class
TMVA_SOFIE_GNN.MLPGraphNetwork
class
TMVA_SOFIE_GNN.SofieGNN
TMVA_SOFIE_GNN.CopyData (
input_data)
TMVA_SOFIE_GNN.GenerateData ()
TMVA_SOFIE_GNN.get_graph_data_dict (
num_nodes,
num_edges,
NODE_FEATURE_SIZE=2,
EDGE_FEATURE_SIZE=2,
GLOBAL_FEATURE_SIZE=1)
TMVA_SOFIE_GNN.make_mlp_model ()
TMVA_SOFIE_GNN.PrintSofie (
output,
printShape=False)
TMVA_SOFIE_GNN.RunGNet (
inputGraphData)
TMVA_SOFIE_GNN.c2 = c0.cd(2)
TMVA_SOFIE_GNN.core = ROOT.TMVA.Experimental.SOFIE.RModel_GNN.ParseFromMemory(ep_model._core._network,
CoreGraphData,
filename = "core")
TMVA_SOFIE_GNN.CoreGraphData =
get_graph_data_dict(
num_nodes,
num_edges, 2*
LATENT_SIZE, 2*
LATENT_SIZE, 2*
LATENT_SIZE)
TMVA_SOFIE_GNN.data =
GenerateData()
list
TMVA_SOFIE_GNN.dataSet = []
TMVA_SOFIE_GNN.DecodeGraphData =
get_graph_data_dict(
num_nodes,
num_edges,
LATENT_SIZE,
LATENT_SIZE,
LATENT_SIZE)
TMVA_SOFIE_GNN.decoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._decoder._network,
DecodeGraphData,
filename = "decoder")
TMVA_SOFIE_GNN.edge_data
TMVA_SOFIE_GNN.edge_index
int TMVA_SOFIE_GNN.edge_size = 4
TMVA_SOFIE_GNN.edgesG =
outGnet[1].edges.numpy()
TMVA_SOFIE_GNN.edgesS = np.asarray(
outSofie[1].
edge_data)
TMVA_SOFIE_GNN.encoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._encoder._network,
GraphData,
filename = "encoder")
TMVA_SOFIE_GNN.end = time.time()
TMVA_SOFIE_GNN.endSC = time.time()
TMVA_SOFIE_GNN.ep_model =
EncodeProcessDecode()
TMVA_SOFIE_GNN.g =
out[1].globals.numpy()
TMVA_SOFIE_GNN.global_data
int TMVA_SOFIE_GNN.global_size = 1
TMVA_SOFIE_GNN.globG =
outGnet[1].globals.numpy()
TMVA_SOFIE_GNN.globS = np.asarray(
outSofie[1].
global_data)
TMVA_SOFIE_GNN.gnet_data_i = utils_tf.data_dicts_to_graphs_tuple([
graphData])
list
TMVA_SOFIE_GNN.gnetData = []
TMVA_SOFIE_GNN.gnn =
SofieGNN()
TMVA_SOFIE_GNN.GraphData =
get_graph_data_dict(
num_nodes,
num_edges,
node_size,
edge_size,
global_size)
list
TMVA_SOFIE_GNN.graphData =
dataSet[i]
TMVA_SOFIE_GNN.hDe = ROOT.TH1D("hDe","Difference
for edge data",40,1,0)
TMVA_SOFIE_GNN.hDg = ROOT.TH1D("hDg","Difference
for global data",40,1,0)
TMVA_SOFIE_GNN.hDn = ROOT.TH1D("hDn","Difference
for node
data",40,1,0)
TMVA_SOFIE_GNN.hG = ROOT.TH1D("hG","Result
from graphnet",20,1,0)
TMVA_SOFIE_GNN.hS = ROOT.TH1D("hS","Result
from SOFIE",20,1,0)
TMVA_SOFIE_GNN.input_core_graph_data = utils_tf.data_dicts_to_graphs_tuple([
CoreGraphData])
TMVA_SOFIE_GNN.input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data()
TMVA_SOFIE_GNN.input_graph_data = utils_tf.data_dicts_to_graphs_tuple([
GraphData])
int TMVA_SOFIE_GNN.LATENT_SIZE = 100
TMVA_SOFIE_GNN.node_data
int TMVA_SOFIE_GNN.node_size = 4
TMVA_SOFIE_GNN.nodesG =
outGnet[1].nodes.numpy()
TMVA_SOFIE_GNN.nodesS = np.asarray(
outSofie[1].
node_data)
int TMVA_SOFIE_GNN.num_edges = 20
int TMVA_SOFIE_GNN.NUM_LAYERS = 4
int TMVA_SOFIE_GNN.num_nodes = 5
int TMVA_SOFIE_GNN.numevts = 40
TMVA_SOFIE_GNN.out =
RunGNet(
gnetData[i])
TMVA_SOFIE_GNN.outGnet =
RunGNet(
gnetData[i])
TMVA_SOFIE_GNN.output_gn =
ep_model(
input_graph_data,
processing_steps)
TMVA_SOFIE_GNN.output_transform = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._output_transform._network,
DecodeGraphData,
filename = "output_transform")
TMVA_SOFIE_GNN.outSofie = gnn.infer(
sofieData[i])
int TMVA_SOFIE_GNN.processing_steps = 5
TMVA_SOFIE_GNN.rec = np.array([0,0,0,0,1,1,1,2,2,3,1,2,3,4,2,3,4,3,4,4],
dtype='
int32')
TMVA_SOFIE_GNN.snd = np.array([1,2,3,4,2,3,4,3,4,4,0,0,0,0,1,1,1,2,2,3],
dtype='
int32')
list
TMVA_SOFIE_GNN.sofieData = []
TMVA_SOFIE_GNN.start = time.time()
TMVA_SOFIE_GNN.start0 = time.time()
