Am 30.11.18 um 07:22 schrieb ashutosh:
> Sir,
>
> I am trying to follow the example on "edge prediction as binary
> classification".
>
> Here is my code:
>
> *import graph_tool as gt
> import pandas as pd*
>
> # create a graph object in data frame format
>
> *ndf =
> pd.DataFrame({'Node1':['a','b','c','d','e'],'Node2':['c','e','b','a','d'],'Weight':[0.2,0.8,0.4,0.5,0.7],
> 'RandProp1':[1,2,3,1,2]})
>
> ng = gt.Graph()
>
> nprop = ng.new_edge_property("float")
> ng.edge_properties['Weight'] = nprop* # important to map the properties to
> the graph
>
> *LayerProp = ng.new_edge_property('float')
> ng.edge_properties['LayerProp'] = LayerProp*
>
>
> *nvp =
> ng.add_edge_list(ndf.values.tolist(),hashed=True,string_vals=True,eprops=[nprop,LayerProp])*
>
>
> # minimizing the graph and inferring partitions
>
> *stateA = gt.inference.minimize_nested_blockmodel_dl(ng,layers=True,
>
> state_args=dict(ec=LayerProp,layers=True),
>
> deg_corr=True,verbose=True)*
> *L = 10
> bs = stateA.get_bs()
> bs += [np.zeros(1)]*(L-len(bs))
>
> stateB = stateA.copy(bs=bs, sampling=True)
> probs=([])
>
> def collect_edge_probs(s):
> p =
> s.get_edges_prob([missing_edges[0]],entropy_args=dict(partition_dl=False))
>
> probs[0].append(p);
>
>
> missing_edges = [(1,2,1)] *# for layered network you need to specify layer
> number
>
> *gt.inference.mcmc_equilibrate(stateB,force_niter=1000,mcmc_args=dict(niter=10),
> callback=collect_edge_probs,verbose=True)*
>
>
> When I run this code, it gives me kernel died error. Please help.
>
>
This might be a bug. Please open an issue in the website with this example, and
I'll take a look at it when I have the time.(Also, please do not post the same email multiple times to the mailing list. If I haven't responded the first time, it's because I did not have the chance to look into it) > I have another query that; how can we get the layer associated with the > node? > > In the above code when I try the command > > *for i in nvp: print(i)* > > I get the output as : *a,c,b,e,d* > > and when I type the command > > *LayerProp.a* > > I get the output: *PropertyArray([ 1., 2., 3., 1., 2.])* > > How do I understand that because the order of addition of nodes depends on > the order they come along with add_edge_list command, while the LayerProp is > added in the order as mentioned in the property map. I'm not sure I understand your question. Nodes do not belong to different layers, only the edges do. -- Tiago de Paula Peixoto <[email protected]>
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