def subgraph_perdb(noise):
lengths = ["8", "15", "30"]
levels = ["10", "20", "30"]
fp = ["echoprint", "chromaprint", "landmark"]
linestyle = ["-", ":", "--"]
x = [8, 15, 30]
pointstyle = ["o", "^", "+"]
plt.figure()
plt.xlim([5, 55])
plt.xlabel("Query length (seconds)")
plt.xticks(x)
plt.ylabel("Accuracy")
plt.ylim([0.0, 1.0])
plt.title("Accuracy with added %s noise" % noise)
count = 1
for p, lev in zip(pointstyle, levels):
plt.subplot(3, 1, count)
dbel = 10 - int(lev)
plt.xlim([5, 45])
plt.xlabel("Query length (seconds)")
plt.xticks(x)
plt.ylabel("Accuracy")
plt.ylim([0.0, 1.0])
plt.title("Accuracy with %ddB %s noise" % (
dbel,
noise,
))
count += 1
print "noise", lev
for line, c in zip(linestyle, fp):
print " fp", c
data = []
for lng in lengths:
print ".",
sys.stdout.flush()
munge = "%s%s,chop%s" % (noise, lev, lng)
row = db.session.query(
evaluation.Run).filter(evaluation.Run.engine == c).filter(
evaluation.Run.munge == munge).one()
i = row.id
s = stats.stats(i)
accuracy = stats.prf(s)["accuracy"]
data.append(accuracy)
print ""
linefmt = "k%s%s" % (line, p)
lab = "%s" % (c, )
plt.plot(x, data, linefmt, label=lab)
plt.legend()
plt.savefig("plot-%s-perdb.png" % noise)
def subgraph_perfp(noise):
lengths = ["8", "15", "30"]
levels = ["10", "20", "30"]
fp = ["echoprint", "chromaprint", "landmark"]
linestyle = ["-", ":", "--"]
x = [8, 15, 30]
pointstyle = ["o", "^", "+"]
plt.figure()
plt.xlim([5, 55])
plt.xlabel("Query length (seconds)")
plt.xticks(x)
plt.ylabel("Accuracy")
plt.ylim([0.0, 1.0])
plt.title("Accuracy with added %s noise" % noise)
count = 1
for line, c in zip(linestyle, fp):
plt.subplot(3, 1, count)
plt.xlim([5, 45])
plt.xlabel("Query length (seconds)")
plt.xticks(x)
plt.ylabel("Accuracy")
plt.ylim([0.0, 1.0])
plt.title("%s accuracy with added %s noise" % (c, noise))
count += 1
print "fp", c
for p, lev in zip(pointstyle, levels):
print " noise", lev
data = []
for lng in lengths:
print ".",
sys.stdout.flush()
munge = "%s%s,chop%s" % (noise, lev, lng)
row = db.session.query(evaluation.Run).filter(evaluation.Run.engine==c).filter(evaluation.Run.munge==munge).one()
i = row.id
s = stats.stats(i)
accuracy = stats.prf(s)["accuracy"]
data.append(accuracy)
print ""
linefmt = "k%s%s" % (line, p)
dbel = 10 - int(lev)
lab = "%ddB" % (dbel, )
plt.plot(x, data, linefmt, label=lab)
plt.legend()
plt.savefig("plot-%s-perfp.png" % noise)
def calc_f(data):
prf = stats.prf(data)
return (("f measure", ), (prf["f"], ), (None, ))
def calc_pr(data):
prf = stats.prf(data)
return (("Precision", "Recall"), (prf["precision"] * 100,
prf["recall"] * 100), ("%%", "%%"))
def calc_f(data):
prf = stats.prf(data)
return (("f measure", ), (prf["f"],), (None, ))
def calc_pr(data):
prf = stats.prf(data)
return (("Precision", "Recall"), (prf["precision"]*100, prf["recall"]*100), ("%%", "%%"))
