'KeysView' object is returned instead of a list

When running this: G_normal = custom_exponential_graph(baseGraph, scale=100)

I get the following error:

==================================================

TypeError Traceback (most recent call last) mtrand.pyx in mtrand.RandomState.choice()

TypeError: ‘KeysView’ object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) <ipython-input-2-1a0d817170a8> in <module> 3 4 # Baseline normal interactions: ----> 5 G_normal = custom_exponential_graph(baseGraph, scale=100) 6 plot_degree_distn(G_normal, max_degree=40)

~\Anaconda3\envs\corona\lib\site-packages\seirsplus\models.py in custom_exponential_graph(base_graph, scale, min_num_edges, m, n) 1175 neighbors = graph[node].keys() 1176 quarantineEdgeNum = int( max(min(numpy.random.exponential(scale=scale, size=1), len(neighbors)), min_num_edges) ) -> 1177 quarantineKeepNeighbors = numpy.random.choice(neighbors, size=quarantineEdgeNum, replace=False) 1178 for neighbor in neighbors: 1179 if(neighbor not in quarantineKeepNeighbors):

mtrand.pyx in mtrand.RandomState.choice()

ValueError: ‘a’ must be 1-dimensional or an integer

solved it by wrapping graph[node].keys() with list(): neighbors = graph[node].keys() -> neighbors = list(graph[node].keys())

Is it right?

[kpelechrinis]

@tomermilo actually the following version does not give any problem (essentially it is what you also mentioned I think) and I was able to run the example provided:

============================================== def custom_exponential_graph(base_graph=None, scale=100, min_num_edges=0, m=9, n=None): # Generate a random preferential attachment power law graph as a starting point. # By the way this graph is constructed, it is expected to have 1 connected component. # Every node is added along with m=8 edges, so the min degree is m=8. if(base_graph): graph = base_graph.copy() else: assert(n is not None), “Argument n (number of nodes) must be provided when no base graph is given.” graph = networkx.barabasi_albert_graph(n=n, m=m)

# To get a graph with power-law-esque properties but without the fixed minimum degree,
# We modify the graph by probabilistically dropping some edges from each node. 
for node in graph:
    neighbors = list(graph[node].keys())
    quarantineEdgeNum = int( max(min(numpy.random.exponential(scale=scale, size=1), len(neighbors)), min_num_edges) )
    quarantineKeepNeighbors = numpy.random.choice(neighbors, size=quarantineEdgeNum, replace=False)
    for neighbor in neighbors:
        if(neighbor not in quarantineKeepNeighbors):
            graph.remove_edge(node, neighbor)

return graph

[ryansmcgee]

The change to list(dict.keys()) has been made throughout the package to make this keys lookup work in both Python 2 and 3. Thank you for flagging this issue.