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return self
def add_time_deltas(self):
'''Adds time deltas (i.e. the time between subsequent transactions) to the edge features'''
reverse_tds = True
adj_list_in, adj_list_out = to_adj_edges_with_times(self)
in_tds = time_deltas(self, adj_list_in)
out_tds = [time_deltas(self, adj_list_out)] if reverse_tds else []
self.edge_attr = torch.cat([self.edge_attr, in_tds] + out_tds, dim=1)
return self
class HeteroGraphData(HeteroData):
'''This is the heterogenous graph object we use for GNN training if reverse MP is enabled'''
def __init__(
self,
readout: str = 'edge',
**kwargs
):
super().__init__(**kwargs)
self.readout = readout
@property
def num_nodes(self):
return self['node'].x.shape[0]
@property
def timestamps(self):
return self['node', 'to', 'node'].timestamps
def add_ports(self):
'''Adds port numberings to the edge features'''
adj_list_in, adj_list_out = to_adj_nodes_with_times(self)
in_ports = ports(self['node', 'to', 'node'].edge_index, adj_list_in)
out_ports = ports(self['node', 'rev_to', 'node'].edge_index, adj_list_out)
self['node', 'to', 'node'].edge_attr = torch.cat([self['node', 'to', 'node'].edge_attr, in_ports], dim=1)
self['node', 'rev_to', 'node'].edge_attr = torch.cat([self['node', 'rev_to', 'node'].edge_attr, out_ports], dim=1)
return self
def add_time_deltas(self):
'''Adds time deltas (i.e. the time between subsequent transactions) to the edge features'''
adj_list_in, adj_list_out = to_adj_edges_with_times(self)
in_tds = time_deltas(self, adj_list_in)
out_tds = time_deltas(self, adj_list_out)
self['node', 'to', 'node'].edge_attr = torch.cat([self['node', 'to', 'node'].edge_attr, in_tds], dim=1)
self['node', 'rev_to', 'node'].edge_attr = torch.cat([self['node', 'rev_to', 'node'].edge_attr, out_tds], dim=1)
return self
def z_norm(data):
std = data.std(0).unsqueeze(0)
std = torch.where(std == 0, torch.tensor(1, dtype=torch.float32).cpu(), std)
return (data - data.mean(0).unsqueeze(0)) / std
def create_hetero_obj(x, y, edge_index, edge_attr, timestamps, args):
'''Creates a heterogenous graph object for reverse message passing'''
data = HeteroGraphData()
data['node'].x = x
data['node', 'to', 'node'].edge_index = edge_index
data['node', 'rev_to', 'node'].edge_index = edge_index.flipud()
data['node', 'to', 'node'].edge_attr = edge_attr
data['node', 'rev_to', 'node'].edge_attr = edge_attr
if args.ports:
#swap the in- and outgoing port numberings for the reverse edges
data['node', 'rev_to', 'node'].edge_attr[:, [-1, -2]] = data['node', 'rev_to', 'node'].edge_attr[:, [-2, -1]]
data['node', 'to', 'node'].y = y
data['node', 'to', 'node'].timestamps = timestamps
return data
# <FILESEP>
"""AXL addLocation sample script, using the Zeep SOAP library
Creates a new Device Pool, then creates a new Media Resource Group List and
updates the Device Pool.
Copyright (c) 2020 Cisco and/or its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from lxml import etree
from requests import Session
from requests.auth import HTTPBasicAuth
from zeep import Client, Settings, Plugin, xsd
from zeep.transports import Transport
from zeep.exceptions import Fault
import sys