def test_CUSUMChangePointAnalyzer(self):
algo1 = cps.CUSUMChangePointAnalyzer()
sig, cpss = signal()
change_points = algo1.detect_change_points(sig)[:-1]
pre, rec = metrics.fuzzy_precall(cpss, change_points, fuzzy=5)
self.assertTrue(
np.median(pre) > 0.6,
"precision {} is smaller than 0.6".format(np.median(pre)))
self.assertTrue(
np.median(rec) > 0.8,
"recall {} is smaller than 0.8".format(np.median(rec)))
def testName(self):
p, r = [], []
pa, pb, pc, ra, rb, rc = [],[],[],[],[],[]
for s in range(5):
print(s)
self.signal, self.cp = signal(s)
change_points = []
a = cps.CUSUMChangePointAnalyzer()
b = cps.BinaryChangePointAnalyzer()
c = cps.WindowChangePointAnalyzer()
ca = a.detect_change_points(self.signal)
cb = b.detect_change_points(self.signal)
cc = c.detect_change_points(self.signal)
change_points += ca
change_points += cb
change_points += cc
change_points.sort()
cpf = Counter(change_points)
N = 5
keks = []
for key in cpf:
# get keys that are +-N of key
keys = list(filter(lambda k: k < key + N and k > key - N, cpf))
kek = []
for keyx in keys:
kek += cpf[keyx] * [keyx]
if len(kek) > 1:
keks.append(int(np.median(kek)))
keks = list(dict.fromkeys(keks))
change_points = list(set(change_points[:-3]) - set(keks))
x, y = metrics.fuzzy_precall(self.cp, keks, fuzzy=5)
p.append(x)
r.append(y)
x, y = metrics.fuzzy_precall(self.cp, ca, fuzzy=5)
pa.append(x)
ra.append(y)
x, y = metrics.fuzzy_precall(self.cp, cb, fuzzy=5)
pb.append(x)
rb.append(y)
x, y = metrics.fuzzy_precall(self.cp, cc, fuzzy=5)
pc.append(x)
rc.append(y)
#plt.hist(p, bins=35, alpha=0.5, label='precision', edgecolor='black', linewidth=0.5)
#plt.hist(r, bins=35, alpha=0.5, label='recall', edgecolor='black', linewidth=0.5)
print(np.mean(p), np.median(p), np.std(p))
print(np.mean(r), np.median(r), np.std(r))
x = range(10)
y = range(10)
fig = plt.figure()
plt.subplot(2, 2, 1)
plt.title("cumulative sum")
sns.distplot(pa, hist=False, rug=True, label="precision (median: {})".format(round(np.mean(pa), 2)))
sns.distplot(ra, hist=False, rug=True, label="recall (median: {})".format(round(np.mean(ra), 2)))
plt.xlim((0,1))
plt.xlabel("precision or recall")
plt.ylabel("frequency")
plt.subplot(2, 2, 2)
plt.title("Binary")
sns.distplot(pb, hist=False, rug=True, label="precision (median: {})".format(round(np.mean(pb), 2)))
sns.distplot(rb, hist=False, rug=True, label="recall (median: {})".format(round(np.mean(rb), 2)))
plt.xlim((0,1))
plt.xlabel("precision or recall")
plt.ylabel("frequency")
plt.subplot(2, 2, 3)
plt.title("Window")
sns.distplot(pc, hist=False, rug=True, label="precision (median: {})".format(round(np.mean(pc), 2)))
sns.distplot(rc, hist=False, rug=True, label="recall (median: {})".format(round(np.mean(rc), 2)))
plt.xlabel("precision or recall")
plt.ylabel("frequency")
plt.xlim((0,1))
plt.subplot(2, 2, 4)
plt.title("Merged")
sns.distplot(p, hist=False, rug=True, label="precision (median: {})".format(round(np.mean(p), 2)))
sns.distplot(r, hist=False, rug=True, label="recall (median: {})".format(round(np.mean(r), 2)))
plt.xlim((0,1))
plt.xlabel("precision or recall")
plt.ylabel("frequency")
plt.show()