mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-08 03:22:38 +02:00
Merge branch 'saul_merge' into devel
This commit is contained in:
Alan Saul
commit
cbb558d751
9 changed files with 797 additions and 317 deletions
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@ -50,19 +50,19 @@ class InferenceMethodList(LatentFunctionInference, list):
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def on_optimization_end(self):
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for inf in self:
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inf.on_optimization_end()
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def __getstate__(self):
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state = []
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for inf in self:
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state.append(inf)
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return state
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def __setstate__(self, state):
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for inf in state:
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self.append(inf)
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from exact_gaussian_inference import ExactGaussianInference
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from laplace import Laplace
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from laplace import Laplace, LaplaceBlock
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from GPy.inference.latent_function_inference.var_dtc import VarDTC
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from expectation_propagation import EP
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from expectation_propagation_dtc import EPDTC
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@ -78,9 +78,9 @@ from svgp import SVGP
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# class EMLikeLatentFunctionInference(LatentFunctionInference):
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# def update_approximation(self):
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# """
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# This function gets called when the
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# This function gets called when the
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# """
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#
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#
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# def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
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# """
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# Do inference on the latent functions given a covariance function `kern`,
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@ -88,7 +88,7 @@ from svgp import SVGP
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# Additional metadata for the outputs `Y` can be given in `Y_metadata`.
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# """
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# raise NotImplementedError, "Abstract base class for full inference"
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#
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#
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# class VariationalLatentFunctionInference(LatentFunctionInference):
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# def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
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# """
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@ -43,28 +43,31 @@ class Laplace(LatentFunctionInference):
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"""
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Returns a Posterior class containing essential quantities of the posterior
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"""
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# Compute K
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K = kern.K(X)
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#Find mode
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if self.bad_fhat or self.first_run:
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Ki_f_init = np.zeros_like(Y)
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first_run = False
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self.first_run = False
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else:
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Ki_f_init = self._previous_Ki_fhat
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Ki_f_init = np.zeros_like(Y)# FIXME: take this out
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f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
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self.f_hat = f_hat
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self.Ki_fhat = Ki_fhat
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self.K = K.copy()
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#self.Ki_fhat = Ki_fhat
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#self.K = K.copy()
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#Compute hessian and other variables at mode
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log_marginal, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
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self._previous_Ki_fhat = Ki_fhat.copy()
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return Posterior(woodbury_vector=Ki_fhat, woodbury_inv=woodbury_inv, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL}
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def rasm_mode(self, K, Y, likelihood, Ki_f_init, Y_metadata=None):
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def rasm_mode(self, K, Y, likelihood, Ki_f_init, Y_metadata=None, *args, **kwargs):
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"""
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Rasmussen's numerically stable mode finding
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For nomenclature see Rasmussen & Williams 2006
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@ -89,7 +92,12 @@ class Laplace(LatentFunctionInference):
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#define the objective function (to be maximised)
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def obj(Ki_f, f):
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return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + np.sum(likelihood.logpdf(f, Y, Y_metadata=Y_metadata))
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ll = -0.5*np.sum(np.dot(Ki_f.T, f)) + np.sum(likelihood.logpdf(f, Y, Y_metadata=Y_metadata))
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if np.isnan(ll):
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return -np.inf
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else:
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return ll
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difference = np.inf
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iteration = 0
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@ -104,7 +112,7 @@ class Laplace(LatentFunctionInference):
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W_f = W*f
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b = W_f + grad # R+W p46 line 6.
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W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave)
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W12BiW12, _, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave, *args, **kwargs)
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W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b))
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#Work out the DIRECTION that we want to move in, but don't choose the stepsize yet
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@ -121,7 +129,9 @@ class Laplace(LatentFunctionInference):
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step = optimize.brent(inner_obj, tol=1e-4, maxiter=12)
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Ki_f_new = Ki_f + step*dKi_f
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f_new = np.dot(K, Ki_f_new)
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#print "new {} vs old {}".format(obj(Ki_f_new, f_new), obj(Ki_f, f))
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if obj(Ki_f_new, f_new) < obj(Ki_f, f):
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raise ValueError("Shouldn't happen, brent optimization failing")
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difference = np.abs(np.sum(f_new - f)) + np.abs(np.sum(Ki_f_new - Ki_f))
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Ki_f = Ki_f_new
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f = f_new
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@ -152,14 +162,10 @@ class Laplace(LatentFunctionInference):
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if np.any(np.isnan(W)):
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raise ValueError('One or more element(s) of W is NaN')
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K_Wi_i, L, LiW12 = self._compute_B_statistics(K, W, likelihood.log_concave)
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#compute vital matrices
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C = np.dot(LiW12, K)
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Ki_W_i = K - C.T.dot(C)
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K_Wi_i, logdet_I_KW, I_KW_i, Ki_W_i = self._compute_B_statistics(K, W, likelihood.log_concave)
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#compute the log marginal
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log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + np.sum(likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata)) - np.sum(np.log(np.diag(L)))
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log_marginal = -0.5*np.sum(np.dot(Ki_f.T, f_hat)) + np.sum(likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata)) - 0.5*logdet_I_KW
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# Compute matrices for derivatives
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dW_df = -likelihood.d3logpdf_df3(f_hat, Y, Y_metadata=Y_metadata) # -d3lik_d3fhat
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@ -196,23 +202,23 @@ class Laplace(LatentFunctionInference):
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dL_dthetaL = np.zeros(num_params)
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for thetaL_i in range(num_params):
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#Explicit
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dL_dthetaL_exp = ( np.sum(dlik_dthetaL[thetaL_i])
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dL_dthetaL_exp = ( np.sum(dlik_dthetaL[thetaL_i,:, :])
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# The + comes from the fact that dlik_hess_dthetaL == -dW_dthetaL
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+ 0.5*np.sum(np.diag(Ki_W_i).flatten()*dlik_hess_dthetaL[:, thetaL_i].flatten())
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+ 0.5*np.sum(np.diag(Ki_W_i)*np.squeeze(dlik_hess_dthetaL[thetaL_i, :, :]))
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)
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#Implicit
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dfhat_dthetaL = mdot(I_KW_i, K, dlik_grad_dthetaL[:, thetaL_i])
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#dfhat_dthetaL = mdot(Ki_W_i, dlik_grad_dthetaL[:, thetaL_i])
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dfhat_dthetaL = mdot(I_KW_i, K, dlik_grad_dthetaL[thetaL_i, :, :])
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#dfhat_dthetaL = mdot(Ki_W_i, dlik_grad_dthetaL[thetaL_i, :, :])
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dL_dthetaL_imp = np.dot(dL_dfhat.T, dfhat_dthetaL)
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dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
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dL_dthetaL[thetaL_i] = np.sum(dL_dthetaL_exp + dL_dthetaL_imp)
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else:
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dL_dthetaL = np.zeros(likelihood.size)
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return log_marginal, K_Wi_i, dL_dK, dL_dthetaL
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def _compute_B_statistics(self, K, W, log_concave):
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def _compute_B_statistics(self, K, W, log_concave, *args, **kwargs):
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"""
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Rasmussen suggests the use of a numerically stable positive definite matrix B
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Which has a positive diagonal elements and can be easily inverted
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@ -225,7 +231,7 @@ class Laplace(LatentFunctionInference):
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"""
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if not log_concave:
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#print "Under 1e-10: {}".format(np.sum(W < 1e-6))
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W[W<1e-6] = 1e-6
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W = np.clip(W, 1e-6, 1e+30)
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# NOTE: when setting a parameter inside parameters_changed it will allways come to closed update circles!!!
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#W.__setitem__(W < 1e-6, 1e-6, update=False) # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
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# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
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@ -247,5 +253,160 @@ class Laplace(LatentFunctionInference):
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#K_Wi_i_2 , _= dpotri(L2)
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#symmetrify(K_Wi_i_2)
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return K_Wi_i, L, LiW12
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#compute vital matrices
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C = np.dot(LiW12, K)
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Ki_W_i = K - C.T.dot(C)
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I_KW_i = np.eye(K.shape[0]) - np.dot(K, K_Wi_i)
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logdet_I_KW = 2*np.sum(np.log(np.diag(L)))
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return K_Wi_i, logdet_I_KW, I_KW_i, Ki_W_i
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class LaplaceBlock(Laplace):
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def rasm_mode(self, K, Y, likelihood, Ki_f_init, Y_metadata=None, *args, **kwargs):
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Ki_f = Ki_f_init.copy()
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f = np.dot(K, Ki_f)
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#define the objective function (to be maximised)
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def obj(Ki_f, f):
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ll = -0.5*np.dot(Ki_f.T, f) + np.sum(likelihood.logpdf_sum(f, Y, Y_metadata=Y_metadata))
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if np.isnan(ll):
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return -np.inf
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else:
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return ll
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difference = np.inf
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iteration = 0
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I = np.eye(K.shape[0])
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while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter:
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W = -likelihood.d2logpdf_df2(f, Y, Y_metadata=Y_metadata)
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W[np.diag_indices_from(W)] = np.clip(np.diag(W), 1e-6, 1e+30)
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W_f = np.dot(W, f)
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grad = likelihood.dlogpdf_df(f, Y, Y_metadata=Y_metadata)
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b = W_f + grad # R+W p46 line 6.
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K_Wi_i, _, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave, *args, **kwargs)
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#Work out the DIRECTION that we want to move in, but don't choose the stepsize yet
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#a = (I - (K+Wi)i*K)*b
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full_step_Ki_f = np.dot(I - np.dot(K_Wi_i, K), b)
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dKi_f = full_step_Ki_f - Ki_f
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#define an objective for the line search (minimize this one)
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def inner_obj(step_size):
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Ki_f_trial = Ki_f + step_size*dKi_f
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f_trial = np.dot(K, Ki_f_trial)
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return -obj(Ki_f_trial, f_trial)
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#use scipy for the line search, the compute new values of f, Ki_f
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step = optimize.brent(inner_obj, tol=1e-4, maxiter=12)
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Ki_f_new = Ki_f + step*dKi_f
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f_new = np.dot(K, Ki_f_new)
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difference = np.abs(np.sum(f_new - f)) + np.abs(np.sum(Ki_f_new - Ki_f))
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Ki_f = Ki_f_new
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f = f_new
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iteration += 1
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#Warn of bad fits
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if difference > self._mode_finding_tolerance:
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if not self.bad_fhat:
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warnings.warn("Not perfect f_hat fit difference: {}".format(difference))
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self._previous_Ki_fhat = np.zeros_like(Y)
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self.bad_fhat = True
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elif self.bad_fhat:
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self.bad_fhat = False
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warnings.warn("f_hat now fine again")
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if iteration > self._mode_finding_max_iter:
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warnings.warn("didn't find the best")
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return f, Ki_f
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def mode_computations(self, f_hat, Ki_f, K, Y, likelihood, kern, Y_metadata):
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#At this point get the hessian matrix (or vector as W is diagonal)
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W = -likelihood.d2logpdf_df2(f_hat, Y, Y_metadata=Y_metadata)
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W[np.diag_indices_from(W)] = np.clip(np.diag(W), 1e-6, 1e+30)
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K_Wi_i, log_B_det, I_KW_i, Ki_W_i = self._compute_B_statistics(K, W, likelihood.log_concave)
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#compute the log marginal
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#FIXME: The derterminant should be output_dim*0.5 I think, gradients may now no longer check
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log_marginal = -0.5*np.dot(f_hat.T, Ki_f) + np.sum(likelihood.logpdf_sum(f_hat, Y, Y_metadata=Y_metadata)) - 0.5*log_B_det
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#Compute vival matrices for derivatives
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dW_df = -likelihood.d3logpdf_df3(f_hat, Y, Y_metadata=Y_metadata) # -d3lik_d3fhat
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#dL_dfhat = np.zeros((f_hat.shape[0]))
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#for i in range(f_hat.shape[0]):
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#dL_dfhat[i] = -0.5*np.trace(np.dot(Ki_W_i, dW_df[:,:,i]))
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dL_dfhat = -0.5*np.einsum('ij,ijk->k', Ki_W_i, dW_df)
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woodbury_vector = likelihood.dlogpdf_df(f_hat, Y, Y_metadata=Y_metadata)
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####################
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#compute dL_dK#
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####################
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if kern.size > 0 and not kern.is_fixed:
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#Explicit
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explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
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#Implicit
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implicit_part = woodbury_vector.dot(dL_dfhat[None,:]).dot(I_KW_i)
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#implicit_part = Ki_f.dot(dL_dfhat[None,:]).dot(I_KW_i)
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dL_dK = explicit_part + implicit_part
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else:
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dL_dK = np.zeros_like(K)
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####################
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#compute dL_dthetaL#
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####################
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if likelihood.size > 0 and not likelihood.is_fixed:
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raise NotImplementedError
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else:
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dL_dthetaL = np.zeros(likelihood.size)
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#self.K_Wi_i = K_Wi_i
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#self.Ki_W_i = Ki_W_i
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#self.W = W
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#self.K = K
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#self.dL_dfhat = dL_dfhat
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#self.explicit_part = explicit_part
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#self.implicit_part = implicit_part
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return log_marginal, K_Wi_i, dL_dK, dL_dthetaL
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def _compute_B_statistics(self, K, W, log_concave, *args, **kwargs):
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"""
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Rasmussen suggests the use of a numerically stable positive definite matrix B
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Which has a positive diagonal element and can be easyily inverted
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:param K: Prior Covariance matrix evaluated at locations X
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:type K: NxN matrix
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:param W: Negative hessian at a point (diagonal matrix)
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:type W: Vector of diagonal values of hessian (1xN)
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:returns: (K_Wi_i, L_B, not_provided)
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"""
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#w = GPy.util.diag.view(W)
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#W[:] = np.where(w<1e-6, 1e-6, w)
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#B = I + KW
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B = np.eye(K.shape[0]) + np.dot(K, W)
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#Bi, L, Li, logdetB = pdinv(B)
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Bi = np.linalg.inv(B)
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#K_Wi_i = np.eye(K.shape[0]) - mdot(W, Bi, K)
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K_Wi_i = np.dot(W, Bi)
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#self.K_Wi_i_brute = np.linalg.inv(K + np.linalg.inv(W))
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#self.B = B
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#self.Bi = Bi
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Ki_W_i = np.dot(Bi, K)
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sign, logdetB = np.linalg.slogdet(B)
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return K_Wi_i, sign*logdetB, Bi, Ki_W_i
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@ -34,7 +34,9 @@ class Gaussian(Likelihood):
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if gp_link is None:
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gp_link = link_functions.Identity()
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assert isinstance(gp_link, link_functions.Identity), "the likelihood only implemented for the identity link"
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if not isinstance(gp_link, link_functions.Identity):
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print "Warning, Exact inference is not implemeted for non-identity link functions,\
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if you are not already, ensure Laplace inference_method is used"
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super(Gaussian, self).__init__(gp_link, name=name)
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@ -263,16 +265,19 @@ class Gaussian(Likelihood):
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return d2logpdf_dlink2_dvar
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def dlogpdf_link_dtheta(self, f, y, Y_metadata=None):
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dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, Y_metadata=Y_metadata)
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return dlogpdf_dvar
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dlogpdf_dtheta = np.zeros((self.size, f.shape[0], f.shape[1]))
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dlogpdf_dtheta[0,:,:] = self.dlogpdf_link_dvar(f, y, Y_metadata=Y_metadata)
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return dlogpdf_dtheta
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def dlogpdf_dlink_dtheta(self, f, y, Y_metadata=None):
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dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, Y_metadata=Y_metadata)
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return dlogpdf_dlink_dvar
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dlogpdf_dlink_dtheta = np.zeros((self.size, f.shape[0], f.shape[1]))
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dlogpdf_dlink_dtheta[0, :, :]= self.dlogpdf_dlink_dvar(f, y, Y_metadata=Y_metadata)
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return dlogpdf_dlink_dtheta
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def d2logpdf_dlink2_dtheta(self, f, y, Y_metadata=None):
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d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, Y_metadata=Y_metadata)
|
||||
return d2logpdf_dlink2_dvar
|
||||
d2logpdf_dlink2_dtheta = np.zeros((self.size, f.shape[0], f.shape[1]))
|
||||
d2logpdf_dlink2_dtheta[0, :, :] = self.d2logpdf_dlink2_dvar(f, y, Y_metadata=Y_metadata)
|
||||
return d2logpdf_dlink2_dtheta
|
||||
|
||||
def _mean(self, gp):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ import numpy as np
|
|||
from scipy import stats,special
|
||||
import scipy as sp
|
||||
import link_functions
|
||||
from ..util.misc import chain_1, chain_2, chain_3
|
||||
from ..util.misc import chain_1, chain_2, chain_3, blockify_dhess_dtheta, blockify_third, blockify_hessian, safe_exp
|
||||
from scipy.integrate import quad
|
||||
import warnings
|
||||
from ..core.parameterization import Parameterized
|
||||
|
|
@ -39,6 +39,7 @@ class Likelihood(Parameterized):
|
|||
assert isinstance(gp_link,link_functions.GPTransformation), "gp_link is not a valid GPTransformation."
|
||||
self.gp_link = gp_link
|
||||
self.log_concave = False
|
||||
self.not_block_really = False
|
||||
|
||||
def _gradients(self,partial):
|
||||
return np.zeros(0)
|
||||
|
|
@ -194,20 +195,27 @@ class Likelihood(Parameterized):
|
|||
|
||||
"""
|
||||
#conditional_mean: the edpected value of y given some f, under this likelihood
|
||||
fmin = -np.inf
|
||||
fmax = np.inf
|
||||
def int_mean(f,m,v):
|
||||
p = np.exp(-(0.5/v)*np.square(f - m))
|
||||
exponent = -(0.5/v)*np.square(f - m)
|
||||
#If exponent is under -30 then exp(exponent) will be very small, so don't exp it!)
|
||||
#If p is zero then conditional_mean will overflow
|
||||
assert v.all() > 0
|
||||
p = safe_exp(exponent)
|
||||
|
||||
#If p is zero then conditional_variance will overflow
|
||||
if p < 1e-10:
|
||||
return 0.
|
||||
else:
|
||||
return self.conditional_mean(f)*p
|
||||
scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
scaled_mean = [quad(int_mean, fmin, fmax,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
|
||||
|
||||
return mean
|
||||
|
||||
def _conditional_mean(self, f):
|
||||
"""Quadrature calculation of the conditional mean: E(Y_star|f)"""
|
||||
"""Quadrature calculation of the conditional mean: E(Y_star|f_star)"""
|
||||
raise NotImplementedError, "implement this function to make predictions"
|
||||
|
||||
def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
|
||||
|
|
@ -215,7 +223,7 @@ class Likelihood(Parameterized):
|
|||
Approximation to the predictive variance: V(Y_star)
|
||||
|
||||
The following variance decomposition is used:
|
||||
V(Y_star) = E( V(Y_star|f_star) ) + V( E(Y_star|f_star) )
|
||||
V(Y_star) = E( V(Y_star|f_star)**2 ) + V( E(Y_star|f_star) )**2
|
||||
|
||||
:param mu: mean of posterior
|
||||
:param sigma: standard deviation of posterior
|
||||
|
|
@ -225,15 +233,22 @@ class Likelihood(Parameterized):
|
|||
#sigma2 = sigma**2
|
||||
normalizer = np.sqrt(2*np.pi*variance)
|
||||
|
||||
fmin_v = -np.inf
|
||||
fmin_m = np.inf
|
||||
fmin = -np.inf
|
||||
fmax = np.inf
|
||||
|
||||
from ..util.misc import safe_exp
|
||||
# E( V(Y_star|f_star) )
|
||||
def int_var(f,m,v):
|
||||
p = np.exp(-(0.5/v)*np.square(f - m))
|
||||
exponent = -(0.5/v)*np.square(f - m)
|
||||
p = safe_exp(exponent)
|
||||
#If p is zero then conditional_variance will overflow
|
||||
if p < 1e-10:
|
||||
return 0.
|
||||
else:
|
||||
return self.conditional_variance(f)*p
|
||||
scaled_exp_variance = [quad(int_var, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
scaled_exp_variance = [quad(int_var, fmin_v, fmax,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
exp_var = np.array(scaled_exp_variance)[:,None] / normalizer
|
||||
|
||||
#V( E(Y_star|f_star) ) = E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2
|
||||
|
|
@ -245,14 +260,15 @@ class Likelihood(Parameterized):
|
|||
|
||||
#E( E(Y_star|f_star)**2 )
|
||||
def int_pred_mean_sq(f,m,v,predictive_mean_sq):
|
||||
p = np.exp(-(0.5/v)*np.square(f - m))
|
||||
exponent = -(0.5/v)*np.square(f - m)
|
||||
p = np.exp(exponent)
|
||||
#If p is zero then conditional_mean**2 will overflow
|
||||
if p < 1e-10:
|
||||
return 0.
|
||||
else:
|
||||
return self.conditional_mean(f)**2*p
|
||||
|
||||
scaled_exp_exp2 = [quad(int_pred_mean_sq, -np.inf, np.inf,args=(mj,s2j,pm2j))[0] for mj,s2j,pm2j in zip(mu,variance,predictive_mean_sq)]
|
||||
scaled_exp_exp2 = [quad(int_pred_mean_sq, fmin_m, fmax,args=(mj,s2j,pm2j))[0] for mj,s2j,pm2j in zip(mu,variance,predictive_mean_sq)]
|
||||
exp_exp2 = np.array(scaled_exp_exp2)[:,None] / normalizer
|
||||
|
||||
var_exp = exp_exp2 - predictive_mean_sq
|
||||
|
|
@ -300,8 +316,18 @@ class Likelihood(Parameterized):
|
|||
:returns: likelihood evaluated for this point
|
||||
:rtype: float
|
||||
"""
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.pdf_link(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.pdf_link(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.pdf_link(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
|
||||
def logpdf_sum(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
Convenience function that can overridden for functions where this could
|
||||
be computed more efficiently
|
||||
"""
|
||||
return np.sum(self.logpdf(f, y, Y_metadata=Y_metadata))
|
||||
|
||||
def logpdf(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
|
|
@ -318,8 +344,11 @@ class Likelihood(Parameterized):
|
|||
:returns: log likelihood evaluated for this point
|
||||
:rtype: float
|
||||
"""
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.logpdf_link(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.logpdf_link(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.logpdf_link(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
|
||||
def dlogpdf_df(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
|
|
@ -337,11 +366,15 @@ class Likelihood(Parameterized):
|
|||
:returns: derivative of log likelihood evaluated for this point
|
||||
:rtype: 1xN array
|
||||
"""
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
return chain_1(dlogpdf_dlink, dlink_df)
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.dlogpdf_dlink(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
return chain_1(dlogpdf_dlink, dlink_df)
|
||||
|
||||
@blockify_hessian
|
||||
def d2logpdf_df2(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
Evaluates the link function link(f) then computes the second derivative of log likelihood using it
|
||||
|
|
@ -358,13 +391,18 @@ class Likelihood(Parameterized):
|
|||
:returns: second derivative of log likelihood evaluated for this point (diagonal only)
|
||||
:rtype: 1xN array
|
||||
"""
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
d2logpdf_dlink2 = self.d2logpdf_dlink2(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
return chain_2(d2logpdf_dlink2, dlink_df, dlogpdf_dlink, d2link_df2)
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
d2logpdf_df2 = self.d2logpdf_dlink2(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
d2logpdf_dlink2 = self.d2logpdf_dlink2(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
d2logpdf_df2 = chain_2(d2logpdf_dlink2, dlink_df, dlogpdf_dlink, d2link_df2)
|
||||
return d2logpdf_df2
|
||||
|
||||
@blockify_third
|
||||
def d3logpdf_df3(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
Evaluates the link function link(f) then computes the third derivative of log likelihood using it
|
||||
|
|
@ -381,64 +419,96 @@ class Likelihood(Parameterized):
|
|||
:returns: third derivative of log likelihood evaluated for this point
|
||||
:rtype: float
|
||||
"""
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
d3logpdf_dlink3 = self.d3logpdf_dlink3(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
d2logpdf_dlink2 = self.d2logpdf_dlink2(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d3link_df3 = self.gp_link.d3transf_df3(f)
|
||||
return chain_3(d3logpdf_dlink3, dlink_df, d2logpdf_dlink2, d2link_df2, dlogpdf_dlink, d3link_df3)
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
d3logpdf_df3 = self.d3logpdf_dlink3(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
d3logpdf_dlink3 = self.d3logpdf_dlink3(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
d2logpdf_dlink2 = self.d2logpdf_dlink2(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
d3link_df3 = self.gp_link.d3transf_df3(f)
|
||||
d3logpdf_df3 = chain_3(d3logpdf_dlink3, dlink_df, d2logpdf_dlink2, d2link_df2, dlogpdf_dlink, d3link_df3)
|
||||
return d3logpdf_df3
|
||||
|
||||
|
||||
def dlogpdf_dtheta(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
TODO: Doc strings
|
||||
"""
|
||||
if self.size > 0:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.dlogpdf_link_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
if self.not_block_really:
|
||||
raise NotImplementedError("Need to make a decorator for this!")
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.dlogpdf_link_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
return self.dlogpdf_link_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
# There are no parameters so return an empty array for derivatives
|
||||
return np.zeros([1, 0])
|
||||
return np.zeros((0, f.shape[0], f.shape[1]))
|
||||
|
||||
def dlogpdf_df_dtheta(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
TODO: Doc strings
|
||||
"""
|
||||
if self.size > 0:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
|
||||
if self.not_block_really:
|
||||
raise NotImplementedError("Need to make a decorator for this!")
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.dlogpdf_dlink_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
|
||||
dlogpdf_df_dtheta = np.zeros((self.size, f.shape[0], f.shape[1]))
|
||||
#Chain each parameter of hte likelihood seperately
|
||||
for p in range(self.size):
|
||||
dlogpdf_df_dtheta[p, :, :] = chain_1(dlogpdf_dlink_dtheta[p,:,:], dlink_df)
|
||||
return dlogpdf_df_dtheta
|
||||
#return chain_1(dlogpdf_dlink_dtheta, dlink_df)
|
||||
else:
|
||||
# There are no parameters so return an empty array for derivatives
|
||||
return np.zeros([f.shape[0], 0])
|
||||
return np.zeros((0, f.shape[0], f.shape[1]))
|
||||
|
||||
def d2logpdf_df2_dtheta(self, f, y, Y_metadata=None):
|
||||
"""
|
||||
TODO: Doc strings
|
||||
"""
|
||||
if self.size > 0:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
|
||||
if self.not_block_really:
|
||||
raise NotImplementedError("Need to make a decorator for this!")
|
||||
if isinstance(self.gp_link, link_functions.Identity):
|
||||
return self.d2logpdf_dlink2_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
else:
|
||||
inv_link_f = self.gp_link.transf(f)
|
||||
dlink_df = self.gp_link.dtransf_df(f)
|
||||
d2link_df2 = self.gp_link.d2transf_df2(f)
|
||||
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
|
||||
|
||||
d2logpdf_df2_dtheta = np.zeros((self.size, f.shape[0], f.shape[1]))
|
||||
#Chain each parameter of hte likelihood seperately
|
||||
for p in range(self.size):
|
||||
d2logpdf_df2_dtheta[p, :, :] = chain_2(d2logpdf_dlink2_dtheta[p,:,:], dlink_df, dlogpdf_dlink_dtheta[p,:,:], d2link_df2)
|
||||
return d2logpdf_df2_dtheta
|
||||
#return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
|
||||
else:
|
||||
# There are no parameters so return an empty array for derivatives
|
||||
return np.zeros([f.shape[0], 0])
|
||||
return np.zeros((0, f.shape[0], f.shape[1]))
|
||||
|
||||
def _laplace_gradients(self, f, y, Y_metadata=None):
|
||||
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, Y_metadata=Y_metadata).sum(axis=0)
|
||||
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
dlogpdf_df_dtheta = self.dlogpdf_df_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
d2logpdf_df2_dtheta = self.d2logpdf_df2_dtheta(f, y, Y_metadata=Y_metadata)
|
||||
|
||||
#Parameters are stacked vertically. Must be listed in same order as 'get_param_names'
|
||||
# ensure we have gradients for every parameter we want to optimize
|
||||
assert len(dlogpdf_dtheta) == self.size #1 x num_param array
|
||||
assert dlogpdf_df_dtheta.shape[1] == self.size #f x num_param matrix
|
||||
assert d2logpdf_df2_dtheta.shape[1] == self.size #f x num_param matrix
|
||||
assert dlogpdf_dtheta.shape[0] == self.size #f, d x num_param array
|
||||
assert dlogpdf_df_dtheta.shape[0] == self.size #f x d x num_param matrix or just f x num_param
|
||||
assert d2logpdf_df2_dtheta.shape[0] == self.size #f x num_param matrix or f x d x num_param matrix, f x f x num_param or f x f x d x num_param
|
||||
|
||||
return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta
|
||||
|
||||
|
|
@ -459,19 +529,98 @@ class Likelihood(Parameterized):
|
|||
|
||||
def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
|
||||
#compute the quantiles by sampling!!!
|
||||
N_samp = 1000
|
||||
N_samp = 50
|
||||
s = np.random.randn(mu.shape[0], N_samp)*np.sqrt(var) + mu
|
||||
#ss_f = s.flatten()
|
||||
#ss_y = self.samples(ss_f, Y_metadata)
|
||||
#ss_y = self.samples(s, Y_metadata, samples=100)
|
||||
ss_y = self.samples(s, Y_metadata)
|
||||
#ss_y = ss_y.reshape(mu.shape[0], N_samp)
|
||||
|
||||
return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles]
|
||||
|
||||
def samples(self, gp, Y_metadata=None):
|
||||
def samples(self, gp, Y_metadata=None, samples=1):
|
||||
"""
|
||||
Returns a set of samples of observations based on a given value of the latent variable.
|
||||
|
||||
:param gp: latent variable
|
||||
:param samples: number of samples to take for each f location
|
||||
"""
|
||||
raise NotImplementedError
|
||||
raise NotImplementedError("""May be possible to use MCMC with user-tuning, see
|
||||
MCMC_pdf_samples in likelihood.py and write samples function
|
||||
using this, beware this is a simple implementation
|
||||
of Metropolis and will not work well for all likelihoods""")
|
||||
|
||||
def MCMC_pdf_samples(self, fNew, num_samples=1000, starting_loc=None, stepsize=0.1, burn_in=1000, Y_metadata=None):
|
||||
"""
|
||||
Simple implementation of Metropolis sampling algorithm
|
||||
|
||||
Will run a parallel chain for each input dimension (treats each f independently)
|
||||
Thus assumes f*_1 independant of f*_2 etc.
|
||||
|
||||
:param num_samples: Number of samples to take
|
||||
:param fNew: f at which to sample around
|
||||
:param starting_loc: Starting locations of the independant chains (usually will be conditional_mean of likelihood), often link_f
|
||||
:param stepsize: Stepsize for the normal proposal distribution (will need modifying)
|
||||
:param burnin: number of samples to use for burnin (will need modifying)
|
||||
:param Y_metadata: Y_metadata for pdf
|
||||
"""
|
||||
print "Warning, using MCMC for sampling y*, needs to be tuned!"
|
||||
if starting_loc is None:
|
||||
starting_loc = fNew
|
||||
from functools import partial
|
||||
logpdf = partial(self.logpdf, f=fNew, Y_metadata=Y_metadata)
|
||||
pdf = lambda y_star: np.exp(logpdf(y=y_star[:, None]))
|
||||
#Should be the link function of f is a good starting point
|
||||
#(i.e. the point before you corrupt it with the likelihood)
|
||||
par_chains = starting_loc.shape[0]
|
||||
chain_values = np.zeros((par_chains, num_samples))
|
||||
chain_values[:, 0][:,None] = starting_loc
|
||||
#Use same stepsize for all par_chains
|
||||
stepsize = np.ones(par_chains)*stepsize
|
||||
accepted = np.zeros((par_chains, num_samples+burn_in))
|
||||
accept_ratio = np.zeros(num_samples+burn_in)
|
||||
#Whilst burning in, only need to keep the previous lot
|
||||
burnin_cache = np.zeros(par_chains)
|
||||
burnin_cache[:] = starting_loc.flatten()
|
||||
burning_in = True
|
||||
for i in xrange(burn_in+num_samples):
|
||||
next_ind = i-burn_in
|
||||
if burning_in:
|
||||
old_y = burnin_cache
|
||||
else:
|
||||
old_y = chain_values[:,next_ind-1]
|
||||
|
||||
old_lik = pdf(old_y)
|
||||
#Propose new y from Gaussian proposal
|
||||
new_y = np.random.normal(loc=old_y, scale=stepsize)
|
||||
new_lik = pdf(new_y)
|
||||
#Accept using Metropolis (not hastings) acceptance
|
||||
#Always accepts if new_lik > old_lik
|
||||
accept_probability = np.minimum(1, new_lik/old_lik)
|
||||
u = np.random.uniform(0,1,par_chains)
|
||||
#print "Accept prob: ", accept_probability
|
||||
accepts = u < accept_probability
|
||||
if burning_in:
|
||||
burnin_cache[accepts] = new_y[accepts]
|
||||
burnin_cache[~accepts] = old_y[~accepts]
|
||||
if i == burn_in:
|
||||
burning_in = False
|
||||
chain_values[:,0] = burnin_cache
|
||||
else:
|
||||
#If it was accepted then new_y becomes the latest sample
|
||||
chain_values[accepts, next_ind] = new_y[accepts]
|
||||
#Otherwise use old y as the sample
|
||||
chain_values[~accepts, next_ind] = old_y[~accepts]
|
||||
|
||||
accepted[~accepts, i] = 0
|
||||
accepted[accepts, i] = 1
|
||||
accept_ratio[i] = np.sum(accepted[:,i])/float(par_chains)
|
||||
|
||||
#Show progress
|
||||
if i % int((burn_in+num_samples)*0.1) == 0:
|
||||
print "{}% of samples taken ({})".format((i/int((burn_in+num_samples)*0.1)*10), i)
|
||||
print "Last run accept ratio: ", accept_ratio[i]
|
||||
|
||||
print "Average accept ratio: ", np.mean(accept_ratio)
|
||||
return chain_values
|
||||
|
|
|
|||
|
|
@ -232,12 +232,13 @@ class StudentT(Likelihood):
|
|||
def dlogpdf_dlink_dtheta(self, f, y, Y_metadata=None):
|
||||
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, Y_metadata=Y_metadata)
|
||||
dlogpdf_dlink_dv = np.zeros_like(dlogpdf_dlink_dvar) #FIXME: Not done yet
|
||||
return np.hstack((dlogpdf_dlink_dvar, dlogpdf_dlink_dv))
|
||||
return np.array((dlogpdf_dlink_dvar, dlogpdf_dlink_dv))
|
||||
|
||||
def d2logpdf_dlink2_dtheta(self, f, y, Y_metadata=None):
|
||||
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, Y_metadata=Y_metadata)
|
||||
d2logpdf_dlink2_dv = np.zeros_like(d2logpdf_dlink2_dvar) #FIXME: Not done yet
|
||||
return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
|
||||
|
||||
return np.array((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
|
||||
|
||||
def predictive_mean(self, mu, sigma, Y_metadata=None):
|
||||
# The comment here confuses mean and median.
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ from GPy.likelihoods import link_functions
|
|||
from GPy.core.parameterization import Param
|
||||
from functools import partial
|
||||
#np.random.seed(300)
|
||||
#np.random.seed(7)
|
||||
#np.random.seed(4)
|
||||
|
||||
#np.seterr(divide='raise')
|
||||
def dparam_partial(inst_func, *args):
|
||||
|
|
@ -52,8 +52,17 @@ def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None,
|
|||
zipped_params = zip(params, params_names)
|
||||
for param_ind, (param_val, param_name) in enumerate(zipped_params):
|
||||
#Check one parameter at a time, make sure it is 2d (as some gradients only return arrays) then strip out the parameter
|
||||
fnum = np.atleast_2d(partial_f(param_val, param_name))[:, param_ind].shape[0]
|
||||
dfnum = np.atleast_2d(partial_df(param_val, param_name))[:, param_ind].shape[0]
|
||||
f_ = partial_f(param_val, param_name)
|
||||
df_ = partial_df(param_val, param_name)
|
||||
#Reshape it such that we have a 3d matrix incase, that is we want it (?, N, D) regardless of whether ? is num_params or not
|
||||
f_ = f_.reshape(-1, f_.shape[0], f_.shape[1])
|
||||
df_ = df_.reshape(-1, f_.shape[0], f_.shape[1])
|
||||
|
||||
#Get the number of f and number of dimensions
|
||||
fnum = f_.shape[-2]
|
||||
fdim = f_.shape[-1]
|
||||
dfnum = df_.shape[-2]
|
||||
|
||||
for fixed_val in range(dfnum):
|
||||
#dlik and dlik_dvar gives back 1 value for each
|
||||
f_ind = min(fnum, fixed_val+1) - 1
|
||||
|
|
@ -61,9 +70,13 @@ def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None,
|
|||
#Make grad checker with this param moving, note that set_params is NOT being called
|
||||
#The parameter is being set directly with __setattr__
|
||||
#Check only the parameter and function value we wish to check at a time
|
||||
grad = GradientChecker(lambda p_val: np.atleast_2d(partial_f(p_val, param_name))[f_ind, param_ind],
|
||||
lambda p_val: np.atleast_2d(partial_df(p_val, param_name))[fixed_val, param_ind],
|
||||
param_val, [param_name])
|
||||
#func = lambda p_val, fnum, fdim, param_ind, f_ind, param_ind: partial_f(p_val, param_name).reshape(-1, fnum, fdim)[param_ind, f_ind, :]
|
||||
#dfunc_dparam = lambda d_val, fnum, fdim, param_ind, fixed_val: partial_df(d_val, param_name).reshape(-1, fnum, fdim)[param_ind, fixed_val, :]
|
||||
|
||||
#First we reshape the output such that it is (num_params, N, D) then we pull out the relavent parameter-findex and checkgrad just this index at a time
|
||||
func = lambda p_val: partial_f(p_val, param_name).reshape(-1, fnum, fdim)[param_ind, f_ind, :]
|
||||
dfunc_dparam = lambda d_val: partial_df(d_val, param_name).reshape(-1, fnum, fdim)[param_ind, fixed_val, :]
|
||||
grad = GradientChecker(func, dfunc_dparam, param_val, [param_name])
|
||||
|
||||
if constraints is not None:
|
||||
for constrain_param, constraint in constraints:
|
||||
|
|
@ -104,37 +117,9 @@ class TestNoiseModels(object):
|
|||
|
||||
self.var = 0.2
|
||||
|
||||
self.var = np.random.rand(1)
|
||||
|
||||
#Make a bigger step as lower bound can be quite curved
|
||||
self.step = 1e-4
|
||||
|
||||
def tearDown(self):
|
||||
self.Y = None
|
||||
self.f = None
|
||||
self.X = None
|
||||
|
||||
def test_scale2_models(self):
|
||||
self.setUp()
|
||||
|
||||
####################################################
|
||||
# Constraint wrappers so we can just list them off #
|
||||
####################################################
|
||||
def constrain_fixed(regex, model):
|
||||
model[regex].constrain_fixed()
|
||||
|
||||
def constrain_negative(regex, model):
|
||||
model[regex].constrain_negative()
|
||||
|
||||
def constrain_positive(regex, model):
|
||||
model[regex].constrain_positive()
|
||||
|
||||
def constrain_bounded(regex, model, lower, upper):
|
||||
"""
|
||||
Used like: partial(constrain_bounded, lower=0, upper=1)
|
||||
"""
|
||||
model[regex].constrain_bounded(lower, upper)
|
||||
|
||||
"""
|
||||
Dictionary where we nest models we would like to check
|
||||
Name: {
|
||||
|
|
@ -149,136 +134,170 @@ class TestNoiseModels(object):
|
|||
"link_f_constraints": [constraint_wrappers, listed_here]
|
||||
}
|
||||
"""
|
||||
noise_models = {"Student_t_default": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
#"constraints": [("t_scale2", constrain_positive), ("deg_free", partial(constrain_fixed, value=5))]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_1_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [1.0],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_small_deg_free": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_small_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [0.001],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_large_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [10.0],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_approx_gauss": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_log": {
|
||||
"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", constrain_positive), (".*deg_free", constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Gaussian_default": {
|
||||
"model": GPy.likelihoods.Gaussian(variance=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*variance"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*variance", constrain_positive)]
|
||||
},
|
||||
"laplace": True,
|
||||
"ep": False # FIXME: Should be True when we have it working again
|
||||
},
|
||||
#"Gaussian_log": {
|
||||
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log(), variance=self.var, D=self.D, N=self.N),
|
||||
#"grad_params": {
|
||||
#"names": ["noise_model_variance"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [constrain_positive]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
#"Gaussian_probit": {
|
||||
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Probit(), variance=self.var, D=self.D, N=self.N),
|
||||
#"grad_params": {
|
||||
#"names": ["noise_model_variance"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [constrain_positive]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
#"Gaussian_log_ex": {
|
||||
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
|
||||
#"grad_params": {
|
||||
#"names": ["noise_model_variance"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [constrain_positive]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
"Bernoulli_default": {
|
||||
"model": GPy.likelihoods.Bernoulli(),
|
||||
"link_f_constraints": [partial(constrain_bounded, lower=0, upper=1)],
|
||||
"laplace": True,
|
||||
"Y": self.binary_Y,
|
||||
"ep": False # FIXME: Should be True when we have it working again
|
||||
},
|
||||
"Exponential_default": {
|
||||
"model": GPy.likelihoods.Exponential(),
|
||||
"link_f_constraints": [constrain_positive],
|
||||
"Y": self.positive_Y,
|
||||
"laplace": True,
|
||||
},
|
||||
"Poisson_default": {
|
||||
"model": GPy.likelihoods.Poisson(),
|
||||
"link_f_constraints": [constrain_positive],
|
||||
"Y": self.integer_Y,
|
||||
"laplace": True,
|
||||
"ep": False #Should work though...
|
||||
}#,
|
||||
#GAMMA needs some work!"Gamma_default": {
|
||||
#"model": GPy.likelihoods.Gamma(),
|
||||
#"link_f_constraints": [constrain_positive],
|
||||
#"Y": self.positive_Y,
|
||||
#"laplace": True
|
||||
#}
|
||||
}
|
||||
self.noise_models = {"Student_t_default": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_1_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [1.0],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_small_deg_free": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_small_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [0.001],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_large_var": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [10.0],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
"Student_t_approx_gauss": {
|
||||
"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*t_scale2"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
#"Student_t_log": {
|
||||
#"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
|
||||
#"grad_params": {
|
||||
#"names": [".*t_noise"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [(".*t_noise", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
"Gaussian_default": {
|
||||
"model": GPy.likelihoods.Gaussian(variance=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*variance"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*variance", self.constrain_positive)]
|
||||
},
|
||||
"laplace": True,
|
||||
"ep": False # FIXME: Should be True when we have it working again
|
||||
},
|
||||
"Gaussian_log": {
|
||||
"model": GPy.likelihoods.Gaussian(gp_link=link_functions.Log(), variance=self.var),
|
||||
"grad_params": {
|
||||
"names": [".*variance"],
|
||||
"vals": [self.var],
|
||||
"constraints": [(".*variance", self.constrain_positive)]
|
||||
},
|
||||
"laplace": True
|
||||
},
|
||||
#"Gaussian_probit": {
|
||||
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Probit(), variance=self.var, D=self.D, N=self.N),
|
||||
#"grad_params": {
|
||||
#"names": ["noise_model_variance"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [constrain_positive]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
#"Gaussian_log_ex": {
|
||||
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
|
||||
#"grad_params": {
|
||||
#"names": ["noise_model_variance"],
|
||||
#"vals": [self.var],
|
||||
#"constraints": [constrain_positive]
|
||||
#},
|
||||
#"laplace": True
|
||||
#},
|
||||
"Bernoulli_default": {
|
||||
"model": GPy.likelihoods.Bernoulli(),
|
||||
"link_f_constraints": [partial(self.constrain_bounded, lower=0, upper=1)],
|
||||
"laplace": True,
|
||||
"Y": self.binary_Y,
|
||||
"ep": False # FIXME: Should be True when we have it working again
|
||||
},
|
||||
"Exponential_default": {
|
||||
"model": GPy.likelihoods.Exponential(),
|
||||
"link_f_constraints": [self.constrain_positive],
|
||||
"Y": self.positive_Y,
|
||||
"laplace": True,
|
||||
},
|
||||
"Poisson_default": {
|
||||
"model": GPy.likelihoods.Poisson(),
|
||||
"link_f_constraints": [self.constrain_positive],
|
||||
"Y": self.integer_Y,
|
||||
"laplace": True,
|
||||
"ep": False #Should work though...
|
||||
},
|
||||
#,
|
||||
#GAMMA needs some work!"Gamma_default": {
|
||||
#"model": GPy.likelihoods.Gamma(),
|
||||
#"link_f_constraints": [constrain_positive],
|
||||
#"Y": self.positive_Y,
|
||||
#"laplace": True
|
||||
#}
|
||||
}
|
||||
|
||||
for name, attributes in noise_models.iteritems():
|
||||
|
||||
####################################################
|
||||
# Constraint wrappers so we can just list them off #
|
||||
####################################################
|
||||
def constrain_fixed(self, regex, model):
|
||||
model[regex].constrain_fixed()
|
||||
|
||||
def constrain_negative(self, regex, model):
|
||||
model[regex].constrain_negative()
|
||||
|
||||
def constrain_positive(self, regex, model):
|
||||
model[regex].constrain_positive()
|
||||
|
||||
def constrain_fixed_below(self, regex, model, up_to):
|
||||
model[regex][0:up_to].constrain_fixed()
|
||||
|
||||
def constrain_fixed_above(self, regex, model, above):
|
||||
model[regex][above:].constrain_fixed()
|
||||
|
||||
def constrain_bounded(self, regex, model, lower, upper):
|
||||
"""
|
||||
Used like: partial(constrain_bounded, lower=0, upper=1)
|
||||
"""
|
||||
model[regex].constrain_bounded(lower, upper)
|
||||
|
||||
|
||||
def tearDown(self):
|
||||
self.Y = None
|
||||
self.f = None
|
||||
self.X = None
|
||||
|
||||
def test_scale2_models(self):
|
||||
self.setUp()
|
||||
|
||||
for name, attributes in self.noise_models.iteritems():
|
||||
model = attributes["model"]
|
||||
if "grad_params" in attributes:
|
||||
params = attributes["grad_params"]
|
||||
|
|
@ -290,7 +309,7 @@ class TestNoiseModels(object):
|
|||
param_vals = []
|
||||
param_names = []
|
||||
constrain_positive = []
|
||||
param_constraints = [] # ??? TODO: Saul to Fix.
|
||||
param_constraints = []
|
||||
if "link_f_constraints" in attributes:
|
||||
link_f_constraints = attributes["link_f_constraints"]
|
||||
else:
|
||||
|
|
@ -303,6 +322,10 @@ class TestNoiseModels(object):
|
|||
f = attributes["f"].copy()
|
||||
else:
|
||||
f = self.f.copy()
|
||||
if "Y_metadata" in attributes:
|
||||
Y_metadata = attributes["Y_metadata"].copy()
|
||||
else:
|
||||
Y_metadata = None
|
||||
if "laplace" in attributes:
|
||||
laplace = attributes["laplace"]
|
||||
else:
|
||||
|
|
@ -317,30 +340,30 @@ class TestNoiseModels(object):
|
|||
|
||||
#Required by all
|
||||
#Normal derivatives
|
||||
yield self.t_logpdf, model, Y, f
|
||||
yield self.t_dlogpdf_df, model, Y, f
|
||||
yield self.t_d2logpdf_df2, model, Y, f
|
||||
yield self.t_logpdf, model, Y, f, Y_metadata
|
||||
yield self.t_dlogpdf_df, model, Y, f, Y_metadata
|
||||
yield self.t_d2logpdf_df2, model, Y, f, Y_metadata
|
||||
#Link derivatives
|
||||
yield self.t_dlogpdf_dlink, model, Y, f, link_f_constraints
|
||||
yield self.t_d2logpdf_dlink2, model, Y, f, link_f_constraints
|
||||
yield self.t_dlogpdf_dlink, model, Y, f, Y_metadata, link_f_constraints
|
||||
yield self.t_d2logpdf_dlink2, model, Y, f, Y_metadata, link_f_constraints
|
||||
if laplace:
|
||||
#Laplace only derivatives
|
||||
yield self.t_d3logpdf_df3, model, Y, f
|
||||
yield self.t_d3logpdf_dlink3, model, Y, f, link_f_constraints
|
||||
yield self.t_d3logpdf_df3, model, Y, f, Y_metadata
|
||||
yield self.t_d3logpdf_dlink3, model, Y, f, Y_metadata, link_f_constraints
|
||||
#Params
|
||||
yield self.t_dlogpdf_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_df_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_d2logpdf2_df2_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_df_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
yield self.t_d2logpdf2_df2_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
#Link params
|
||||
yield self.t_dlogpdf_link_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_dlink_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_link_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
yield self.t_dlogpdf_dlink_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, Y_metadata, param_vals, param_names, param_constraints
|
||||
|
||||
#laplace likelihood gradcheck
|
||||
yield self.t_laplace_fit_rbf_white, model, self.X, Y, f, self.step, param_vals, param_names, param_constraints
|
||||
yield self.t_laplace_fit_rbf_white, model, self.X, Y, f, Y_metadata, self.step, param_vals, param_names, param_constraints
|
||||
if ep:
|
||||
#ep likelihood gradcheck
|
||||
yield self.t_ep_fit_rbf_white, model, self.X, Y, f, self.step, param_vals, param_names, param_constraints
|
||||
yield self.t_ep_fit_rbf_white, model, self.X, Y, f, Y_metadata, self.step, param_vals, param_names, param_constraints
|
||||
|
||||
|
||||
self.tearDown()
|
||||
|
|
@ -349,41 +372,41 @@ class TestNoiseModels(object):
|
|||
# dpdf_df's #
|
||||
#############
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_logpdf(self, model, Y, f):
|
||||
def t_logpdf(self, model, Y, f, Y_metadata):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
#print model._get_params()
|
||||
np.testing.assert_almost_equal(
|
||||
model.pdf(f.copy(), Y.copy()).prod(),
|
||||
np.exp(model.logpdf(f.copy(), Y.copy()).sum())
|
||||
model.pdf(f.copy(), Y.copy(), Y_metadata=Y_metadata).prod(),
|
||||
np.exp(model.logpdf(f.copy(), Y.copy(), Y_metadata=Y_metadata).sum())
|
||||
)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_df(self, model, Y, f):
|
||||
def t_dlogpdf_df(self, model, Y, f, Y_metadata):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
self.description = "\n{}".format(inspect.stack()[0][3])
|
||||
logpdf = functools.partial(np.sum(model.logpdf), y=Y)
|
||||
dlogpdf_df = functools.partial(model.dlogpdf_df, y=Y)
|
||||
logpdf = functools.partial(np.sum(model.logpdf), y=Y, Y_metadata=Y_metadata)
|
||||
dlogpdf_df = functools.partial(model.dlogpdf_df, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(logpdf, dlogpdf_df, f.copy(), 'g')
|
||||
grad.randomize()
|
||||
print model
|
||||
assert grad.checkgrad(verbose=1)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d2logpdf_df2(self, model, Y, f):
|
||||
def t_d2logpdf_df2(self, model, Y, f, Y_metadata):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
dlogpdf_df = functools.partial(model.dlogpdf_df, y=Y)
|
||||
d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=Y)
|
||||
dlogpdf_df = functools.partial(model.dlogpdf_df, y=Y, Y_metadata=Y_metadata)
|
||||
d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(dlogpdf_df, d2logpdf_df2, f.copy(), 'g')
|
||||
grad.randomize()
|
||||
print model
|
||||
assert grad.checkgrad(verbose=1)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d3logpdf_df3(self, model, Y, f):
|
||||
def t_d3logpdf_df3(self, model, Y, f, Y_metadata):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=Y)
|
||||
d3logpdf_df3 = functools.partial(model.d3logpdf_df3, y=Y)
|
||||
d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=Y, Y_metadata=Y_metadata)
|
||||
d3logpdf_df3 = functools.partial(model.d3logpdf_df3, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(d2logpdf_df2, d3logpdf_df3, f.copy(), 'g')
|
||||
grad.randomize()
|
||||
print model
|
||||
|
|
@ -393,32 +416,32 @@ class TestNoiseModels(object):
|
|||
# df_dparams #
|
||||
##############
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_dparams(self, model, Y, f, params, params_names, param_constraints):
|
||||
def t_dlogpdf_dparams(self, model, Y, f, Y_metadata, params, params_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
|
||||
params, params_names, args=(f, Y), constraints=param_constraints,
|
||||
params, params_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_df_dparams(self, model, Y, f, params, params_names, param_constraints):
|
||||
def t_dlogpdf_df_dparams(self, model, Y, f, Y_metadata, params, params_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
|
||||
params, params_names, args=(f, Y), constraints=param_constraints,
|
||||
params, params_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d2logpdf2_df2_dparams(self, model, Y, f, params, params_names, param_constraints):
|
||||
def t_d2logpdf2_df2_dparams(self, model, Y, f, Y_metadata, params, params_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
|
||||
params, params_names, args=(f, Y), constraints=param_constraints,
|
||||
params, params_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
|
|
@ -426,10 +449,10 @@ class TestNoiseModels(object):
|
|||
# dpdf_dlink's #
|
||||
################
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_dlink(self, model, Y, f, link_f_constraints):
|
||||
def t_dlogpdf_dlink(self, model, Y, f, Y_metadata, link_f_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
logpdf = functools.partial(model.logpdf_link, y=Y)
|
||||
dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=Y)
|
||||
logpdf = functools.partial(model.logpdf_link, y=Y, Y_metadata=Y_metadata)
|
||||
dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(logpdf, dlogpdf_dlink, f.copy(), 'g')
|
||||
|
||||
#Apply constraints to link_f values
|
||||
|
|
@ -442,10 +465,10 @@ class TestNoiseModels(object):
|
|||
assert grad.checkgrad(verbose=1)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d2logpdf_dlink2(self, model, Y, f, link_f_constraints):
|
||||
def t_d2logpdf_dlink2(self, model, Y, f, Y_metadata, link_f_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=Y)
|
||||
d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=Y)
|
||||
dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=Y, Y_metadata=Y_metadata)
|
||||
d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(dlogpdf_dlink, d2logpdf_dlink2, f.copy(), 'g')
|
||||
|
||||
#Apply constraints to link_f values
|
||||
|
|
@ -458,10 +481,10 @@ class TestNoiseModels(object):
|
|||
assert grad.checkgrad(verbose=1)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d3logpdf_dlink3(self, model, Y, f, link_f_constraints):
|
||||
def t_d3logpdf_dlink3(self, model, Y, f, Y_metadata, link_f_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=Y)
|
||||
d3logpdf_dlink3 = functools.partial(model.d3logpdf_dlink3, y=Y)
|
||||
d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=Y, Y_metadata=Y_metadata)
|
||||
d3logpdf_dlink3 = functools.partial(model.d3logpdf_dlink3, y=Y, Y_metadata=Y_metadata)
|
||||
grad = GradientChecker(d2logpdf_dlink2, d3logpdf_dlink3, f.copy(), 'g')
|
||||
|
||||
#Apply constraints to link_f values
|
||||
|
|
@ -477,32 +500,32 @@ class TestNoiseModels(object):
|
|||
# dlink_dparams #
|
||||
#################
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_link_dparams(self, model, Y, f, params, param_names, param_constraints):
|
||||
def t_dlogpdf_link_dparams(self, model, Y, f, Y_metadata, params, param_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.logpdf_link, model.dlogpdf_link_dtheta,
|
||||
params, param_names, args=(f, Y), constraints=param_constraints,
|
||||
params, param_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_dlogpdf_dlink_dparams(self, model, Y, f, params, param_names, param_constraints):
|
||||
def t_dlogpdf_dlink_dparams(self, model, Y, f, Y_metadata, params, param_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.dlogpdf_dlink, model.dlogpdf_dlink_dtheta,
|
||||
params, param_names, args=(f, Y), constraints=param_constraints,
|
||||
params, param_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, params, param_names, param_constraints):
|
||||
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, Y_metadata, params, param_names, param_constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
print model
|
||||
assert (
|
||||
dparam_checkgrad(model.d2logpdf_dlink2, model.d2logpdf_dlink2_dtheta,
|
||||
params, param_names, args=(f, Y), constraints=param_constraints,
|
||||
params, param_names, args=(f, Y, Y_metadata), constraints=param_constraints,
|
||||
randomize=False, verbose=True)
|
||||
)
|
||||
|
||||
|
|
@ -510,14 +533,15 @@ class TestNoiseModels(object):
|
|||
# laplace test #
|
||||
################
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_laplace_fit_rbf_white(self, model, X, Y, f, step, param_vals, param_names, constraints):
|
||||
def t_laplace_fit_rbf_white(self, model, X, Y, f, Y_metadata, step, param_vals, param_names, constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
#Normalize
|
||||
Y = Y/Y.max()
|
||||
white_var = 1e-6
|
||||
white_var = 1e-5
|
||||
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
|
||||
laplace_likelihood = GPy.inference.latent_function_inference.Laplace()
|
||||
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, inference_method=laplace_likelihood)
|
||||
|
||||
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=laplace_likelihood)
|
||||
m['.*white'].constrain_fixed(white_var)
|
||||
|
||||
#Set constraints
|
||||
|
|
@ -526,6 +550,7 @@ class TestNoiseModels(object):
|
|||
|
||||
print m
|
||||
m.randomize()
|
||||
m.randomize()
|
||||
|
||||
#Set params
|
||||
for param_num in range(len(param_names)):
|
||||
|
|
@ -545,14 +570,15 @@ class TestNoiseModels(object):
|
|||
# EP test #
|
||||
###########
|
||||
@with_setup(setUp, tearDown)
|
||||
def t_ep_fit_rbf_white(self, model, X, Y, f, step, param_vals, param_names, constraints):
|
||||
def t_ep_fit_rbf_white(self, model, X, Y, f, Y_metadata, step, param_vals, param_names, constraints):
|
||||
print "\n{}".format(inspect.stack()[0][3])
|
||||
#Normalize
|
||||
Y = Y/Y.max()
|
||||
white_var = 1e-6
|
||||
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
|
||||
ep_inf = GPy.inference.latent_function_inference.EP()
|
||||
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, inference_method=ep_inf)
|
||||
|
||||
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=ep_inf)
|
||||
m['.*white'].constrain_fixed(white_var)
|
||||
|
||||
for param_num in range(len(param_names)):
|
||||
|
|
@ -571,8 +597,8 @@ class LaplaceTests(unittest.TestCase):
|
|||
"""
|
||||
|
||||
def setUp(self):
|
||||
self.N = 5
|
||||
self.D = 3
|
||||
self.N = 15
|
||||
self.D = 1
|
||||
self.X = np.random.rand(self.N, self.D)*10
|
||||
|
||||
self.real_std = 0.1
|
||||
|
|
@ -636,20 +662,20 @@ class LaplaceTests(unittest.TestCase):
|
|||
exact_inf = GPy.inference.latent_function_inference.ExactGaussianInference()
|
||||
m1 = GPy.core.GP(X, Y.copy(), kernel=kernel1, likelihood=gauss_distr1, inference_method=exact_inf)
|
||||
m1['.*white'].constrain_fixed(1e-6)
|
||||
m1['.*rbf.variance'] = initial_var_guess
|
||||
m1['.*rbf.variance'].constrain_bounded(1e-4, 10)
|
||||
m1['.*Gaussian_noise.variance'].constrain_bounded(1e-4, 10)
|
||||
m1.randomize()
|
||||
|
||||
gauss_distr2 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
|
||||
laplace_inf = GPy.inference.latent_function_inference.Laplace()
|
||||
m2 = GPy.core.GP(X, Y.copy(), kernel=kernel2, likelihood=gauss_distr2, inference_method=laplace_inf)
|
||||
m2['.*white'].constrain_fixed(1e-6)
|
||||
m2['.*rbf.variance'].constrain_bounded(1e-4, 10)
|
||||
m2['.*Gaussian_noise.variance'].constrain_bounded(1e-4, 10)
|
||||
m2.randomize()
|
||||
|
||||
if debug:
|
||||
print m1
|
||||
print m2
|
||||
|
||||
optimizer = 'scg'
|
||||
print "Gaussian"
|
||||
m1.optimize(optimizer, messages=debug, ipython_notebook=False)
|
||||
|
|
@ -687,8 +713,6 @@ class LaplaceTests(unittest.TestCase):
|
|||
pb.scatter(X, m1.likelihood.Y, c='g')
|
||||
pb.scatter(X, m2.likelihood.Y, c='r', marker='x')
|
||||
|
||||
|
||||
|
||||
#Check Y's are the same
|
||||
np.testing.assert_almost_equal(m1.Y, m2.Y, decimal=5)
|
||||
#Check marginals are the same
|
||||
|
|
|
|||
18
GPy/testing/misc_tests.py
Normal file
18
GPy/testing/misc_tests.py
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
import numpy as np
|
||||
import scipy as sp
|
||||
import GPy
|
||||
|
||||
class MiscTests(np.testing.TestCase):
|
||||
"""
|
||||
Testing some utilities of misc
|
||||
"""
|
||||
def setUp(self):
|
||||
self._lim_val = np.finfo(np.float64).max
|
||||
self._lim_val_exp = np.log(self._lim_val)
|
||||
|
||||
def test_safe_exp_upper(self):
|
||||
assert np.exp(self._lim_val_exp + 1) == np.inf
|
||||
assert GPy.util.misc.safe_exp(self._lim_val_exp + 1) < np.inf
|
||||
|
||||
def test_safe_exp_lower(self):
|
||||
assert GPy.util.misc.safe_exp(1e-10) < np.inf
|
||||
|
|
@ -17,6 +17,54 @@ def get_blocks(A, blocksizes):
|
|||
count_i += i
|
||||
return B
|
||||
|
||||
def get_block_shapes(B):
|
||||
assert B.dtype is np.dtype('object'), "Must be a block matrix"
|
||||
return [B[b,b].shape[0] for b in range(0, B.shape[0])]
|
||||
|
||||
def unblock(B):
|
||||
assert B.dtype is np.dtype('object'), "Must be a block matrix"
|
||||
block_shapes = get_block_shapes(B)
|
||||
num_elements = np.sum(block_shapes)
|
||||
A = np.empty(shape=(num_elements, num_elements))
|
||||
count_i = 0
|
||||
for Bi, i in enumerate(block_shapes):
|
||||
count_j = 0
|
||||
for Bj, j in enumerate(block_shapes):
|
||||
A[count_i:count_i + i, count_j:count_j + j] = B[Bi, Bj]
|
||||
count_j += j
|
||||
count_i += i
|
||||
return A
|
||||
|
||||
def block_dot(A, B):
|
||||
"""
|
||||
Element wise dot product on block matricies
|
||||
|
||||
+------+------+ +------+------+ +-------+-------+
|
||||
| | | | | | |A11.B11|B12.B12|
|
||||
| A11 | A12 | | B11 | B12 | | | |
|
||||
+------+------+ o +------+------| = +-------+-------+
|
||||
| | | | | | |A21.B21|A22.B22|
|
||||
| A21 | A22 | | B21 | B22 | | | |
|
||||
+-------------+ +------+------+ +-------+-------+
|
||||
|
||||
..Note
|
||||
If either (A or B) of the diagonal matrices are stored as vectors then a more
|
||||
efficient dot product using numpy broadcasting will be used, i.e. A11*B11
|
||||
"""
|
||||
#Must have same number of blocks and be a block matrix
|
||||
assert A.dtype is np.dtype('object'), "Must be a block matrix"
|
||||
assert B.dtype is np.dtype('object'), "Must be a block matrix"
|
||||
Ashape = A.shape
|
||||
Bshape = B.shape
|
||||
assert Ashape == Bshape
|
||||
def f(A,B):
|
||||
if Ashape[0] == Ashape[1] or Bshape[0] == Bshape[1]:
|
||||
#FIXME: Careful if one is transpose of other, would make a matrix
|
||||
return A*B
|
||||
else:
|
||||
return np.dot(A,B)
|
||||
dot = np.vectorize(f, otypes = [np.object])
|
||||
return dot(A,B)
|
||||
|
||||
|
||||
if __name__=='__main__':
|
||||
|
|
@ -24,3 +72,5 @@ if __name__=='__main__':
|
|||
B = get_blocks(A,[2,3])
|
||||
B[0,0] += 7
|
||||
print B
|
||||
|
||||
assert np.all(unblock(B) == A)
|
||||
|
|
|
|||
|
|
@ -4,6 +4,16 @@
|
|||
import numpy as np
|
||||
from config import *
|
||||
|
||||
_lim_val = np.finfo(np.float64).max
|
||||
|
||||
_lim_val_exp = np.log(_lim_val)
|
||||
_lim_val_square = np.sqrt(_lim_val)
|
||||
_lim_val_cube = np.power(_lim_val, -3)
|
||||
|
||||
def safe_exp(f):
|
||||
clip_f = np.clip(f, -np.inf, _lim_val_exp)
|
||||
return np.exp(clip_f)
|
||||
|
||||
def chain_1(df_dg, dg_dx):
|
||||
"""
|
||||
Generic chaining function for first derivative
|
||||
|
|
@ -11,6 +21,11 @@ def chain_1(df_dg, dg_dx):
|
|||
.. math::
|
||||
\\frac{d(f . g)}{dx} = \\frac{df}{dg} \\frac{dg}{dx}
|
||||
"""
|
||||
if np.all(dg_dx==1.):
|
||||
return df_dg
|
||||
if len(df_dg) > 1 and df_dg.shape[-1] > 1:
|
||||
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
|
||||
raise NotImplementedError('Not implemented for matricies yet')
|
||||
return df_dg * dg_dx
|
||||
|
||||
def chain_2(d2f_dg2, dg_dx, df_dg, d2g_dx2):
|
||||
|
|
@ -20,7 +35,13 @@ def chain_2(d2f_dg2, dg_dx, df_dg, d2g_dx2):
|
|||
.. math::
|
||||
\\frac{d^{2}(f . g)}{dx^{2}} = \\frac{d^{2}f}{dg^{2}}(\\frac{dg}{dx})^{2} + \\frac{df}{dg}\\frac{d^{2}g}{dx^{2}}
|
||||
"""
|
||||
return d2f_dg2*(dg_dx**2) + df_dg*d2g_dx2
|
||||
if np.all(dg_dx==1.) and np.all(d2g_dx2 == 0):
|
||||
return d2f_dg2
|
||||
if len(d2f_dg2) > 1 and d2f_dg2.shape[-1] > 1:
|
||||
raise NotImplementedError('Not implemented for matricies yet')
|
||||
#dg_dx_2 = np.clip(dg_dx, 1e-12, _lim_val_square)**2
|
||||
dg_dx_2 = dg_dx**2
|
||||
return d2f_dg2*(dg_dx_2) + df_dg*d2g_dx2
|
||||
|
||||
def chain_3(d3f_dg3, dg_dx, d2f_dg2, d2g_dx2, df_dg, d3g_dx3):
|
||||
"""
|
||||
|
|
@ -29,11 +50,18 @@ def chain_3(d3f_dg3, dg_dx, d2f_dg2, d2g_dx2, df_dg, d3g_dx3):
|
|||
.. math::
|
||||
\\frac{d^{3}(f . g)}{dx^{3}} = \\frac{d^{3}f}{dg^{3}}(\\frac{dg}{dx})^{3} + 3\\frac{d^{2}f}{dg^{2}}\\frac{dg}{dx}\\frac{d^{2}g}{dx^{2}} + \\frac{df}{dg}\\frac{d^{3}g}{dx^{3}}
|
||||
"""
|
||||
return d3f_dg3*(dg_dx**3) + 3*d2f_dg2*dg_dx*d2g_dx2 + df_dg*d3g_dx3
|
||||
if np.all(dg_dx==1.) and np.all(d2g_dx2==0) and np.all(d3g_dx3==0):
|
||||
return d3f_dg3
|
||||
if ( (len(d2f_dg2) > 1 and d2f_dg2.shape[-1] > 1)
|
||||
or (len(d3f_dg3) > 1 and d3f_dg3.shape[-1] > 1)):
|
||||
raise NotImplementedError('Not implemented for matricies yet')
|
||||
#dg_dx_3 = np.clip(dg_dx, 1e-12, _lim_val_cube)**3
|
||||
dg_dx_3 = dg_dx**3
|
||||
return d3f_dg3*(dg_dx_3) + 3*d2f_dg2*dg_dx*d2g_dx2 + df_dg*d3g_dx3
|
||||
|
||||
def opt_wrapper(m, **kwargs):
|
||||
"""
|
||||
This function just wraps the optimization procedure of a GPy
|
||||
Thit function just wraps the optimization procedure of a GPy
|
||||
object so that optimize() pickleable (necessary for multiprocessing).
|
||||
"""
|
||||
m.optimize(**kwargs)
|
||||
|
|
@ -96,3 +124,47 @@ from :class:ndarray)"""
|
|||
if len(param) == 1:
|
||||
return param[0].view(np.ndarray)
|
||||
return [x.view(np.ndarray) for x in param]
|
||||
|
||||
def blockify_hessian(func):
|
||||
def wrapper_func(self, *args, **kwargs):
|
||||
# Invoke the wrapped function first
|
||||
retval = func(self, *args, **kwargs)
|
||||
# Now do something here with retval and/or action
|
||||
if self.not_block_really and (retval.shape[0] != retval.shape[1]):
|
||||
return np.diagflat(retval)
|
||||
else:
|
||||
return retval
|
||||
return wrapper_func
|
||||
|
||||
def blockify_third(func):
|
||||
def wrapper_func(self, *args, **kwargs):
|
||||
# Invoke the wrapped function first
|
||||
retval = func(self, *args, **kwargs)
|
||||
# Now do something here with retval and/or action
|
||||
if self.not_block_really and (len(retval.shape) < 3):
|
||||
num_data = retval.shape[0]
|
||||
d3_block_cache = np.zeros((num_data, num_data, num_data))
|
||||
diag_slice = range(num_data)
|
||||
d3_block_cache[diag_slice, diag_slice, diag_slice] = np.squeeze(retval)
|
||||
return d3_block_cache
|
||||
else:
|
||||
return retval
|
||||
return wrapper_func
|
||||
|
||||
def blockify_dhess_dtheta(func):
|
||||
def wrapper_func(self, *args, **kwargs):
|
||||
# Invoke the wrapped function first
|
||||
retval = func(self, *args, **kwargs)
|
||||
# Now do something here with retval and/or action
|
||||
if self.not_block_really and (len(retval.shape) < 3):
|
||||
num_data = retval.shape[0]
|
||||
num_params = retval.shape[-1]
|
||||
dhess_dtheta = np.zeros((num_data, num_data, num_params))
|
||||
diag_slice = range(num_data)
|
||||
for param_ind in range(num_params):
|
||||
dhess_dtheta[diag_slice, diag_slice, param_ind] = np.squeeze(retval[:,param_ind])
|
||||
return dhess_dtheta
|
||||
else:
|
||||
return retval
|
||||
return wrapper_func
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue