Thank you! I'll try that out. On Sat, Aug 5, 2017 at 11:51 AM, Alexandre Hannud Abdo <abdo@member.fsf.org> wrote:
Ni!
What you're looking for is the 'project_level' method of NestedBlockState:
some_level = 2 blocks = state.project_level( some_level ).get_blocks() block_for_v_at_level = blocks[ some_vertex ]
Hope this helps,
ale .~´
On Sat, Aug 5, 2017 at 5:29 PM, lenail <lenail@mit.edu> wrote:
Hello Graph Tool developers,
I'm interested in the nested stochastic block model (nsbm). What interests me most is: when I fit the model, where did each of my nodes get clustered? The closest function I can find to this in the API by reading the docs is:
nsbm.get_bs()
which returns
[PropertyArray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32), PropertyArray([1, 1, 2, 2, 3, 4, 5, 0, 6, 4, 7, 1, 1, 4, 0, 5, 0, 0, 8, 2, 6, 5, 6, 6, 2, 2, 3, 3, 1, 1, 0, 7, 5, 5, 7, 3, 3, 6, 3, 7, 9, 8, 0, 8, 6, 7, 7], dtype=int32), PropertyArray([ 0, 1, 1, 13, 4, 5, 6, 7, 8, 9, 10, 4, 11, 0, 12, 13, 10, 14, 15, 4, 16, 17, 18, 5, 19, 20, 21, 22, 23, 9, 16, 14, 7, 24, 25, 26, 9, 27, 28, 29, 30, 5, 35, 14, 23, 30, 11, 41, 31, 13, 32, 6, 25, 33, 8, 34, 0, 12, 4, 16, 32, 35, 0, 28, 36, 13, 30, 27, 36, 11, 19, 13, 26, 13, 36, 37, 23, 28, 32, 19, 25, 29, 5, 24, 20, 27, 25, 4, 17, 36, 22, 11, 15, 12, 14, 2, 5, 38, 9, 9, 24, 39, 29, 13, 34, 17, 8, 20, 9, 5, 23, 8, 9, 40, 40, 27, 31, 40, 41, 10, 3, 12, 25, 38, 20, 40, 9, 9, 25, 42, 10, 24, 43, 3, 37, 2, 17, 34, 35, 21, 38, 32, 26, 22, 28, 13, 17, 44, 45, 36, 42, 26, 17, 27, 24, 40, 39, 9, 13, 5, 43, 38, 35, 30, 13, 36, 13, 11, 14, 40, 40, 12, 3, 40, 38, 1, 40, 21, 42, 9, 10, 29, 43, 45, 40, 31, 46, 40, 31, 5, 42, 40, 14, 11, 38, 34, 31, 34, 40, 31, 31, 45, 10, 4], dtype=int32), PropertyArray([ 0, 1, 2, ..., 163, 98, 18], dtype=int32)]
The solution I ended up using was:
vertex_name = nsbm.g.vertex_properties['_graphml_vertex_id']
clustering = [(nsbm.g.vertex_index[v], vertex_name[v], nsbm.get_bs()[0][nsbm.g.vertex_index[v]]) for v in nsbm.g.vertices()]
clustering = [(i, name, base_clustering, nsbm.get_bs()[1][level0]) for i, name, level0 in clustering]
clustering = [(i, name, level0, level1, nsbm.get_bs()[2][level1]) for i, name, level0, level1 in clustering]
clustering = [(i, name, level0, level1, level2, nsbm.get_bs()[3][level2]) for i, name, level0, level1, level2 in clustering]
at which point I had my result. Is there a less verbose way of putting this? If not, this serves as a feature request to add such a method, maybe called "get_clabels" ?
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