@author: musselle
"""
import scipy.io as sio
from utils import analysis
from PedrosFrahst import frahst_pedro
from Frahst_v3 import FRAHST_V3
from artSigs import genCosSignals_no_rand , genCosSignals
#AbileneMat = sio.loadmat('C:\DataSets\Abilene\Abilene.mat')
#data = AbileneMat['F']
data = genCosSignals_no_rand()
data = genCosSignals(0,-3.0)
e_high = 0.98
e_low = 0.96
alpha = 0.96
holdTime = 0
# My version
res_me = FRAHST_V3(data, alpha=0.96, e_low=0.96, e_high=0.98, sci = -1, \
holdOffTime = holdTime, r = 1, evalMetrics = 'T')
#metric_me_5, sets_me, anom_det_tab_me = analysis(res_me, AbileneMat['F_g_truth_tab'])
# Pedros Version
res_ped = frahst_pedro(data, r=1, alpha=0.96, energy_low=0.96, energy_high=0.98, \
holdOffTime = holdTime, evaluateTruth='T')
#metric_ped_5, sets_ped, anom_det_tab_ped = analysis(res_ped, AbileneMat['F_g_truth_tab'])
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)
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_RawResults.append(res_fr)
FRAHST_metricList.append(analysis(res_fr, ground_truths, 300,
epsilon = epsilon, ignoreUpTo = ignore))
# pltSummary2(res_fr, streams, (el, eh))
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_RawResults.append(res_frped)
PEDRO_FRAHST_metricList.append(analysis(res_frped, ground_truths, 300,
epsilon = epsilon, ignoreUpTo = ignore))
# pltSummary2(res_frped, streams, (el, eh))
#
#
print 'Finished running alg(s) with parameter set ' + str(alg_count) + ' of ' + str(total_alg) + ' for this dataset\n'
print '\t' + str(total_data - dataset_count) + ' datasets remaining\n'
alg_count += 1
res_fr['Alg'] = 'My Frahst with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
pltSummary2(res_fr, streams, (e_low, e_high))
data = res_fr
Title = 'My Frahst with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
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:
# My version of Frahst
res_frped = frahst_pedro(streams, alpha=alpha, e_low=e_low, e_high=e_high,
holdOffTime=holdOff, r = 1, evalMetrics = 'T')
res_frped['Alg'] = 'Pedros Frahst with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
pltSummary2(res_frped, streams, (e_low, e_high))
data = res_frped
Title = 'Pedros Frahst with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
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 = ', finish
Title = 'My Frahst with alpha = ' + str(
valA) + ' and e_low = ' + str(valB)
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:
# My version of Frahst
res_frped = frahst_pedro(streams,
alpha=alpha,
e_low=e_low,
e_high=e_high,
holdOffTime=holdOff,
r=1,
evalMetrics='T')
res_frped['Alg'] = 'Pedros Frahst with alpha = ' + str(
valA) + ' and e_low = ' + str(valB)
pltSummary2(res_frped, streams, (e_low, e_high))
data = res_frped
Title = 'Pedros Frahst with alpha = ' + str(
valA) + ' and e_low = ' + str(valB)
plot_4x1(streams, data['hidden'], data['orthog_error'],
data['subspace_error'], [
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)
e_high = 0.98
e_low = 0.96
alpha = 0.96
sci = -1
holdOFF = 50
# My version
res_me = FRAHST_V3(data, alpha=0.96, e_low=0.96, e_high=0.98, sci = -1, \
holdOffTime=holdOFF, r = 1, evalMetrics = 'F')
metric_me, sets_me, anom_det_tab_me = analysis(res_me, AbileneMat['P_g_truth_tab_alt'], n)
new_metrics_me = scoreEvaluation(n, res_me['anomalies'],
AbileneMat['P_g_truth_tab_alt'], ignoreUpTo = 400)
# Pedros Version
res_ped = frahst_pedro(data, r = 1, alpha=0.96, energy_low=0.96, energy_high=0.98, \
holdOffTime=holdOFF, evaluateTruth='FALSE')
metric_ped, sets_ped, anom_det_tab_ped = analysis(res_ped, AbileneMat['P_g_truth_tab_alt'], n)
new_metrics_ped = scoreEvaluation(n, res_ped['anomalies'],
AbileneMat['P_g_truth_tab_alt'], ignoreUpTo = 400)
res_me3['Alg'] = 'My First Implimentation of FRAUST sci = -1'
pltSummary(res_me3, data)
# My second version
res_me2 = FRAHST_V3_1(data, r = 1,
alpha = param['alpha'],
e_low = param['e_low'],
e_high = param['e_high'],
holdOffTime=param['holdOffTime'],
fix_init_Q = 1,
evalMetrics = 'F')
res_me2['Alg'] = 'My Second Implimentation of FRAUST'
pltSummary(res_me2, data)
# Pedros Version
res_ped = frahst_pedro(data, r = 1,
alpha = param['alpha'],
energy_low = param['e_low'],
energy_high = param['e_high'],
holdOffTime=param['holdOffTime'],
evaluateTruth='FALSE')
res_ped['Alg'] = 'Pedros Implimentation of FRAUST'
pltSummary(res_ped, data)
