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Remove symbolic import.
This commit is contained in:
Neil Lawrence
parent
4a93e75ce2
commit
4be2ef267d
4 changed files with 0 additions and 361 deletions
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@ -6,18 +6,3 @@ from poisson import Poisson
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from student_t import StudentT
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from likelihood import Likelihood
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from mixed_noise import MixedNoise
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#TODO need to fix this in a config file.
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#TODO need to add the files to the git repo!
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try:
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import sympy as sym
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sympy_available=True
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except ImportError:
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sympy_available=False
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if sympy_available:
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#These are likelihoods that rely on symbolic.
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from symbolic import Symbolic
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from sstudent_t import SstudentT
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from negative_binomial import Negative_binomial
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from skew_normal import Skew_normal
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from skew_exponential import Skew_exponential
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# from null_category import Null_category
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@ -1,279 +0,0 @@
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# Copyright (c) 2014 GPy Authors
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import sympy as sym
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import numpy as np
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from likelihood import Likelihood
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from ..core.symbolic import Symbolic_core
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class Symbolic(Likelihood, Symbolic_core):
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"""
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Symbolic likelihood.
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Likelihood where the form of the likelihood is provided by a sympy expression.
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"""
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def __init__(self, log_pdf=None, logZ=None, missing_log_pdf=None, gp_link=None, name='symbolic', log_concave=False, parameters=None, func_modules=[]):
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if gp_link is None:
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gp_link = link_functions.Identity()
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if log_pdf is None:
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raise ValueError, "You must provide an argument for the log pdf."
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Likelihood.__init__(self, gp_link, name=name)
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functions = {'log_pdf':log_pdf}
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self.cacheable = ['F', 'Y']
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self.missing_data = False
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if missing_log_pdf:
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self.missing_data = True
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functions['missing_log_pdf']=missing_log_pdf
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self.ep_analytic = False
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if logZ:
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self.ep_analytic = True
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functions['logZ'] = logZ
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self.cacheable += ['M', 'V']
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Symbolic_core.__init__(self, functions, cacheable=self.cacheable, derivatives = ['F', 'theta'], parameters=parameters, func_modules=func_modules)
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# TODO: Is there an easy way to check whether the pdf is log
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self.log_concave = log_concave
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def _set_derivatives(self, derivatives):
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# these are arguments for computing derivatives.
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print "Whoop"
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Symbolic_core._set_derivatives(self, derivatives)
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# add second and third derivatives for Laplace approximation.
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derivative_arguments = []
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if derivatives is not None:
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for derivative in derivatives:
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derivative_arguments += self.variables[derivative]
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exprs = ['log_pdf']
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if self.missing_data:
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exprs.append('missing_log_pdf')
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for expr in exprs:
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self.expressions[expr]['second_derivative'] = {theta.name : self.stabilize(sym.diff(self.expressions[expr]['derivative']['f_0'], theta)) for theta in derivative_arguments}
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self.expressions[expr]['third_derivative'] = {theta.name : self.stabilize(sym.diff(self.expressions[expr]['second_derivative']['f_0'], theta)) for theta in derivative_arguments}
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if self.ep_analytic:
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derivative_arguments = [M]
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# add second derivative for EP
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exprs = ['logZ']
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if self.missing_data:
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exprs.append('missing_logZ')
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for expr in exprs:
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self.expressions[expr]['second_derivative'] = {theta.name : self.stabilize(sym.diff(self.expressions[expr]['derivative'], theta)) for theta in derivative_arguments}
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def eval_update_cache(self, Y, **kwargs):
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# TODO: place checks for inf/nan in here
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# for all provided keywords
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Symbolic_core.eval_update_cache(self, Y=Y, **kwargs)
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# Y = np.atleast_2d(Y)
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# for variable, code in sorted(self.code['parameters_changed'].iteritems()):
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# self._set_attribute(variable, eval(code, self.namespace))
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# for i, theta in enumerate(self.variables['Y']):
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# missing = np.isnan(Y[:, i])
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# self._set_attribute('missing_' + str(i), missing)
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# self._set_attribute(theta.name, value[missing, i][:, None])
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# for variable, value in kwargs.items():
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# # update their cached values
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# if value is not None:
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# if variable == 'F' or variable == 'M' or variable == 'V' or variable == 'Y_metadata':
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# for i, theta in enumerate(self.variables[variable]):
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# self._set_attribute(theta.name, value[:, i][:, None])
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# else:
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# self._set_attribute(theta.name, value[:, i])
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# for variable, code in sorted(self.code['update_cache'].iteritems()):
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# self._set_attribute(variable, eval(code, self.namespace))
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def parameters_changed(self):
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pass
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def update_gradients(self, grads):
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"""
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Pull out the gradients, be careful as the order must match the order
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in which the parameters are added
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"""
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# The way the Laplace approximation is run requires the
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# covariance function to compute the true gradient (because it
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# is dependent on the mode). This means we actually compute
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# the gradient outside this object. This function would
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# normally ask the object to update its gradients internally,
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# but here it provides them externally, because they are
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# computed in the inference code. TODO: Thought: How does this
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# effect EP? Shouldn't this be done by a separate
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# Laplace-approximation specific call?
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for theta, grad in zip(self.variables['theta'], grads):
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parameter = getattr(self, theta.name)
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setattr(parameter, 'gradient', grad)
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def pdf_link(self, f, y, Y_metadata=None):
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"""
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Likelihood function given inverse link of f.
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:param f: inverse link of latent variables.
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:type f: Nx1 array
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:param y: data
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:type y: Nx1 array
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:param Y_metadata: Y_metadata which is not used in student t distribution
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:returns: likelihood evaluated for this point
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:rtype: float
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"""
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return np.exp(self.logpdf_link(f, y, Y_metadata=None))
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def logpdf_link(self, f, y, Y_metadata=None):
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"""
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Log Likelihood Function given inverse link of latent variables.
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:param f: latent variables (inverse link of f)
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:type f: Nx1 array
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:param y: data
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:type y: Nx1 array
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:param Y_metadata: Y_metadata
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:returns: likelihood evaluated for this point
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:rtype: float
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"""
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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if self.missing_data:
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missing_flag = np.isnan(y)
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not_missing_flag = np.logical_not(missing_flag)
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ll = self.eval_function('missing_log_pdf', F=f[missing_flag]).sum()
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ll += self.eval_function('log_pdf', F=f[not_missing_flag], Y=y[not_missing_flag], Y_metadata=Y_metadata[not_missing_flag]).sum()
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else:
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ll = self.eval_function('log_pdf', F=f, Y=y, Y_metadata=Y_metadata).sum()
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return ll
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def dlogpdf_dlink(self, f, y, Y_metadata=None):
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"""
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Gradient of log likelihood with respect to the inverse link function.
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:param f: latent variables (inverse link of f)
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:type f: Nx1 array
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:param y: data
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:type y: Nx1 array
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:param Y_metadata: Y_metadata
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:returns: gradient of likelihood with respect to each point.
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:rtype: Nx1 array
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"""
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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if self.missing_data:
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return np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['derivative']['f_0'], self.namespace),
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eval(self.code['log_pdf']['derivative']['f_0'], self.namespace))
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else:
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return np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['derivative']['f_0'], self.namespace))
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def d2logpdf_dlink2(self, f, y, Y_metadata=None):
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"""
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Hessian of log likelihood given inverse link of latent variables with respect to that inverse link.
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i.e. second derivative logpdf at y given inv_link(f_i) and inv_link(f_j) w.r.t inv_link(f_i) and inv_link(f_j).
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:param f: inverse link of the latent variables.
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:type f: Nx1 array
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:param y: data
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:type y: Nx1 array
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:param Y_metadata: Y_metadata which is not used in student t distribution
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:returns: Diagonal of Hessian matrix (second derivative of likelihood evaluated at points f)
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:rtype: Nx1 array
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.. Note::
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Returns diagonal of Hessian, since every where else it is
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0, as the likelihood factorizes over cases (the
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distribution for y_i depends only on link(f_i) not on
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link(f_(j!=i))
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"""
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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if self.missing_data:
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return np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['second_derivative']['f_0'], self.namespace),
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eval(self.code['log_pdf']['second_derivative']['f_0'], self.namespace))
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else:
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return np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['second_derivative']['f_0'], self.namespace))
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def d3logpdf_dlink3(self, f, y, Y_metadata=None):
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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if self.missing_data:
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return np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['third_derivative']['f_0'], self.namespace),
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eval(self.code['log_pdf']['third_derivative']['f_0'], self.namespace))
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else:
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return np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['third_derivative']['f_0'], self.namespace))
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def dlogpdf_link_dtheta(self, f, y, Y_metadata=None):
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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g = np.zeros((np.atleast_1d(y).shape[0], len(self.variables['theta'])))
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for i, theta in enumerate(self.variables['theta']):
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if self.missing_data:
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g[:, i:i+1] = np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['derivative'][theta.name], self.namespace),
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eval(self.code['log_pdf']['derivative'][theta.name], self.namespace))
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else:
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g[:, i:i+1] = np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['derivative'][theta.name], self.namespace))
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return g.sum(0)
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def dlogpdf_dlink_dtheta(self, f, y, Y_metadata=None):
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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g = np.zeros((np.atleast_1d(y).shape[0], len(self.variables['theta'])))
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for i, theta in enumerate(self.variables['theta']):
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if self.missing_data:
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g[:, i:i+1] = np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['second_derivative'][theta.name], self.namespace),
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eval(self.code['log_pdf']['second_derivative'][theta.name], self.namespace))
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else:
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g[:, i:i+1] = np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['second_derivative'][theta.name], self.namespace))
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return g
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def d2logpdf_dlink2_dtheta(self, f, y, Y_metadata=None):
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assert np.atleast_1d(f).shape == np.atleast_1d(y).shape
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self.eval_update_cache(F=f, Y=y, Y_metadata=Y_metadata)
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g = np.zeros((np.atleast_1d(y).shape[0], len(self.variables['theta'])))
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for i, theta in enumerate(self.variables['theta']):
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if self.missing_data:
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g[:, i:i+1] = np.where(np.isnan(y),
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eval(self.code['missing_log_pdf']['third_derivative'][theta.name], self.namespace),
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eval(self.code['log_pdf']['third_derivative'][theta.name], self.namespace))
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else:
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g[:, i:i+1] = np.where(np.isnan(y),
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0.,
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eval(self.code['log_pdf']['third_derivative'][theta.name], self.namespace))
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return g
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def predictive_mean(self, mu, sigma, Y_metadata=None):
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raise NotImplementedError
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def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
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raise NotImplementedError
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def conditional_mean(self, gp):
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raise NotImplementedError
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def conditional_variance(self, gp):
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raise NotImplementedError
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def samples(self, gp, Y_metadata=None):
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raise NotImplementedError
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@ -5,13 +5,3 @@ from kernel import Kernel
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from linear import Linear
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from mlp import MLP
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#from rbf import RBF
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# TODO need to fix this in a config file.
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try:
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import sympy as sym
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sympy_available=True
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except ImportError:
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sympy_available=False
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if sympy_available:
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# These are likelihoods that rely on symbolic.
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from symbolic import Symbolic
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@ -1,57 +0,0 @@
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# Copyright (c) 2014 GPy Authors
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import sympy as sym
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from ..core.mapping import Mapping, Bijective_mapping
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from ..core.symbolic import Symbolic_core
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import numpy as np
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class Symbolic(Mapping, Symbolic_core):
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"""
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Symbolic mapping
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Mapping where the form of the mapping is provided by a sympy expression.
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"""
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def __init__(self, input_dim, output_dim, f=None, name='symbolic', parameters=None, func_modules=[]):
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if f is None:
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raise ValueError, "You must provide an argument for the function."
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Mapping.__init__(self, input_dim, output_dim, name=name)
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Symbolic_core.__init__(self, {'f': f}, ['X'], derivatives = ['X', 'theta'], parameters=parameters, func_modules=func_modules)
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self._initialize_cache()
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self.parameters_changed()
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def _initialize_cache(self):
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self._set_attribute('x_0', np.random.normal(size=(3, self.input_dim)))
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def parameters_changed(self):
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self.eval_parameters_changed()
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def update_cache(self, X=None):
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self.eval_update_cache(X=X)
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def update_gradients(self, partial, X=None):
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for name, val in self.eval_update_gradients('f', partial, X=X).iteritems():
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setattr(getattr(self, name), 'gradient', val)
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def gradients_X(self, partial, X=None):
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return self.eval_gradients_X('f', partial, X=X)
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def f(self, X=None):
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"""
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"""
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return self.eval_function('f', X=X)
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def df_dX(self, X):
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"""
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"""
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pass
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def df_dtheta(self, X):
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pass
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