def testPriorAndPrefs(self):
S5 = Shekel5()
pX = lhcSample(S5.bounds, 100, seed=13)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, S5.bounds, k=10)
hv = .1
hyper = [hv, hv, hv, hv]
gkernel = GaussianKernel_ard(hyper)
GP = PrefGaussianProcess(gkernel, prior=prior)
X = [array([i + .5] * 4) for i in range(5)]
valX = [x.copy() for x in X]
prefs = []
for i in range(len(X)):
for j in range(i):
if S5.f(X[i]) > S5.f(X[j]):
prefs.append((X[i], X[j], 0))
else:
prefs.append((X[j], X[i], 0))
GP.addPreferences(prefs)
opt, optx = maximizeEI(GP, S5.bounds)
def testFastGallery(self):
tf = Hartman3()
kernel = tf.createKernel(GaussianKernel_ard)
X = lhcSample(tf.bounds, 10, seed=23)
Y = [tf.f(x) for x in X]
prefs = query2prefs(X, tf.f)
GP = PrefGaussianProcess(kernel)
GP.addPreferences(prefs)
gallery = fastUCBGallery(GP, tf.bounds, 4)
print 'gallery returned:'
for x in gallery:
print '\t', x
GP.addPreferences(query2prefs(gallery, tf.f))
# make sure we don't return anything out of bounds
bounds = copy(tf.bounds)
bounds[0] = [0., 0.]
gallery = fastUCBGallery(GP, bounds, 4)
print 'gallery returned:'
for x in gallery:
print '\t', x
for v, b in zip(x, bounds):
self.failUnless(v>=b[0] and v<=b[1])
def testPriorAndPrefs(self):
S5 = Shekel5()
pX = lhcSample(S5.bounds, 100, seed=13)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, S5.bounds, k=10)
hv = .1
hyper = [hv, hv, hv, hv]
gkernel = GaussianKernel_ard(hyper)
GP = PrefGaussianProcess(gkernel, prior=prior)
X = [array([i+.5]*4) for i in xrange(5)]
valX = [x.copy() for x in X]
prefs = []
for i in xrange(len(X)):
for j in xrange(i):
if S5.f(X[i]) > S5.f(X[j]):
prefs.append((X[i], X[j], 0))
else:
prefs.append((X[j], X[i], 0))
GP.addPreferences(prefs)
opt, optx = maximizeEI(GP, S5.bounds)
def demoPrefGallery():
"""
A simpler demo, showing how to use a preference gallery. This demo
is not interactive -- it uses the 6D Hartman test function to generate
the preferences.
"""
N = 3 # gallery size
# use the Hartman6 test function, which has a kernel and bounds predefined
tf = Hartman6()
bounds = tf.bounds
kernel = tf.createKernel(GaussianKernel_ard)
# set up a Preference Gaussian Process, in which the observations are
# preferences, rather than scalars
GP = PrefGaussianProcess(kernel)
# initial preferences -- since we have no informative prior on the space,
# the gallery will be a set of points that maximize variance
gallery = fastUCBGallery(GP, bounds, N)
# this is a utility function for automtically testing the preferences --
# given a test functions and some sample points, it will return a list of
# preferences
prefs = query2prefs(gallery, tf.f)
# preferences have the form [r, c, degree], where r is preferred to c.
# degree is degree of preference. Just leave degree at 0 for now.
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# add preferences to the model
GP.addPreferences(prefs)
# get another gallery, but with the first three dimensions fixed to .5
nbounds = deepcopy(bounds)
nbounds[:3] = [[.5,.5]]*3
gallery = fastUCBGallery(GP, nbounds, N)
prefs = query2prefs(gallery, tf.f)
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# get another gallery, but with the *last* three dimensions fixed to .5
nbounds = deepcopy(bounds)
nbounds[3:] = [[.5,.5]]*3
gallery = fastUCBGallery(GP, nbounds, N)
prefs = query2prefs(gallery, tf.f)
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# preferences don't have to come from the gallery
r = array([0, 0, .5, 0, 1, .25])
c = array([1, 1, .75, 1, 0, .5])
pref = (r, c, 0)
GP.addPreferences([pref])
def learn(request,pref=None,unpref=None,canvas_size=400):
# set up
if not request.session.has_key('session_id'):
raise Exception('session_id not found in request.session')
session = BanditProblemSession.objects.get(id=request.session['session_id'])
problem = session.problem
# push to database if needed
if pref is not None and unpref is not None:
pref = BanditContext.objects.get(id=pref)
unpref = BanditContext.objects.get(id=unpref)
pc = PairedComparison.objects.create(
unpreferred_context=unpref,
preferred_context=pref,
session=session,
)
# now pull historical preferences
gp = PrefGaussianProcess(GaussianKernel_iso([problem.length_scale, problem.noise_magnitude]))
pcs = session.comparisons.all()
if pcs is not None and len(pcs) > 0:
gp.addPreferences([
(pc.preferred_context.vector, pc.unpreferred_context.vector, 0)
for pc in pcs ])
# generate art (it just sounds wrong!!!)
gal_db = {}
gallery = fastUCBGallery(gp, [[0,1]]*problem.dim(), 2, useBest=True, xi=problem.xi,passback=gal_db)
ctx1,_ = BanditContext.objects.get_or_create(dimension=len(gallery[0]),vector=gallery[0].tolist())
ctx2,_ = BanditContext.objects.get_or_create(dimension=len(gallery[1]),vector=gallery[1].tolist())
left_choice = problem.generate(ctx1, id='left')
right_choice = problem.generate(ctx2, id='right')
plots = _make_plots_from_gallery_passback(problem,gal_db)
# ...
t = loader.get_template('compare.html')
c = Context({
'left_choice': left_choice,
'right_choice': right_choice,
'context_1': ctx1,
'context_2': ctx2,
'problem': problem,
'plots': json.dumps(plots),
})
return HttpResponse(t.render(c))
def testFastGallery(self):
tf = Hartman3()
kernel = tf.createKernel(GaussianKernel_ard)
X = lhcSample(tf.bounds, 10, seed=23)
Y = [tf.f(x) for x in X]
prefs = query2prefs(X, tf.f)
GP = PrefGaussianProcess(kernel)
GP.addPreferences(prefs)
gallery = fastUCBGallery(GP, tf.bounds, 4)
print('gallery returned:')
for x in gallery:
print('\t', x)
GP.addPreferences(query2prefs(gallery, tf.f))
# make sure we don't return anything out of bounds
bounds = copy(tf.bounds)
bounds[0] = [0., 0.]
gallery = fastUCBGallery(GP, bounds, 4)
print('gallery returned:')
for x in gallery:
print('\t', x)
for v, b in zip(x, bounds):
self.failUnless(v >= b[0] and v <= b[1])
def demoPrefGallery():
"""
A simpler demo, showing how to use a preference gallery. This demo
is not interactive -- it uses the 6D Hartman test function to generate
the preferences.
"""
N = 3 # gallery size
# use the Hartman6 test function, which has a kernel and bounds predefined
tf = Hartman6()
bounds = tf.bounds
kernel = tf.createKernel(GaussianKernel_ard)
# set up a Preference Gaussian Process, in which the observations are
# preferences, rather than scalars
GP = PrefGaussianProcess(kernel)
# initial preferences -- since we have no informative prior on the space,
# the gallery will be a set of points that maximize variance
gallery = fastUCBGallery(GP, bounds, N)
# this is a utility function for automtically testing the preferences --
# given a test functions and some sample points, it will return a list of
# preferences
prefs = query2prefs(gallery, tf.f)
# preferences have the form [r, c, degree], where r is preferred to c.
# degree is degree of preference. Just leave degree at 0 for now.
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# add preferences to the model
GP.addPreferences(prefs)
# get another gallery, but with the first three dimensions fixed to .5
nbounds = deepcopy(bounds)
nbounds[:3] = [[.5, .5]] * 3
gallery = fastUCBGallery(GP, nbounds, N)
prefs = query2prefs(gallery, tf.f)
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# get another gallery, but with the *last* three dimensions fixed to .5
nbounds = deepcopy(bounds)
nbounds[3:] = [[.5, .5]] * 3
gallery = fastUCBGallery(GP, nbounds, N)
prefs = query2prefs(gallery, tf.f)
for r, c, _ in prefs:
print '%s preferred to %s' % (r, c)
# preferences don't have to come from the gallery
r = array([0, 0, .5, 0, 1, .25])
c = array([1, 1, .75, 1, 0, .5])
pref = (r, c, 0)
GP.addPreferences([pref])
