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Adding update_gradients to sympy.py.

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This commit is contained in:
Neil Lawrence 2014-02-19 19:39:24 -05:00
parent f6484bcbd0
commit 5214c3c1ac
2 changed files with 115 additions and 86 deletions
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2
GPy/kern/parts/rbf.py
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@ -109,7 +109,7 @@ class RBF(Kernpart):
self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dK, X, None)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)
b
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
#contributions from Kdiag
self.variance.gradient = np.sum(dL_dKdiag)

199
GPy/kern/parts/sympykern.py
View file

@ -26,6 +26,8 @@ import ast
from kernpart import Kernpart
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
# TODO have this set up in a set up file!
user_code_storage = tempfile.gettempdir()
class spkern(Kernpart):
"""
@ -61,13 +63,12 @@ class spkern(Kernpart):
assert all([x.name=='x_%i'%i for i,x in enumerate(self._sp_x)])
assert all([z.name=='z_%i'%i for i,z in enumerate(self._sp_z)])
assert len(self._sp_x)==len(self._sp_z)
assert len(self._sp_x)==input_dim
x_dim=len(self._sp_x)
# If it is a multi-output covariance, add an input for indexing the outputs.
self._real_input_dim = self.input_dim
if output_dim > 1:
self.input_dim += 1
assert self.input_dim == input_dim
self._real_input_dim = x_dim
# Check input dim is number of xs + 1 if output_dim is >1
assert self.input_dim == x_dim + int(output_dim > 1)
self.output_dim = output_dim
# extract parameter names from the covariance
@ -113,7 +114,6 @@ class spkern(Kernpart):
#setattr(self, theta.name, val)
setattr(self, theta.name, Param(theta.name, val, None))
self.add_parameters(getattr(self, theta.name))
self.parameters_changed() # initializes cache
#deal with param
#self._set_params(self._get_params())
@ -139,13 +139,15 @@ class spkern(Kernpart):
extra_compile_args = []
self.weave_kwargs = {
'support_code':self._function_code,
'include_dirs':[tempfile.gettempdir(), os.path.join(current_dir,'parts/')],
'headers':['"sympy_helpers.h"'],
'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")],
'support_code': None, #self._function_code,
'include_dirs':[user_code_storage, os.path.join(current_dir,'parts/')],
'headers':['"sympy_helpers.h"', '"'+self.name+'.h"'],
'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp"), os.path.join(user_code_storage, self.name+'.cpp')],
'extra_compile_args':extra_compile_args,
'extra_link_args':['-lgomp'],
'verbose':True}
self.parameters_changed() # initializes caches
def __add__(self,other):
return spkern(self._sp_k+other._sp_k)
@ -177,31 +179,39 @@ class spkern(Kernpart):
# Use weave to compute the underlying functions.
if weave_available:
# put the header file where we can find it
f = file(os.path.join(tempfile.gettempdir(), self.name + '.h'),'w')
f = file(os.path.join(user_code_storage, self.name + '.h'),'w')
f.write(self._function_header)
f.close()
# Substitute any known derivatives which sympy doesn't compute
self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
if weave_available:
# Substitute any known derivatives which sympy doesn't compute
self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
# put the cpp file in user code storage (defaults to temp file location)
f = file(os.path.join(user_code_storage, self.name + '.cpp'),'w')
else:
# put the python file in user code storage
f = file(os.path.join(user_code_storage, self.name + '.py'),'w')
f.write(self._function_code)
f.close()
if weave_available:
# arg_list will store the arguments required for the C code.
arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
input_arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
+ ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
# for multiple outputs reverse argument list is also required
if self.output_dim>1:
reverse_arg_list = list(arg_list)
reverse_arg_list.reverse()
reverse_input_arg_list = list(input_arg_list)
reverse_input_arg_list.reverse()
# This gives the parameters for the arg list.
param_arg_list = [shared_params.name for shared_params in self._sp_theta]
arg_list += param_arg_list
arg_list = input_arg_list + param_arg_list
precompute_list=[]
if self.output_dim > 1:
reverse_arg_list+=list(param_arg_list)
reverse_arg_list= reverse_input_arg_list + list(param_arg_list)
# For multiple outputs, also need the split parameters.
split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
@ -218,9 +228,9 @@ class spkern(Kernpart):
# Any precomputations will be done here eventually.
self._precompute = \
"""
// Precompute code would go here. It will be called when parameters are updated.
"""
"""
// Precompute code would go here. It will be called when parameters are updated.
"""
# Here's the code to do the looping for K
self._K_code =\
@ -229,11 +239,11 @@ class spkern(Kernpart):
// Code for computing the covariance function.
int i;
int j;
int N = target_array->dimensions[0];
int n = target_array->dimensions[0];
int num_inducing = target_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N;i++){
for (i=0;i<n;i++){
for (j=0;j<num_inducing;j++){
%s
//target[i*num_inducing+j] =
@ -244,6 +254,7 @@ class spkern(Kernpart):
"""%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
# adding a string representation of the function in the
# comment forces recompile when needed
self._K_code_X = self._K_code.replace('Z2(', 'X2(')
# Code to compute diagonal of covariance.
@ -259,10 +270,10 @@ class spkern(Kernpart):
// _Kdiag_code
// Code for computing diagonal of covariance function.
int i;
int N = target_array->dimensions[0];
int n = target_array->dimensions[0];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for
for (i=0;i<N;i++){
for (i=0;i<n;i++){
%s
//target[i] =
TARGET1(i)=k(%s);
@ -274,9 +285,9 @@ class spkern(Kernpart):
grad_func_list = []
if self.output_dim>1:
grad_func_list += c_define_output_indices
grad_func_list += [' '*16 + 'TARGET1(%i+ii) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, arg_string) for i, theta in enumerate(self._sp_theta_i)]
grad_func_list += [' '*16 + 'TARGET1(%i+jj) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, reverse_arg_string) for i, theta in enumerate(self._sp_theta_i)]
grad_func_list += ([' '*16 + 'TARGET1(%i) += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in enumerate(self._sp_theta)])
grad_func_list += [' '*16 + 'TARGET1(%i+ii) += PARTIAL2(i, j)*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, arg_string) for i, theta in enumerate(self._sp_theta_i)]
grad_func_list += [' '*16 + 'TARGET1(%i+jj) += PARTIAL2(i, j)*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, reverse_arg_string) for i, theta in enumerate(self._sp_theta_i)]
grad_func_list += ([' '*16 + 'TARGET1(%i) += PARTIAL2(i, j)*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in enumerate(self._sp_theta)])
grad_func_string = '\n'.join(grad_func_list)
self._dK_dtheta_code =\
@ -285,17 +296,18 @@ class spkern(Kernpart):
// Code for computing gradient of covariance with respect to parameters.
int i;
int j;
int N = partial_array->dimensions[0];
int n = partial_array->dimensions[0];
int num_inducing = partial_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N;i++){
for (i=0;i<n;i++){
for (j=0;j<num_inducing;j++){
%s
%s
}
}
%s
"""%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z2(', 'X2(')
# Code to compute gradients for Kdiag TODO: needs clean up
@ -308,9 +320,9 @@ class spkern(Kernpart):
// _dKdiag_dtheta_code
// Code for computing gradient of diagonal with respect to parameters.
int i;
int N = partial_array->dimensions[0];
int n = partial_array->dimensions[0];
int input_dim = X_array->dimensions[1];
for (i=0;i<N;i++){
for (i=0;i<n;i++){
%s
}
%s
@ -329,32 +341,33 @@ class spkern(Kernpart):
// Code for computing gradient of covariance with respect to inputs.
int i;
int j;
int N = partial_array->dimensions[0];
int n = partial_array->dimensions[0];
int num_inducing = partial_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N; i++){
for (i=0;i<n; i++){
for (j=0; j<num_inducing; j++){
%s
}
}
%s
"""%(gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
self._dK_dX_code_X = self._dK_dX_code.replace('Z2(', 'X2(')
diag_gradX_func_string = re.sub('Z','X',gradX_func_string,count=0)
diag_gradX_func_string = re.sub('int jj','//int jj',diag_gradX_func_string)
diag_gradX_func_string = re.sub('j','i',diag_gradX_func_string)
diag_gradX_func_string = re.sub('partial\[i\*num_inducing\+i\]','2*partial[i]',diag_gradX_func_string)
diag_gradX_func_string = re.sub('PARTIAL2\(i\, i\)','2*PARTIAL(i)',diag_gradX_func_string)
# Code for gradients of Kdiag wrt X
self._dKdiag_dX_code= \
"""
// _dKdiag_dX_code
// Code for computing gradient of diagonal with respect to inputs.
int N = partial_array->dimensions[0];
int n = partial_array->dimensions[0];
int input_dim = X_array->dimensions[1];
for (int i=0;i<N; i++){
for (int i=0;i<n; i++){
%s
}
%s
@ -364,13 +377,7 @@ class spkern(Kernpart):
# diag_func_string called here? Need to check that.
#self._dKdiag_dX_code = self._dKdiag_dX_code.replace('Z[j', 'X[i')
# Code to use when only X is provided.
self._K_code_X = self._K_code.replace('Z[', 'X[')
self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z[', 'X[')
self._dK_dX_code_X = self._dK_dX_code.replace('Z[', 'X[').replace('+= partial[', '+= 2*partial[')
self._K_code_X = self._K_code.replace('Z2(', 'X2(')
self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z2(', 'X2(')
self._dK_dX_code_X = self._dK_dX_code.replace('Z2(', 'X2(')
#TODO: insert multiple functions here via string manipulation
@ -378,9 +385,10 @@ class spkern(Kernpart):
#TODO: similar functions when cython available.
#TODO: similar functions when only python available.
def _get_arg_names(self, Z=None, partial=None):
arg_names = ['target','X']
def _get_arg_names(self, target=None, Z=None, partial=None):
arg_names = ['X']
if target is not None:
arg_names += ['target']
for shared_params in self._sp_theta:
arg_names += [shared_params.name]
if Z is not None:
@ -392,20 +400,21 @@ class spkern(Kernpart):
arg_names += ['output_dim']
return arg_names
def _generate_inline(self, code, X, target, Z=None, partial=None):
def _generate_inline(self, code, X, target=None, Z=None, partial=None):
output_dim = self.output_dim
# Need to extract parameters to local variables first
for shared_params in self._sp_theta:
locals()[shared_params.name] = getattr(self, shared_params.name)
# Need to extract parameters first
for split_params in self._split_theta_names:
locals()[split_params] = getattr(self, split_params)
arg_names = self._get_arg_names(Z, partial)
locals()[split_params] = np.asarray(getattr(self, split_params))
arg_names = self._get_arg_names(target, Z, partial)
if weave_available:
weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
return weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
else:
raise RuntimeError('Weave not available and other variants of sympy covariance not yet implemented')
def K(self,X,Z,target):
if Z is None:
self._generate_inline(self._K_code_X, X, target)
@ -421,9 +430,9 @@ class spkern(Kernpart):
self._generate_inline(self._dK_dtheta_code_X, X, target, Z, partial)
else:
self._generate_inline(self._dK_dtheta_code, X, target, Z, partial)
def dKdiag_dtheta(self,partial,X,target):
self._generate_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
self._generate_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial).namelocals()[shared_params.name] = getattr(self, shared_params.name)
def gradients_X(self,partial,X,Z,target):
if Z is None:
@ -461,34 +470,54 @@ class spkern(Kernpart):
self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
clear_cache()
self._sp_psi2 = self._sp_psi2.simplify()
def parameters_changed(self):
# Do anything here that needs to happen when parameters change, like precompute.
self._generate_inline(self._precompute, X, target, Z, partial)
# Reset the caches
self._cache, self._cache2 = np.empty(shape=(2, 1))
self._cache3, self._cache4, self._cache5 = np.empty(shape=(3, 1))
def update_gradients_full(self, dL_dK, X):
# Need to extract parameters to local variables first
self._K_computations(X, None)
for shared_params in self._sp_theta:
parameter = getattr(self, shared_params.name)
code = getattr(self, '_dK_d' + shared_params.name + '_code')
setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
for split_params in self._split_theta_names:
parameter = getattr(self, split_params.name)
code = getattr(self, '_dK_d' + split_params.name + '_code')
setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
#contributions from Kdiag
self.variance.gradient = np.sum(dL_dKdiag)
#from Knm
self._K_computations(X, Z)
self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
if self.ARD:
self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
#from Kmm
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
# def _set_params(self,param):
# assert param.size == (self.num_params)
# for i, shared_params in enumerate(self._sp_theta):
# setattr(self, shared_params.name, param[i])
# if self.output_dim>1:
# for i, split_params in enumerate(self._split_theta_names):
# start = self.num_shared_params + i*self.output_dim
# end = self.num_shared_params + (i+1)*self.output_dim
# setattr(self, split_params, param[start:end])
#---------------------------------------#
# Precomputations #
#---------------------------------------#
# def _get_params(self):
# params = np.zeros(0)
# for shared_params in self._sp_theta:
# params = np.hstack((params, getattr(self, shared_params.name)))
# if self.output_dim>1:
# for split_params in self._split_theta_names:
# params = np.hstack((params, getattr(self, split_params).flatten()))
# return params
# def _get_param_names(self):
# if self.output_dim>1:
# 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)]
# else:
# return [x.name for x in self._sp_theta]
def _K_computations(self, X, Z):
if Z is None:
self._generate_inline(self._precompute, X)
else:
self._generate_inline(self._precompute, X, Z=Z)