def test():
# Testing
import numpy.random as rand
import pylab
############## Make synthetic data #################
# Simulate protein binding site by having few positive
# class examples
folds = []
labs = [-1] * 500
labs.extend([1] * 20)
N = 40 # number of sequences
# set means of decision values
mu_n = -1
mu_p = 1
# draw decision values from normal distribution
# for each class
for i in xrange(N):
preds = rand.normal(mu_n, 1, 500).tolist()
preds.extend(rand.normal(mu_p, 1, 20).tolist())
folds.append((labs[:], preds))
# Calculate average ROC curve
fpr, tpr, tpr_ci, area, area_ci = roc_VA(folds)
# Print AUC with confidence interval
print "area: %f, 95%% CI: (%f, %f)" % (area, area - area_ci,
area + area_ci)
####### Plot ROC curve with 95% confidence band #######
fig = pylab.figure()
ax = fig.add_subplot(111)
# fill upper band
xs, ys = pylab.poly_between(fpr, tpr,
numpy.array(tpr) + numpy.array(tpr_ci))
ax.fill(xs, ys, 'r')
# fill lower band
xs, ys = pylab.poly_between(fpr, tpr,
numpy.array(tpr) - numpy.array(tpr_ci))
ax.fill(xs, ys, 'r')
# label axes and main
pylab.xlabel("FPR")
pylab.ylabel("TPR")
pylab.title("Example ROC curve with 95% confidence band")
ax.plot(numpy.arange(0, 1.1, .1), numpy.arange(0, 1.1, .1), 'k--')
pylab.xlim(-.01, 1.01)
pylab.ylim(-.01, 1.01)
pylab.show()
def test():
# Testing
import numpy.random as rand
import pylab
############## Make synthetic data #################
# Simulate protein binding site by having few positive
# class examples
folds = [ ]
labs = [ -1 ] * 500
labs.extend( [ 1 ] * 20 )
N = 40 # number of sequences
# set means of decision values
mu_n = -1
mu_p = 1
# draw decision values from normal distribution
# for each class
for i in xrange( N ):
preds = rand.normal( mu_n, 1, 500 ).tolist()
preds.extend( rand.normal( mu_p, 1, 20 ).tolist() )
folds.append( ( labs[ : ], preds ) )
# Calculate average ROC curve
fpr, tpr, tpr_ci, area, area_ci = roc_VA( folds )
# Print AUC with confidence interval
print "area: %f, 95%% CI: (%f, %f)" % ( area, area-area_ci, area+area_ci )
####### Plot ROC curve with 95% confidence band #######
fig = pylab.figure( )
ax = fig.add_subplot( 111 )
# fill upper band
xs, ys = pylab.poly_between( fpr, tpr, numpy.array( tpr ) + numpy.array( tpr_ci ) )
ax.fill( xs, ys, 'r' )
# fill lower band
xs, ys = pylab.poly_between( fpr, tpr, numpy.array( tpr ) - numpy.array( tpr_ci ) )
ax.fill( xs, ys, 'r' )
# label axes and main
pylab.xlabel( "FPR" )
pylab.ylabel( "TPR" )
pylab.title( "Example ROC curve with 95% confidence band" )
ax.plot( numpy.arange( 0, 1.1, .1 ), numpy.arange( 0, 1.1, .1 ), 'k--')
pylab.xlim( -.01, 1.01 )
pylab.ylim( -.01, 1.01 )
pylab.show( )
def test():
GP = GaussianProcess(GaussianKernel_iso([0.2, 1.0]))
X = array([[0.2], [0.3], [0.5], [1.5]])
Y = [1, 0, 1, 0.75]
GP.addData(X, Y)
figure(1)
A = arange(0, 2, 0.01)
mu = array([GP.mu(x) for x in A])
sig2 = array([GP.posterior(x)[1] for x in A])
Ei = EI(GP)
ei = [-Ei.negf(x) for x in A]
Pi = PI(GP)
pi = [-Pi.negf(x) for x in A]
Ucb = UCB(GP, 1, T=2)
ucb = [-Ucb.negf(x) for x in A]
ax = subplot(1, 1, 1)
ax.plot(A, mu, "k-", lw=2)
xv, yv = poly_between(A, mu - sig2, mu + sig2)
ax.fill(xv, yv, color="#CCCCCC")
ax.plot(A, ei, "g-", lw=2, label="EI")
ax.plot(A, ucb, "g--", lw=2, label="UCB")
ax.plot(A, pi, "g:", lw=2, label="PI")
ax.plot(X, Y, "ro")
ax.legend()
draw()
show()
def test():
GP = GaussianProcess(GaussianKernel_iso([.2, 1.0]))
X = array([[.2], [.3], [.5], [1.5]])
Y = [1, 0, 1, .75]
GP.addData(X, Y)
figure(1)
A = arange(0, 2, 0.01)
mu = array([GP.mu(x) for x in A])
sig2 = array([GP.posterior(x)[1] for x in A])
Ei = EI(GP)
ei = [-Ei.negf(x) for x in A]
Pi = PI(GP)
pi = [-Pi.negf(x) for x in A]
Ucb = UCB(GP, 1, T=2)
ucb = [-Ucb.negf(x) for x in A]
ax = subplot(1, 1, 1)
ax.plot(A, mu, 'k-', lw=2)
xv, yv = poly_between(A, mu - sig2, mu + sig2)
ax.fill(xv, yv, color="#CCCCCC")
ax.plot(A, ei, 'g-', lw=2, label='EI')
ax.plot(A, ucb, 'g--', lw=2, label='UCB')
ax.plot(A, pi, 'g:', lw=2, label='PI')
ax.plot(X, Y, 'ro')
ax.legend()
draw()
show()
def stackareaplot(self):
fig = pyplot.figure()
lineas = []
stack = []
k = 0
for i in self.yval:
stack.append(i)
m = 0
for j in i:
if k > 0:
stack[k][m] = stack[k][m] + stack[k - 1][m]
m = m + 1
k = k + 1
j = 0
xss = []
yss = []
used_colors = []
for i in stack:
linea = pyplot.plot(self.xval, i, self.colors[j])
used_colors.append(self.colors[j])
lineas.append(linea[0])
xs, ys = pylab.poly_between(self.xval, 0, i)
xss.append(xs)
yss.append(ys)
j = j + 1
if j > 19:
j = 0
j = 0
k = -1
used_colors_inv = used_colors[::-1]
for i in yss:
pylab.fill(xss[0], yss[k], used_colors_inv[j])
j = j + 1
k = k - 1
pyplot.legend(lineas, self.tags, 'best')
pyplot.title(self.title)
pyplot.xlabel('Time (s)')
pyplot.ylabel('Airtime consumption (%)')
pyplot.grid(True)
pyplot.show()
def stackareaplot(self):
fig = pyplot.figure()
lineas=[]
stack = []
k=0
for i in self.yval:
stack.append(i)
m=0
for j in i:
if k > 0:
stack[k][m]=stack[k][m]+stack[k-1][m]
m = m+1
k=k+1
j=0
xss=[]
yss=[]
used_colors=[]
for i in stack:
linea = pyplot.plot(self.xval,i,self.colors[j])
used_colors.append(self.colors[j])
lineas.append(linea[0])
xs,ys = pylab.poly_between(self.xval,0,i)
xss.append(xs)
yss.append(ys)
j=j+1
if j>19:
j = 0
j=0
k=-1
used_colors_inv = used_colors[::-1]
for i in yss:
pylab.fill(xss[0], yss[k], used_colors_inv[j])
j=j+1
k=k-1
pyplot.legend(lineas,self.tags,'best')
pyplot.title(self.title)
pyplot.xlabel('Time (s)')
pyplot.ylabel('Airtime consumption (%)')
pyplot.grid(True)
pyplot.show()
