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merge the current devel into psi2
This commit is contained in:
Zhenwen Dai
commit
785c580032
49 changed files with 1839 additions and 581 deletions
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@ -32,7 +32,7 @@ class EP(LatentFunctionInference):
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pass
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def inference(self, kern, X, likelihood, Y, Y_metadata=None, Z=None):
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num_data, output_dim = X.shape
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num_data, output_dim = Y.shape
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assert output_dim ==1, "ep in 1D only (for now!)"
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K = kern.K(X)
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@ -56,7 +56,7 @@ class EPDTC(LatentFunctionInference):
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self._ep_approximation = None
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def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
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num_data, output_dim = X.shape
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num_data, output_dim = Y.shape
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assert output_dim ==1, "ep in 1D only (for now!)"
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Kmm = kern.K(Z)
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@ -9,6 +9,8 @@ import numpy as np
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from ...util.misc import param_to_array
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from . import LatentFunctionInference
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log_2_pi = np.log(2*np.pi)
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import logging, itertools
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logger = logging.getLogger('vardtc')
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class VarDTC(LatentFunctionInference):
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"""
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@ -180,11 +182,12 @@ class VarDTC(LatentFunctionInference):
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return post, log_marginal, grad_dict
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class VarDTCMissingData(LatentFunctionInference):
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const_jitter = 1e-6
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const_jitter = 1e-10
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def __init__(self, limit=1, inan=None):
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from ...util.caching import Cacher
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self._Y = Cacher(self._subarray_computations, limit)
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self._inan = inan
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if inan is not None: self._inan = ~inan
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else: self._inan = None
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pass
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def set_limit(self, limit):
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@ -205,21 +208,35 @@ class VarDTCMissingData(LatentFunctionInference):
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if self._inan is None:
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inan = np.isnan(Y)
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has_none = inan.any()
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self._inan = ~inan
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else:
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inan = self._inan
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has_none = True
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if has_none:
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from ...util.subarray_and_sorting import common_subarrays
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self._subarray_indices = []
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for v,ind in common_subarrays(inan, 1).iteritems():
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if not np.all(v):
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v = ~np.array(v, dtype=bool)
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ind = np.array(ind, dtype=int)
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if ind.size == Y.shape[1]:
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ind = slice(None)
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self._subarray_indices.append([v,ind])
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Ys = [Y[v, :][:, ind] for v, ind in self._subarray_indices]
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traces = [(y**2).sum() for y in Ys]
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#print "caching missing data slices, this can take several minutes depending on the number of unique dimensions of the data..."
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#csa = common_subarrays(inan, 1)
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size = Y.shape[1]
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#logger.info('preparing subarrays {:3.3%}'.format((i+1.)/size))
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Ys = []
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next_ten = [0.]
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count = itertools.count()
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for v, y in itertools.izip(inan.T, Y.T[:,:,None]):
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i = count.next()
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if ((i+1.)/size) >= next_ten[0]:
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logger.info('preparing subarrays {:>6.1%}'.format((i+1.)/size))
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next_ten[0] += .1
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Ys.append(y[v,:])
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next_ten = [0.]
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count = itertools.count()
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def trace(y):
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i = count.next()
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if ((i+1.)/size) >= next_ten[0]:
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logger.info('preparing traces {:>6.1%}'.format((i+1.)/size))
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next_ten[0] += .1
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y = y[inan[:,i],i:i+1]
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return np.einsum('ij,ij->', y,y)
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traces = [trace(Y) for _ in xrange(size)]
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return Ys, traces
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else:
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self._subarray_indices = [[slice(None),slice(None)]]
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@ -241,7 +258,6 @@ class VarDTCMissingData(LatentFunctionInference):
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beta_all = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
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het_noise = beta_all.size != 1
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import itertools
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num_inducing = Z.shape[0]
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dL_dpsi0_all = np.zeros(Y.shape[0])
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@ -261,22 +277,17 @@ class VarDTCMissingData(LatentFunctionInference):
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Lm = jitchol(Kmm)
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if uncertain_inputs: LmInv = dtrtri(Lm)
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VVT_factor_all = np.empty(Y.shape)
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full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1]
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if not full_VVT_factor:
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psi1V = np.dot(Y.T*beta_all, psi1_all).T
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for y, trYYT, [v, ind] in itertools.izip(Ys, traces, self._subarray_indices):
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if het_noise: beta = beta_all[ind]
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size = Y.shape[1]
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next_ten = 0
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for i, [y, v, trYYT] in enumerate(itertools.izip(Ys, self._inan.T, traces)):
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if ((i+1.)/size) >= next_ten:
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logger.info('inference {:> 6.1%}'.format((i+1.)/size))
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next_ten += .1
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if het_noise: beta = beta_all[i]
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else: beta = beta_all
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VVT_factor = (beta*y)
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try:
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VVT_factor_all[v, ind].flat = VVT_factor.flat
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except ValueError:
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mult = np.ravel_multi_index((v.nonzero()[0][:,None],ind[None,:]), VVT_factor_all.shape)
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VVT_factor_all.flat[mult] = VVT_factor
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output_dim = y.shape[1]
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VVT_factor = (y*beta)
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output_dim = 1#len(ind)
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psi0 = psi0_all[v]
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psi1 = psi1_all[v, :]
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@ -318,7 +329,6 @@ class VarDTCMissingData(LatentFunctionInference):
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VVT_factor, Cpsi1Vf, DBi_plus_BiPBi,
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psi1, het_noise, uncertain_inputs)
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#import ipdb;ipdb.set_trace()
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dL_dpsi0_all[v] += dL_dpsi0
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dL_dpsi1_all[v, :] += dL_dpsi1
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if uncertain_inputs:
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@ -335,19 +345,20 @@ class VarDTCMissingData(LatentFunctionInference):
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psi0, psi1, beta,
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data_fit, num_data, output_dim, trYYT, Y)
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if full_VVT_factor: woodbury_vector[:, ind] = Cpsi1Vf
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else:
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print 'foobar'
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tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
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tmp, _ = dpotrs(LB, tmp, lower=1)
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woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0]
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#if full_VVT_factor:
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woodbury_vector[:, i:i+1] = Cpsi1Vf
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#else:
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# print 'foobar'
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# tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
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# tmp, _ = dpotrs(LB, tmp, lower=1)
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# woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0]
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#import ipdb;ipdb.set_trace()
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Bi, _ = dpotri(LB, lower=1)
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symmetrify(Bi)
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Bi = -dpotri(LB, lower=1)[0]
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diag.add(Bi, 1)
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woodbury_inv_all[:, :, ind] = backsub_both_sides(Lm, Bi)[:,:,None]
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woodbury_inv_all[:, :, i:i+1] = backsub_both_sides(Lm, Bi)[:,:,None]
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dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
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@ -364,23 +375,6 @@ class VarDTCMissingData(LatentFunctionInference):
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'dL_dKnm':dL_dpsi1_all,
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'dL_dthetaL':dL_dthetaL}
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#get sufficient things for posterior prediction
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#TODO: do we really want to do this in the loop?
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#if not full_VVT_factor:
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# print 'foobar'
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# psi1V = np.dot(Y.T*beta_all, psi1_all).T
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# tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
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# tmp, _ = dpotrs(LB_all, tmp, lower=1)
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# woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
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#import ipdb;ipdb.set_trace()
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#Bi, _ = dpotri(LB_all, lower=1)
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#symmetrify(Bi)
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#Bi = -dpotri(LB_all, lower=1)[0]
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#from ...util import diag
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#diag.add(Bi, 1)
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#woodbury_inv = backsub_both_sides(Lm, Bi)
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post = Posterior(woodbury_inv=woodbury_inv_all, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
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return post, log_marginal, grad_dict
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