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debug HMC shortcut

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This commit is contained in:
Zhenwen Dai 2014-08-07 14:28:46 +01:00
parent e0a7884270
commit c3bb7b28a1
2 changed files with 57 additions and 31 deletions
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19
GPy/core/parameterization/parameter_core.py
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@ -888,6 +888,25 @@ class Parameterizable(OptimizationHandlable):
self._param_array_ = np.empty(self.size, dtype=np.float64) self._param_array_ = np.empty(self.size, dtype=np.float64)
return self._param_array_ return self._param_array_
@property
def unfixed_param_array(self):
"""
Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization.
!WARNING!: setting the parameter array MUST always be done in memory:
m.param_array[:] = m_copy.param_array
"""
if self.__dict__.get('_param_array_', None) is None:
self._param_array_ = np.empty(self.size, dtype=np.float64)
if self.constraints[__fixed__].size !=0:
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
return self._param_array_[fixes]
else:
return self._param_array_
@param_array.setter @param_array.setter
def param_array(self, arr): def param_array(self, arr):
self._param_array_ = arr self._param_array_ = arr

69
GPy/inference/optimization/hmc.py
View file

@ -15,13 +15,12 @@ class HMC:
self.Minv = np.linalg.inv(self.M) self.Minv = np.linalg.inv(self.M)
def sample(self, m_iters=1000, hmc_iters=20): def sample(self, m_iters=1000, hmc_iters=20):
thetas = np.empty((m_iters,self.p.size)) params = np.empty((m_iters,self.p.size))
ps = np.empty((m_iters,self.p.size))
for i in xrange(m_iters): for i in xrange(m_iters):
self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M) self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
H_old = self._computeH() H_old = self._computeH()
p_old = self.p.copy()
theta_old = self.model.optimizer_array.copy() theta_old = self.model.optimizer_array.copy()
params[i] = self.model.unfixed_param_array
#Matropolis #Matropolis
self._update(hmc_iters) self._update(hmc_iters)
H_new = self._computeH() H_new = self._computeH()
@ -31,13 +30,10 @@ class HMC:
else: else:
k = np.exp(H_old-H_new) k = np.exp(H_old-H_new)
if np.random.rand()<k: if np.random.rand()<k:
thetas[i] = self.model.optimizer_array params[i] = self.model.unfixed_param_array
ps[i] = self.p
else: else:
thetas[i] = theta_old
ps[i] = p_old
self.model.optimizer_array = theta_old self.model.optimizer_array = theta_old
return thetas, ps return params
def _update(self, hmc_iters): def _update(self, hmc_iters):
for i in xrange(hmc_iters): for i in xrange(hmc_iters):
@ -49,9 +45,9 @@ class HMC:
return self.model.objective_function()+self.p.size*np.log(2*np.pi)/2.+np.log(np.linalg.det(self.M))/2.+np.dot(self.p, np.dot(self.Minv,self.p[:,None]))/2. return self.model.objective_function()+self.p.size*np.log(2*np.pi)/2.+np.log(np.linalg.det(self.M))/2.+np.dot(self.p, np.dot(self.Minv,self.p[:,None]))/2.
class HMC_shortcut: class HMC_shortcut:
def __init__(self,model,M=None,stepsize_range=[1e-6, 1e-1],groupsize=5, Hstd_th=[1e-3, 20.]): def __init__(self,model,M=None,stepsize_range=[1e-6, 1e-1],groupsize=5, Hstd_th=[1e-5, 3.]):
self.model = model self.model = model
self.stepsize_range = np.log10(stepsize_range) self.stepsize_range = np.log(stepsize_range)
self.p = np.empty_like(model.optimizer_array.copy()) self.p = np.empty_like(model.optimizer_array.copy())
self.groupsize = groupsize self.groupsize = groupsize
self.Hstd_th = Hstd_th self.Hstd_th = Hstd_th
@ -62,14 +58,13 @@ class HMC_shortcut:
self.Minv = np.linalg.inv(self.M) self.Minv = np.linalg.inv(self.M)
def sample(self, m_iters=1000, hmc_iters=20): def sample(self, m_iters=1000, hmc_iters=20):
thetas = np.empty((m_iters,self.p.size)) params = np.empty((m_iters,self.p.size))
ps = np.empty((m_iters,self.p.size))
for i in xrange(m_iters): for i in xrange(m_iters):
# sample a stepsize from the uniform distribution # sample a stepsize from the uniform distribution
stepsize = np.exp10(np.random.rand()*(self.stepsize_range[1]-self.stepsize_range[0])+self.stepsize_range[0]) stepsize = np.exp(np.random.rand()*(self.stepsize_range[1]-self.stepsize_range[0])+self.stepsize_range[0])
self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M) self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
H_old = self._computeH() H_old = self._computeH()
p_old = self.p.copy() params[i] = self.model.unfixed_param_array
theta_old = self.model.optimizer_array.copy() theta_old = self.model.optimizer_array.copy()
#Matropolis #Matropolis
self._update(hmc_iters, stepsize) self._update(hmc_iters, stepsize)
@ -80,13 +75,10 @@ class HMC_shortcut:
else: else:
k = np.exp(H_old-H_new) k = np.exp(H_old-H_new)
if np.random.rand()<k: if np.random.rand()<k:
thetas[i] = self.model.optimizer_array params[i] = self.model.unfixed_param_array
ps[i] = self.p
else: else:
thetas[i] = theta_old
ps[i] = p_old
self.model.optimizer_array = theta_old self.model.optimizer_array = theta_old
return thetas, ps return params
def _update(self, hmc_iters, stepsize): def _update(self, hmc_iters, stepsize):
theta_buf = np.empty((2*hmc_iters+1,self.model.optimizer_array.size)) theta_buf = np.empty((2*hmc_iters+1,self.model.optimizer_array.size))
@ -99,31 +91,43 @@ class HMC_shortcut:
reversal = [] reversal = []
pos = 1 pos = 1
for i in xrange(hmc_iters): i=0
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients()) while i<hmc_iters:
self.model.optimizer_array = self.model.optimizer_array + self.stepsize*np.dot(self.Minv, self.p) self.p[:] += -stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients()) self.model.optimizer_array = self.model.optimizer_array + stepsize*np.dot(self.Minv, self.p)
self.p[:] += -stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
theta_buf[hmc_iters+pos] = self.model.optimizer_array theta_buf[hmc_iters+pos] = self.model.optimizer_array
p_buf[hmc_iters+pos] = self.p p_buf[hmc_iters+pos] = self.p
H_buf[hmc_iters+pos] = self._computeH() H_buf[hmc_iters+pos] = self._computeH()
i+=1
if i<self.groupsize: if i<self.groupsize:
pos += 1 pos += 1
continue continue
else: else:
if len(reversal)==0: if len(reversal)==0:
Hlist = range(pos,pos-self.groupsize,-1) Hlist = range(hmc_iters+pos,hmc_iters+pos-self.groupsize,-1)
if self._testH(H_buf[Hlist]): if self._testH(H_buf[Hlist]):
pos += 1 pos += 1
else: else:
# Reverse the trajectory for the 1st time # Reverse the trajectory for the 1st time
reversal.add(pos) reversal.append(pos)
pos = -1 if hmc_iters-i>pos:
self.model.optimizer_array = theta_buf[hmc_iters] pos = -1
self.p[:] = -p_buf[hmc_iters] i += pos
self.model.optimizer_array = theta_buf[hmc_iters]
self.p[:] = -p_buf[hmc_iters]
else:
pos_new = pos-hmc_iters+i
self.model.optimizer_array = theta_buf[hmc_iters+pos_new]
self.p[:] = -p_buf[hmc_iters+pos_new]
break
else: else:
Hlist = range(pos,pos+self.groupsize) Hlist = range(hmc_iters+pos,hmc_iters+pos+self.groupsize)
# print Hlist
# print self._testH(H_buf[Hlist])
if self._testH(H_buf[Hlist]): if self._testH(H_buf[Hlist]):
pos += -1 pos += -1
else: else:
@ -132,14 +136,17 @@ class HMC_shortcut:
if r>(reversal[0]-pos): if r>(reversal[0]-pos):
pos_new = 2*reversal[0] - r - pos pos_new = 2*reversal[0] - r - pos
else: else:
pos_new = 2*pos + r - reversal[0] pos_new = pos + r
self.model.optimizer_array = theta_buf[hmc_iters+pos_new] self.model.optimizer_array = theta_buf[hmc_iters+pos_new]
self.p[:] = p_buf[hmc_iters+pos_new] # the sign of momentum might be wrong! self.p[:] = p_buf[hmc_iters+pos_new] # the sign of momentum might be wrong!
# print reversal[0],pos,pos_new
# print H_buf
break break
def _testH(self, Hlist): def _testH(self, Hlist):
Hstd = np.std(Hlist) Hstd = np.std(Hlist)
# print Hlist
# print Hstd
if Hstd<self.Hstd_th[0] or Hstd>self.Hstd_th[1]: if Hstd<self.Hstd_th[0] or Hstd>self.Hstd_th[1]:
return False return False
else: else: