def threeDplot():
n = 250
censorP = 0.7
avgRuns = 5
Xs, Ys, Zs = [], [], []
p, q = Decimal(str(0.0)), Decimal(str(0.0))
increment = 0.05
while p <= 1:
while q <= 1:
Xs.append(float(p))
Ys.append(float(q))
#
G, truth = cg.construct_adj(n, p, q)
correct, total = sim.runsim(truth, censorP,
voting.scipy_weighted_knn,
metrics.dsd_mat(G), avgRuns)
acc = correct / total
#
Zs.append(float(acc))
q += Decimal(str(increment))
p += Decimal(str(increment))
q = Decimal(str(0.0))
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.scatter3D(Xs, Ys, Zs, cmap='Greens')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
return Xs, Ys, Zs
def test():
n = 250
p = Decimal(str(0.0))
q = Decimal(str(0.0))
censorP = 0.3
avgRuns = 5
increment = Decimal(str(0.05))
spdaccs1 = []
spdaccs2 = []
dsdaccs = []
rdaccs = []
params = []
r = 100
q = Decimal(str(0.5))
while float(p) <= 1:
A, truth = cg.construct_adj(n, float(p), float(q))
#A, truth = cg.constructWithHubs(n,float(p),float(q),r)
#spdcorr1, spdtotal1 = sim.runsim(truth, censorP, voting.scipy_weighted_knn, metrics.spd_mat(A), avgRuns)
spdcorr1, spdtotal1 = sim.runsim(truth, censorP,
voting.sklearn_weighted_knn,
metrics.dsd_mat(A), avgRuns)
spdcorr2, spdtotal2 = sim.runsim(truth, censorP,
voting.knn_weighted_majority_vote,
metrics.dsd_mat(A), avgRuns)
#dsdcorr, dsdtotal = sim.runsim(truth, censorP, voting.scipy_weighted_knn, metrics.dsd_mat(A), avgRuns)
#rdcorr, rdtotal = sim.runsim(truth, censorP, voting.scipy_weighted_knn, metrics.rd_mat(A), avgRuns)
spdaccs1.append(spdcorr1 / spdtotal1)
spdaccs2.append(spdcorr2 / spdtotal2)
#dsdaccs.append(dsdcorr/dsdtotal)
#rdaccs.append(rdcorr/rdtotal)
print(p)
print(spdcorr1 / spdtotal1)
print(spdcorr2 / spdtotal2)
p += increment
params.append(float(p))
#plotting.plot_params_vs_accuracy(params, [spdaccs, dsdaccs, rdaccs], "Edge addition probability (p)", ["SPD","DSD", "RD"], "NCC (q=0.5)")
plotting.plot_params_vs_accuracy(params, [spdaccs1, spdaccs2],
"Edge addition probability (p)",
["SPD Scipy", "SPD Mine"],
"NCC (q=0.5, censorP=0.6)")
return
def test_censor():
n = 250
p = Decimal(str(0.3))
q = Decimal(str(0.5))
censorP = Decimal(str(0.05))
avgRuns = 10
increment = Decimal(str(0.05))
spdaccs = []
dsdaccs = []
rdaccs = []
params = []
r = 100
#p += increment
while float(censorP) < 1:
A, truth = cg.construct_adj(n, float(p), float(q))
#A, truth = cg.constructWithHubs(n,float(p),float(q),r)
spdcorr, spdtotal = sim.runsim(truth, float(censorP),
voting.scipy_weighted_knn,
metrics.spd_mat(A), avgRuns)
dsdcorr, dsdtotal = sim.runsim(truth, float(censorP),
voting.scipy_weighted_knn,
metrics.dsd_mat(A), avgRuns)
rdcorr, rdtotal = sim.runsim(truth, float(censorP),
voting.scipy_weighted_knn,
metrics.rd_mat(A), avgRuns)
spdaccs.append(spdcorr / spdtotal)
dsdaccs.append(dsdcorr / dsdtotal)
rdaccs.append(rdcorr / rdtotal)
print(censorP)
censorP += increment
params.append(float(censorP))
plotting.plot_params_vs_accuracy(params, [spdaccs, dsdaccs, rdaccs],
"Censoring probability (censorP)",
["SPD", "DSD", "RD"], "NCC (q=0.5)")
return
def test_cg():
n = 250
p = Decimal(str(0.0))
q = Decimal(str(0.0))
censorP = 0.6
avgRuns = 10
increment = Decimal(str(0.05))
spdaccs = []
dsdaccs = []
rdaccs = []
params = []
r = 100
#p += increment
while float(p) <= 1:
q = Decimal(str(0.5))
A, truth = cg.construct_adj(n, float(p), float(q))
#A, truth = cg.constructWithHubs(n,float(p),float(q),r)
spdcorr, spdtotal = sim.runsim(truth, censorP,
voting.scipy_weighted_knn,
metrics.spd_mat(A), avgRuns)
dsdcorr, dsdtotal = sim.runsim(truth, censorP,
voting.scipy_weighted_knn,
metrics.dsd_mat(A), avgRuns)
rdcorr, rdtotal = sim.runsim(truth, censorP, voting.scipy_weighted_knn,
metrics.rd_mat(A), avgRuns)
spdaccs.append(spdcorr / spdtotal)
dsdaccs.append(dsdcorr / dsdtotal)
rdaccs.append(rdcorr / rdtotal)
print(p)
p += increment
params.append(float(p))
plotting.plot_params_vs_accuracy(params, [spdaccs, dsdaccs, rdaccs],
"Edge addition probability (p)",
["SPD", "DSD", "RD"], "NCC (q=0.5)")
p = Decimal(str(0.5))
q = Decimal(str(0.0))
spdaccs = []
dsdaccs = []
rdaccs = []
params = []
q += increment
while float(q) <= 1:
A, truth = cg.construct_adj(n, float(p), float(q))
#A, truth = cg.constructWithHubs(n,float(p),float(q),r)
spdcorr, spdtotal = sim.runsim(truth, censorP,
voting.knn_weighted_majority_vote,
metrics.spd_mat(A), avgRuns)
dsdcorr, dsdtotal = sim.runsim(truth, censorP,
voting.knn_weighted_majority_vote,
metrics.dsd_mat(A), avgRuns)
rdcorr, rdtotal = sim.runsim(truth, censorP,
voting.knn_weighted_majority_vote,
metrics.rd_mat(A), avgRuns)
spdaccs.append(spdcorr / spdtotal)
dsdaccs.append(dsdcorr / dsdtotal)
rdaccs.append(rdcorr / rdtotal)
print(q)
q += increment
params.append(float(q))
plotting.plot_params_vs_accuracy(params, [spdaccs, dsdaccs, rdaccs],
"Edge deletion probability (q)",
["SPD", "DSD", "RD"],
"NCCH (p=0.5, number of hubs=100)")
return
def test_completeGraphs(n, p, q, censorP, vote, metric, avgRuns):
A, truth = cg.construct_adj(n, p, q)
correct, total = sim.runsim(truth, censorP, vote, metric(A), avgRuns)
return correct / total