Hi Dr. Peixoto, The attached is a complete example with the input simulated data and the python scripts. The main code is shown below as well, it's mainly one "load edges to graph" function and "minimize_nested_blockmodel_dl". Thank you! import os import numpy as np import pandas as pd import pickle import warnings from collections import defaultdict import graph_tool.all as gt def load_edges_to_graph(tidy_edges,verbose = True): # Grab lists of unique nodes for both types node_a_names = tidy_edges['node_a'].unique() n_node_a = len(node_a_names) node_b_names = tidy_edges['node_b'].unique() n_node_b = len(node_b_names) # create a graph-tool graph g = gt.Graph(directed=False) # Setup and pull out node property vectors for easy setting name = g.vp["name"] = g.new_vp("string") kind = g.vp["kind"] = g.new_vp("int") # Dictionaries for adding and grabbing nodes from either class node_a_add = defaultdict(lambda: g.add_vertex()) node_b_add = defaultdict(lambda: g.add_vertex()) num_edges, _ = tidy_edges.shape # Start by adding all unique node_a's to make sure nodes are not jumbled by type if(verbose): print(f'Adding all unique node As to model nodes') for node_a_name in node_a_names: node_a = node_a_add[node_a_name] name[node_a] = node_a_name kind[node_a] = 0 # Next do node b's if(verbose): print(f'Adding all unique node Bs to model nodes') for node_b_name in node_b_names: node_b = node_b_add[node_b_name] name[node_b] = node_b_name kind[node_b] = 1 # Now go edge by edge and add the model. five_percent = np.ceil(num_edges/20) for edge_num in range(num_edges): if((edge_num%five_percent == 0) and verbose): print(f'{np.round((edge_num/num_edges)*100, decimals=1)}% done | {edge_num}/{num_edges}') node_a_name, node_b_name = tidy_edges.iloc[edge_num,:] # Grab the nodes for each end of the edge. node_a = node_a_add[node_a_name] node_b = node_b_add[node_b_name] # Add the edge to the model edge = g.add_edge(node_a, node_b) return g # Load network edges (aka phenome vectors) tidy_edges = pd.read_csv("bisbm_sim_edge_complex.csv", dtype = 'str') g = load_edges_to_graph(tidy_edges, verbose = True) node_types = g.vp['kind'] niter = 1 beta = 1 # mcmc state = gt.minimize_nested_blockmodel_dl( g, state_args = dict(deg_corr = True,clabel=node_types), multilevel_mcmc_args = dict(niter=niter,beta=beta)) Best, Siwei ________________________________ From: Tiago de Paula Peixoto <tiago@skewed.de> Sent: Friday, July 22, 2022 7:18 To: graph-tool@skewed.de <graph-tool@skewed.de> Subject: [graph-tool] Re: bipartite minimize_nested_blockmodel_dl() error: cannot move vertex across clabel barriers Am 20.07.22 um 08:02 schrieb Siwei Zhang:
Hi Dr. Peixoto,
I am using graph-tool version 2.45 and I have two questions.
1. I am trying to reproduce the script in the document 2. g = gt.collection.data["celegansneural"] state = gt.minimize_nested_blockmodel_dl(g, state_args=dict(overlap=True))
and have the error: /usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py:390: UserWarning: unrecognized keyword arguments: ['overlap'] warnings.warn("unrecognized keyword arguments: " +
It seems the argument of "overlap" is removed.
The proper way to use an overlapping model is to pass the option: state_args=dict(base_type=OverlapBlockState)
2. Regardless of the question1, I am trying to do a bipartite version stochastic block model and I define "clabel" to constraint labels on the vertices so that vertices with different label values will not be clustered in the same group. But I always have the error of "ValueError: cannot move vertex across clabel barriers". The below is the code: node_types = g.vp['kind'] node_types.get_array() Output: PropertyArray([1, 1, 1, ..., 2, 2, 2], dtype=int32)
state = gt.minimize_nested_blockmodel_dl( g, state_args=dict(clabel=node_types,pclabel=node_types,deg_corr=True), multilevel_mcmc_args = dict(niter=niter,beta=beta)) Could you please help me with these questions? Thanks!
In order to understand what is happening you would need to send us a minimal but complete working example that shows the problem. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>