def extr(self, name='testing'):
self.error = self.y_test - self.yqcnn
fraction = 0.5
width = 512
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all',
sharey='all',
gridspec_kw={'hspace': 0.5})
#ax.scatter(range(0,len(self.y_test)),self.y_test, s=50, marker='*', c='blue', alpha=0.7, label='test data')
plt.scatter(np.where(
np.logical_or(self.y_test > self.yqcnnu,
self.y_test < self.yqcnnl)),
self.uncertainty[self.id_anomaly],
c='red',
s=50,
marker='*',
alpha=0.7)
fig.text(-0.005,
0.5,
'eXtreme scores',
va='center',
rotation='vertical',
fontsize=plt.rcParams['axes.labelsize'])
plt.xlabel(r'time')
plt.tight_layout()
def _save(self, name='wiki1'):
'''
Parameters
----------
name : TYPE, optional
DESCRIPTION. The default is 'wiki1'.
Returns
-------
None.
'''
fraction = 1
width = 212
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
#plt.plot(self.y10, color='green', alpha=0.9)
plt.plot(self.y50, color='red', alpha=0.8, label='OFAT')
plt.plot(self.y_test, color='olive', alpha=0.8, label='ground truth')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.05), ncol=2)
plt.xlabel('Time (days)')
#print('{} smae: {} std: {}'.format(name, self.smae, self.std_s))
plt.ylabel('Weekly prices')
plt.tight_layout()
plt.savefig('../../ofat/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def plot(self, name='wikipgs'):
fraction = 1
width = 512
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
#plt.plot(self.uncertainty, color='green', alpha=0.9)
#plt.plot(self.y50,alpha=0.8, label='ofat')
plt.plot(self.y_test, alpha=0.8, label='gt')
plt.plot(self.yqcnn, alpha=0.8, label='qcnn')
plt.plot(self.ycnn, alpha=0.8, label='cnn')
plt.plot(self.yqclnn, alpha=0.8, label='qconvl')
plt.plot(self.yqlstm[1], alpha=0.8, label='qlstm')
plt.plot(self.ylstm, alpha=0.6, label='lstm')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right',
bbox_to_anchor=(1.01, 1.05),
ncol=3,
columnspacing=0.5)
plt.xlabel('Time (days)')
#print('{} smae: {} std: {}'.format(name, self.smae, self.std_s))
plt.ylabel('Daily page visits')
plt.tight_layout()
def plotuncertanty(self, name='preduncer'):
fraction = 0.5
width = 510
timex = 2000
y = np.concatenate((self.y_train[-timex:], self.yqcnn), axis=0)
#self.y_test[91]=9.06
fig, ax = plt.subplots(figsize=set_size(width, fraction), sharey='all')
#plt.plot(self.y_train[-timex:],'grey', label='fitted model')
plt.plot(y + 0.1, 'red', label='ground truth', alpha=0.9)
ax.fill_between(range(timex,
len(self.y_test) + timex),
self.yqcnnl,
self.yqcnnu - 0.5,
color='royalblue',
alpha=0.5,
label='predictive uncertainty')
plt.plot(range(timex,
len(self.y_test) + timex),
self.y_test + 0.1,
'orange',
label='fitted model',
alpha=0.8)
ax.axvline(x=timex,
ymin=0,
ymax=5,
color='grey',
ls='--',
lw=2,
alpha=0.7)
#plt.scatter(timex+91, self.y_test[91], marker='*', s=70, color='red', label='predicted extreme event')#extreme event
#ax.grid(True, zorder=5)
#plt.plot(self.y_test, color='olive', alpha=0.8, label ='ground truth')
#plt.plot(range(timex,len(self.y_test)+timex), self.yqlstm[0],color='grey',alpha=0.3)
#plt.plot(range(timex,len(self.y_test)+timex), self.yqlstm[2],color='grey',alpha=0.3)
plt.ylim([8.8, 10.2])
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right',
bbox_to_anchor=(0.75, 1.03),
ncol=1,
columnspacing=0.05)
ax.set_xlabel('Time ')
ax.set_ylabel(r'EDF stock price (\pounds)')
plt.tight_layout()
plt.savefig('../_8th/myphd/thesis_1/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def plot(self, name='testing'):
'''
Parameters
----------
name : TYPE, optional
DESCRIPTION. The default is 'testing'.
Returns
-------
None.
'''
fraction = 0.5
width = 510
fig, ax = plt.subplots(figsize=set_size(width, fraction), sharex='all')
plt.plot(self.y50, 'red')
plt.plot(self.y_test, 'orange')
ax.fill_between(range(0, len(self.y50)),
self.y10,
self.y90,
color='grey',
alpha=0.5)
#ax.grid(True, zorder=5)
plt.plot(self.y50, color='red', alpha=0.8, label='OFAT')
plt.plot(self.y_test, color='olive', alpha=0.8, label='ground truth')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
#plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.05), ncol=2)
ax.set_xlabel('Time (weeks)')
ax.set_ylabel('Average price')
plt.tight_layout()
plt.savefig('../../ofat/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def _predict(self, name='stock'):
fraction = 0.5
width = 510
fig, ax = plt.subplots(3,
figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
ax[0].plot(self.y10[28:], color='green', alpha=0.9)
ax[0].plot(self.y50[28:], color='blue', alpha=0.6)
ax[0].plot(self.y90[28:], color='purple', alpha=0.8)
ax[0].yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax[0].yaxis.set_major_locator(plt.LinearLocator(3))
ax[0].set_title('fitted model (blue) and thresholds')
#ax[0].setylabel(r'thresholds')
ax[1].plot(self.y_test[28:], alpha=0.9, lw=2, c='orange')
ax[1].yaxis.set_major_locator(plt.LinearLocator(3))
ax[1].yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax[1].set_title('test data')
ax[2].plot(self.anom_scores[28:],
c='navy',
label='scores',
alpha=0.9,
lw=2)
ax[2].yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax[2].yaxis.set_major_locator(plt.LinearLocator(3))
#ax[2].plot(self.id_anomaly)
ax[2].scatter(np.where(
np.logical_or(self.y_test[28:] > self.y90[28:],
self.y_test[28:] < self.y10[28:]))[0],
self.anom_scores[28:][np.logical_or(
self.y_test[28:] > self.y90[28:],
self.y_test[28:] < self.y10[28:])],
c='red',
s=50,
marker='*',
alpha=0.7)
# ax[2].scatter(np.where(np.logical_or(self.y_test[28:]>self.y90[28:], self.y_test[28:]<self.y10[28:]))[0],
# self.anom_scores[28:][self.id_anomaly], c='red', s=50, marker='*', alpha=0.7) #best simplified version for marking anomalies
plt.ylim([-0.27, 0.1])
ax[2].xaxis.set_major_locator(plt.LinearLocator(6))
fig.text(-0.06,
0.5,
'OFA scores',
va='center',
rotation='vertical',
fontsize=plt.rcParams['axes.labelsize'])
plt.xlabel(r'time', fontsize=plt.rcParams['axes.labelsize'])
plt.tight_layout()
#plot anomalies
#ax.xaxis.set_major_locator(plt.LinearLocator(6))
#ax.yaxis.set_major_locator(plt.LinearLocator(4))
#ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
#ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
#ax.yaxis.set_major_formatter(ScalarFormatter())
#ax.yaxis.major.formatter._useMathText = True
#ax.yaxis.major.formatter._useMathText = True
#ax.yaxis.set_minor_locator(AutoMinorLocator(1))
#plt.ylim([self.y10.min()-1, self.y90.max()+1])
#plt.legend(loc='lower right')
#plt.tight_layout()
#fig, ax = plt.subplots(figsize=set_size(width, fraction))
#ax[1].plot(self.error, c='green', label='scores', alpha=0.9, lw=2)
plt.savefig('/latex/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def plot(self, name='testing'):
#plt.figure(figsize=(16,8))
#plt.xlabel(r'$\bf {running time(s)} x10$', fontsize=45, fontweight ='bold')
#plt.ylabel(r'$\bf {window size~}x100$', fontsize=45, fontweight ='bold')
#plt.xlabel(r'\textbf{execution time(s)} $x10$', fontsize=10, fontweight ='bold')
#plt.ylabel(r'$\psi~x10^2$', fontsize=10, fontweight ='bold')
fraction = 0.5
width = 512
fig, ax = plt.subplots(3,
figsize=set_size(width, fraction),
sharex='all',
sharey='all',
gridspec_kw={'hspace': 0.5})
ax[0].scatter(range(0, len(self.y_test)),
self.y_test,
s=50,
marker='*',
c='blue',
alpha=0.7,
label='test data')
ax[1].plot(self.error, c='green', label='scores', alpha=0.9, lw=2)
for i in range(2):
#ax[i].xaxis.set_major_locator(plt.LinearLocator(6))
#ax[i].yaxis.set_major_locator(plt.LinearLocator(3))
#ax[i].xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
#ax[i].yaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
ax[i].yaxis.set_minor_locator(AutoMinorLocator(2))
#ax[i].xaxis.set_minor_locator(AutoMinorLocator(2))
#plot anomalies
plt.scatter(np.where(
np.logical_or(self.y_test > self.y90, self.y_test < self.y10))[0],
self.anom_scores,
c='red',
s=50,
marker='*',
alpha=0.7)
plt.plot(self.error, c='green', alpha=0.9, lw=2)
fig.text(-0.005,
0.5,
'OFA scores',
va='center',
rotation='vertical',
fontsize=plt.rcParams['axes.labelsize'])
plt.xlabel(r'time')
#plt.ylabel(r'OFA score')
plt.tight_layout()
# plt.savefig('/media/c1ph3r/colls/Dropbox/_China/_Xidian/_6th/vldb/vldb_style_sample/latex/figures/'+name+'.pdf',
# format='pdf', bbox_inches='tight')
fraction = 0.5
width = 510
fig, ax = plt.subplots(3,
figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
ax[0].plot(self.y90, c='orange', alpha=0.9, lw=2)
ax[1].plot(self.y50, c='cyan', alpha=0.9, lw=2)
ax[2].plot(self.y10, c='purple', alpha=0.9, lw=2)
for i in range(3):
#ax[i].xaxis.set_major_locator(plt.LinearLocator(6))
ax[i].yaxis.set_major_locator(plt.LinearLocator(3))
# ax[i].xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax[i].yaxis.set_major_formatter(plt.FormatStrFormatter('%.5f'))
#ax[i].yaxis.set_minor_locator(AutoMinorLocator(1))
#ax[i].xaxis.set_minor_locator(AutoMinorLocator(2))
plt.xlabel(r'time')
#plt.ylim([-0.001, 0.001])
plt.tight_layout()
def evt(self, tx=[], name='', mode=3):
fraction = 0.5
width = 512
if mode == 0:
tx = tx.strftime('%H:%M')
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all')
#plt.scatter(range(len(self.uncertainty[3900:9500])), self.uncertainty[3900:9500], color='gray', alpha=0.7, label='uncertainty')
# plt.plot(self.y_test[3900:9500],color='red', alpha=0.3, label='ground truth')
#plt.ylabel(r'SYN packets/ms')
plt.plot(self.yqcnnu[:400],
ls='--',
alpha=0.5,
color='orange',
label='UB')
plt.plot(self.yqcnnl[:400],
ls='--',
alpha=0.6,
color='purple',
label='LB')
plt.plot(self.y_test[:400], label='Gt', color='grey', alpha=0.8)
plt.scatter([self.id_anomaly[:4]],
self.anomaly[:4],
s=30,
marker='*',
alpha=0.8,
color='red',
label='Ext')
plt.xlabel(r'time (hour)')
plt.ylabel('EDF Stock price')
#fig.text(0.005, 0.5, 'EDF Stock price', va='center', rotation='vertical', fontsize=plt.rcParams['axes.labelsize'])
#ax.fill_between(range(len(self.y_test)), self.yqcnnl, self.yqcnnu,color='royalblue',alpha=0.5, label='PU')
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(5))
#plt.scatter(range(len(self.y_test)),self.uncertainty)
#plt.ylim([9,9.7])
plt.xticks([x for x in range(0, len(tx[:400]), 9 * 5)],
[tx[x] for x in range(0, len(tx[:400]), 9 * 5)],
rotation=40)
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
#ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
plt.legend(loc='upper right',
bbox_to_anchor=(1, 1.05),
ncol=5,
labelspacing=0.1,
columnspacing=1)
plt.tight_layout()
# plt.savefig('../_8th/papers/vldb/pvldbstyle-master/figures/'+
# name+'.pdf',format='pdf', bbox_inches='tight')
elif mode == 1:
name = ''
fraction = 0.5
width = 512
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all')
plt.scatter(range(len(self.uncertainty[3900:9500])),
self.uncertainty[3900:9500],
color='gray',
alpha=0.7,
label='Uncertainty ')
plt.plot(self.y_test[3900:9500],
color='red',
alpha=0.3,
label='ground truth')
plt.ylabel(r'SYN packets/ms')
plt.xlabel(r'time (s)')
ax.xaxis.set_major_locator(plt.LinearLocator(8))
ax.yaxis.set_major_locator(plt.LinearLocator(8))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
plt.legend(loc='upper right',
bbox_to_anchor=(0.9, 1.05),
ncol=2,
labelspacing=0.1,
columnspacing=1)
plt.tight_layout()
# plt.savefig('../_8th/papers/vldb/pvldbstyle-master/figures/'+
# name+'.pdf',format='pdf', bbox_inches='tight')
else:
name = 'field'
fraction = 0.5
width = 512
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all')
# plt.scatter(range(len(self.uncertainty)), self.uncertainty, color='gray', alpha=0.7, label='Uncertainty ')
# plt.plot(self.y_test,color='red', alpha=0.3, label='ground truth')
plt.plot(self.yqcnnu,
ls='--',
alpha=0.5,
color='orange',
label='UB')
plt.plot(self.yqcnnl,
ls='--',
alpha=0.6,
color='purple',
label='LB')
plt.plot(self.y_test, label='Gt', color='grey', alpha=0.8)
plt.scatter([self.id_anomaly],
self.anomaly,
s=30,
marker='*',
alpha=0.8,
color='red',
label='Ext')
plt.ylabel(r'Magnetic field (nT)')
plt.xlabel(r'time (s)')
ax.xaxis.set_major_locator(plt.LinearLocator(8))
ax.yaxis.set_major_locator(plt.LinearLocator(8))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
plt.legend(loc='upper right',
bbox_to_anchor=(0.9, 1.05),
ncol=4,
labelspacing=0.1,
columnspacing=1)
plt.tight_layout()