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Moved transf_data to make data -1 or 1 from 0 or 1 for bernoulli

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with probit into the analytical moment match (but it 10% slower),
needs removing from epmixednoise
This commit is contained in:
Alan Saul 2013-10-22 15:30:56 +01:00
parent 5f9d7eb709
commit 7c9eda482c
2 changed files with 18 additions and 13 deletions
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7
GPy/likelihoods/ep.py
View file

@ -19,7 +19,6 @@ class EP(likelihood):
self.num_data, self.output_dim = self.data.shape self.num_data, self.output_dim = self.data.shape
self.is_heteroscedastic = True self.is_heteroscedastic = True
self.num_params = 0 self.num_params = 0
self._transf_data = self.noise_model._preprocess_values(data)
#Initial values - Likelihood approximation parameters: #Initial values - Likelihood approximation parameters:
#p(y|f) = t(f|tau_tilde,v_tilde) #p(y|f) = t(f|tau_tilde,v_tilde)
@ -134,7 +133,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
@ -233,7 +232,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
@ -336,7 +335,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])

24
GPy/likelihoods/noise_models/bernoulli_noise.py
View file

@ -45,18 +45,24 @@ class Bernoulli(NoiseDistribution):
:param tau_i: precision of the cavity distribution (float) :param tau_i: precision of the cavity distribution (float)
:param v_i: mean/variance of the cavity distribution (float) :param v_i: mean/variance of the cavity distribution (float)
""" """
if data_i == 1:
sign = 1.
elif data_i == 0:
sign = -1
else:
raise ValueError("bad value for Bernouilli observation (0,1)")
if isinstance(self.gp_link,gp_transformations.Probit): if isinstance(self.gp_link,gp_transformations.Probit):
z = data_i*v_i/np.sqrt(tau_i**2 + tau_i) z = sign*v_i/np.sqrt(tau_i**2 + tau_i)
Z_hat = std_norm_cdf(z) Z_hat = std_norm_cdf(z)
phi = std_norm_pdf(z) phi = std_norm_pdf(z)
mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i)) mu_hat = v_i/tau_i + sign*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat) sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
elif isinstance(self.gp_link,gp_transformations.Heaviside): elif isinstance(self.gp_link,gp_transformations.Heaviside):
a = data_i*v_i/np.sqrt(tau_i) a = sign*v_i/np.sqrt(tau_i)
Z_hat = std_norm_cdf(a) Z_hat = std_norm_cdf(a)
N = std_norm_pdf(a) N = std_norm_pdf(a)
mu_hat = v_i/tau_i + data_i*N/Z_hat/np.sqrt(tau_i) mu_hat = v_i/tau_i + sign*N/Z_hat/np.sqrt(tau_i)
sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i
if np.any(np.isnan([Z_hat, mu_hat, sigma2_hat])): if np.any(np.isnan([Z_hat, mu_hat, sigma2_hat])):
stop stop
@ -97,7 +103,7 @@ class Bernoulli(NoiseDistribution):
.. Note: .. Note:
Each y_i must be in {0,1} Each y_i must be in {0,1}
""" """
assert np.asarray(link_f).shape == np.asarray(y).shape assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
objective = (link_f**y) * ((1.-link_f)**(1.-y)) objective = (link_f**y) * ((1.-link_f)**(1.-y))
return np.exp(np.sum(np.log(objective))) return np.exp(np.sum(np.log(objective)))
@ -116,7 +122,7 @@ class Bernoulli(NoiseDistribution):
:returns: log likelihood evaluated at points link(f) :returns: log likelihood evaluated at points link(f)
:rtype: float :rtype: float
""" """
assert np.asarray(link_f).shape == np.asarray(y).shape assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
#objective = y*np.log(link_f) + (1.-y)*np.log(link_f) #objective = y*np.log(link_f) + (1.-y)*np.log(link_f)
objective = np.where(y==1, np.log(link_f), np.log(1-link_f)) objective = np.where(y==1, np.log(link_f), np.log(1-link_f))
return np.sum(objective) return np.sum(objective)
@ -136,7 +142,7 @@ class Bernoulli(NoiseDistribution):
:returns: gradient of log likelihood evaluated at points link(f) :returns: gradient of log likelihood evaluated at points link(f)
:rtype: Nx1 array :rtype: Nx1 array
""" """
assert np.asarray(link_f).shape == np.asarray(y).shape assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
grad = (y/link_f) - (1.-y)/(1-link_f) grad = (y/link_f) - (1.-y)/(1-link_f)
return grad return grad
@ -161,7 +167,7 @@ class Bernoulli(NoiseDistribution):
Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i)) (the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
""" """
assert np.asarray(link_f).shape == np.asarray(y).shape assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d2logpdf_dlink2 = -y/(link_f**2) - (1-y)/((1-link_f)**2) d2logpdf_dlink2 = -y/(link_f**2) - (1-y)/((1-link_f)**2)
return d2logpdf_dlink2 return d2logpdf_dlink2
@ -180,7 +186,7 @@ class Bernoulli(NoiseDistribution):
:returns: third derivative of log likelihood evaluated at points link(f) :returns: third derivative of log likelihood evaluated at points link(f)
:rtype: Nx1 array :rtype: Nx1 array
""" """
assert np.asarray(link_f).shape == np.asarray(y).shape assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d3logpdf_dlink3 = 2*(y/(link_f**3) - (1-y)/((1-link_f)**3)) d3logpdf_dlink3 = 2*(y/(link_f**3) - (1-y)/((1-link_f)**3))
return d3logpdf_dlink3 return d3logpdf_dlink3