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Adding weibull likelihood, requires 'extra_data' to be passed to likelihood, i.e. the censoring information

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Alan Saul 2013-04-10 15:43:31 +01:00
parent 65481d7a73
commit 9bbb11b825
2 changed files with 104 additions and 19 deletions
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24
python/likelihoods/Laplace.py
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@ -30,7 +30,7 @@ def pddet(A):
class Laplace(likelihood):
"""Laplace approximation to a posterior"""
def __init__(self, data, likelihood_function, rasm=True):
def __init__(self, data, likelihood_function, extra_data=None, rasm=True):
"""
Laplace Approximation
@ -44,13 +44,15 @@ class Laplace(likelihood):
Arguments
---------
:data: @todo
:data: array of data the likelihood function is approximating
:likelihood_function: likelihood function - subclass of likelihood_function
:extra_data: additional data used by some likelihood functions, for example survival likelihoods need censoring data
:rasm: Flag of whether to use rasmussens numerically stable mode finding or simple ncg optimisation
"""
self.data = data
self.likelihood_function = likelihood_function
self.extra_data = extra_data
self.rasm = rasm
#Inital values
@ -179,7 +181,7 @@ class Laplace(likelihood):
self.f_hat = self.ncg_mode(K)
#At this point get the hessian matrix
self.W = -np.diag(self.likelihood_function.link_hess(self.data, self.f_hat))
self.W = -np.diag(self.likelihood_function.link_hess(self.data, self.f_hat, extra_data=self.extra_data))
if not self.likelihood_function.log_concave:
self.W[self.W < 0] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
@ -194,7 +196,7 @@ class Laplace(likelihood):
Ki_W_i = self.K - mdot(self.K, self.W_12, self.Bi, self.W_12, self.K)
self.ln_Ki_W_i_det = np.linalg.det(Ki_W_i)
b = np.dot(self.W, self.f_hat) + self.likelihood_function.link_grad(self.data, self.f_hat)[:, None]
b = np.dot(self.W, self.f_hat) + self.likelihood_function.link_grad(self.data, self.f_hat, extra_data=self.extra_data)[:, None]
solve_chol = cho_solve((self.B_chol, True), mdot(self.W_12, (K, b)))
a = b - mdot(self.W_12, solve_chol)
self.f_Ki_f = np.dot(self.f_hat.T, a)
@ -203,7 +205,7 @@ class Laplace(likelihood):
self.ln_z_hat = (- 0.5*self.f_Ki_f
- 0.5*self.ln_K_det
+ 0.5*self.ln_Ki_W_i_det
+ self.likelihood_function.link_function(self.data, self.f_hat)
+ self.likelihood_function.link_function(self.data, self.f_hat, extra_data=self.extra_data)
)
return self._compute_GP_variables()
@ -236,16 +238,16 @@ class Laplace(likelihood):
#FIXME: Can we get rid of this horrible reshaping?
#ONLY WORKS FOR 1D DATA
def obj(f):
res = -1 * (self.likelihood_function.link_function(self.data[:, 0], f) - 0.5 * np.dot(f.T, np.dot(self.Ki, f))
res = -1 * (self.likelihood_function.link_function(self.data[:, 0], f, extra_data=self.extra_data) - 0.5 * np.dot(f.T, np.dot(self.Ki, f))
+ self.NORMAL_CONST)
return float(res)
def obj_grad(f):
res = -1 * (self.likelihood_function.link_grad(self.data[:, 0], f) - np.dot(self.Ki, f))
res = -1 * (self.likelihood_function.link_grad(self.data[:, 0], f, extra_data=self.extra_data) - np.dot(self.Ki, f))
return np.squeeze(res)
def obj_hess(f):
res = -1 * (--np.diag(self.likelihood_function.link_hess(self.data[:, 0], f)) - self.Ki)
res = -1 * (--np.diag(self.likelihood_function.link_hess(self.data[:, 0], f, extra_data=self.extra_data)) - self.Ki)
return np.squeeze(res)
f_hat = sp.optimize.fmin_ncg(obj, f, fprime=obj_grad, fhess=obj_hess)
@ -267,7 +269,7 @@ class Laplace(likelihood):
def obj(a, f):
#Careful of shape of data!
return -0.5*np.dot(a.T, f) + self.likelihood_function.link_function(self.data, f)
return -0.5*np.dot(a.T, f) + self.likelihood_function.link_function(self.data, f, extra_data=self.extra_data)
difference = np.inf
epsilon = 1e-6
@ -276,7 +278,7 @@ class Laplace(likelihood):
i = 0
while difference > epsilon and i < MAX_ITER and rs < MAX_RESTART:
f_old = f.copy()
W = -np.diag(self.likelihood_function.link_hess(self.data, f))
W = -np.diag(self.likelihood_function.link_hess(self.data, f, extra_data=self.extra_data))
if not self.likelihood_function.log_concave:
W[W < 0] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
@ -285,7 +287,7 @@ class Laplace(likelihood):
B, L, W_12 = self._compute_B_statistics(K, W)
W_f = np.dot(W, f)
grad = self.likelihood_function.link_grad(self.data, f)[:, None]
grad = self.likelihood_function.link_grad(self.data, f, extra_data=self.extra_data)[:, None]
#Find K_i_f
b = W_f + grad