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Merge branch 'feature-multioutput' of https://github.com/esiivola/GPy into esiivola-feature-multioutput
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mzwiessele
commit
179ffa76dd
3 changed files with 254 additions and 2 deletions
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@ -9,3 +9,4 @@ from .mixed_noise import MixedNoise
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from .binomial import Binomial
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from .weibull import Weibull
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from .loglogistic import LogLogistic
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from .multioutput_likelihood import MultioutputLikelihood
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233
GPy/likelihoods/multioutput_likelihood.py
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233
GPy/likelihoods/multioutput_likelihood.py
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@ -0,0 +1,233 @@
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# Copyright (c) 2012-2014 The GPy authors (see AUTHORS.txt)
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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# Multioutput likelihood structure is similar to the
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# corresponding structure in GPstuff. If building complex
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# multioutput models on top of this class and need a reference,
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# check GPstuff project.
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import numpy as np
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from scipy import stats, special
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from . import link_functions
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from .likelihood import Likelihood
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from .mixed_noise import MixedNoise
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from .gaussian import Gaussian
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from ..core.parameterization import Param
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from paramz.transformations import Logexp
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from ..core.parameterization import Parameterized
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from ..kern.src.independent_outputs import index_to_slices
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import itertools
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class MultioutputLikelihood(MixedNoise):
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'''
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CombinedLikelihood is used to combine different likelihoods for
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multioutput models, where different outputs have different observation models.
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As input the likelihood takes a list of likelihoods used. The likelihood
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uses "output_index" in Y_metadata to connect observations to likelihoods.
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'''
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def __init__(self, likelihoods_list, name='multioutput_likelihood'):
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super(Likelihood, self).__init__(name=name)
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indices, inverse = self._unique_likelihoods(likelihoods_list)
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self.link_parameters(*[likelihoods_list[i] for i in indices])
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self.index_map = [indices[i] for i in inverse]
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self.inverse = inverse
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self.gradient_sizes = [likelihoods_list[i].size for i in indices]
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self.gradient_index = np.cumsum(self.gradient_sizes) - self.gradient_sizes[0]
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self.likelihoods_list = likelihoods_list
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self.gp_link = None
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self.log_concave = False
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self.not_block_really = False
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def _unique_likelihoods(self, likelihoods_list):
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indices = []
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inverse = []
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for i in range(len(likelihoods_list)):
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for j in indices:
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if likelihoods_list[i] is likelihoods_list[j]:
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inverse += [j]
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break
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if len(inverse) <= i:
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indices += [i]
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inverse += [i]
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return indices, inverse
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def moments_match_ep(self, data_i, tau_i, v_i, Y_metadata_i):
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return self.likelihoods_list[Y_metadata_i["output_index"][0]].moments_match_ep(data_i, tau_i, v_i, Y_metadata_i)
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def exact_inference_gradients(self, dL_dKdiag, Y_metadata):
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assert all([isinstance(l, Gaussian) for l in self.likelihoods_list])
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ind = [self.index_map[i] for i in Y_metadata['output_index'].flatten()]
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return np.array([dL_dKdiag[ind==i].sum() for i in np.unique(self.index_map)])
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def ep_gradients(self, Y, cav_tau, cav_v, dL_dKdiag, Y_metadata=None, quad_mode='gk', boost_grad=1.):
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ind = [self.index_map[i] for i in Y_metadata['output_index'].flatten()]
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slices = index_to_slices(ind)
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grads = np.zeros((self.size))
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for i in range(len(slices)):
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if self.likelihoods_list[i].size > 0:
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ii = self.inverse[i] ## index in our gradient_sizes and gradient_index -lists
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for j in range(len(slices[i])):
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grads[self.gradient_index[ii]:self.gradient_index[ii]+self.gradient_sizes[ii]] += self.likelihoods_list[i].ep_gradients(Y[slices[i][j],:], cav_tau[slices[i][j]], cav_v[slices[i][j]], dL_dKdiag = dL_dKdiag[slices[i][j]], Y_metadata=Y_metadata, quad_mode=quad_mode, boost_grad=boost_grad)
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return grads
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def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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mu_new = np.zeros(mu.shape )
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var_new = np.zeros(var.shape )
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for j in outputs:
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m, v = self.likelihoods_list[j].predictive_values(mu[ind==j,:], var[ind==j,:], full_cov, Y_metadata=None)
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mu_new[ind==j,:] = m
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var_new[ind==j,:] = v
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return mu_new, var_new
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def predictive_variance(self, mu, sigma, Y_metadata):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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var = np.zeros( (sigma.size) )
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for j in outputs:
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v = self.likelihoods_list[j].predictive_variance(mu[ind==j,:],
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sigma[ind==j,:],Y_metadata=None)
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var[ind==j,:] = np.hstack(v)
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return [v[:,None] for v in var.T]
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def pdf(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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pdf = np.zeros(y.shape)
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for j in outputs:
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pdf[ind==j,:] = self.likelihoods_list[j].pdf(f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return pdf
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def pdf_link(self, inv_link_f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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pdf_link = np.zeros(y.shape)
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for j in outputs:
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pdf_link[ind==j,:] = self.likelihoods_list[j].pdf_link(inv_link_f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return pdf_link
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def logpdf(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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if ind.shape[0]==1:
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ind = ind[0]*np.ones(f.shape[0])
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y = y*np.ones(f.shape)
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lpdf = np.zeros(f.shape)
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for j in outputs:
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lpdf[np.where(ind==j)[0],:] = self.likelihoods_list[j].logpdf(f[np.where(ind==j)[0],:], y[np.where(ind==j)[0],:], Y_metadata=None)
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return lpdf
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def logpdf_link(self, inv_link_f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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logpdf_link = np.zeros(y.shape)
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for j in outputs:
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logpdf_link[ind==j,:] = self.likelihoods_list[j].logpdf_link(inv_link_f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return logpdf_link
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def dlogpdf_dlink(self, inv_link_f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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dlogpdf_dlink = np.zeros(y.shape)
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for j in outputs:
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dlogpdf_dlink[ind==j,:] = self.likelihoods_list[j].dlogpdf_dlink(inv_link_f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return dlogpdf_dlink
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def d2logpdf_dlink2(self, inv_link_f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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d2logpdf_dlink2 = np.zeros(y.shape)
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for j in outputs:
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d2logpdf_dlink2[ind==j,:] = self.likelihoods_list[j].d2logpdf_dlink2(inv_link_f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return d2logpdf_dlink2
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def d3logpdf_dlink3(self, inv_link_f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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d3logpdf_dlink3 = np.zeros(y.shape)
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for j in outputs:
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d3logpdf_dlink3[ind==j,:] = self.likelihoods_list[j].d3logpdf_dlink3(inv_link_f[ind==j,:], y[ind==j,:], Y_metadata=None)
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return d3logpdf_dlink3
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def log_predictive_density(self, y_test, mu_star, var_star, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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log_pred = np.zeros(y_test.shape)
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for j in outputs:
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log_pred[ind==j,:] = self.likelihoods_list[j].log_predictive_density(y_test[ind==j,:], mu_star[ind==j,:], var_star[ind==j,:], Y_metadata=None)
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return log_pred
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def dlogpdf_dtheta(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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if ind.shape[0]==1:
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ind = ind[0]*np.ones(f.shape[0])
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y = y*np.ones(f.shape)
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slices = index_to_slices(ind)
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pdf = np.zeros((self.size, f.shape[0], f.shape[1]) )
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for i in range(len(slices)):
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if self.likelihoods_list[i].size > 0:
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ii = self.inverse[i]
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for j in range(len(slices[i])):
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pdf[self.gradient_index[ii]:self.gradient_index[ii]+self.gradient_sizes[ii], slices[i][j],:] = self.likelihoods_list[i].dlogpdf_dtheta(f[slices[i][j],:], y[slices[i][j],:], Y_metadata=None)
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return pdf
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def d2logpdf_df2(self, f, y, Y_metadata):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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Q = np.zeros(f.shape)
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for j in outputs:
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Q[ind==j,:] = self.likelihoods_list[j].d2logpdf_df2(f[ind==j,:],
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y[ind==j,:],Y_metadata=None)
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return Q
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def dlogpdf_df(self, f, y, Y_metadata):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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Q = np.zeros(f.shape)
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for j in outputs:
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Q[ind==j,:] = self.likelihoods_list[j].dlogpdf_df(f[ind==j,:],
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y[ind==j,:],Y_metadata=None)
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return Q
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def d3logpdf_df3(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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Q = np.zeros(f.shape)
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for j in outputs:
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Q[ind==j,:] = self.likelihoods_list[j].d3logpdf_df3(f[ind==j,:],
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y[ind==j,:],Y_metadata=None)
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return Q
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def dlogpdf_df_dtheta(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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if ind.shape[0]==1:
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ind = ind[0]*np.ones(f.shape[0])
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y = y*np.ones(f.shape)
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slices = index_to_slices(ind)
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pdf = np.zeros((self.size, f.shape[0], f.shape[1]) )
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for i in range(len(slices)):
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if self.likelihoods_list[i].size > 0:
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ii = self.inverse[i]
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for j in range(len(slices[i])):
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pdf[self.gradient_index[ii]:self.gradient_index[ii]+self.gradient_sizes[ii], slices[i][j],:] = self.likelihoods_list[i].dlogpdf_df_dtheta(f[slices[i][j],:], y[slices[i][j],:], Y_metadata=None)
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return pdf
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def d2logpdf_df2_dtheta(self, f, y, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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if ind.shape[0]==1:
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ind = ind[0]*np.ones(f.shape[0])
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y = y*np.ones(f.shape)
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slices = index_to_slices(ind)
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pdf = np.zeros((self.size, f.shape[0], f.shape[1]) )
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for i in range(len(slices)):
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if self.likelihoods_list[i].size > 0:
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ii = self.inverse[i]
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for j in range(len(slices[i])):
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pdf[self.gradient_index[ii]:self.gradient_index[ii]+self.gradient_sizes[ii], slices[i][j],:] = self.likelihoods_list[i].d2logpdf_df2_dtheta(f[slices[i][j],:], y[slices[i][j],:], Y_metadata=None)
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return pdf
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@ -128,7 +128,16 @@ class TestNoiseModels(object):
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censored[random_inds] = 1
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self.Y_metadata = dict()
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self.Y_metadata['censored'] = censored
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self.Y_metadata['output_index'] = np.zeros((self.N,1), dtype=int)
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self.Y_metadata2 = dict()
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self.Y_metadata2['censored'] = censored
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inds = np.zeros((self.N,1), dtype=int)
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inds[5:10] = 1
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inds[10:] = 2
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self.Y_metadata2['output_index'] = inds
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self.combY = self.Y
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self.combY[10:] = np.where(self.binary_Y[10:] >0, self.binary_Y[10:], 0)
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print(self.combY)
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#Make a bigger step as lower bound can be quite curved
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self.step = 1e-4
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@ -292,6 +301,15 @@ class TestNoiseModels(object):
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"Y": self.positive_Y,
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"Y_metadata": self.Y_metadata,
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"laplace": True
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},
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"multioutput_default": {
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"model": GPy.likelihoods.MultioutputLikelihood([GPy.likelihoods.Gaussian(), GPy.likelihoods.Poisson(), GPy.likelihoods.Bernoulli()]),
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"link_f_constraints": [partial(self.constrain_bounded, lower=0, upper=1)],
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"laplace": True,
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"Y": self.combY,
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"Y_metadata": self.Y_metadata2,
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"ep": True,
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"variational_expectations": True,
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}
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#,
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#GAMMA needs some work!"Gamma_default": {
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@ -618,7 +636,7 @@ class TestNoiseModels(object):
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# Y = Y/Y.max()
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white_var = 1e-4
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kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
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ep_inf = GPy.inference.latent_function_inference.EP()
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ep_inf = GPy.inference.latent_function_inference.EP(always_reset=True)
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m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=ep_inf)
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m['.*white'].constrain_fixed(white_var)
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