def run_improve(g,
gname,
method,
methodname,
delta,
nthreads=24,
timeout=1000):
ratio = 1.0
if g._num_vertices > 1000000:
ratio = 0.05
elif g._num_vertices > 100000:
ratio = 0.1
elif g._num_vertices > 10000:
ratio = 0.4
elif g._num_vertices > 7500:
ratio = 0.6
elif g._num_vertices > 5000:
ratio = 0.8
print("ratio: ", ratio)
start = time.time()
ncp = lgc.NCPData(g, store_output_clusters=True)
ncp.approxPageRank(ratio=ratio,
nthreads=nthreads,
localmins=False,
neighborhoods=False,
random_neighborhoods=False)
end = time.time()
print("Elapsed time for acl-ncp for dataset ", gname, " is ", end - start,
" the method is ", methodname, " delta is ", delta)
sets = [st["output_cluster"] for st in ncp.results]
print("Make an NCP object for Improve Algo")
start2 = time.time()
ncp2 = lgc.NCPData(g)
print("Going into improve mode")
output = ncp2.refine(sets,
method=method,
methodname=methodname,
nthreads=nthreads,
timeout=timeout,
**{"delta": delta})
end2 = time.time()
print("Elapsed time for improve-ncp for dataset ", gname, " is ",
end2 - start2, " the method is ", methodname, " delta is ", delta)
fig = lgc.NCPPlots(ncp2).mqi_input_output_cond_plot()[0]
fig.axes[0].set_title(gname + " " + methodname + "-NCP")
fig.savefig("figures/" + method + "-ncp-" + gname + ".pdf",
bbox_inches="tight",
figsize=(100, 100))
plt.show()
pickle.dump(ncp,
open('results/' + method + "-ncp-" + gname + '.pickle', 'wb'))
ncp.write('results/' + method + "-ncp-csv-" + gname, writepython=False)
pickle.dump(ncp2,
open('results/' + method + "-ncp2-" + gname + '.pickle', 'wb'))
ncp2.write('results/' + method + "-ncp2-csv-" + gname, writepython=False)
def test_ncpplots():
G = load_example_graph()
ncp = lgc.NCPData(G)
ncp.mqi()
plots = lgc.NCPPlots(ncp)
plots.cond_by_vol()
plots.cond_by_size()
plots.isop_by_size()
plots.mqi_input_output_cond_plot()
plots.cond_by_vol_itrv(alpha=0.2)
plots.cond_by_size_itrv(alpha=0.2)
plots.isop_by_size_itrv(alpha=0.2)
plots = lgc.NCPPlots(ncp, method_name="mqi")
G = load_example_graph()
ncp = lgc.NCPData(G)
ncp.approxPageRank()
df = ncp.as_data_frame()
plots = lgc.NCPPlots(df)
plots.cond_by_vol()
ncp.crd()
ncp.l1reg()
plots = lgc.NCPPlots(ncp, method_name="crd")
plots = lgc.NCPPlots(ncp, method_name="l1reg")
plots = lgc.NCPPlots(ncp, method_name="ncpapr")
sep = ' '
if isinstance(gfile, tuple):
sep = gfile[1]
gfile = gfile[0]
print("Running " + gname)
g = lgc.GraphLocal(os.path.join("..", "data", gfile),'edgelist', " ")
g.discard_weights()
start = time.time()
ncp_instance = lgc.NCPData(g)
ncp_instance.approxPageRank(ratio=0.1,timeout=5000000,nthreads=24)
ncp_plots = lgc.NCPPlots(ncp_instance,method_name = "acl")
#plot conductance vs size
fig, ax, min_tuples = ncp_plots.cond_by_size()
plt.savefig('figures/cond_card_' + gname + '.png', bbox_inches='tight')
plt.show()
#plot conductance vs volume
fig, ax, min_tuples = ncp_plots.cond_by_vol()
plt.savefig('figures/cond_vol_' + gname + '.png', bbox_inches='tight')
plt.show()
#plot isoperimetry vs size
fig, ax, min_tuples = ncp_plots.isop_by_size()
plt.savefig('figures/expand_card_' + gname + '.png', bbox_inches='tight')
plt.show()
end = time.time()
print(" Elapsed time: ", end - start)