def r_igraph_max_ave_degree(g):
"""
Compute local triangle count of graph g and save as necessary
*Global graph attributes can only be stored in the graph*
@param g: The igraph graph loaded via Rpy2 i.e. an R object
@return: Same graph an input but with added invariant as an attribute
"""
mad = r_igraph_get_attr(
g, "eigvals",
"g") # See if we already have computed eigenvalues for the graph
if mad == NULL: # Ok then compute top 1 eig ourself
mad = r_igraph_eigs(g, 1, return_eigs=True, save_fn=None)[0]
else:
mad = float(mad[0].split(",")[0]
[1:]) # The largest eigenvalue is held at index 0
if mad is not None:
g = r_igraph_set_graph_attribute(g, "max_ave_degree", mad)
else: # More than likely ran out of memory
print "Failed to estimate max ave degree because eigensolver failed ..."
return g # return so we can use for other attributes
inv_dict["k"] = min(100,
r_igraph_vcount(G, False) -
3) # Max of 100 eigenvalues
# Test if graph is too big for invariants
print "Computing eigen decompositon ..."
lcc = True if r_igraph_ecount(G, False) > 500000 else False
if sep_save:
inv_dict["eigvl_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvl.npy")
inv_dict["eigvect_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvect.npy")
G = r_igraph_eigs(G,
inv_dict['k'],
save_fn=(inv_dict["eigvl_fn"],
inv_dict["eigvect_fn"]),
lcc=lcc)
else:
G = r_igraph_eigs(G, inv_dict['k'], save_fn=None, lcc=lcc)
#else: G = r_igraph_eigs(G, 4, save_fn=None, lcc=True)
if inv_dict.get("mad", False) != False:
if r_igraph_vcount(
G, False) < 1000000: # Cannot compute eigs on very big graphs
if sep_save:
inv_dict["mad_fn"] = os.path.join(inv_dict["save_dir"], "MAD", \
getBaseName(inv_dict["graph_fn"]) + "_mad.npy")
print "Computing MAD ..."
G = r_igraph_max_ave_degree(G)
if inv_dict.get("k") is None:
inv_dict["k"] = min(100, r_igraph_vcount(G, False) - 3) # Max of 100 eigenvalues
# Test if graph is too big for invariants
print "Computing eigen decompositon ..."
lcc = True if r_igraph_ecount(G, False) > 500000 else False
if sep_save:
inv_dict["eigvl_fn"] = os.path.join(
inv_dict["save_dir"], "Eigen", getBaseName(inv_dict["graph_fn"]) + "_eigvl.npy"
)
inv_dict["eigvect_fn"] = os.path.join(
inv_dict["save_dir"], "Eigen", getBaseName(inv_dict["graph_fn"]) + "_eigvect.npy"
)
G = r_igraph_eigs(G, inv_dict["k"], save_fn=(inv_dict["eigvl_fn"], inv_dict["eigvect_fn"]), lcc=lcc)
else:
G = r_igraph_eigs(G, inv_dict["k"], save_fn=None, lcc=lcc)
# else: G = r_igraph_eigs(G, 4, save_fn=None, lcc=True)
if inv_dict.get("mad", False) != False:
if r_igraph_vcount(G, False) < 1000000: # Cannot compute eigs on very big graphs
if sep_save:
inv_dict["mad_fn"] = os.path.join(
inv_dict["save_dir"], "MAD", getBaseName(inv_dict["graph_fn"]) + "_mad.npy"
)
print "Computing MAD ..."
G = r_igraph_max_ave_degree(G)
else:
print "Graph too big to compute spectral embedding"
