# Load test data
data = datasets.loadDatasets(dataset, 'interval')
# Try different thresholds for interval proposing
results = OrderedDict()
for propmeth in PROPMETHODS:
results[propmeth] = OrderedDict()
for sd_th in THS:
ygts = []
regions = []
for ftype in data:
for func in data[ftype]:
ygts.append(func['gt'])
ts = preproc.normalize_time_series(func['ts'])
if td_dim > 1:
ts = preproc.td(ts, td_dim, td_lag)
regions.append(list(pointwiseRegionProposals(ts, method = propmeth, sd_th = sd_th,
extint_min_len = 10, extint_max_len = extint_max_len)))
results[propmeth][sd_th] = eval.recall_precision(ygts, regions, multiAsFP = False)
# Print results as table
labels = ('Recall', 'Precision', 'F1-Score')
hdiv_len = 5 + sum(len(lbl) + 3 for lbl in labels) # length of horizontal divider
for propmeth, res in results.items():
print('\n-- {} --\n'.format(propmeth))
print('Period of {}. right singular vector: {} -> {}'.format(i+1, period, float(minSVDLen) / period))
plt.show()
# Remove some leading singular values
try:
rsRem = raw_input('Enter number of right-singular vectors to remove: ')
except:
rsRem = input('Enter number of right-singular vectors to remove: ')
rsRem = int(rsRem)
mm_s[:rsRem] = 0.0
mm_norm = mm_u.dot(np.diag(mm_s).dot(mm_vt))
"""
# Individual De-seasonalization by DFT, Hourly Z Score and OLS
print('-- DFT, Hourly Z Score, OLS --')
for id in ids:
func = preproc.normalize_time_series(data[id]['ts']).ravel()
# Search non-trivial peak in power-spectrum
freq = np.fft.fft(func)
ps = (freq * freq.conj()).real
ps[0] = 0
th = np.mean(ps) + 3 * np.std(ps)
period = (ps > th)
period[0:7] = False
period[-6:] = False
period_ind = np.where(period)[0]
print('{}: period = {} -> {}'.format(
id, period_ind[:len(period_ind) // 2],
periods2time(period_ind[:len(period_ind) // 2], len(func))))
# Remove seasonal frequency and reconstruct deseasonalized time series
a = datetime.datetime.combine(a, datetime.datetime.min.time())
if isinstance(b, datetime.date):
b = datetime.datetime.combine(b, datetime.datetime.min.time())
return int((a - b).total_seconds()) / 3600
if __name__ == '__main__':
import sys
method = sys.argv[1] if len(sys.argv) > 1 else 'gaussian_cov_ts'
propmeth = sys.argv[2] if len(sys.argv) > 2 else 'dense'
# Load data
data, dates = read_hpw_csv('HPW_2012_41046.csv')
data = preproc.normalize_time_series(data)
# Detect
if method in ['hotellings_t', 'kde']:
if method == 'kde':
scores = baselines_noninterval.pointwiseKDE(preproc.td(data))
else:
scores = baselines_noninterval.hotellings_t(preproc.td(data))
regions = baselines_noninterval.pointwiseScoresToIntervals(scores, 24)
elif method == 'gaussian_cov_ts':
regions = maxdiv.maxdiv(data,
'gaussian_cov',
mode='TS',
td_dim=3,
td_lag=1,
proposals=propmeth,