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modifiying stationary.py
This commit is contained in:
James Hensman
parent
59cf0b3c9a
commit
780cf85687
1 changed files with 21 additions and 98 deletions
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@ -9,14 +9,15 @@ from ...util.linalg import tdot
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from ... import util
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import numpy as np
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from scipy import integrate
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from ...util.config import config # for assesing whether to use weave
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from ...util.config import config # for assesing whether to use cython
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from ...util.caching import Cache_this
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import stationary_cython
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try:
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from scipy import weave
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import stationary_cython
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except ImportError:
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config.set('weave', 'working', 'False')
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print('warning: failed to import cython module: falling back to numpy')
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config.set('cython', 'working', 'false')
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class Stationary(Kern):
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"""
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@ -159,21 +160,11 @@ class Stationary(Kern):
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#now the lengthscale gradient(s)
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dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
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if self.ARD:
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#rinv = self._inv_dis# this is rather high memory? Should we loop instead?t(X, X2)
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#d = X[:, None, :] - X2[None, :, :]
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#x_xl3 = np.square(d)
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#self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3
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tmp = dL_dr*self._inv_dist(X, X2)
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if X2 is None: X2 = X
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if config.getboolean('weave', 'working'):
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try:
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self.lengthscale.gradient = self.weave_lengthscale_grads(tmp, X, X2)
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except:
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print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n")
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config.set('weave', 'working', 'False')
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self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in range(self.input_dim)])
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if config.getboolean('cython', 'working'):
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self.lengthscale.gradient = self._lengthscale_grads_cython(tmp, X, X2)
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else:
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self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in range(self.input_dim)])
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else:
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@ -190,29 +181,7 @@ class Stationary(Kern):
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dist = self._scaled_dist(X, X2).copy()
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return 1./np.where(dist != 0., dist, np.inf)
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def weave_lengthscale_grads(self, tmp, X, X2):
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"""Use scipy.weave to compute derivatives wrt the lengthscales"""
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N,M = tmp.shape
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Q = X.shape[1]
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if hasattr(X, 'values'):X = X.values
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if hasattr(X2, 'values'):X2 = X2.values
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grads = np.zeros(self.input_dim)
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code = """
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double gradq;
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for(int q=0; q<Q; q++){
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gradq = 0;
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for(int n=0; n<N; n++){
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for(int m=0; m<M; m++){
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gradq += tmp(n,m)*(X(n,q)-X2(m,q))*(X(n,q)-X2(m,q));
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}
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}
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grads(q) = gradq;
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}
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"""
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weave.inline(code, ['tmp', 'X', 'X2', 'grads', 'N', 'M', 'Q'], type_converters=weave.converters.blitz, support_code="#include <math.h>")
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return -grads/self.lengthscale**3
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def cython_lengthscale_grads(self, tmp, X, X2):
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def _lengthscale_grads_cython(self, tmp, X, X2):
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N,M = tmp.shape
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Q = X.shape[1]
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if hasattr(X, 'values'):X = X.values
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@ -221,23 +190,16 @@ class Stationary(Kern):
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stationary_cython.lengthscale_grads(N, M, Q, tmp, X, X2, grads)
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return -grads/self.lengthscale**3
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def gradients_X(self, dL_dK, X, X2=None):
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"""
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Given the derivative of the objective wrt K (dL_dK), compute the derivative wrt X
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"""
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if config.getboolean('weave', 'working'):
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try:
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return self.gradients_X_weave(dL_dK, X, X2)
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except:
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print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n")
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config.set('weave', 'working', 'False')
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return self.gradients_X_(dL_dK, X, X2)
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if config.getboolean('cython', 'working'):
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return self._gradients_X_cython(dL_dK, X, X2)
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else:
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return self.gradients_X_(dL_dK, X, X2)
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return self._gradients_X_pure(dL_dK, X, X2)
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def gradients_X_(self, dL_dK, X, X2=None):
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def _gradients_X_pure(self, dL_dK, X, X2=None):
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invdist = self._inv_dist(X, X2)
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dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
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tmp = invdist*dL_dr
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@ -247,17 +209,15 @@ class Stationary(Kern):
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#The high-memory numpy way:
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#d = X[:, None, :] - X2[None, :, :]
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#ret = np.sum(tmp[:,:,None]*d,1)/self.lengthscale**2
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#grad = np.sum(tmp[:,:,None]*d,1)/self.lengthscale**2
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#the lower memory way with a loop
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ret = np.empty(X.shape, dtype=np.float64)
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grad = np.empty(X.shape, dtype=np.float64)
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for q in range(self.input_dim):
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np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q])
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ret /= self.lengthscale**2
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np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=grad[:,q])
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return grad/self.lengthscale**2
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return ret
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def gradients_X_cython(self, dL_dK, X, X2=None):
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def _gradients_X_cython(self, dL_dK, X, X2=None):
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invdist = self._inv_dist(X, X2)
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dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
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tmp = invdist*dL_dr
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@ -270,52 +230,15 @@ class Stationary(Kern):
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stationary_cython.grad_X(X.shape[0], X.shape[1], X2.shape[0], X, X2, tmp, grad)
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return grad/self.lengthscale**2
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def gradients_X_weave(self, dL_dK, X, X2=None):
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invdist = self._inv_dist(X, X2)
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dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
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tmp = invdist*dL_dr
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if X2 is None:
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tmp = tmp + tmp.T
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X2 = X
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code = """
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int n,m,d;
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double retnd;
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#pragma omp parallel for private(n,d, retnd, m)
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for(d=0;d<D;d++){
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for(n=0;n<N;n++){
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retnd = 0.0;
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for(m=0;m<M;m++){
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retnd += tmp(n,m)*(X(n,d)-X2(m,d));
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}
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ret(n,d) = retnd;
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}
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}
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"""
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if hasattr(X, 'values'):X = X.values #remove the GPy wrapping to make passing into weave safe
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if hasattr(X2, 'values'):X2 = X2.values
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ret = np.zeros(X.shape)
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N,D = X.shape
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N,M = tmp.shape
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from scipy import weave
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support_code = """
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#include <omp.h>
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#include <stdio.h>
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"""
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weave_options = {'headers' : ['<omp.h>'],
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'extra_compile_args': ['-fopenmp -O3'], # -march=native'],
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'extra_link_args' : ['-lgomp']}
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weave.inline(code, ['ret', 'N', 'D', 'M', 'tmp', 'X', 'X2'], type_converters=weave.converters.blitz, support_code=support_code, **weave_options)
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return ret/self.lengthscale**2
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def gradients_X_diag(self, dL_dKdiag, X):
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return np.zeros(X.shape)
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def input_sensitivity(self, summarize=True):
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return self.variance*np.ones(self.input_dim)/self.lengthscale**2
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class Exponential(Stationary):
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def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Exponential'):
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super(Exponential, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
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