def start_path(self, ui, region):
"""Start new trajectory path."""
# print("new direction:", self.scale, self.noutside_regions, self.nrejects, self.naccepts)
v = self.generate_direction(ui, region, scale=self.scale)
assert (v**2).sum() > 0, (v, self.scale)
assert region.inside(ui.reshape((1, -1))).all(), ui
self.path = ContourSamplingPath(SamplingPath(ui, v, 0.0), region)
if self.grad_function is not None:
self.path.gradient = self.grad_function
if not (ui > 0).all() or not (ui < 1).all() or not region.inside(ui.reshape((1, -1))):
assert False, ui
self.direction = +1
self.lasti = 0
self.cache = {0: (True, ui, self.last[1])}
self.deadends = set()
# self.iresets += 1
if self.log:
print()
print("starting new direction", v, 'from', ui)
def start_direction(self, region):
"""Choose a new random direction."""
if self.log:
print("choosing random direction")
ui, Li = self.last
v = generate_random_direction(ui, region, scale=self.scale)
# v = generate_region_random_direction(ui, region, scale=self.scale)
self.nrestarts += 1
if self.sampler is None or True:
samplingpath = SamplingPath(ui, v, Li)
contourpath = ContourSamplingPath(samplingpath, region)
if self.samplername == 'steps':
self.sampler = ClockedStepSampler(contourpath, log=self.log)
self.stepper = DirectJumper(self.sampler, self.nsteps, log=self.log)
elif self.samplername == 'bisect':
self.sampler = ClockedBisectSampler(contourpath, log=self.log)
self.stepper = DirectJumper(self.sampler, self.nsteps, log=self.log)
elif self.samplername == 'nuts':
self.sampler = ClockedNUTSSampler(contourpath, log=self.log)
self.stepper = IntervalJumper(self.sampler, self.nsteps, log=self.log)
else:
assert False
def test_reversible_gradient(plot=False):
def loglike(x):
x, y = x.transpose()
return -0.5 * (x**2 + ((y - 0.5) / 0.2)**2)
def transform(u):
return u
Lmin = -0.5
for i in [84] + list(range(1, 100)):
print("setting seed = %d" % i)
np.random.seed(i)
points = np.random.uniform(size=(10000, 2))
L = loglike(points)
mask = L > Lmin
points = points[mask, :][:100, :]
active_u = points
active_values = L[mask][:100]
transformLayer = AffineLayer(wrapped_dims=[])
transformLayer.optimize(points, points)
region = MLFriends(points, transformLayer)
region.maxradiussq, region.enlarge = region.compute_enlargement(
nbootstraps=30)
region.create_ellipsoid()
nclusters = transformLayer.nclusters
assert nclusters == 1
assert np.allclose(region.unormed,
region.transformLayer.transform(
points)), "transform should be reproducible"
assert region.inside(
points).all(), "live points should lie near live points"
if i == 84:
v = np.array([0.03477044, -0.01977415])
reflpoint = np.array([0.09304075, 0.29114574])
elif i == 4:
v = np.array([0.03949306, -0.00634806])
reflpoint = np.array([0.9934771, 0.55358031])
else:
v = np.random.normal(size=2)
v /= (v**2).sum()**0.5
v *= 0.04
j = np.random.randint(len(active_u))
reflpoint = np.random.normal(active_u[j, :], 0.04)
if not (reflpoint < 1).all() and not (reflpoint > 0).all():
continue
bpts = region.transformLayer.transform(reflpoint).reshape((1, -1))
tt = get_sphere_tangents(region.unormed, bpts)
t = region.transformLayer.untransform(tt * 1e-3 +
region.unormed) - region.u
# compute new vector
normal = t / norm(t, axis=1).reshape((-1, 1))
print("reflecting at ", reflpoint, "with direction", v)
mask_forward1, angles, anglesnew = get_reflection_angles(normal, v)
if mask_forward1.any():
j = np.argmin(
((region.unormed[mask_forward1, :] - bpts)**2).sum(axis=1))
k = np.arange(len(normal))[mask_forward1][j]
angles_used = angles[k]
normal_used = normal[k, :]
print("chose normal", normal_used, k)
#chosen_point = region.u[k,:]
vnew = -(v - 2 * angles_used * normal_used)
assert vnew.shape == v.shape
mask_forward2, angles2, anglesnew2 = get_reflection_angles(
normal, vnew)
#j2 = np.argmin(((region.unormed[mask_forward2,:] - bpts)**2).sum(axis=1))
#chosen_point2 = region.u[mask_forward2,:][0,:]
#assert j2 == j, (j2, j)
assert mask_forward2[k]
#assert_allclose(chosen_point, chosen_point2)
#for m, a, b, m2, a2, b2 in zip(mask_forward1, angles, anglesnew, mask_forward2, angles2, anglesnew2):
# if m != m2:
# print(' ', m, a, b, m2, a2, b2)
#print("using normal", normal)
#print("changed v from", v, "to", vnew)
#angles2 = -(normal * (vnew / norm(vnew))).sum(axis=1)
#mask_forward2 = angles < 0
if plot:
plt.figure(figsize=(5, 5))
plt.title('%d' % mask_forward1.sum())
plt.plot((reflpoint + v)[0], (reflpoint + v)[1],
'^',
color='orange')
plt.plot((reflpoint + vnew)[:, 0], (reflpoint + vnew)[:, 1],
'^ ',
color='lime')
plt.plot(reflpoint[0], reflpoint[1], '^ ', color='r')
plt.plot(region.u[:, 0], region.u[:, 1], 'x ', ms=2, color='k')
plt.plot(region.u[mask_forward1, 0],
region.u[mask_forward1, 1],
'o ',
ms=6,
mfc='None',
mec='b')
plt.plot(region.u[mask_forward2, 0],
region.u[mask_forward2, 1],
's ',
ms=8,
mfc='None',
mec='g')
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.savefig('test_flatnuts_reversible_gradient_%d.png' % i,
bbox_inches='tight')
plt.close()
assert mask_forward1[k] == mask_forward2[k], (mask_forward1[k],
mask_forward2[k])
print("reflecting at ", reflpoint, "with direction", v)
# make that step, then try to go back
j = np.arange(len(normal))[mask_forward1][0]
normal = normal[j, :]
angles = (normal * (v / norm(v))).sum()
v2 = v - 2 * angle(normal, v) * normal
print("reflecting with", normal, "new direction", v2)
#newpoint = reflpoint + v2
#angles2 = (normal * (v2 / norm(v2))).sum()
v3 = v2 - 2 * angle(normal, v2) * normal
print("re-reflecting gives direction", v3)
assert_allclose(v3, v)
print()
print("FORWARD:", v, reflpoint)
samplingpath = SamplingPath(reflpoint - v, v, active_values[0])
contourpath = ContourSamplingPath(samplingpath, region)
normal = contourpath.gradient(reflpoint)
if normal is not None:
assert normal.shape == v.shape, (normal.shape, v.shape)
print("BACKWARD:", v, reflpoint)
v2 = -(v - 2 * angle(normal, v) * normal)
normal2 = contourpath.gradient(reflpoint)
assert_allclose(normal, normal2)
normal2 = normal
v3 = -(v2 - 2 * angle(normal2, v2) * normal2)
assert_allclose(v3, v)
def test_detailed_balance():
def loglike(x):
x, y = x.transpose()
return -0.5 * (x**2 + ((y - 0.5) / 0.2)**2)
def transform(u):
return u
Lmin = -0.5
for i in range(1, 100):
print()
print("---- seed=%d ----" % i)
print()
np.random.seed(i)
points = np.random.uniform(size=(10000, 2))
L = loglike(points)
mask = L > Lmin
points = points[mask, :][:400, :]
active_u = points
active_values = L[mask][:400]
transformLayer = AffineLayer(wrapped_dims=[])
transformLayer.optimize(points, points)
region = MLFriends(points, transformLayer)
region.maxradiussq, region.enlarge = region.compute_enlargement(
nbootstraps=30)
region.create_ellipsoid()
nclusters = transformLayer.nclusters
assert nclusters == 1
assert np.allclose(region.unormed,
region.transformLayer.transform(
points)), "transform should be reproducible"
assert region.inside(
points).all(), "live points should lie near live points"
v = np.random.normal(size=2)
v /= (v**2).sum()**0.5
v *= 0.04
print("StepSampler ----")
print("FORWARD SAMPLING FROM", 0, active_u[0], v, active_values[0])
samplingpath = SamplingPath(active_u[0], v, active_values[0])
problem = dict(loglike=loglike, transform=transform, Lmin=Lmin)
sampler = ClockedStepSampler(ContourSamplingPath(samplingpath, region))
check_starting_point(sampler, active_u[0], active_values[0], **problem)
sampler.expand_onestep(fwd=True, **problem)
sampler.expand_onestep(fwd=True, **problem)
sampler.expand_onestep(fwd=True, **problem)
sampler.expand_onestep(fwd=True, **problem)
sampler.expand_onestep(fwd=False, **problem)
sampler.expand_to_step(4, **problem)
sampler.expand_to_step(-4, **problem)
check_starting_point(sampler, active_u[0], active_values[0], **problem)
starti, startx, startv, startL = max(sampler.points)
print()
print("BACKWARD SAMPLING FROM", starti, startx, startv, startL)
samplingpath2 = SamplingPath(startx, -startv, startL)
sampler2 = ClockedStepSampler(
ContourSamplingPath(samplingpath2, region))
check_starting_point(sampler2, startx, startL, **problem)
sampler2.expand_to_step(starti, **problem)
check_starting_point(sampler2, startx, startL, **problem)
starti2, startx2, startv2, startL2 = max(sampler2.points)
assert_allclose(active_u[0], startx2)
assert_allclose(v, -startv2)
starti, startx, startv, startL = min(sampler.points)
print()
print("BACKWARD SAMPLING FROM", starti, startx, startv, startL)
samplingpath3 = SamplingPath(startx, startv, startL)
sampler3 = ClockedStepSampler(
ContourSamplingPath(samplingpath3, region))
check_starting_point(sampler3, startx, startL, **problem)
sampler3.expand_to_step(-starti, **problem)
check_starting_point(sampler3, startx, startL, **problem)
starti3, startx3, startv3, startL3 = max(sampler3.points)
assert_allclose(active_u[0], startx3)
assert_allclose(v, startv3)
print()
print("BisectSampler ----")
log = dict(log=True)
print("FORWARD SAMPLING FROM", 0, active_u[0], v, active_values[0])
samplingpath = SamplingPath(active_u[0], v, active_values[0])
sampler = ClockedBisectSampler(
ContourSamplingPath(samplingpath, region), **log)
check_starting_point(sampler, active_u[0], active_values[0], **problem)
sampler.expand_to_step(10, **problem)
check_starting_point(sampler, active_u[0], active_values[0], **problem)
starti, startx, startv, startL = max(sampler.points)
print()
print("BACKWARD SAMPLING FROM", starti, startx, startv, startL)
samplingpath2 = SamplingPath(startx, -startv, startL)
sampler2 = ClockedBisectSampler(
ContourSamplingPath(samplingpath2, region), **log)
check_starting_point(sampler2, startx, startL, **problem)
sampler2.expand_to_step(starti, **problem)
check_starting_point(sampler2, startx, startL, **problem)
starti2, startx2, startv2, startL2 = max(sampler2.points)
if gap_free_path(sampler, 0, starti, **problem) and gap_free_path(
sampler2, 0, starti2, **problem):
assert_allclose(active_u[0], startx2)
assert_allclose(v, -startv2)
starti, startx, startv, startL = min(sampler.points)
print()
print("BACKWARD SAMPLING FROM", starti, startx, startv, startL)
samplingpath3 = SamplingPath(startx, -startv, startL)
sampler3 = ClockedBisectSampler(
ContourSamplingPath(samplingpath3, region), **log)
check_starting_point(sampler3, startx, startL, **problem)
sampler3.expand_to_step(starti, **problem)
check_starting_point(sampler3, startx, startL, **problem)
starti3, startx3, startv3, startL3 = min(sampler3.points)
if gap_free_path(sampler, 0, starti, **problem) and gap_free_path(
sampler3, 0, starti3, **problem):
assert_allclose(active_u[0], startx3)
assert_allclose(v, -startv3)
print()
print("NUTSSampler ----")
print("FORWARD SAMPLING FROM", 0, active_u[0], v, active_values[0])
samplingpath = SamplingPath(active_u[0], v, active_values[0])
np.random.seed(i)
sampler = ClockedNUTSSampler(ContourSamplingPath(samplingpath, region))
sampler.get_independent_sample(**problem)
def test_directjumper():
Lmin = -1.0
us = 0.5 + np.zeros((100, 2))
#Ls = np.zeros(100)
region = make_region(2)
def transform(x):
return x
def loglike(x):
return 0.0
def gradient(x, plot=False):
j = np.argmax(np.abs(x - 0.5))
v = np.zeros(len(x))
v[j] = -1 if x[j] > 0.5 else 1
return v
def nocall(x):
assert False
ui = us[np.random.randint(len(us)), :]
v = np.array([0.01, 0.01])
path = ContourSamplingPath(SamplingPath(ui, v, 0.0), region)
path.gradient = nocall
sampler = ClockedBisectSampler(path)
stepper = DirectJumper(sampler, 4)
assert (stepper.naccepts, stepper.nrejects) == (0, 0), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 0, stepper.isteps
x, L = makejump(stepper, sampler, transform, loglike, Lmin)
assert_allclose(x, [0.54, 0.54])
assert (stepper.naccepts, stepper.nrejects) == (4, 0), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 4, stepper.isteps
print()
print("make reflect")
print()
def loglike(x):
return 0.0 if x[0] < 0.505 else -100
path = ContourSamplingPath(SamplingPath(ui, v, 0.0), region)
path.gradient = gradient
sampler = ClockedBisectSampler(path)
stepper = DirectJumper(sampler, 4)
assert (stepper.naccepts, stepper.nrejects) == (0, 0), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 0, stepper.isteps
x, L = makejump(stepper, sampler, transform, loglike, Lmin)
assert_allclose(x, [0.47, 0.55])
assert (stepper.naccepts, stepper.nrejects) == (4, 0), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 4, stepper.isteps
print()
print("make stuck")
print()
# make stuck
def loglike(x):
return -100
path = ContourSamplingPath(SamplingPath(ui, v, 0.0), region)
path.gradient = gradient
sampler = ClockedBisectSampler(path)
stepper = DirectJumper(sampler, 4)
assert (stepper.naccepts, stepper.nrejects) == (0, 0), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 0, stepper.isteps
x, L = makejump(stepper, sampler, transform, loglike, Lmin)
assert_allclose(x, [0.50, 0.50])
assert (stepper.naccepts, stepper.nrejects) == (0, 4), (stepper.naccepts,
stepper.nrejects)
assert stepper.isteps == 4, stepper.isteps
def move(self, ui, region, ndraw=1, plot=False):
"""Advance by slice sampling on the path."""
if self.interval is None:
v = self.generate_direction(ui, region, scale=self.scale)
self.path = ContourSamplingPath(
SamplingPath(ui, v, 0.0), region)
if not (ui > 0).all() or not (ui < 1).all() or not region.inside(ui.reshape((1, -1))):
assert False, ui
# unit hypercube diagonal gives a reasonable maximum path length
maxlength = len(ui)**0.5
# expand direction until it is surely outside
left = -1
right = +1
while abs(left * self.scale) < maxlength:
xj, vj = self.path.extrapolate(left)
if not (xj > 0).all() or not (xj < 1).all() or not region.inside(xj.reshape((1, -1))):
break
# self.path.add(left, xj, vj, 0.0)
left *= 2
while abs(right * self.scale) < maxlength:
xj, _ = self.path.extrapolate(right)
if not (xj > 0).all() or not (xj < 1).all() or not region.inside(xj.reshape((1, -1))):
break
# self.path.add(right, xj, vj, 0.0)
right *= 2
scale = max(-left, right)
# print("scale %f gave %d %d " % (self.scale, left, right))
if scale < 5:
self.scale /= 1.1
# if scale > 100:
# self.scale *= 1.1
assert self.scale > 1e-10, self.scale
self.interval = (left, right, None)
else:
left, right, mid = self.interval
# we rejected mid, and shrink corresponding side
if mid < 0:
left = mid
elif mid > 0:
right = mid
# shrink direction if outside
while True:
mid = np.random.randint(left, right + 1)
# print("interpolating %d - %d - %d" % (left, mid, right),
# self.path.points)
if mid == 0:
_, xj, _, _ = self.path.points[0]
else:
xj, _ = self.path.extrapolate(mid)
if region.inside(xj.reshape((1, -1))):
self.interval = (left, right, mid)
return xj.reshape((1, -1))
else:
if mid < 0:
left = mid
else:
right = mid
self.interval = (left, right, mid)
class SamplingPathSliceSampler(StepSampler):
"""Slice sampler, respecting the region, on the sampling path.
This first builds up a complete trajectory, respecting reflections.
Then, from the trajectory a new point is drawn with slice sampling.
The trajectory is built by doubling the length to each side and
checking if the point is still inside. If not, reflection is
attempted with the gradient (either provided or region-based estimate).
"""
def __init__(self, nsteps):
"""Initialise sampler.
Parameters
-----------
nsteps: int
number of accepted steps until the sample is considered independent.
"""
StepSampler.__init__(self, nsteps=nsteps)
self.interval = None
self.path = None
def generate_direction(self, ui, region, scale=1):
"""Choose new initial direction according to region.transformLayer axes."""
return generate_region_oriented_direction(ui, region, tscale=1, scale=scale)
def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
"""Adjust proposal given that we have been *accepted* at a new point after *nc* calls."""
if accepted:
# start with a new interval next time
self.interval = None
self.last = unew, Lnew
self.history.append((unew, Lnew))
else:
self.nrejects += 1
# continue on current interval
pass
self.logstat.append([accepted, self.scale])
def adjust_outside_region(self):
"""Adjust proposal given that we have stepped out of region."""
self.logstat.append([False, self.scale])
def move(self, ui, region, ndraw=1, plot=False):
"""Advance by slice sampling on the path."""
if self.interval is None:
v = self.generate_direction(ui, region, scale=self.scale)
self.path = ContourSamplingPath(
SamplingPath(ui, v, 0.0), region)
if not (ui > 0).all() or not (ui < 1).all() or not region.inside(ui.reshape((1, -1))):
assert False, ui
# unit hypercube diagonal gives a reasonable maximum path length
maxlength = len(ui)**0.5
# expand direction until it is surely outside
left = -1
right = +1
while abs(left * self.scale) < maxlength:
xj, vj = self.path.extrapolate(left)
if not (xj > 0).all() or not (xj < 1).all() or not region.inside(xj.reshape((1, -1))):
break
# self.path.add(left, xj, vj, 0.0)
left *= 2
while abs(right * self.scale) < maxlength:
xj, _ = self.path.extrapolate(right)
if not (xj > 0).all() or not (xj < 1).all() or not region.inside(xj.reshape((1, -1))):
break
# self.path.add(right, xj, vj, 0.0)
right *= 2
scale = max(-left, right)
# print("scale %f gave %d %d " % (self.scale, left, right))
if scale < 5:
self.scale /= 1.1
# if scale > 100:
# self.scale *= 1.1
assert self.scale > 1e-10, self.scale
self.interval = (left, right, None)
else:
left, right, mid = self.interval
# we rejected mid, and shrink corresponding side
if mid < 0:
left = mid
elif mid > 0:
right = mid
# shrink direction if outside
while True:
mid = np.random.randint(left, right + 1)
# print("interpolating %d - %d - %d" % (left, mid, right),
# self.path.points)
if mid == 0:
_, xj, _, _ = self.path.points[0]
else:
xj, _ = self.path.extrapolate(mid)
if region.inside(xj.reshape((1, -1))):
self.interval = (left, right, mid)
return xj.reshape((1, -1))
else:
if mid < 0:
left = mid
else:
right = mid
self.interval = (left, right, mid)
class SamplingPathStepSampler(StepSampler):
"""Step sampler on a sampling path."""
def __init__(self, nresets, nsteps, scale=1.0, balance=0.01, nudge=1.1, log=False):
"""Initialise sampler.
Parameters
------------
nresets: int
after this many iterations, select a new direction
nsteps: int
how many steps to make in total
scale: float
initial step size
balance: float
acceptance rate to target
if below, scale is increased, if above, scale is decreased
nudge: float
factor for increasing scale (must be >=1)
nudge=1 implies no step size adaptation.
"""
StepSampler.__init__(self, nsteps=nsteps)
# self.lasti = None
self.path = None
self.nresets = nresets
# initial step scale in transformed space
self.scale = scale
# fraction of times a reject is expected
self.balance = balance
# relative increase in step scale
self.nudge = nudge
assert nudge >= 1
self.log = log
self.grad_function = None
self.istep = 0
self.iresets = 0
self.start()
self.terminate_path()
self.logstat_labels = ['acceptance rate', 'reflection rate', 'scale', 'nstuck']
def __str__(self):
"""Get string representation."""
return '(nsteps=%d, nresets=%d, AR=%d%%)' % (
type(self).__name__, self.nsteps, self.nresets, (1 - self.balance) * 100)
def start(self):
"""Start sampler, reset all counters."""
if hasattr(self, 'naccepts') and self.nrejects + self.naccepts > 0:
nr, na = self.nrejects, self.naccepts
self.logstat.append([
self.naccepts / (self.nrejects + self.naccepts),
self.nreflects / (self.nreflects + self.nrejects + self.naccepts),
self.scale, self.nstuck])
self.nrejects = 0
self.naccepts = 0
self.nreflects = 0
self.nstuck = 0
self.istep = 0
self.iresets = 0
self.noutside_regions = 0
self.last = None, None
self.history = []
self.direction = +1
self.deadends = set()
self.path = None
def start_path(self, ui, region):
"""Start new trajectory path."""
# print("new direction:", self.scale, self.noutside_regions, self.nrejects, self.naccepts)
v = self.generate_direction(ui, region, scale=self.scale)
assert (v**2).sum() > 0, (v, self.scale)
assert region.inside(ui.reshape((1, -1))).all(), ui
self.path = ContourSamplingPath(SamplingPath(ui, v, 0.0), region)
if self.grad_function is not None:
self.path.gradient = self.grad_function
if not (ui > 0).all() or not (ui < 1).all() or not region.inside(ui.reshape((1, -1))):
assert False, ui
self.direction = +1
self.lasti = 0
self.cache = {0: (True, ui, self.last[1])}
self.deadends = set()
# self.iresets += 1
if self.log:
print()
print("starting new direction", v, 'from', ui)
def terminate_path(self):
"""Terminate current path, and reset path counting variable."""
# check if we went anywhere:
if -1 in self.deadends and +1 in self.deadends:
# self.scale /= self.nudge
self.nstuck += 1
# self.nrejects = 0
# self.naccepts = 0
# self.istep = 0
# self.noutside_regions = 0
self.direction = +1
self.deadends = set()
self.path = None
self.iresets += 1
if self.log:
print("reset %d" % self.iresets)
def set_gradient(self, grad_function):
"""Set gradient function."""
print("set gradient function to %s" % grad_function.__name__)
def plot_gradient_wrapper(x, plot=False):
"""wrapper that makes plots (when desired)"""
v = grad_function(x)
if plot:
plt.plot(x[0], x[1], '+ ', color='k', ms=10)
plt.plot([x[0], v[0] * 1e-2 + x[0]],
[x[1], v[1] * 1e-2 + x[1]], color='gray')
return v
self.grad_function = plot_gradient_wrapper
def generate_direction(self, ui, region, scale):
"""Choose a random axis from region.transformLayer."""
return generate_region_random_direction(ui, region, scale=scale)
# return generate_random_direction(ui, region, scale=scale)
def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
"""Adjust proposal given that we have been *accepted* at a new point after *nc* calls."""
self.cache[self.nexti] = (accepted, unew, Lnew)
if accepted:
# start at new point next time
self.lasti = self.nexti
self.last = unew, Lnew
self.history.append((unew, Lnew))
self.naccepts += 1
else:
# continue on current point, do not update self.last
self.nrejects += 1
self.history.append((unew, Lnew))
assert self.scale > 1e-10, (self.scale, self.istep, self.nrejects)
def adjust_outside_region(self):
"""Adjust proposal given that we landed outside region."""
self.noutside_regions += 1
self.nrejects += 1
def adjust_scale(self, maxlength):
"""Adjust scale, but not above maxlength."""
# print("%2d | %2d | %2d | %2d %2d %2d %2d | %f" % (self.iresets, self.istep,
# len(self.history), self.naccepts, self.nrejects,
# self.noutside_regions, self.nstuck, self.scale))
assert len(self.history) > 1
if self.naccepts < (self.nrejects + self.naccepts) * self.balance:
if self.log:
print("adjusting scale %f down: istep=%d inside=%d outside=%d region=%d nstuck=%d" % (
self.scale, len(self.history), self.naccepts, self.nrejects, self.noutside_regions, self.nstuck))
self.scale /= self.nudge
else:
if self.scale < maxlength or True:
if self.log:
print("adjusting scale %f up: istep=%d inside=%d outside=%d region=%d nstuck=%d" % (
self.scale, len(self.history), self.naccepts, self.nrejects, self.noutside_regions, self.nstuck))
self.scale *= self.nudge
assert self.scale > 1e-10, self.scale
def movei(self, ui, region, ndraw=1, plot=False):
"""Make a move and return the proposed index."""
if self.path is not None:
if self.lasti - 1 in self.deadends and self.lasti + 1 in self.deadends:
# stuck, cannot go anywhere. Stay.
self.nexti = self.lasti
return self.nexti
if self.path is None:
self.start_path(ui, region)
assert not (self.lasti - 1 in self.deadends and self.lasti + 1 in self.deadends), \
(self.deadends, self.lasti)
if self.lasti + self.direction in self.deadends:
self.direction *= -1
self.nexti = self.lasti + self.direction
# print("movei", self.nexti)
# self.nexti = self.lasti + np.random.randint(0, 2) * 2 - 1
return self.nexti
def move(self, ui, region, ndraw=1, plot=False):
"""Advance move."""
u, v = self.get_point(self.movei(ui, region=region, ndraw=ndraw, plot=plot))
return u.reshape((1, -1))
def reflect(self, reflpoint, v, region, plot=False):
"""Reflect at *reflpoint* going in direction *v*. Return new direction."""
normal = self.path.gradient(reflpoint, plot=plot)
if normal is None:
return -v
return v - 2 * (normal * v).sum() * normal
def get_point(self, inew):
"""Get point corresponding to index *inew*."""
ipoints = [(u, v) for i, u, p, v in self.path.points if i == inew]
if len(ipoints) == 0:
# print("getting point %d" % inew, self.path.points) #, "->", self.path.extrapolate(self.nexti))
return self.path.extrapolate(inew)
else:
return ipoints[0]
def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=False):
"""Get next point.
Parameters
----------
region: MLFriends
region.
Lmin: float
loglikelihood threshold
us: array of vectors
current live points
Ls: array of floats
current live point likelihoods
transform: function
transform function
loglike: function
loglikelihood function
ndraw: int
number of draws to attempt simultaneously.
plot: bool
whether to produce debug plots.
"""
# find most recent point in history conforming to current Lmin
ui, Li = self.last
if Li is not None and not Li >= Lmin:
if self.log:
print("wandered out of L constraint; resetting", ui[0])
ui, Li = None, None
if Li is not None and not region.inside(ui.reshape((1,-1))):
# region was updated and we are not inside anymore
# so reset
if self.log:
print("region change; resetting")
ui, Li = None, None
if Li is None and self.history:
# try to resume from a previous point above the current contour
for uj, Lj in self.history[::-1]:
if Lj >= Lmin and region.inside(uj.reshape((1,-1))):
ui, Li = uj, Lj
if self.log:
print("recovered using history", ui)
break
# select starting point
if Li is None:
# choose a new random starting point
mask = region.inside(us)
assert mask.any(), (
"None of the live points satisfies the current region!",
region.maxradiussq, region.u, region.unormed, us)
i = np.random.randint(mask.sum())
self.starti = i
ui = us[mask,:][i]
if self.log:
print("starting at", ui)
assert np.logical_and(ui > 0, ui < 1).all(), ui
Li = Ls[mask][i]
self.start()
self.history.append((ui, Li))
self.last = (ui, Li)
inew = self.movei(ui, region, ndraw=ndraw)
if self.log:
print("i: %d->%d (step %d)" % (self.lasti, inew, self.istep))
# uold, _ = self.get_point(self.lasti)
_, uold, Lold = self.cache[self.lasti]
if plot:
plt.plot(uold[0], uold[1], 'd', color='brown', ms=4)
uret, pret, Lret = uold, transform(uold), Lold
nc = 0
if inew != self.lasti:
accept = False
if inew not in self.cache:
unew, _ = self.get_point(inew)
if plot:
plt.plot(unew[0], unew[1], 'x', color='k', ms=4)
accept = np.logical_and(unew > 0, unew < 1).all() and region.inside(unew.reshape((1, -1)))
if accept:
if plot:
plt.plot(unew[0], unew[1], '+', color='orange', ms=4)
pnew = transform(unew)
Lnew = loglike(pnew.reshape((1, -1)))
nc = 1
else:
Lnew = -np.inf
if self.log:
print("outside region: ", unew, "from", ui)
self.deadends.add(inew)
self.adjust_outside_region()
else:
_, unew, Lnew = self.cache[self.nexti]
# if plot:
# plt.plot(unew[0], unew[1], 's', color='r', ms=2)
if self.log:
print(" suggested point:", unew)
pnew = transform(unew)
if Lnew >= Lmin:
if self.log:
print(" -> inside.")
if plot:
plt.plot(unew[0], unew[1], 'o', color='g', ms=4)
self.adjust_accept(True, unew, pnew, Lnew, nc)
uret, pret, Lret = unew, pnew, Lnew
else:
if plot:
plt.plot(unew[0], unew[1], '+', color='k', ms=2, alpha=0.3)
if self.log:
print(" -> outside.")
jump_successful = False
if inew not in self.cache and inew not in self.deadends:
# first time we try to go beyond
# try to reflect:
reflpoint, v = self.get_point(inew)
if self.log:
print(" trying to reflect at", reflpoint)
self.nreflects += 1
sign = -1 if inew < 0 else +1
vnew = self.reflect(reflpoint, v * sign, region=region) * sign
xk, vk = extrapolate_ahead(sign, reflpoint, vnew, contourpath=self.path)
if plot:
plt.plot([reflpoint[0], (-v + reflpoint)[0]], [reflpoint[1], (-v + reflpoint)[1]], '-', color='k', lw=0.5, alpha=0.5)
plt.plot([reflpoint[0], (vnew + reflpoint)[0]], [reflpoint[1], (vnew + reflpoint)[1]], '-', color='k', lw=1)
if self.log:
print(" trying", xk)
accept = np.logical_and(xk > 0, xk < 1).all() and region.inside(xk.reshape((1, -1)))
if accept:
pk = transform(xk)
Lk = loglike(pk.reshape((1, -1)))[0]
nc += 1
if Lk >= Lmin:
jump_successful = True
uret, pret, Lret = xk, pk, Lk
if self.log:
print("successful reflect!")
self.path.add(inew, xk, vk, Lk)
self.adjust_accept(True, xk, pk, Lk, nc)
else:
if self.log:
print("unsuccessful reflect")
self.adjust_accept(False, xk, pk, Lk, nc)
else:
if self.log:
print("unsuccessful reflect out of region")
self.adjust_outside_region()
if plot:
plt.plot(xk[0], xk[1], 'x', color='g' if jump_successful else 'r', ms=8)
if not jump_successful:
# unsuccessful. mark as deadend
self.deadends.add(inew)
# print("deadends:", self.deadends)
else:
self.adjust_accept(False, uret, pret, Lret, nc)
# self.adjust_accept(False, unew, pnew, Lnew, nc)
assert inew in self.cache or inew in self.deadends, (inew in self.cache, inew in self.deadends)
else:
# stuck, proposal did not move us
self.nstuck += 1
self.adjust_accept(False, uret, pret, Lret, nc)
# increase step count
self.istep += 1
if self.istep == self.nsteps:
if self.log:
print("triggering re-orientation")
# reset path so we go in a new direction
self.terminate_path()
self.istep = 0
# if had enough resets, return final point
if self.iresets >= self.nresets:
if self.log:
print("walked %d paths; returning sample" % self.iresets)
self.adjust_scale(maxlength=len(uret)**0.5)
self.start()
self.last = None, None
return uret, pret, Lret, nc
# do not have a independent sample yet
return None, None, None, nc