TMVA_SOFIE_GNN_Parser.py File Reference

  class  TMVA_SOFIE_GNN_Parser.MLPGraphNetwork  

Functions

 TMVA_SOFIE_GNN_Parser.get_dynamic_graph_data_dict (NODE_FEATURE_SIZE=2, EDGE_FEATURE_SIZE=2, GLOBAL_FEATURE_SIZE=1)    TMVA_SOFIE_GNN_Parser.get_fix_graph_data_dict (num_nodes, num_edges, NODE_FEATURE_SIZE=2, EDGE_FEATURE_SIZE=2, GLOBAL_FEATURE_SIZE=1)    TMVA_SOFIE_GNN_Parser.make_mlp_model ()    TMVA_SOFIE_GNN_Parser.printMemory (s="")      TMVA_SOFIE_GNN_Parser.CoreGraphData = get_fix_graph_data_dict(num_max_nodes, num_max_edges, 2*LATENT_SIZE, 2*LATENT_SIZE, 2*LATENT_SIZE)   list TMVA_SOFIE_GNN_Parser.dataset = []    TMVA_SOFIE_GNN_Parser.DecodeGraphData = get_fix_graph_data_dict(num_max_nodes, num_max_edges, LATENT_SIZE, LATENT_SIZE, LATENT_SIZE)    TMVA_SOFIE_GNN_Parser.decoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._decoder._network, DecodeGraphData, filename = "decoder")    TMVA_SOFIE_GNN_Parser.edge_data = ROOT.std.vector['float'](num_max_edges*edge_size)   int TMVA_SOFIE_GNN_Parser.edge_size = 4    TMVA_SOFIE_GNN_Parser.encoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._encoder._network, GraphData, filename = "encoder")    TMVA_SOFIE_GNN_Parser.end = time.time()    TMVA_SOFIE_GNN_Parser.ep_model = EncodeProcessDecode()    TMVA_SOFIE_GNN_Parser.fileOut = ROOT.TFile.Open("graph_data.root","RECREATE")   bool TMVA_SOFIE_GNN_Parser.firstEvent = True    TMVA_SOFIE_GNN_Parser.global_data = ROOT.std.vector['float'](global_size)   int TMVA_SOFIE_GNN_Parser.global_size = 1    TMVA_SOFIE_GNN_Parser.GraphData = get_fix_graph_data_dict(num_max_nodes, num_max_edges, node_size, edge_size, global_size)    TMVA_SOFIE_GNN_Parser.graphData = get_dynamic_graph_data_dict(node_size, edge_size, global_size)    TMVA_SOFIE_GNN_Parser.h1 = ROOT.TH1D("h1","GraphNet nodes output",40,1,0)    TMVA_SOFIE_GNN_Parser.h2 = ROOT.TH1D("h2","GraphNet edges output",40,1,0)    TMVA_SOFIE_GNN_Parser.h3 = ROOT.TH1D("h3","GraphNet global output",40,1,0)    TMVA_SOFIE_GNN_Parser.input_core_graph_data = utils_tf.data_dicts_to_graphs_tuple([CoreGraphData])    TMVA_SOFIE_GNN_Parser.input_graph_data = utils_tf.data_dicts_to_graphs_tuple([GraphData])   int TMVA_SOFIE_GNN_Parser.LATENT_SIZE = 100    TMVA_SOFIE_GNN_Parser.node_data = ROOT.std.vector['float'](num_max_nodes*node_size)   int TMVA_SOFIE_GNN_Parser.node_size = 4    TMVA_SOFIE_GNN_Parser.num_edges = graphData['edges'].shape[0]   int TMVA_SOFIE_GNN_Parser.NUM_LAYERS = 4   int TMVA_SOFIE_GNN_Parser.num_max_edges = 300   int TMVA_SOFIE_GNN_Parser.num_max_nodes = 100   int TMVA_SOFIE_GNN_Parser.numevts = 100    TMVA_SOFIE_GNN_Parser.outgnn = ROOT.std.vector['float'](3)    TMVA_SOFIE_GNN_Parser.output_edges = output_gnn[-1].edges.numpy()    TMVA_SOFIE_GNN_Parser.output_globals = output_gnn[-1].globals.numpy()    TMVA_SOFIE_GNN_Parser.output_gn = ep_model(input_graph_data, processing_steps)    TMVA_SOFIE_GNN_Parser.output_gnn = ep_model(dataset[0], processing_steps)    TMVA_SOFIE_GNN_Parser.output_nodes = output_gnn[-1].nodes.numpy()    TMVA_SOFIE_GNN_Parser.output_transform = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._output_transform._network, DecodeGraphData, filename = "output_transform")   int TMVA_SOFIE_GNN_Parser.processing_steps = 5    TMVA_SOFIE_GNN_Parser.receivers = ROOT.std.vector['int'](num_max_edges)    TMVA_SOFIE_GNN_Parser.s_edges = graphData['edges'].size    TMVA_SOFIE_GNN_Parser.s_nodes = graphData['nodes'].size    TMVA_SOFIE_GNN_Parser.senders = ROOT.std.vector['int'](num_max_edges)    TMVA_SOFIE_GNN_Parser.start = time.time()    TMVA_SOFIE_GNN_Parser.tf_graph_data = utils_tf.data_dicts_to_graphs_tuple([graphData])    TMVA_SOFIE_GNN_Parser.tmp = ROOT.std.vector['float'](graphData['nodes'].reshape((graphData['nodes'].size)))    TMVA_SOFIE_GNN_Parser.tree = ROOT.TTree("gdata","GNN data")   bool TMVA_SOFIE_GNN_Parser.verbose = False