def plot_2d(property):
# Plot average degree on 2d-graph
figure()
for g in GRAPH_TYPES:
fname = '%s/%s_%sdeg%d.txt' % (results_dir,
snap.GetAttributeAbbr(property),
snap.GetGraphAbbr(g), AVG_DEG)
A = loadtxt(fname)
A = sort(A, 0)
Y = A[:, -1] # Last column
X = A[:, :-1] # Columns 0-(n-1)
loglog(X[:, 0], Y, 'o', label=snap.GetGraphDesc(g))
legend(loc='lower right')
xlabel('Num Nodes (d_avg = %.1f)' % AVG_DEG)
ylabel('time')
title('%s runtime (avg degree = %d)' %
(snap.GetAttributeDesc(property), AVG_DEG))
pname = '%s/plot2d_%s.png' % (results_dir,
snap.GetAttributeAbbr(property))
print "Saving figure %s" % pname
savefig(pname)
def plot_3d(property):
import mpl_toolkits.mplot3d.axes3d as p3
fig3d = figure()
ax = fig3d.add_subplot(111, projection='3d')
for g in GRAPH_TYPES:
fname = '%s/%s_%s.txt' % (results_dir, snap.GetAttributeAbbr(property),
snap.GetGraphAbbr(g))
if not os.path.exists(fname):
print "No such file: %s" % fname
return
A = loadtxt(fname)
A = sort(A, 0)
Y = A[:, -1] # Last column
X = A[:, :-1] # Columns 0-(n-1)
# savetxt(fname+'_sorted.txt', A)
Nodes = X[:, 0]
Edges = X[:, 1]
ax.plot(Nodes,
Edges,
Y,
'o',
label="%s-%s" %
(snap.GetAttributeAbbr(property), snap.GetGraphAbbr(g)))
ax.set_xlabel('# of nodes', fontsize=9)
ax.set_ylabel('# of edges', fontsize=9)
ax.set_zlabel('Run time %s (sec)' % snap.GetAttributeAbbr(property),
fontsize=9)
ax.legend()
# ax.set_xlim3d([0, 10**max_nodes_exponent])
# ax.set_ylim3d([0, 10**max_nodes_exponent*AVG_DEGREE_RANGE[1]])
# ax.set_zlim3d([0, max(Y)])
# ax.set_xscale('log')
# ax.w_xaxis.set_scale('log')
# ax.w_yaxis.set_scale('log')
# ax.set_zscale('log')
# ax.auto_scale_xyz([0, max(Nodes)], [0, max(Edges)], [0, max(Y)])
# ax.title("%s run time" % snap.GetAttributeAbbr(property))
pname = '%s/plot3d_%s.png' % (results_dir, snap.GetAttributeAbbr(property))
print "Saving figure %s" % pname
fig3d.savefig(pname)
def calc_stats():
for g in GRAPH_TYPES:
for e in range(min_nodes_exponent, max_nodes_exponent + 1):
# Random number of nodes of degree i
NNodes = randrange(10**e, 10**(e + 1))
for avg in DEGREE_TYPES:
if avg:
# Use average degree
NEdges = NNodes * AVG_DEG
else:
# Random number of edges (from 1-3x nodes)
NEdges = NNodes * choice(AVG_DEGREE_RANGE)
print "%s graph: NNodes=%.2e, %.2e" % \
(snap.GetGraphDesc(g), NNodes, NEdges)
fname = "%s%s" % (snap.GetGraphAbbr(g),
'deg%d' % AVG_DEG if avg else '')
# Repeat for all graph types
for j in PROPERTY_TYPES:
print "Calculating %s..." % snap.GetAttributeDesc(j)
t = snap.GetStats(NNodes, NEdges, j, g)
f = open(
'%s/%s_%s.txt' %
(results_dir, snap.GetAttributeAbbr(j), fname), 'a')
f.write("%d %d %.5f\n" % (NNodes, NEdges, t))
f_all = open(
'%s/%s_all.txt' %
(results_dir, snap.GetAttributeAbbr(j)), 'a')
f_all.write("%d %d %.5f\n" % (NNodes, NEdges, t))
# For each characteristic:
# Write out test data to same file (linear fit using matlab?)
# NNodes NEdges Time
print "-" * 75
def plot_residuals(property):
# Calculate residuals for all graph types, and combined
figure()
# All graphs
fname = '%s/%s_all.txt' % (results_dir, snap.GetAttributeAbbr(property))
A = loadtxt(fname)
A = sort(A, 0)
Y = A[:, -1] # Last column
X = A[:, :-1] # Columns 0-(n-1)
# savetxt(fname+'_sorted.txt', A)
best_r2 = 0.0
best_model = None
best_p = None
desc = 'all'
abbr = 'all'
print "Fitting %s for %s" % (snap.GetAttributeDesc(property), desc)
fname = '%s/coeff_%s.txt' % (results_dir, snap.GetAttributeAbbr(property))
f = open(fname, 'w')
cname = '%s/coeff_%s.txt' % (combined_dir, snap.GetAttributeAbbr(property))
combined_file = open(cname, 'a+')
for model in ['poly', 'exp', 'log']:
# Plot residuals with multiple fitting types
rsquared, pfinal = plot_fit(X, Y, desc, model)
f.write("%s, model=%s r2=%.4f pfinal=%s\n" %
(abbr, model, rsquared, pfinal))
if (rsquared > best_r2):
best_r2 = rsquared
best_model = model
best_p = pfinal
title('Residual error for approx. of run-time, %s (%s)' %
(snap.GetAttributeDesc(property).title(), desc))
xscale('log')
yscale('symlog')
grid(True)
xlabel('Number of Nodes')
ylabel('Residual')
legend(loc='lower right')
pname = '%s/residuals_%s_%s.png' % (results_dir,
snap.GetAttributeAbbr(property), abbr)
print "Saving figure %s" % pname
savefig(pname)
print "Best model: %s, r2 = %.3f, pfinal: %s" % \
(best_model, best_r2, repr(best_p))
# TODO: Get most recent date of data
print "Best results to %s" % cname
combined_file.write(
'hostname=%s, model=%s, type=%s, n=%d, r2=%.4f, pfinal=%s\n' % \
(hostname, best_model, abbr, len(X), best_r2,
["%.4e" % p for p in best_p]))