def return_top(ts_stand, oglist, n, ret=False, file='', test=False):
'''
Prints or saves the n closest timeseries to ts_stand from a list of timeseries names (i.e. as string)
'''
if n > 1:
topn = []
for k in oglist:
if test:
v = pickle.load(open("../ts_data/{}.p".format(k), "rb"))
else:
v = pickle.load(open("ts_data/{}.p".format(k), "rb"))
v_stand = util.stand(v, v.mean(), v.std())
curr_distance = 2 * (1 - util.kernel_corr(ts_stand, v_stand))
if len(topn) < n:
topn.append((k, curr_distance))
else:
val = [x[1] for x in topn]
i = val.index(max(val))
if val[i] < curr_distance:
continue
else:
topn[i] = (k, curr_distance)
topn.sort(key=lambda x: x[1])
# return topn
if not ret:
print('Results: \n')
for k, v in topn:
print(k + ':', v)
else:
pickle.dump(topn, open(file, "wb"))
else:
m = float("inf")
best = None
for k in oglist:
if test:
v = pickle.load(open("../ts_data/{}.p".format(k), "rb"))
else:
v = pickle.load(open("ts_data/{}.p".format(k), "rb"))
v_stand = util.stand(v, v.mean(), v.std())
curr_distance = 2 * (1 - util.kernel_corr(ts_stand, v_stand))
if m > curr_distance:
m = curr_distance
best = k
if not ret:
print('Closest: ' + best + ', Distance: ' + str(m))
else:
pickle.dump([m, best], open(file, "wb"))
def sanity(ts_stand, n):
"This function is a sanity check used in the testing file: brute force n closest"
ds = []
for i in range(1000):
v = pickle.load(open("../ts_data/ts_{}.p".format(i), "rb"))
v_stand = util.stand(v, v.mean(), v.std())
ds.append(('ts_data/ts_' + str(i) + '.p',
2 * (1 - util.kernel_corr(ts_stand, v_stand))))
return sorted(ds, key=lambda d: d[1])[:n]
def main(arguments):
#Clean the databases or create the directory
try:
shutil.rmtree('dbs')
except:
pass
os.mkdir('dbs')
#Parse the number of vantage points wanted
parser = argparse.ArgumentParser(
description='Number of vantage points to generate')
parser.add_argument('--n',
help='Number of vantage points',
type=int,
default=20)
parser.add_argument('--nts',
help='Number of vantage points',
type=int,
default=1000)
args = parser.parse_args(arguments)
n = args.n
nts = args.nts
#sample the vantage points without replacement
vantage = random.sample(range(nts), n)
pickle.dump(vantage, open("ts_data/vantage_points.p", "wb"))
print('Vantage Points are: ')
for i in vantage:
print('TS ' + str(i))
tss = []
#load the ts
print('Loading the TS')
for i in range(nts):
tss.append(pickle.load(open("ts_data/ts_{}.p".format(i), "rb")))
#Store the negative of the k_corr of each vantage points to the eveyr other timeseries in the DB
print('Creating the DBS')
for ct, i in enumerate(vantage):
print(str(ct + 1) + '/' + str(len(vantage)) + ' Done')
db = binarytree.connect('dbs/db_' + str(i) + '.dbdb')
ts_i_stand = stand(tss[i], tss[i].mean(), tss[i].std())
for j in range(0, nts):
if j == i:
continue
kc = kernel_corr(ts_i_stand,
stand(tss[j], tss[j].mean(), tss[j].std()))
db.set(2 * (1 - kc), 'ts_' + str(j))
db.commit()
db.close()
def in_radius_from_vantage(ts_stand, v_str, test=False):
'''
Return the points in the circle of radius 2*k_corr(ts_stand, vantage_stand)
Args:
-----
- ts_stand: standardised ArrayTimeSeries
- v_str: index of the vantage point as a string
'''
if test:
v = pickle.load(open("../ts_data/ts_{}.p".format(v_str), "rb"))
else:
v = pickle.load(open("ts_data/ts_{}.p".format(v_str), "rb"))
v_stand = util.stand(v, v.mean(), v.std())
d = 2 * (1 - util.kernel_corr(ts_stand, v_stand))
if not test:
db = binarytree.connect('dbs/db_{}.dbdb'.format(v_str))
else:
db = binarytree.connect('../dbs/db_{}.dbdb'.format(v_str))
closest_in_vantage = [x[1] for x in db.get_closer_than(2 * d)]
db.close()
return closest_in_vantage
def eval_closest_vantage(ts_stand, vantage, test=False):
'''
Return the index of the closest vantage point
Args:
-----
- ts_stand: standardised ArrayTimeSeries
- vantage point: list of indices of vantage points
'''
closest_vantage = None
closest_distance = float("inf")
for i in vantage:
if test:
v = pickle.load(open("../ts_data/ts_{}.p".format(i), "rb"))
else:
v = pickle.load(open("ts_data/ts_{}.p".format(i), "rb"))
v_stand = util.stand(v, v.mean(), v.std())
curr_distance = 2 * (1 - util.kernel_corr(ts_stand, v_stand))
if curr_distance < closest_distance:
if test:
closest_vantage = (i, "../ts_data/ts_{}.p".format(i))
else:
closest_vantage = (i, "ts_data/ts_{}.p".format(i))
closest_distance = curr_distance
return closest_vantage, closest_distance
def test_kc_smaller_1(self):
#Checks that the kc is smaller than 1 for any pair of random of timeseries
t1 = util.random_ts(1)
t2 = util.random_ts(10)
self.assertTrue(util.kernel_corr(t1,t2)<=1)
def test_kc_with_itself(self):
#Checks that the kc is equal to 1 when evaluating the kc of a timeseries with itself
t1 = util.tsmaker(0.5, 0.1, 0.01)
self.assertTrue(abs(util.kernel_corr(t1, t1)-1)<0.000001)