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[missing data] general implementation for subsetting data
This commit is contained in:
Max Zwiessele
parent
d93fac8c13
commit
fa7807ee6f
7 changed files with 329 additions and 108 deletions
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@ -62,7 +62,9 @@ class VarDTC(LatentFunctionInference):
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def get_VVTfactor(self, Y, prec):
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return Y * prec # TODO chache this, and make it effective
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def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
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def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None, dL_dKmm=None):
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_, output_dim = Y.shape
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uncertain_inputs = isinstance(X, VariationalPosterior)
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@ -84,8 +86,8 @@ class VarDTC(LatentFunctionInference):
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Kmm = kern.K(Z).copy()
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diag.add(Kmm, self.const_jitter)
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Lm = jitchol(Kmm)
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if Lm is None:
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Lm = jitchol(Kmm)
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# The rather complex computations of A, and the psi stats
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if uncertain_inputs:
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@ -100,7 +102,6 @@ class VarDTC(LatentFunctionInference):
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else:
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psi0 = kern.Kdiag(X)
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psi1 = kern.K(X, Z)
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psi2 = None
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if het_noise:
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tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))
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else:
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@ -122,11 +123,12 @@ class VarDTC(LatentFunctionInference):
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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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# Compute dL_dKmm
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dL_dKmm = backsub_both_sides(Lm, delit)
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if dL_dKmm is None:
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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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# Compute dL_dKmm
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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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@ -208,6 +210,7 @@ class VarDTCMissingData(LatentFunctionInference):
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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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@ -241,7 +244,7 @@ class VarDTCMissingData(LatentFunctionInference):
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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):
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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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@ -260,7 +263,7 @@ class VarDTCMissingData(LatentFunctionInference):
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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((num_inducing, num_inducing))
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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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@ -271,31 +274,31 @@ class VarDTCMissingData(LatentFunctionInference):
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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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Lm = jitchol(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 = Y.shape[1]
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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: psi2 = kern.psi2(Z, X[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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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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@ -330,11 +333,11 @@ class VarDTCMissingData(LatentFunctionInference):
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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 += dL_dpsi2
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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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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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@ -393,7 +396,6 @@ def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, C
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# subsume back into psi1 (==Kmn)
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dL_dpsi1 += 2.*np.dot(psi1, dL_dpsi2)
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dL_dpsi2 = None
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return dL_dpsi0, dL_dpsi1, dL_dpsi2
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