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[var dtc missing] deleted code for missing data inference
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Max Zwiessele
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1 changed files with 0 additions and 198 deletions
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@ -182,204 +182,6 @@ class VarDTC(LatentFunctionInference):
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post = Posterior(woodbury_inv=woodbury_inv, 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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class VarDTCMissingData(LatentFunctionInference):
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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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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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self._Y.limit = limit
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def __getstate__(self):
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# has to be overridden, as Cacher objects cannot be pickled.
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return self._Y.limit, self._inan
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def __setstate__(self, state):
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# has to be overridden, as Cacher objects cannot be pickled.
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from ...util.caching import Cacher
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self.limit = state[0]
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self._inan = state[1]
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self._Y = Cacher(self._subarray_computations, self.limit)
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def _subarray_computations(self, Y):
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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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inan = self._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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#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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return [Y], [(Y**2).sum()]
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def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None):
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if isinstance(X, VariationalPosterior):
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uncertain_inputs = True
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psi0_all = kern.psi0(Z, X)
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psi1_all = kern.psi1(Z, X)
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else:
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uncertain_inputs = False
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psi0_all = kern.Kdiag(X)
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psi1_all = kern.K(X, Z)
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Ys, traces = self._Y(Y)
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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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num_inducing = Z.shape[0]
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dL_dpsi0_all = np.zeros(Y.shape[0])
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dL_dpsi1_all = np.zeros((Y.shape[0], num_inducing))
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if uncertain_inputs:
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dL_dpsi2_all = np.zeros((Y.shape[0], num_inducing, num_inducing))
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dL_dR = 0
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woodbury_vector = np.zeros((num_inducing, Y.shape[1]))
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woodbury_inv_all = np.zeros((num_inducing, num_inducing, Y.shape[1]))
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dL_dKmm = 0
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log_marginal = 0
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Kmm = kern.K(Z).copy()
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diag.add(Kmm, self.const_jitter)
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#factor Kmm
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if Lm is None:
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Lm = jitchol(Kmm)
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if uncertain_inputs: LmInv = dtrtri(Lm)
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size = len(Ys)
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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 = (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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if uncertain_inputs:
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psi2 = kern.psi2(Z, X[v, :])
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else: psi2 = None
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num_data = psi1.shape[0]
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if uncertain_inputs:
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if het_noise: psi2_beta = psi2 * (beta.flatten().reshape(num_data, 1, 1)).sum(0)
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else: psi2_beta = psi2 * (beta)
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A = LmInv.dot(psi2_beta.dot(LmInv.T))
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else:
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if het_noise: tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))
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else: tmp = psi1 * (np.sqrt(beta))
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tmp, _ = dtrtrs(Lm, tmp.T, lower=1)
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A = tdot(tmp) #print A.sum()
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# factor B
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B = np.eye(num_inducing) + A
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LB = jitchol(B)
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psi1Vf = psi1.T.dot(VVT_factor)
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tmp, _ = dtrtrs(Lm, psi1Vf, lower=1, trans=0)
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_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, tmp, lower=1, trans=0)
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tmp, _ = dtrtrs(LB, _LBi_Lmi_psi1Vf, lower=1, trans=1)
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Cpsi1Vf, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
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# data fit and derivative of L w.r.t. Kmm
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delit = tdot(_LBi_Lmi_psi1Vf)
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data_fit = np.trace(delit)
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DBi_plus_BiPBi = backsub_both_sides(LB, output_dim * np.eye(num_inducing) + delit)
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delit = -0.5 * DBi_plus_BiPBi
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delit += -0.5 * B * output_dim
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delit += output_dim * np.eye(num_inducing)
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dL_dKmm += backsub_both_sides(Lm, delit)
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# derivatives of L w.r.t. psi
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dL_dpsi0, dL_dpsi1, dL_dpsi2 = _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm,
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VVT_factor, Cpsi1Vf, DBi_plus_BiPBi,
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psi1, het_noise, uncertain_inputs)
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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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dL_dpsi2_all[v, :, :] += dL_dpsi2
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# log marginal likelihood
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log_marginal += _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise,
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psi0, A, LB, trYYT, data_fit, VVT_factor)
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#put the gradients in the right places
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dL_dR += _compute_dL_dR(likelihood,
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het_noise, uncertain_inputs, LB,
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_LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A,
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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:
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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[:, :, 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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# gradients:
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if uncertain_inputs:
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grad_dict = {'dL_dKmm': dL_dKmm,
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'dL_dpsi0':dL_dpsi0_all,
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'dL_dpsi1':dL_dpsi1_all,
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'dL_dpsi2':dL_dpsi2_all,
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'dL_dthetaL':dL_dthetaL}
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else:
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grad_dict = {'dL_dKmm': dL_dKmm,
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'dL_dKdiag':dL_dpsi0_all,
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'dL_dKnm':dL_dpsi1_all,
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'dL_dthetaL':dL_dthetaL}
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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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def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, Cpsi1Vf, DBi_plus_BiPBi, psi1, het_noise, uncertain_inputs):
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dL_dpsi0 = -0.5 * output_dim * (beta[:,None] * np.ones([num_data, 1])).flatten()
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dL_dpsi1 = np.dot(VVT_factor, Cpsi1Vf.T)
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