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Using params class with sympy covariance. Adding conditional statements for presence of weave.
This commit is contained in:
Neil Lawrence
parent
d1b6d18ddf
commit
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1 changed files with 284 additions and 227 deletions
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@ -1,17 +1,31 @@
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import numpy as np
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import sympy as sp
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from sympy.utilities.codegen import codegen
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from sympy.core.cache import clear_cache
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try:
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import sympy as sp
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sympy_available=True
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except ImportError:
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sympy_available=False
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exit()
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from sympy.core.cache import clear_cache
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from sympy.utilities.codegen import codegen
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try:
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from scipy import weave
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weave_available = True
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except ImportError:
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weave_available = False
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from scipy import weave
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import re
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import os
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import sys
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current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
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import sys
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import numpy as np
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import re
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import tempfile
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import pdb
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import ast
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from kernpart import Kernpart
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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class spkern(Kernpart):
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"""
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@ -75,17 +89,20 @@ class spkern(Kernpart):
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self.num_split_params = len(self._sp_theta_i)
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self._split_theta_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i]
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for theta in self._split_theta_names:
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setattr(self, theta, np.ones(self.output_dim))
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setattr(self, theta, Param(theta, np.ones(self.output_dim), None))
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self.add_parameters(getattr(self, theta))
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#setattr(self, theta, np.ones(self.output_dim))
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self.num_shared_params = len(self._sp_theta)
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self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
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#self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
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else:
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self.num_split_params = 0
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self._split_theta_names = []
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self._sp_theta = thetas
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self.num_shared_params = len(self._sp_theta)
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self.num_params = self.num_shared_params
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#self.num_params = self.num_shared_params
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# Add parameters to the model.
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for theta in self._sp_theta:
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@ -93,9 +110,12 @@ class spkern(Kernpart):
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if param is not None:
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if param.has_key(theta):
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val = param[theta]
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setattr(self, theta.name, val)
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#setattr(self, theta.name, val)
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setattr(self, theta.name, Param(theta.name, val, None))
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self.add_parameters(getattr(self, theta.name))
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self.parameters_changed() # initializes cache
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#deal with param
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self._set_params(self._get_params())
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#self._set_params(self._get_params())
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# Differentiate with respect to parameters.
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self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
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@ -112,7 +132,7 @@ class spkern(Kernpart):
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# generate the code for the covariance functions
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self._gen_code()
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if weave
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if weave_available:
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if False:
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extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
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else:
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@ -131,7 +151,7 @@ class spkern(Kernpart):
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return spkern(self._sp_k+other._sp_k)
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def _gen_code(self):
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#generate c functions from sympy objects
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argument_sequence = self._sp_x+self._sp_z+self._sp_theta
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code_list = [('k',self._sp_k)]
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# gradients with respect to covariance input
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@ -142,31 +162,47 @@ class spkern(Kernpart):
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if self.output_dim > 1:
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argument_sequence += self._sp_theta_i + self._sp_theta_j
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code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta_i,self._sp_dk_dtheta_i)]
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# generate c functions from sympy objects
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if weave_available:
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code_type = "C"
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else:
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code_type = "PYTHON"
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(foo_c,self._function_code), (foo_h,self._function_header) = \
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codegen(code_list, "C",'foobar',argument_sequence=argument_sequence)
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#put the header file where we can find it
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f = file(os.path.join(tempfile.gettempdir(),'foobar.h'),'w')
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codegen(code_list,
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code_type,
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self.name,
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argument_sequence=argument_sequence)
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# Use weave to compute the underlying functions.
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if weave_available:
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# put the header file where we can find it
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f = file(os.path.join(tempfile.gettempdir(), self.name + '.h'),'w')
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f.write(self._function_header)
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f.close()
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# Substitute any known derivatives which sympy doesn't compute
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self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
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# This is the basic argument construction for the C code.
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#arg_list = (["X[i*input_dim+%s]"%x.name[2:] for x in self._sp_x]
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# + ["Z[j*input_dim+%s]"%z.name[2:] for z in self._sp_z])
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if weave_available:
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# arg_list will store the arguments required for the C code.
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arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
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+ ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
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# for multiple outputs reverse argument list is also required
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if self.output_dim>1:
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reverse_arg_list = list(arg_list)
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reverse_arg_list.reverse()
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# This gives the parameters for the arg list.
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param_arg_list = [shared_params.name for shared_params in self._sp_theta]
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arg_list += param_arg_list
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precompute_list=[]
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if self.output_dim > 1:
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reverse_arg_list+=list(param_arg_list)
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# For multiple outputs, also need the split parameters.
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split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
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split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
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arg_list += split_param_arg_list
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@ -177,6 +213,15 @@ class spkern(Kernpart):
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reverse_arg_string = ", ".join(reverse_arg_list)
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arg_string = ", ".join(arg_list)
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precompute_string = "\n".join(precompute_list)
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# Now we use the arguments in code that computes the separate parts.
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# Any precomputations will be done here eventually.
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self._precompute = \
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"""
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// Precompute code would go here. It will be called when parameters are updated.
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"""
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# Here's the code to do the looping for K
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self._K_code =\
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"""
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@ -190,13 +235,15 @@ class spkern(Kernpart):
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//#pragma omp parallel for private(j)
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for (i=0;i<N;i++){
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for (j=0;j<num_inducing;j++){
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%s
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%s
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//target[i*num_inducing+j] =
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TARGET2(i, j) += k(%s);
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}
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}
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%s
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"""%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
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"""%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
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# adding a string representation of the function in the
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# comment forces recompile when needed
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# Code to compute diagonal of covariance.
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@ -244,7 +291,7 @@ class spkern(Kernpart):
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//#pragma omp parallel for private(j)
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for (i=0;i<N;i++){
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for (j=0;j<num_inducing;j++){
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%s
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%s
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}
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}
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%s
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@ -328,7 +375,11 @@ class spkern(Kernpart):
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#TODO: insert multiple functions here via string manipulation
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#TODO: similar functions for psi_stats
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#TODO: similar functions when cython available.
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#TODO: similar functions when only python available.
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def _get_arg_names(self, Z=None, partial=None):
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arg_names = ['target','X']
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for shared_params in self._sp_theta:
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arg_names += [shared_params.name]
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@ -341,7 +392,7 @@ class spkern(Kernpart):
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arg_names += ['output_dim']
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return arg_names
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def _weave_inline(self, code, X, target, Z=None, partial=None):
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def _generate_inline(self, code, X, target, Z=None, partial=None):
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output_dim = self.output_dim
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for shared_params in self._sp_theta:
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locals()[shared_params.name] = getattr(self, shared_params.name)
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@ -350,35 +401,38 @@ class spkern(Kernpart):
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for split_params in self._split_theta_names:
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locals()[split_params] = getattr(self, split_params)
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arg_names = self._get_arg_names(Z, partial)
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if weave_available:
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weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
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else:
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raise RuntimeError('Weave not available and other variants of sympy covariance not yet implemented')
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def K(self,X,Z,target):
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if Z is None:
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self._weave_inline(self._K_code_X, X, target)
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self._generate_inline(self._K_code_X, X, target)
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else:
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self._weave_inline(self._K_code, X, target, Z)
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self._generate_inline(self._K_code, X, target, Z)
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def Kdiag(self,X,target):
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self._weave_inline(self._Kdiag_code, X, target)
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self._generate_inline(self._Kdiag_code, X, target)
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def _param_grad_helper(self,partial,X,Z,target):
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if Z is None:
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self._weave_inline(self._dK_dtheta_code_X, X, target, Z, partial)
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self._generate_inline(self._dK_dtheta_code_X, X, target, Z, partial)
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else:
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self._weave_inline(self._dK_dtheta_code, X, target, Z, partial)
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self._generate_inline(self._dK_dtheta_code, X, target, Z, partial)
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def dKdiag_dtheta(self,partial,X,target):
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self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
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self._generate_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
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def gradients_X(self,partial,X,Z,target):
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if Z is None:
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self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
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self._generate_inline(self._dK_dX_code_X, X, target, Z, partial)
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else:
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self._weave_inline(self._dK_dX_code, X, target, Z, partial)
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self._generate_inline(self._dK_dX_code, X, target, Z, partial)
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def dKdiag_dX(self,partial,X,target):
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self._weave.inline(self._dKdiag_dX_code, X, target, Z, partial)
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self._generate_inline(self._dKdiag_dX_code, X, target, Z, partial)
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def compute_psi_stats(self):
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#define some normal distributions
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@ -407,31 +461,34 @@ class spkern(Kernpart):
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self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
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clear_cache()
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self._sp_psi2 = self._sp_psi2.simplify()
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def parameters_changed(self):
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# Do anything here that needs to happen when parameters change, like precompute.
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self._generate_inline(self._precompute, X, target, Z, partial)
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def _set_params(self,param):
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assert param.size == (self.num_params)
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for i, shared_params in enumerate(self._sp_theta):
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setattr(self, shared_params.name, param[i])
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# def _set_params(self,param):
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# assert param.size == (self.num_params)
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# for i, shared_params in enumerate(self._sp_theta):
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# setattr(self, shared_params.name, param[i])
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if self.output_dim>1:
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for i, split_params in enumerate(self._split_theta_names):
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start = self.num_shared_params + i*self.output_dim
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end = self.num_shared_params + (i+1)*self.output_dim
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setattr(self, split_params, param[start:end])
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# if self.output_dim>1:
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# for i, split_params in enumerate(self._split_theta_names):
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# start = self.num_shared_params + i*self.output_dim
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# end = self.num_shared_params + (i+1)*self.output_dim
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# setattr(self, split_params, param[start:end])
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def _get_params(self):
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params = np.zeros(0)
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for shared_params in self._sp_theta:
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params = np.hstack((params, getattr(self, shared_params.name)))
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if self.output_dim>1:
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for split_params in self._split_theta_names:
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params = np.hstack((params, getattr(self, split_params).flatten()))
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return params
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# def _get_params(self):
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# params = np.zeros(0)
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# for shared_params in self._sp_theta:
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# params = np.hstack((params, getattr(self, shared_params.name)))
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# if self.output_dim>1:
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# for split_params in self._split_theta_names:
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# params = np.hstack((params, getattr(self, split_params).flatten()))
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# return params
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def _get_param_names(self):
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if self.output_dim>1:
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return [x.name for x in self._sp_theta] + [x.name[:-2] + str(i) for x in self._sp_theta_i for i in range(self.output_dim)]
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else:
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return [x.name for x in self._sp_theta]
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# def _get_param_names(self):
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# if self.output_dim>1:
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# return [x.name for x in self._sp_theta] + [x.name[:-2] + str(i) for x in self._sp_theta_i for i in range(self.output_dim)]
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# else:
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# return [x.name for x in self._sp_theta]
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