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111 lines
4.7 KiB
Python
111 lines
4.7 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from ..core.sparse_gp_mpi import SparseGP_MPI
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from .. import kern
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from ..likelihoods import Gaussian
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from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior
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from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
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from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
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from ..kern._src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU
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class SSGPLVM(SparseGP_MPI):
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"""
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Spike-and-Slab Gaussian Process Latent Variable Model
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:param Y: observed data (np.ndarray) or GPy.likelihood
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:type Y: np.ndarray| GPy.likelihood instance
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:param input_dim: latent dimensionality
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:type input_dim: int
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:param init: initialisation method for the latent space
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:type init: 'PCA'|'random'
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"""
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def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10,
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Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True,normalizer=False, **kwargs):
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self.group_spike = group_spike
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if X == None:
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from ..util.initialization import initialize_latent
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X, fracs = initialize_latent(init, input_dim, Y)
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else:
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fracs = np.ones(input_dim)
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self.init = init
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if X_variance is None: # The variance of the variational approximation (S)
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X_variance = np.random.uniform(0,.1,X.shape)
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if Gamma is None:
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gamma = np.empty_like(X) # The posterior probabilities of the binary variable in the variational approximation
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gamma[:] = 0.5 + 0.1 * np.random.randn(X.shape[0], input_dim)
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gamma[gamma>1.-1e-9] = 1.-1e-9
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gamma[gamma<1e-9] = 1e-9
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else:
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gamma = Gamma.copy()
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if Z is None:
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Z = np.random.permutation(X.copy())[:num_inducing]
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assert Z.shape[1] == X.shape[1]
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if likelihood is None:
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likelihood = Gaussian()
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if kernel is None:
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kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=True) # + kern.white(input_dim)
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if kernel.useGPU:
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kernel.psicomp = PSICOMP_SSRBF_GPU()
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if inference_method is None:
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inference_method = VarDTC_minibatch(mpi_comm=mpi_comm)
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if pi is None:
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pi = np.empty((input_dim))
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pi[:] = 0.5
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self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi) # the prior probability of the latent binary variable b
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X = SpikeAndSlabPosterior(X, X_variance, gamma)
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super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
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# self.X.unfix()
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# self.X.variance.constrain_positive()
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if self.group_spike:
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[self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together
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def set_X_gradients(self, X, X_grad):
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"""Set the gradients of the posterior distribution of X in its specific form."""
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X.mean.gradient, X.variance.gradient, X.binary_prob.gradient = X_grad
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def get_X_gradients(self, X):
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"""Get the gradients of the posterior distribution of X in its specific form."""
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return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient
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def parameters_changed(self):
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super(SSGPLVM,self).parameters_changed()
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if isinstance(self.inference_method, VarDTC_minibatch):
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return
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self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
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self.X.mean.gradient, self.X.variance.gradient, self.X.binary_prob.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2'])
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# update for the KL divergence
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self.variational_prior.update_gradients_KL(self.X)
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def input_sensitivity(self):
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if self.kern.ARD:
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return self.kern.input_sensitivity()
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else:
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return self.variational_prior.pi
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def plot_latent(self, plot_inducing=True, *args, **kwargs):
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import dim_reduction_plots
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return dim_reduction_plots.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs)
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