mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-15 06:52:39 +02:00
epsilon and power_ep now are parameters of update_likelihood.
This commit is contained in:
Ricardo
parent
c8fec98071
commit
a51af5b8c4
5 changed files with 62 additions and 30 deletions
|
|
@ -29,7 +29,7 @@ class FITC(SparseGP):
|
|||
SparseGP.__init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False)
|
||||
assert self.output_dim == 1, "FITC model is not defined for handling multiple outputs"
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
def update_likelihood_approximation(self, **kwargs):
|
||||
"""
|
||||
Approximates a non-Gaussian likelihood using Expectation Propagation
|
||||
|
||||
|
|
@ -37,7 +37,7 @@ class FITC(SparseGP):
|
|||
this function does nothing
|
||||
"""
|
||||
self.likelihood.restart()
|
||||
self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0, **kwargs)
|
||||
self._set_params(self._get_params())
|
||||
|
||||
def _compute_kernel_matrices(self):
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ class GP(GPBase):
|
|||
def _get_param_names(self):
|
||||
return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
def update_likelihood_approximation(self, **kwargs):
|
||||
"""
|
||||
Approximates a non-gaussian likelihood using Expectation Propagation
|
||||
|
||||
|
|
@ -70,7 +70,7 @@ class GP(GPBase):
|
|||
this function does nothing
|
||||
"""
|
||||
self.likelihood.restart()
|
||||
self.likelihood.fit_full(self.kern.K(self.X))
|
||||
self.likelihood.fit_full(self.kern.K(self.X), **kwargs)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
def _model_fit_term(self):
|
||||
|
|
|
|||
|
|
@ -538,22 +538,16 @@ class Model(Parameterized):
|
|||
return k.variances
|
||||
|
||||
|
||||
def pseudo_EM(self, epsilon=.1, **kwargs):
|
||||
def pseudo_EM(self, stop_crit=.1, **kwargs):
|
||||
"""
|
||||
TODO: Should this not bein the GP class?
|
||||
EM - like algorithm for Expectation Propagation and Laplace approximation
|
||||
|
||||
kwargs are passed to the optimize function. They can be:
|
||||
:stop_crit: convergence criterion
|
||||
:type stop_crit: float
|
||||
|
||||
:epsilon: convergence criterion
|
||||
:max_f_eval: maximum number of function evaluations
|
||||
:messages: whether to display during optimisation
|
||||
:param optimzer: whice optimizer to use (defaults to self.preferred optimizer)
|
||||
:type optimzer: string TODO: valid strings?
|
||||
|
||||
"""
|
||||
..Note: kwargs are passed to update_likelihood and optimize functions. """
|
||||
assert isinstance(self.likelihood, likelihoods.EP) or isinstance(self.likelihood, likelihoods.EP_Mixed_Noise), "pseudo_EM is only available for EP likelihoods"
|
||||
ll_change = epsilon + 1.
|
||||
ll_change = stop_crit + 1.
|
||||
iteration = 0
|
||||
last_ll = -np.inf
|
||||
|
||||
|
|
@ -561,10 +555,25 @@ class Model(Parameterized):
|
|||
alpha = 0
|
||||
stop = False
|
||||
|
||||
#Handle **kwargs
|
||||
ep_args = {}
|
||||
for arg in kwargs.keys():
|
||||
if arg in ('epsilon','power_ep'):
|
||||
ep_args[arg] = kwargs[arg]
|
||||
del kwargs[arg]
|
||||
|
||||
while not stop:
|
||||
last_approximation = self.likelihood.copy()
|
||||
last_params = self._get_params()
|
||||
self.update_likelihood_approximation()
|
||||
if len(ep_args) == 2:
|
||||
self.update_likelihood_approximation(epsilon=ep_args['epsilon'],power_ep=ep_args['power_ep'])
|
||||
elif len(ep_args) == 1:
|
||||
if ep_args.keys()[0] == 'epsilon':
|
||||
self.update_likelihood_approximation(epsilon=ep_args['epsilon'])
|
||||
elif ep_args.keys()[0] == 'power_ep':
|
||||
self.update_likelihood_approximation(power_ep=ep_args['power_ep'])
|
||||
else:
|
||||
self.update_likelihood_approximation()
|
||||
new_ll = self.log_likelihood()
|
||||
ll_change = new_ll - last_ll
|
||||
|
||||
|
|
@ -576,7 +585,7 @@ class Model(Parameterized):
|
|||
else:
|
||||
self.optimize(**kwargs)
|
||||
last_ll = self.log_likelihood()
|
||||
if ll_change < epsilon:
|
||||
if ll_change < stop_crit:
|
||||
stop = True
|
||||
iteration += 1
|
||||
if stop:
|
||||
|
|
|
|||
|
|
@ -215,7 +215,7 @@ class SparseGP(GPBase):
|
|||
#def _get_print_names(self):
|
||||
# return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
def update_likelihood_approximation(self, **kwargs):
|
||||
"""
|
||||
Approximates a non-gaussian likelihood using Expectation Propagation
|
||||
|
||||
|
|
@ -229,10 +229,10 @@ class SparseGP(GPBase):
|
|||
Kmmi = tdot(Lmi.T)
|
||||
diag_tr_psi2Kmmi = np.array([np.trace(psi2_Kmmi) for psi2_Kmmi in np.dot(self.psi2, Kmmi)])
|
||||
|
||||
self.likelihood.fit_FITC(self.Kmm, self.psi1.T, diag_tr_psi2Kmmi) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
|
||||
self.likelihood.fit_FITC(self.Kmm, self.psi1.T, diag_tr_psi2Kmmi, **kwargs) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
|
||||
# raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
|
||||
else:
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1.T)
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1.T, **kwargs)
|
||||
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
|
|
|
|||
|
|
@ -4,18 +4,17 @@ from ..util.linalg import pdinv,mdot,jitchol,chol_inv,DSYR,tdot,dtrtrs
|
|||
from likelihood import likelihood
|
||||
|
||||
class EP(likelihood):
|
||||
def __init__(self,data,noise_model,epsilon=1e-3,power_ep=[1.,1.]):
|
||||
def __init__(self,data,noise_model):
|
||||
"""
|
||||
Expectation Propagation
|
||||
|
||||
Arguments
|
||||
---------
|
||||
epsilon : Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
|
||||
noise_model : a likelihood function (see likelihood_functions.py)
|
||||
:param data: data to model
|
||||
:type data: numpy array
|
||||
:param noise_model: noise distribution
|
||||
:type noise_model: A GPy noise model
|
||||
|
||||
"""
|
||||
self.noise_model = noise_model
|
||||
self.epsilon = epsilon
|
||||
self.eta, self.delta = power_ep
|
||||
self.data = data
|
||||
self.N, self.output_dim = self.data.shape
|
||||
self.is_heteroscedastic = True
|
||||
|
|
@ -87,11 +86,19 @@ class EP(likelihood):
|
|||
self.VVT_factor = self.V
|
||||
self.trYYT = np.trace(self.YYT)
|
||||
|
||||
def fit_full(self,K):
|
||||
def fit_full(self, K, epsilon=1e-3,power_ep=[1.,1.]):
|
||||
"""
|
||||
The expectation-propagation algorithm.
|
||||
For nomenclature see Rasmussen & Williams 2006.
|
||||
|
||||
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
|
||||
:type epsilon: float
|
||||
:param power_ep: Power EP parameters
|
||||
:type power_ep: list of floats
|
||||
"""
|
||||
self.epsilon = epsilon
|
||||
self.eta, self.delta = power_ep
|
||||
|
||||
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
|
||||
mu = np.zeros(self.N)
|
||||
Sigma = K.copy()
|
||||
|
|
@ -149,11 +156,19 @@ class EP(likelihood):
|
|||
|
||||
return self._compute_GP_variables()
|
||||
|
||||
def fit_DTC(self, Kmm, Kmn):
|
||||
def fit_DTC(self, Kmm, Kmn, epsilon=1e-3,power_ep=[1.,1.]):
|
||||
"""
|
||||
The expectation-propagation algorithm with sparse pseudo-input.
|
||||
For nomenclature see ... 2013.
|
||||
|
||||
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
|
||||
:type epsilon: float
|
||||
:param power_ep: Power EP parameters
|
||||
:type power_ep: list of floats
|
||||
"""
|
||||
self.epsilon = epsilon
|
||||
self.eta, self.delta = power_ep
|
||||
|
||||
num_inducing = Kmm.shape[0]
|
||||
|
||||
#TODO: this doesn't work with uncertain inputs!
|
||||
|
|
@ -245,11 +260,19 @@ class EP(likelihood):
|
|||
|
||||
self._compute_GP_variables()
|
||||
|
||||
def fit_FITC(self, Kmm, Kmn, Knn_diag):
|
||||
def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):
|
||||
"""
|
||||
The expectation-propagation algorithm with sparse pseudo-input.
|
||||
For nomenclature see Naish-Guzman and Holden, 2008.
|
||||
|
||||
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
|
||||
:type epsilon: float
|
||||
:param power_ep: Power EP parameters
|
||||
:type power_ep: list of floats
|
||||
"""
|
||||
self.epsilon = epsilon
|
||||
self.eta, self.delta = power_ep
|
||||
|
||||
num_inducing = Kmm.shape[0]
|
||||
|
||||
"""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue