoff=reset_connection_if_connection_issue,
on_success=publish_metrics,
interval=2,
jitter=backoff.full_jitter)
def execute(self, rows, operations, **kwargs):
if not self.conn:
self.conn = self.gremlin_utils.remote_connection(
pool_size=self.pool_size, **self.kwargs)
self.g = self.gremlin_utils.traversal_source(
connection=self.conn)
t = self.g
for operation in operations:
for row in rows:
t = operation(t, row, **kwargs)
t.next()
return execute(self, rows, operations, **kwargs)
def execute_batch(self, rows, operations=[], batch_size=50, **kwargs):
if 'mappings' not in kwargs:
kwargs['mappings'] = Mappings()
rows_list = []
for row in rows:
rows_list.append(self.to_dict(row))
if len(rows_list) == batch_size:
self.__execute_batch_internal(rows_list, operations, **kwargs)
rows_list = []
if rows_list:
self.__execute_batch_internal(rows_list, operations, **kwargs)
def add_vertices(self, batch_size=50, rows=None, **kwargs):
def batch_op(rows):
self.execute_batch(rows,
operations=[add_vertex],
batch_size=batch_size,
**kwargs)
return batch_op(rows) if rows else batch_op
def upsert_vertices(self, batch_size=50, rows=None, **kwargs):
def batch_op(rows):
operations = [upsert_vertex]
on_upsert = kwargs.get('on_upsert', None)
if on_upsert and on_upsert == 'replaceAllProperties':
operations.append(replace_vertex_properties)
self.execute_batch(rows,
operations=operations,
batch_size=batch_size,
**kwargs)
return batch_op(rows) if rows else batch_op
def add_edges(self, batch_size=50, rows=None, **kwargs):
def batch_op(rows):
self.execute_batch(rows,
operations=[add_edge],
batch_size=batch_size,
**kwargs)
return batch_op(rows) if rows else batch_op
def upsert_edges(self, batch_size=50, rows=None, **kwargs):
def batch_op(rows):
operations = [upsert_edge]
on_upsert = kwargs.get('on_upsert', None)
if on_upsert and on_upsert == 'replaceAllProperties':
operations.append(replace_edge_properties)
self.execute_batch(rows,
operations=operations,
batch_size=batch_size,
**kwargs)
return batch_op(rows) if rows else batch_op
def add_edge_properties(self, batch_size=50, rows=None, **kwargs):
def batch_op(rows):
self.execute_batch(rows,
operations=[add_properties_to_edge],
batch_size=batch_size,
**kwargs)
return batch_op(rows) if rows else batch_op
class GraphModelClient:
def __init__(self, endpoint):
self.gremlin_utils = GremlinUtils(endpoint)
GremlinUtils.init_statics(globals())
def insert_new_transaction_vertex_and_edge(self,
tr_dict,
connectted_node_dict,
target_id,
vertex_type='Transaction'):
"""Load transaction data, insert transaction object and related domain objects into GraphDB as vertex,
with their properties as values, and insert their relation as edges.
Example:
>>> insert_new_transaction_vertex_and_edge(tr_dict, connectted_node_dict, target_id, vertex_type = 'Transaction')
"""
def insert_attr(graph_conn, attr_val_dict, target_id, node_id,
vertex_type):
if (not g.V().has(id, node_id).hasNext()):
logger.info(f'Insert_Vertex: {node_id}.')
g.inject(attr_val_dict).unfold().as_(vertex_type).\
addV(vertex_type).as_('v').property(id,node_id).\
sideEffect(__.select(vertex_type).unfold().as_('kv').select('v').\
property(__.select('kv').by(Column.keys),
__.select('kv').by(Column.values)
)
).iterate()
# Insert_edge
to_node = g.V().has(id, node_id).next()
if (not g.E().has(id, target_id + '-' + node_id).hasNext()):
logger.info(f'Insert_Edge: {target_id} --> {node_id}.')
g.V().has(id, target_id).addE('CATEGORY').to(to_node).property(
id, target_id + '-' + node_id).iterate()
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
if (not g.V().has(id, target_id).hasNext()):
logger.info(f'Insert_Vertex: {target_id}.')
g.inject(tr_dict).unfold().as_(vertex_type).\
addV(vertex_type).as_('v').property(id,target_id).\
sideEffect(__.select(vertex_type).unfold().as_('kv').select('v').\
property(__.select('kv').by(Column.keys),
__.select('kv').by(Column.values)
)
).iterate()
attr_cols = [f'val{x}' for x in range(1, 391)]
empty_node_dict = {}
for attr in attr_cols:
empty_node_dict[attr] = 0.0
for node_k, node_v in connectted_node_dict[0].items():
node_id = node_k + '-' + str(node_v)
insert_attr(g, [empty_node_dict],
target_id,
node_id,
vertex_type=node_k)
conn.close()
def query_target_subgraph(self, target_id, tr_dict, transaction_value_cols,
union_id_cols, dummied_col):
"""Extract 2nd degree subgraph of target transaction.Dump data into subgraph dict and n_feats dict.
subgraph_dict: related transactions' id list and values through edges
n_feats dict: related 1 degree vertex and transactions' embeded elements vectors.
Usually after insert new test sample's vertex and edges into graphDB.
Example:
>>> query_target_subgraph('3661635', load_data_from_event(), 'M2_T,M3_F,M3_T,...')
"""
subgraph_dict = {}
neighbor_list = []
neighbor_dict = {}
transaction_embed_value_dict = {}
ii = 0
s_t = dt.now()
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
t1 = self.gremlin_utils.traversal_source(connection=conn)
target_name = target_id[(target_id.find('-') + 1):]
feature_list = g.V().has(id, target_id).out().id().toList()
for feat in feature_list:
ii += 1
feat_name = feat[:feat.find('-')]
feat_value = feat[(feat.find('-') + 1):]
node_list = g.V().has(
id, feat).both().limit(MAX_FEATURE_NODE).id().toList()
target_and_conn_node_list = [int(target_name)] + [
int(target_conn_node[(target_conn_node.find('-') + 1):])
for target_conn_node in node_list
]
target_and_conn_node_list = list(set(target_and_conn_node_list))
neighbor_list += target_and_conn_node_list
nodes_and_feature_value_array = (target_and_conn_node_list,
[feat_value] *
len(target_and_conn_node_list))
subgraph_dict['target<>' +
feat_name] = nodes_and_feature_value_array
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: subgraph_dict used {(e_t - s_t).total_seconds()} seconds'
)
logger.info(
f'subgraph_dict len: {len(subgraph_dict.keys())} key: {subgraph_dict.keys()}'
)
logger.info(f'subgraph_dict: {subgraph_dict}')
new_s_t = e_t
union_li = [
t1.V().has(id, target_id).both().hasLabel(label).both().limit(
MAX_FEATURE_NODE) for label in union_id_cols
]
logger.info(
f'union_id_cols len: {len(union_id_cols)} key: {union_id_cols}')
logger.info(f'union_li len: {len(union_li)} key: {union_li}')
if len(union_id_cols) == 51:
node_dict = g.V().has(id,target_id).union(__.both().hasLabel('card1').both().limit(MAX_FEATURE_NODE),\
union_li[1], union_li[2], union_li[3], union_li[4], union_li[5],\
union_li[6], union_li[7], union_li[8], union_li[9], union_li[10],\
union_li[11], union_li[12], union_li[13], union_li[14], union_li[15],\
union_li[16], union_li[17], union_li[18], union_li[19], union_li[20],\
union_li[21], union_li[22], union_li[23], union_li[24], union_li[25],\
union_li[26], union_li[27], union_li[28], union_li[29], union_li[30],\
union_li[31], union_li[32], union_li[33], union_li[34], union_li[35],\
union_li[36], union_li[37], union_li[38], union_li[39], union_li[40],\
union_li[41], union_li[42], union_li[43], union_li[44], union_li[45],\
union_li[46], union_li[47], union_li[48], union_li[49], union_li[50]).elementMap().toList()
else:
node_dict = g.V().has(id,target_id).union(__.both().hasLabel('card1').both().limit(MAX_FEATURE_NODE),\
union_li[1], union_li[2], union_li[3], union_li[4], union_li[5],\
union_li[6], union_li[7], union_li[8], union_li[9], union_li[10]).elementMap().toList()
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: node_dict used {(e_t - new_s_t).total_seconds()} seconds.'
)
new_s_t = e_t
logger.info(f'node_dict len: {len(node_dict)} key: {node_dict}')
for item in node_dict:
node = item.get(list(item)[0])
node_value = node[(node.find('-') + 1):]
neighbor_dict[node_value] = [
item.get(key) for key in transaction_value_cols
]
target_value = target_id[(target_id.find('-') + 1):]
neighbor_dict[target_value] = [
tr_dict[0].get(key) for key in transaction_value_cols
]
logger.info(
f'INSIDE query_target_subgraph: node_dict used {(e_t - new_s_t).total_seconds()} seconds.'
)
logger.info(
f'neighbor_dict len: {len(neighbor_dict.keys())} key: {neighbor_dict.keys()}'
)
logger.info(f'neighbor_dict: {neighbor_dict}')
attr_cols = ['val' + str(x) for x in range(1, 391)]
for attr in feature_list:
attr_name = attr[:attr.find('-')]
attr_value = attr[(attr.find('-') + 1):]
attr_dict = g.V().has(id, attr).valueMap().toList()[0]
attr_dict = [attr_dict.get(key)[-1] for key in attr_cols]
attr_input_dict = {}
attr_input_dict[attr_value] = attr_dict
transaction_embed_value_dict[attr_name] = attr_input_dict
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: transaction_embed_value_dict used {(e_t - new_s_t).total_seconds()} seconds. Total test cost {(e_t - s_t).total_seconds()} seconds.'
)
new_s_t = e_t
transaction_embed_value_dict['target'] = neighbor_dict
conn.close()
logger.info(
f'transaction_embed_value_dict len: {len(transaction_embed_value_dict.keys())} key: {transaction_embed_value_dict.keys()}'
)
logger.info(
f'transaction_embed_value_dict: {transaction_embed_value_dict}')
return subgraph_dict, transaction_embed_value_dict
def __init__(self):
GremlinUtils.init_statics(globals())
gremlin_utils = GremlinUtils()
self.neptune_connection = gremlin_utils.remote_connection()
self.g = gremlin_utils.traversal_source(
connection=self.neptune_connection)
# Simple way of creating endpoints, requires creating a dummy connection in Glue
endpoints = GlueNeptuneConnectionInfo(
'us-east-1', args['neptune_connection_role']).neptune_endpoints(
args['neptune_connection_name'])
# Complex way of creating endpoints - no connection required, but needs the neptune url
# sts = boto3.client('sts', region_name='us-east-1')
# role = sts.assume_role(RoleArn=role_arn, RoleSessionName='bananananame', DurationSeconds=3600)
# credentials = Credentials(
# access_key=role['Credentials']['AccessKeyId'],
# secret_key=role['Credentials']['SecretAccessKey'],
# token=role['Credentials']['SessionToken'])
gremlin_utils = GremlinUtils(endpoints)
conn = gremlin_utils.remote_connection(show_endpoint=True)
g = gremlin_utils.traversal_source(connection=conn)
print("Endpoints created")
print(g.V().limit(10).valueMap().toList())
print("Sanity checked")
bulkload = BulkLoad(source='s3://co-resource-ingestion-bucket-dev/output-dir/',
role=args['neptune_to_s3_role'],
region='us-east-1',
endpoints=endpoints)
bulkload.load()
class GlueGremlinClient:
def __init__(self, endpoints):
self.gremlin_utils = GremlinUtils(endpoints)
GremlinUtils.init_statics(globals())
def not_cme(e):
return '"code":"ConcurrentModificationException"' not in str(e)
@backoff.on_exception(backoff.expo,
GremlinServerError,
max_tries=5,
giveup=not_cme)
def retry_query(self, query):
q = query
q.next()
def add_vertices(self, label, batch_size=1):
"""Adds a vertex with the supplied label for each row in a DataFrame partition.
If the DataFrame contains an '~id' column, the values in this column will be treated as user-supplied IDs for the new vertices.
If the DataFrame does not have an '~id' column, Neptune will autogenerate a UUID for each vertex.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.add_vertices('Product'))
"""
def add_vertices_for_label(rows):
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
t = g
i = 0
for row in rows:
entries = row.asDict()
t = t.addV(label)
for key, value in entries.items():
key = key.split(':')[0]
if key == '~id':
t = t.property(id, value)
elif key == '~label':
pass
else:
t = t.property(key, value)
i += 1
if i == batch_size:
self.retry_query(t)
t = g
i = 0
if i > 0:
self.retry_query(t)
conn.close()
return add_vertices_for_label
def upsert_vertices(self, label, batch_size=1):
"""Conditionally adds vertices for the rows in a DataFrame partition using the Gremlin coalesce() idiom.
The DataFrame must contain an '~id' column.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.upsert_vertices('Product'))
"""
def upsert_vertices_for_label(rows):
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
t = g
i = 0
for row in rows:
entries = row.asDict()
create_traversal = __.addV(label)
for key, value in entries.items():
key = key.split(':')[0]
if key == '~id':
create_traversal = create_traversal.property(id, value)
elif key == '~label':
pass
else:
create_traversal = create_traversal.property(
key, value)
t = t.V(entries['~id']).fold().coalesce(
__.unfold(), create_traversal)
i += 1
if i == batch_size:
self.retry_query(t)
t = g
i = 0
if i > 0:
self.retry_query(t)
conn.close()
return upsert_vertices_for_label
def add_edges(self, label, batch_size=1):
"""Adds an edge with the supplied label for each row in a DataFrame partition.
If the DataFrame contains an '~id' column, the values in this column will be treated as user-supplied IDs for the new edges.
If the DataFrame does not have an '~id' column, Neptune will autogenerate a UUID for each edge.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.add_edges('ORDER_DETAIL'))
"""
def add_edges_for_label(rows):
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
t = g
i = 0
for row in rows:
entries = row.asDict()
t = t.V(entries['~from']).addE(label).to(V(
entries['~to'])).property(id, entries['~id'])
for key, value in entries.items():
key = key.split(':')[0]
if key not in ['~id', '~from', '~to', '~label']:
t = t.property(key, value)
i += 1
if i == batch_size:
self.retry_query(t)
t = g
i = 0
if i > 0:
self.retry_query(t)
conn.close()
return add_edges_for_label
def upsert_edges(self, label, batch_size=1):
"""Conditionally adds edges for the rows in a DataFrame partition using the Gremlin coalesce() idiom.
The DataFrame must contain '~id', '~from', '~to' and '~label' columns.
Example:
>>> dynamicframe.toDF().foreachPartition(neptune.upsert_edges('ORDER_DETAIL'))
"""
def add_edges_for_label(rows):
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
t = g
i = 0
for row in rows:
entries = row.asDict()
create_traversal = __.V(entries['~from']).addE(label).to(
V(entries['~to'])).property(id, entries['~id'])
for key, value in entries.items():
key = key.split(':')[0]
if key not in ['~id', '~from', '~to', '~label']:
create_traversal.property(key, value)
t = t.V(entries['~from']).outE(label).hasId(
entries['~id']).fold().coalesce(__.unfold(),
create_traversal)
i += 1
if i == batch_size:
self.retry_query(t)
t = g
i = 0
if i > 0:
self.retry_query(t)
conn.close()
return add_edges_for_label
class GraphModelClient:
def __init__(self, endpoint):
self.gremlin_utils = GremlinUtils(endpoint)
GremlinUtils.init_statics(globals())
def insert_new_transaction_vertex_and_edge(self,
tr_dict,
connectted_node_dict,
target_id,
vertex_type='Transaction'):
"""Load transaction data, insert transaction object and related domain objects into GraphDB as vertex,
with their properties as values, and insert their relation as edges.
Example:
>>> insert_new_transaction_vertex_and_edge(tr_dict, connectted_node_dict, target_id, vertex_type = 'Transaction')
"""
def insert_attr(graph_conn, attr_val_dict, target_id, node_id,
vertex_type):
if (not g.V().has(id, node_id).hasNext()):
logger.info(f'Insert_Vertex: {node_id}.')
g.inject(attr_val_dict).unfold().as_(vertex_type).\
addV(vertex_type).as_('v').property(id,node_id).\
sideEffect(__.select(vertex_type).unfold().as_('kv').select('v').\
property(Cardinality.single, __.select('kv').by(Column.keys),
__.select('kv').by(Column.values)
)
).iterate()
else:
logger.debug(
f'Ignore inserting existing Vertex with id {node_id}')
# Insert_edge
to_node = g.V().has(id, node_id).next()
edgeId = target_id + '-' + node_id
if (not g.E().has(id, edgeId).hasNext()):
logger.info(f'Insert_Edge: {target_id} --> {node_id}.')
g.V().has(id, target_id).addE('CATEGORY').to(to_node).property(
id, edgeId).iterate()
else:
logger.debug(
f'Ignore inserting existing edge with id {edgeId}')
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
if (not g.V().has(id, target_id).hasNext()):
logger.info(f'Insert_Vertex: {target_id}.')
g.inject(tr_dict).unfold().as_(vertex_type).\
addV(vertex_type).as_('v').property(id,target_id).\
sideEffect(__.select(vertex_type).unfold().as_('kv').select('v').\
property(Cardinality.single, __.select('kv').by(Column.keys),
__.select('kv').by(Column.values)
)
).iterate()
cols = {'val' + str(i + 1): '0.0' for i in range(390)}
for node_k, node_v in connectted_node_dict[0].items():
node_id = node_k + '-' + str(node_v)
empty_node_dict = {}
empty_node_dict[attr_version_key] = json.dumps(cols)
empty_node_dict = [empty_node_dict]
insert_attr(g,
empty_node_dict,
target_id,
node_id,
vertex_type=node_k)
conn.close()
def query_target_subgraph(self, target_id, tr_dict, transaction_value_cols,
union_id_cols, dummied_col):
"""Extract 2nd degree subgraph of target transaction.Dump data into subgraph dict and n_feats dict.
subgraph_dict: related transactions' id list and values through edges
n_feats dict: related 1 degree vertex and transactions' embeded elements vectors.
Usually after insert new test sample's vertex and edges into graphDB.
Example:
>>> query_target_subgraph('3661635', load_data_from_event(), 'M2_T,M3_F,M3_T,...')
"""
subgraph_dict = {}
neighbor_list = []
neighbor_dict = {}
transaction_embed_value_dict = {}
ii = 0
s_t = dt.now()
conn = self.gremlin_utils.remote_connection()
g = self.gremlin_utils.traversal_source(connection=conn)
target_name = target_id[(target_id.find('-') + 1):]
feature_list = g.V().has(id, target_id).out().id().toList()
for feat in feature_list:
ii += 1
feat_name = feat[:feat.find('-')]
feat_value = feat[(feat.find('-') + 1):]
node_list = g.V().has(
id, feat).both().limit(MAX_FEATURE_NODE).id().toList()
target_and_conn_node_list = [int(target_name)] + [
int(target_conn_node[(target_conn_node.find('-') + 1):])
for target_conn_node in node_list
]
target_and_conn_node_list = list(set(target_and_conn_node_list))
neighbor_list += target_and_conn_node_list
nodes_and_feature_value_array = (target_and_conn_node_list,
[feat_value] *
len(target_and_conn_node_list))
subgraph_dict['target<>' +
feat_name] = nodes_and_feature_value_array
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: subgraph_dict used {(e_t - s_t).total_seconds()} seconds'
)
new_s_t = e_t
union_li = [
__.V().has(id, target_id).both().hasLabel(label).both().limit(
MAX_FEATURE_NODE) for label in union_id_cols
]
if len(union_id_cols) == 51:
node_dict = g.V().has(id,target_id).union(__.both().hasLabel('card1').both().limit(MAX_FEATURE_NODE),\
union_li[1], union_li[2], union_li[3], union_li[4], union_li[5],\
union_li[6], union_li[7], union_li[8], union_li[9], union_li[10],\
union_li[11], union_li[12], union_li[13], union_li[14], union_li[15],\
union_li[16], union_li[17], union_li[18], union_li[19], union_li[20],\
union_li[21], union_li[22], union_li[23], union_li[24], union_li[25],\
union_li[26], union_li[27], union_li[28], union_li[29], union_li[30],\
union_li[31], union_li[32], union_li[33], union_li[34], union_li[35],\
union_li[36], union_li[37], union_li[38], union_li[39], union_li[40],\
union_li[41], union_li[42], union_li[43], union_li[44], union_li[45],\
union_li[46], union_li[47], union_li[48], union_li[49], union_li[50]).elementMap().toList()
else:
node_dict = g.V().has(id,target_id).union(__.both().hasLabel('card1').both().limit(MAX_FEATURE_NODE),\
union_li[1], union_li[2], union_li[3], union_li[4], union_li[5],\
union_li[6], union_li[7], union_li[8], union_li[9], union_li[10]).elementMap().toList()
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: node_dict used {(e_t - new_s_t).total_seconds()} seconds.'
)
new_s_t = e_t
logger.debug(f'Found {len(node_dict)} nodes from graph dbs...')
class Item():
def __init__(self, item):
self.item = item
def __hash__(self):
return hash(self.item.get(list(self.item)[0]))
def __eq__(self, other):
if isinstance(other, self.__class__):
return self.__hash__() == other.__hash__()
else:
return NotImplemented
def __repr__(self):
return "Item(%s)" % (self.item)
node_dict = list(set([Item(node) for node in node_dict]))
logger.debug(f'Found {len(node_dict)} nodes without duplication')
for item in node_dict:
item = item.item
node = item.get(list(item)[0])
node_value = node[(node.find('-') + 1):]
try:
logger.debug(
f'the props of node {node} is {item.get(attr_version_key)}'
)
jsonVal = json.loads(item.get(attr_version_key))
neighbor_dict[node_value] = [
jsonVal[key] for key in transaction_value_cols
]
logger.debug(
f'neighbor pair is {node_value}, {neighbor_dict[node_value]}'
)
except json.JSONDecodeError:
logger.warn(
f'Malform node value {node} is {item.get(attr_version_key)}, run below cmd to remove it'
)
logger.info(f'g.V(\'{node}\').drop()')
target_value = target_id[(target_id.find('-') + 1):]
jsonVal = json.loads(tr_dict[0].get(attr_version_key))
neighbor_dict[target_value] = [
jsonVal[key] for key in transaction_value_cols
]
logger.info(
f'INSIDE query_target_subgraph: neighbor_dict used {(e_t - new_s_t).total_seconds()} seconds.'
)
attr_cols = ['val' + str(x) for x in range(1, 391)]
for attr in feature_list:
attr_name = attr[:attr.find('-')]
attr_value = attr[(attr.find('-') + 1):]
attr_dict = g.V().has(id, attr).valueMap().toList()[0]
logger.debug(f'attr is {attr}, dict is {attr_dict}')
jsonVal = json.loads(attr_dict.get(attr_version_key)[0])
attr_dict = [float(jsonVal[key]) for key in attr_cols]
attr_input_dict = {}
attr_input_dict[attr_value] = attr_dict
transaction_embed_value_dict[attr_name] = attr_input_dict
e_t = dt.now()
logger.info(
f'INSIDE query_target_subgraph: transaction_embed_value_dict used {(e_t - new_s_t).total_seconds()} seconds. Total test cost {(e_t - s_t).total_seconds()} seconds.'
)
new_s_t = e_t
transaction_embed_value_dict['target'] = neighbor_dict
conn.close()
return subgraph_dict, transaction_embed_value_dict