F.batch_analysis(gt_list, anomalies_list, keep_sets=0)
# Fill in R
E.R[i, j, k]['met']['TP'] = F.metric['TP']
E.R[i, j, k]['met']['FP'] = F.metric['FP']
E.R[i, j, k]['met']['TN'] = F.metric['TN']
E.R[i, j, k]['met']['FN'] = F.metric['FN']
E.R[i, j, k]['met']['precision'] = F.metric['precision']
E.R[i, j, k]['met']['recall'] = F.metric['recall']
E.R[i, j, k]['met']['F05'] = F.metric['F05']
E.R[i, j, k]['met']['F1'] = F.metric['F1']
E.R[i, j, k]['met']['F2'] = F.metric['F2']
E.R[i, j, k]['met']['ACC'] = F.metric['ACC']
E.R[i, j, k]['met']['FDR'] = F.metric['FDR']
E.R[i, j, k]['met']['FPR'] = F.metric['FPR']
ti = time.time() - start
tim = GetInHMS(ti)
print 'Finished Algorithm ver %i of %i at %s' % (i + 1, alg_ver_count,
tim)
fin = time.time() - start
hms = GetInHMS(fin)
print 'Finished a total of %i iterations in %s' % (total_loops, hms)
print 'Average of %f seconds per iteration' % (fin / total_loops)
with open(path + '/' + "E.p", "wb") as f:
pickle.dump(E, f)
E.write2csv('TP', path + '/' + "TP.csv")
E.write2csv('FP', path + '/' + "FP.csv")
ehighStr = '[ '
for s in e_highs:
ehighStr = ehighStr + "%.2f" % s + ', '
alphasStr = '[ '
for s in alphas:
alphasStr = alphasStr + "%.2f" % s + ', '
f.write('Algorithm Parameters\n')
f.write('--------------------\n')
f.write('Lower Energy Thresholds: ' + elowStr + ']\n')
f.write('Upper Energy Thresholds: ' + ehighStr + ']\n')
f.write('Alphas: ' + alphasStr + ']\n')
f.write('Hold Current r lengths: ' + str(holdOffs) + '\n.\n')
f.write('Algorithms Run\n')
f.write('--------------------\n')
if run_spirit :
f.write('SPIRIT\n')
if run_frahst :
f.write('FRAHST - My Most Current Version\n')
if run_frahst_pedro :
f.write('FRAHST - Pedros Published Version\n')
os.chdir(cwd)
finish = time.time() - start
print 'Runtime = ' + str(finish) + 'seconds\n'
print 'In H:M:S = ' + GetInHMS(finish)
def singleShiftRerun(parameter_string):
params = parameter_string.split(', ')
# Data Sets
baseLine = 0.0
num_streams = [int(params[0].split()[-1])] # n
SNRs = [int(params[1].split()[-1])] # snr
anomaly_lengths = [int(params[2].split()[-1])] # l
anomaly_magnitudes = [int(params[3].split()[-1])] # m
initial_conditions = 20 # i
# Algorithm Parameters
thresholds = params[4].split()[-1].split(',')
e_highs = [float(thresholds[1][0:-1])] # eh
e_lows = [float(thresholds[0][1:])] # el
alphas = [float(params[5].split()[-1])] # a
holdOffs = [int(params[6].split()[-1])] # h
# Algorithm flags
run_spirit = 0
run_frahst = 1
run_frahst_pedro = 0
#===============================================================================
# Initialise Data sets
#========-=======================================================================
# For Profiling
start = time.time()
for n in num_streams:
for snr in SNRs:
for l in anomaly_lengths:
for m in anomaly_magnitudes:
A = 0
for i in range(initial_conditions):
# Seed random number generator
np.random.seed(i)
# Two ts that have anomalous shift
s0 = Tseries(0)
s1 = Tseries(0)
s0.makeSeries([1, 3, 1], [100, l, 200 - l],
[baseLine, baseLine, baseLine + m],
gradient=float(m) / float(l),
noise_type='none')
s1.makeSeries([1, 4, 1], [200, l, 100 - l],
[baseLine, baseLine, baseLine - m],
gradient=float(m) / float(l),
noise_type='none')
# The rest of the ts
for k in range(2, n):
name = 's' + str(k)
vars()[name] = Tseries(0)
vars()[name].makeSeries([1], [300], [baseLine],
noise_type='none')
# Concat into one matrix
S = scipy.c_[s0]
for k in range(1, n):
S = scipy.c_[S, vars()['s' + str(k)]]
# Concatonate to 3d array, timesteps x streams x initial condition
if type(A) == int:
A = S
else:
A = np.dstack((A, S))
# Calculate the noise
Ps = np.sum(A[:, :, 0]**2)
Pn = Ps / (10.**(snr / 10.))
scale = Pn / (n * 300.)
noise = np.random.randn(A.shape[0], A.shape[1],
A.shape[2]) * np.sqrt(scale)
A = A + noise
#===============================================================================
# Ground Truths
#===============================================================================
# # time step | length
ground_truths = np.array([[100, l], [200, l]])
#==============================================================================
# Run Algorithm
#==============================================================================
alg_count = 1
for eh in e_highs:
for el in e_lows:
for a in alphas:
for h in holdOffs:
print 'Running Algorithm(s) with:\nE_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
SPIRIT_metricList = []
FRAHST_metricList = []
PEDRO_FRAHST_metricList = []
for i in range(initial_conditions):
# Load Data
streams = A[:, :, i]
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams,
a, [el, eh],
evalMetrics='F',
reorthog=False,
holdOffTime=h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
pltSummary2(
res_sp, streams, (el, eh))
SPIRIT_metricList.append(
analysis(
res_sp, ground_truths,
300))
# data = res_sp
# Title = 'SPIRIT alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['e_ratio'], data['orthog_error'],
# ['Input Data','Hidden\nVariables',
# 'Energy Ratio', 'Orthogonality\nError (dB)'] , 'Time Steps', Title)
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
#
if run_frahst == 1:
# My version of Frahst
res_fr = FRAHST_V3_1(
streams,
alpha=a,
e_low=el,
e_high=eh,
holdOffTime=h,
fix_init_Q=1,
r=1,
evalMetrics='F')
res_fr[
'Alg'] = 'MyFrahst: alpha = ' + str(
a
) + ' ,E_Thresh = (' + str(
el) + ',' + str(eh) + ')'
FRAHST_metricList.append(
analysis(
res_fr, ground_truths,
300))
pltSummary2(
res_fr, streams, (el, eh))
#
# data = res_fr
# Title = 'My Frahst with alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
if run_frahst_pedro == 1:
# Pedros version of Frahst
res_frped = frahst_pedro(
streams,
alpha=a,
e_low=el,
e_high=eh,
holdOffTime=h,
r=1,
evalMetrics='F')
res_frped['Alg'] = 'Pedros Frahst: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
PEDRO_FRAHST_metricList.append(
analysis(
res_frped, ground_truths,
300))
pltSummary2(
res_frped, streams, (el, eh))
#
# data = res_frped
# Title = 'Pedros Frahst: alpha = ' + str(a) + \
# ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
finish = time.time() - start
print 'Runtime = ' + str(finish) + 'seconds\n'
print 'In H:M:S = ' + GetInHMS(finish)