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MRD updates merge
- termination rule changes for SCG - oil flow updates
This commit is contained in:
Max Zwiessele
commit
50b7958051
5 changed files with 30 additions and 26 deletions
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@ -63,7 +63,7 @@ def GPLVM_oil_100(optimize=True):
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m.plot_latent(labels=m.data_labels)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300, plot=False):
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=20, max_f_eval=300, plot=False):
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data = GPy.util.datasets.oil()
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# create simple GP model
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@ -72,19 +72,19 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300, plot=False):
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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m.constrain('variance', logexp_clipped())
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m.constrain('length', logexp_clipped())
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m['lengt'] = 100.
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m.ensure_default_constraints()
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# optimize
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if optimize:
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m.constrain_fixed('noise', 1. / Y.var())
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m.constrain('variance', logexp_clipped())
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m['lengt'] = 1000
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m.unconstrain('noise'); m.constrain_fixed('noise', Y.var() / 100.)
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m.optimize('scg', messages=1, max_f_eval=150)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=max(80, max_f_eval))
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m.unconstrain('noise')
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m.constrain_positive('noise')
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m.optimize('scg', messages=1, max_f_eval=max(0, max_f_eval - 80))
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else:
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m.ensure_default_constraints()
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m.constrain('noise', logexp_clipped())
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m.optimize('scg', messages=1, max_f_eval=max_f_eval)
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if plot:
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y = m.likelihood.Y[0, :]
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@ -182,7 +182,7 @@ def bgplvm_simulation_matlab_compare():
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Y = sim_data['Y']
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S = sim_data['S']
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mu = sim_data['mu']
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M, [_, Q] = 20, mu.shape
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M, [_, Q] = 3, mu.shape
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from GPy.models import mrd
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from GPy import kern
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@ -192,7 +192,7 @@ def bgplvm_simulation_matlab_compare():
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
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# X=mu,
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# X_variance=S,
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_debug=True)
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_debug=False)
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m.ensure_default_constraints()
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m.auto_scale_factor = True
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m['noise'] = Y.var() / 100.
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@ -371,7 +371,7 @@ def brendan_faces():
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# optimize
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m.ensure_default_constraints()
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# m.optimize(messages=1, max_f_eval=10000)
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m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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@ -49,6 +49,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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function_eval = 1
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fnow = fold
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gradnew = gradf(x, *optargs) # Initial gradient.
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current_grad = np.dot(gradnew, gradnew)
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gradold = gradnew.copy()
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d = -gradnew # Initial search direction.
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success = True # Force calculation of directional derivs.
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@ -110,7 +111,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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iteration += 1
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if display:
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print '\r',
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print 'i: {0:>5g} f:{1:> 12e} b:{2:> 12e} |g|:{3:> 12e}'.format(iteration, fnow, beta, np.dot(gradnew, gradnew)),
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print 'i: {0:>5g} f:{1:> 12e} b:{2:> 12e} |g|:{3:> 12e}'.format(iteration, fnow, beta, current_grad),
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# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
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sys.stdout.flush()
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@ -125,8 +126,9 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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fold = fnew
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gradold = gradnew
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gradnew = gradf(x, *optargs)
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current_grad = np.dot(gradnew, gradnew)
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# If the gradient is zero then we are done.
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if (np.dot(gradnew, gradnew)) <= gtol:
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if current_grad <= gtol:
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status = 'converged'
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return x, flog, function_eval, status
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@ -136,14 +136,16 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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self._savedparams.append([self.f_call, self._get_params()])
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self._savedgradients.append([self.f_call, self._log_likelihood_gradients()])
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self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
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sf2 = self.scale_factor ** 2
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# sf2 = self.scale_factor ** 2
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if self.likelihood.is_heteroscedastic:
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A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
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else:
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A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
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B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
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C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5 * self.M * np.log(sf2))
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
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C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
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D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
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self._savedABCD.append([self.f_call, A, B, C, D])
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@ -273,7 +273,7 @@ class MRD(model):
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def _handle_plotting(self, fig_num, axes, plotf):
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if axes is None:
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fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3 * len(self.bgplvms)))
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fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3))
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for i, g in enumerate(self.bgplvms):
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if axes is None:
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ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
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@ -104,7 +104,7 @@ class sparse_GP(GP):
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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else:
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# tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
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# tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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@ -163,7 +163,7 @@ class sparse_GP(GP):
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else:
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# likelihood is not heterscedatic
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self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
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# self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
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# self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
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self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
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self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
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@ -177,7 +177,7 @@ class sparse_GP(GP):
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
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else:
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A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
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A = -0.5 * self.N * self.D * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
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# C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5 * self.M * np.log(sf2))
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