def get_H(self,mvals=None,customdir=None):
"""Computes the objective function contribution and its gradient / Hessian.
First the low-level 'get' method is called with the analytic gradient
and Hessian both turned on. Then we loop through the fd1_pids and compute
the corresponding elements of the gradient by finite difference,
if the 'fdgrad' switch is turned on.
This is followed by looping through the fd2_pids and computing the corresponding
Hessian elements by finite difference. Forward finite difference is used
throughout for the sake of speed.
"""
Ans = self.meta_get(mvals,1,1,customdir=customdir)
if self.fdhess:
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd1_pids]) or 'ALL' in self.fd1_pids:
Ans['G'][i] = f1d2p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd2_pids]) or 'ALL' in self.fd2_pids:
FDSlice = f1d2p(fdwrap_H(self,mvals,i),self.h,f0 = Ans['G'])
Ans['H'][i,:] = FDSlice
Ans['H'][:,i] = FDSlice
elif self.fdhessdiag:
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd2_pids]) or 'ALL' in self.fd2_pids:
Ans['G'][i], Ans['H'][i,i] = f12d3p(fdwrap_G(self,mvals,i),self.h, f0 = Ans['X'])
if Counter() == self.zerograd and self.zerograd >= 0:
self.write_0grads(Ans)
self.hct += 1
return Ans
def get_H(self,mvals=None):
"""Computes the objective function contribution and its gradient / Hessian.
First the low-level 'get' method is called with the analytic gradient
and Hessian both turned on. Then we loop through the fd1_pids and compute
the corresponding elements of the gradient by finite difference,
if the 'fdgrad' switch is turned on.
This is followed by looping through the fd2_pids and computing the corresponding
Hessian elements by finite difference. Forward finite difference is used
throughout for the sake of speed.
"""
Ans = self.meta_get(mvals,1,1)
if self.fdhess:
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd1_pids]) or 'ALL' in self.fd1_pids:
Ans['G'][i] = f1d2p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd2_pids]) or 'ALL' in self.fd2_pids:
FDSlice = f1d2p(fdwrap_H(self,mvals,i),self.h,f0 = Ans['G'])
Ans['H'][i,:] = FDSlice
Ans['H'][:,i] = FDSlice
elif self.fdhessdiag:
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd2_pids]) or 'ALL' in self.fd2_pids:
Ans['G'][i], Ans['H'][i,i] = f12d3p(fdwrap_G(self,mvals,i),self.h, f0 = Ans['X'])
self.write_0grads(Ans)
self.hct += 1
return Ans
def get_G(self,mvals=None):
"""Computes the objective function contribution and its gradient.
First the low-level 'get' method is called with the analytic gradient
switch turned on. Then we loop through the fd1_pids and compute
the corresponding elements of the gradient by finite difference,
if the 'fdgrad' switch is turned on. Alternately we can compute
the gradient elements and diagonal Hessian elements at the same time
using central difference if 'fdhessdiag' is turned on.
In this function we also record which parameters cause a
nonzero change in the objective function contribution.
Parameters which do not change the objective function will
not be differentiated in subsequent calculations. This is
recorded in a text file in the targets directory.
"""
Ans = self.meta_get(mvals,1,0)
for i in self.pgrad:
if any([j in self.FF.plist[i] for j in self.fd1_pids]) or 'ALL' in self.fd1_pids:
if self.fdhessdiag:
Ans['G'][i], Ans['H'][i,i] = f12d3p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
elif self.fdgrad:
Ans['G'][i] = f1d2p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
self.gct += 1
self.write_0grads(Ans)
return Ans
def get_G(self,mvals=None):
"""Computes the objective function contribution and its gradient.
First the low-level 'get' method is called with the analytic gradient
switch turned on. Then we loop through the fd1_pids and compute
the corresponding elements of the gradient by finite difference,
if the 'fdgrad' switch is turned on. Alternately we can compute
the gradient elements and diagonal Hessian elements at the same time
using central difference if 'fdhessdiag' is turned on.
"""
Ans = self.sget(mvals,1,0)
for i in range(self.FF.np):
if any([j in self.FF.plist[i] for j in self.fd1_pids]) or 'ALL' in self.fd1_pids:
if self.fdhessdiag:
Ans['G'][i], Ans['H'][i,i] = f12d3p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
elif self.fdgrad:
Ans['G'][i] = f1d2p(fdwrap_G(self,mvals,i),self.h,f0 = Ans['X'])
self.gct += 1
return Ans
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 05-30-2012
This subroutine builds the objective function (and optionally
its derivatives) from a general software.
This subroutine interfaces with simulation software 'drivers'.
The driver is expected to give exact values, fitting values, and weights.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
global LAST_MVALS, CHECK_BASIS
# print mvals
# print LAST_MVALS
# print mvals == LAST_MVALS
if LAST_MVALS is None or not (mvals == LAST_MVALS).all():
CHECK_BASIS = False
else:
CHECK_BASIS = False
Answer = {}
Fac = 1000000
## Dictionary for derivative terms
dM = {}
# Create the new force field!!
NP = len(mvals)
G = np.zeros(NP)
H = np.zeros((NP, NP))
pvals = self.FF.make(mvals)
if float('Inf') in pvals:
return {'X': 1e10, 'G': G, 'H': H}
Ans = self.driver()
W = Ans[:, 2]
M = Ans[:, 1]
Q = Ans[:, 0]
D = M - Q
self.MAQ = np.mean(np.abs(Q))
ns = len(M)
# Wrapper to the driver, which returns just the part that changes.
def callM(mvals_):
self.FF.make(mvals_)
Ans2 = self.driver()
M_ = Ans2[:, 1]
D_ = M_ - Q
return Ans2[:, 1]
if AGrad:
# Leaving comment here if we want to reintroduce second deriv someday.
# dM[p,:], ddM[p,:] = f12d3p(fdwrap(callM, mvals, p), h = self.h, f0 = M)
xgrad = []
for p in self.pgrad:
dM_arr = f1d2p(fdwrap(callM, mvals, p), h=self.h, f0=M)
if np.max(np.abs(dM_arr)) == 0.0 and (not self.evaluated):
logger.info(
"\r Simulation %s will skip over parameter %i in subsequent steps\n"
% (self.name, p))
xgrad.append(p)
else:
dM[p] = dM_arr.copy()
for p in xgrad:
self.pgrad.remove(p)
Objective = np.dot(W, D**2) * Fac
if AGrad:
for p in self.pgrad:
G[p] = 2 * np.dot(W, D * dM[p])
if not AHess: continue
H[p, p] = 2 * np.dot(W, dM[p]**2)
for q in range(p):
if q not in self.pgrad: continue
GNP = 2 * np.dot(W, dM[p] * dM[q])
H[q, p] = GNP
H[p, q] = GNP
G *= Fac
H *= Fac
Answer = {'X': Objective, 'G': G, 'H': H}
if not in_fd():
self.D = D
self.objective = Answer['X']
LAST_MVALS = mvals.copy()
return Answer
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 05-30-2012
This subroutine builds the objective function (and optionally
its derivatives) from a general software.
This subroutine interfaces with simulation software 'drivers'.
The driver is expected to give exact values, fitting values, and weights.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
global LAST_MVALS, CHECK_BASIS
# print mvals
# print LAST_MVALS
# print mvals == LAST_MVALS
if LAST_MVALS is None or not (mvals == LAST_MVALS).all():
CHECK_BASIS = False
else:
CHECK_BASIS = False
Answer = {}
Fac = 1000000
## Dictionary for derivative terms
dM = {}
# Create the new force field!!
NP = len(mvals)
G = np.zeros(NP)
H = np.zeros((NP,NP))
pvals = self.FF.make(mvals)
if float('Inf') in pvals:
return {'X' : 1e10, 'G' : G, 'H' : H}
Ans = self.driver()
W = Ans[:,2]
M = Ans[:,1]
Q = Ans[:,0]
D = M - Q
self.MAQ = np.mean(np.abs(Q))
ns = len(M)
# Wrapper to the driver, which returns just the part that changes.
def callM(mvals_):
self.FF.make(mvals_)
Ans2 = self.driver()
M_ = Ans2[:,1]
D_ = M_ - Q
return Ans2[:,1]
if AGrad:
# Leaving comment here if we want to reintroduce second deriv someday.
# dM[p,:], ddM[p,:] = f12d3p(fdwrap(callM, mvals, p), h = self.h, f0 = M)
xgrad = []
for p in self.pgrad:
dM_arr = f1d2p(fdwrap(callM, mvals, p), h = self.h, f0 = M)
if np.max(np.abs(dM_arr)) == 0.0 and (not self.evaluated):
logger.info("\r Simulation %s will skip over parameter %i in subsequent steps\n" % (self.name, p))
xgrad.append(p)
else:
dM[p] = dM_arr.copy()
for p in xgrad:
self.pgrad.remove(p)
Objective = np.dot(W, D**2) * Fac
if AGrad:
for p in self.pgrad:
G[p] = 2 * np.dot(W, D*dM[p])
if not AHess: continue
H[p, p] = 2 * np.dot(W, dM[p]**2)
for q in range(p):
if q not in self.pgrad: continue
GNP = 2 * np.dot(W, dM[p] * dM[q])
H[q,p] = GNP
H[p,q] = GNP
G *= Fac
H *= Fac
Answer = {'X':Objective, 'G':G, 'H':H}
if not in_fd():
self.D = D
self.objective = Answer['X']
LAST_MVALS = mvals.copy()
return Answer
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 04-17-2013
This subroutine builds the objective function from Psi4.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
Answer = {}
Fac = 1000000
n = len(mvals)
X = 0.0
G = np.zeros(n)
H = np.zeros((n,n))
pvals = self.FF.make(mvals)
self.tdir = os.getcwd()
self.objd = OrderedDict()
self.gradd = OrderedDict()
self.hdiagd = OrderedDict()
wq = getWorkQueue()
def fdwrap2(func,mvals0,pidx,qidx,key=None,**kwargs):
def func2(arg1,arg2):
mvals = list(mvals0)
mvals[pidx] += arg1
mvals[qidx] += arg2
logger.info("\rfdwrap2:" + func.__name__ + "[%i] = % .1e , [%i] = % .1e" % (pidx, arg1, qidx, arg2) + ' '*50)
if key != None:
return func(mvals,**kwargs)[key]
else:
return func(mvals,**kwargs)
return func2
def f2d5p(f, h):
fpp, fpm, fmp, fmm = [f(i*h,j*h) for i,j in [(1,1),(1,-1),(-1,1),(-1,-1)]]
fpp = (fpp-fpm-fmp+fmm)/(4*h*h)
return fpp
def f2d4p(f, h, f0 = None):
if f0 == None:
fpp, fp0, f0p, f0 = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1),(0,0)]]
else:
fpp, fp0, f0p = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1)]]
fpp = (fpp-fp0-f0p+f0)/(h*h)
return fpp
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50*' ' + '\r')
if wq == None:
x = self.driver(mvals, d)
grad = np.zeros(n)
hdiag = np.zeros(n)
hess = np.zeros((n,n))
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
for p in range(self.FF.np):
if self.callderivs[d][p]:
def reader(mvals_,h):
apath = os.path.join(self.tdir, d, str(p), str(h))
answer = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
return answer
if AHess:
if wq != None:
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
grad[p], hdiag[p] = f12d3p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p], hdiag[p] = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
hess[p,p] = hdiag[p]
elif AGrad:
if self.bidirect:
if wq != None:
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
grad[p], _ = f12d3p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p], _ = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
else:
if wq != None:
# Since the calculations are submitted as 3-point finite difference, this part of the code
# actually only reads from half of the completed calculations.
grad[p] = f1d2p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p] = f1d2p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
self.objd[d] = x
self.gradd[d] = grad
self.hdiagd[d] = hdiag
X += x
G += grad
#H += np.diag(hdiag)
H += hess
if not in_fd():
self.objective = X
self.objvals = self.objd
# print self.objd
# print self.gradd
# print self.hdiagd
if float('Inf') in pvals:
return {'X' : 1e10, 'G' : G, 'H' : H}
return {'X' : X, 'G' : G, 'H' : H}
def submit_jobs(self, mvals, AGrad=True, AHess=True):
# This routine is called by Objective.stage() will run before "get".
# It submits the jobs to the Work Queue and the stage() function will wait for jobs to complete.
#
self.tdir = os.getcwd()
wq = getWorkQueue()
if wq == None:
return
def submit_psi(this_apath, mname, these_mvals):
""" Create a grid file and a psi4 input file in the absolute path and submit it to the work queue. """
cwd = os.getcwd()
if not os.path.exists(this_apath) : os.makedirs(this_apath)
os.chdir(this_apath)
self.FF.make(these_mvals)
o = wopen('objective.dat')
for line in self.objfiles[d]:
s = line.split()
if len(s) > 2 and s[0] == 'path' and s[1] == '=':
print >> o, "path = '%s'" % os.getcwd()
elif len(s) > 2 and s[0] == 'set' and s[1] == 'objective_path':
print >> o, "opath = '%s'" % os.getcwd()
print >> o, "set objective_path $opath"
else:
print >> o, line,
o.close()
os.system("rm -f objective.out")
if wq == None:
logger.info("There is no Work Queue!!!\n")
sys.exit()
else:
input_files = [(os.path.join(this_apath, i), i) for i in glob.glob("*")]
# input_files += [(os.path.join(self.tgtdir,d,"build.dat"), "build.dat")]
input_files += [(os.path.join(os.path.split(__file__)[0],"data","run_psi_rdvr3_objective.sh"), "run_psi_rdvr3_objective.sh")]
logger.info("\r")
queue_up_src_dest(wq,"sh run_psi_rdvr3_objective.sh %s &> run_psi_rdvr3_objective.log" % mname,
input_files=input_files,
output_files=[(os.path.join(this_apath, i),i) for i in ["run_psi_rdvr3_objective.log", "output.dat"]], verbose=False)
os.chdir(cwd)
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50*' ' + '\r')
odir = os.path.join(os.getcwd(),d)
#if os.path.exists(odir):
# shutil.rmtree(odir)
if not os.path.exists(odir): os.makedirs(odir)
apath = os.path.join(odir, "current")
submit_psi(apath, d, mvals)
for p in range(self.FF.np):
def subjob(mvals_,h):
apath = os.path.join(odir, str(p), str(h))
submit_psi(apath, d, mvals_)
#logger.info("Will set up a job for %s, parameter %i\n" % (d, p))
return 0.0
if self.callderivs[d][p]:
if AHess:
f12d3p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
elif AGrad:
if self.bidirect:
f12d3p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
else:
f1d2p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 04-17-2013
This subroutine builds the objective function from Psi4.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
Answer = {}
Fac = 1000000
n = len(mvals)
X = 0.0
G = np.zeros(n)
H = np.zeros((n, n))
pvals = self.FF.make(mvals)
self.tdir = os.getcwd()
self.objd = OrderedDict()
self.gradd = OrderedDict()
self.hdiagd = OrderedDict()
wq = getWorkQueue()
def fdwrap2(func, mvals0, pidx, qidx, key=None, **kwargs):
def func2(arg1, arg2):
mvals = list(mvals0)
mvals[pidx] += arg1
mvals[qidx] += arg2
logger.info("\rfdwrap2:" + func.__name__ +
"[%i] = % .1e , [%i] = % .1e" %
(pidx, arg1, qidx, arg2) + ' ' * 50)
if key is not None:
return func(mvals, **kwargs)[key]
else:
return func(mvals, **kwargs)
return func2
def f2d5p(f, h):
fpp, fpm, fmp, fmm = [
f(i * h, j * h)
for i, j in [(1, 1), (1, -1), (-1, 1), (-1, -1)]
]
fpp = (fpp - fpm - fmp + fmm) / (4 * h * h)
return fpp
def f2d4p(f, h, f0=None):
if f0 is None:
fpp, fp0, f0p, f0 = [
f(i * h, j * h)
for i, j in [(1, 1), (1, 0), (0, 1), (0, 0)]
]
else:
fpp, fp0, f0p = [
f(i * h, j * h) for i, j in [(1, 1), (1, 0), (0, 1)]
]
fpp = (fpp - fp0 - f0p + f0) / (h * h)
return fpp
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50 * ' ' + '\r')
if wq is None:
x = self.driver(mvals, d)
grad = np.zeros(n)
hdiag = np.zeros(n)
hess = np.zeros((n, n))
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(
apath,
'objective.out')).readlines()[0].split()[1]) * self.factor
for p in range(self.FF.np):
if self.callderivs[d][p]:
def reader(mvals_, h):
apath = os.path.join(self.tdir, d, str(p), str(h))
answer = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
return answer
if AHess:
if wq is not None:
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
grad[p], hdiag[p] = f12d3p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p], hdiag[p] = f12d3p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
hess[p, p] = hdiag[p]
elif AGrad:
if self.bidirect:
if wq is not None:
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
grad[p], _ = f12d3p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p], _ = f12d3p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
else:
if wq is not None:
# Since the calculations are submitted as 3-point finite difference, this part of the code
# actually only reads from half of the completed calculations.
grad[p] = f1d2p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p] = f1d2p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
self.objd[d] = x
self.gradd[d] = grad
self.hdiagd[d] = hdiag
X += x
G += grad
#H += np.diag(hdiag)
H += hess
if not in_fd():
self.objective = X
self.objvals = self.objd
# print self.objd
# print self.gradd
# print self.hdiagd
if float('Inf') in pvals:
return {'X': 1e10, 'G': G, 'H': H}
return {'X': X, 'G': G, 'H': H}
def submit_jobs(self, mvals, AGrad=True, AHess=True):
# This routine is called by Objective.stage() will run before "get".
# It submits the jobs to the Work Queue and the stage() function will wait for jobs to complete.
#
self.tdir = os.getcwd()
wq = getWorkQueue()
if wq is None:
return
def submit_psi(this_apath, dname, these_mvals):
""" Create a grid file and a psi4 input file in the absolute path and submit it to the work queue. """
cwd = os.getcwd()
if not os.path.exists(this_apath): os.makedirs(this_apath)
os.chdir(this_apath)
self.FF.make(these_mvals)
o = wopen('objective.dat')
for line in self.objfiles[d]:
s = line.split()
if len(s) > 2 and s[0] == 'path' and s[1] == '=':
print("path = '%s'" % os.getcwd(), file=o)
elif len(s) > 2 and s[0] == 'set' and s[1] == 'objective_path':
print("opath = '%s'" % os.getcwd(), file=o)
print("set objective_path $opath", file=o)
else:
print(line, end=' ', file=o)
o.close()
os.system("rm -f objective.out")
if wq is None:
logger.info("There is no Work Queue!!!\n")
sys.exit()
else:
input_files = [(os.path.join(this_apath, i), i)
for i in glob.glob("*")]
input_files += [(os.path.join(self.root, self.tgtdir, dname,
"build.dat"), "build.dat")]
input_files += [(os.path.join(
os.path.split(__file__)[0], "data",
"run_psi_rdvr3_objective.sh"),
"run_psi_rdvr3_objective.sh")]
logger.info("\r")
queue_up_src_dest(
wq,
"sh run_psi_rdvr3_objective.sh -c %s &> run_psi_rdvr3_objective.log"
% os.path.join(self.root, self.tgtdir, dname),
input_files=input_files,
output_files=[
(os.path.join(this_apath, i), i)
for i in ["run_psi_rdvr3_objective.log", "output.dat"]
],
verbose=False)
os.chdir(cwd)
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50 * ' ' + '\r')
odir = os.path.join(os.getcwd(), d)
#if os.path.exists(odir):
# shutil.rmtree(odir)
if not os.path.exists(odir): os.makedirs(odir)
apath = os.path.join(odir, "current")
submit_psi(apath, d, mvals)
for p in range(self.FF.np):
def subjob(mvals_, h):
apath = os.path.join(odir, str(p), str(h))
submit_psi(apath, d, mvals_)
#logger.info("Will set up a job for %s, parameter %i\n" % (d, p))
return 0.0
if self.callderivs[d][p]:
if AHess:
f12d3p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
elif AGrad:
if self.bidirect:
f12d3p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
else:
f1d2p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 04-17-2013
This subroutine builds the objective function from Psi4.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
Answer = {}
Fac = 1000000
n = len(mvals)
X = 0.0
G = np.zeros(n,dtype=float)
H = np.zeros((n,n),dtype=float)
pvals = self.FF.make(mvals)
self.tdir = os.getcwd()
self.objd = OrderedDict()
self.gradd = OrderedDict()
self.hdiagd = OrderedDict()
bidirect = False
def fdwrap2(func,mvals0,pidx,qidx,key=None,**kwargs):
def func2(arg1,arg2):
mvals = list(mvals0)
mvals[pidx] += arg1
mvals[qidx] += arg2
print "\rfdwrap2:", func.__name__, "[%i] = % .1e , [%i] = % .1e" % (pidx, arg1, qidx, arg2), ' '*50,
if key != None:
return func(mvals,**kwargs)[key]
else:
return func(mvals,**kwargs)
return func2
def f2d5p(f, h):
fpp, fpm, fmp, fmm = [f(i*h,j*h) for i,j in [(1,1),(1,-1),(-1,1),(-1,-1)]]
fpp = (fpp-fpm-fmp+fmm)/(4*h*h)
return fpp
def f2d4p(f, h, f0 = None):
if f0 == None:
fpp, fp0, f0p, f0 = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1),(0,0)]]
else:
fpp, fp0, f0p = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1)]]
fpp = (fpp-fp0-f0p+f0)/(h*h)
return fpp
for d in self.objfiles:
print "\rNow working on", d, 50*' ','\r',
x = self.driver(mvals, d)
grad = np.zeros(n,dtype=float)
hdiag = np.zeros(n,dtype=float)
hess = np.zeros((n,n),dtype=float)
for p in range(self.FF.np):
if self.callderivs[d][p]:
if AHess:
grad[p], hdiag[p] = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
hess[p,p] = hdiag[p]
# for q in range(p):
# if self.callderivs[d][q]:
# if bidirect:
# hessentry = f2d5p(fdwrap2(self.driver, mvals, p, q, d=d), h = self.h)
# else:
# hessentry = f2d4p(fdwrap2(self.driver, mvals, p, q, d=d), h = self.h, f0 = x)
# hess[p,q] = hessentry
# hess[q,p] = hessentry
elif AGrad:
if bidirect:
grad[p], _ = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
else:
grad[p] = f1d2p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
self.objd[d] = x
self.gradd[d] = grad
self.hdiagd[d] = hdiag
X += x
G += grad
#H += np.diag(hdiag)
H += hess
if not in_fd():
self.objective = X
self.objvals = self.objd
# print self.objd
# print self.gradd
# print self.hdiagd
if float('Inf') in pvals:
return {'X' : 1e10, 'G' : G, 'H' : H}
return {'X' : X, 'G' : G, 'H' : H}
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 05-30-2012
This subroutine builds the objective function (and optionally
its derivatives) from a general software.
This subroutine interfaces with simulation software 'drivers'.
The driver is expected to give exact values, fitting values, and weights.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
global LAST_MVALS, CHECK_BASIS
# print mvals
# print LAST_MVALS
# print mvals == LAST_MVALS
if LAST_MVALS == None or not (mvals == LAST_MVALS).all():
CHECK_BASIS = False
else:
CHECK_BASIS = False
Answer = {}
Fac = 1000000
## Dictionary for derivative terms
dM = {}
# Create the new force field!!
np = len(mvals)
G = zeros(np, dtype=float)
H = zeros((np, np), dtype=float)
pvals = self.FF.make(mvals)
if float("Inf") in pvals:
return {"X": 1e10, "G": G, "H": H}
Ans = self.driver()
W = Ans[:, 2]
M = Ans[:, 1]
Q = Ans[:, 0]
D = M - Q
self.MAQ = mean(abs(Q))
ns = len(M)
# Wrapper to the driver, which returns just the part that changes.
def callM(mvals_):
self.FF.make(mvals_)
Ans2 = self.driver()
M_ = Ans2[:, 1]
D_ = M_ - Q
return Ans2[:, 1]
if AGrad:
# Leaving comment here if we want to reintroduce second deriv someday.
# dM[p,:], ddM[p,:] = f12d3p(fdwrap(callM, mvals, p), h = self.h, f0 = M)
for p in range(np):
if self.call_derivatives[p] == False:
continue
dM_arr = f1d2p(fdwrap(callM, mvals, p), h=self.h, f0=M)
if max(abs(dM_arr)) == 0.0 and Counter() == 0:
print "\r Simulation %s will skip over parameter %i in subsequent steps" % (self.name, p)
self.call_derivatives[p] = False
else:
dM[p] = dM_arr.copy()
Objective = dot(W, D ** 2) * Fac
if AGrad:
for p in range(np):
if self.call_derivatives[p] == False:
continue
G[p] = 2 * dot(W, D * dM[p])
if not AHess:
continue
H[p, p] = 2 * dot(W, dM[p] ** 2)
for q in range(p):
if self.call_derivatives[q] == False:
continue
GNP = 2 * dot(W, dM[p] * dM[q])
H[q, p] = GNP
H[p, q] = GNP
G *= Fac
H *= Fac
Answer = {"X": Objective, "G": G, "H": H}
if not in_fd():
self.D = D
self.objective = Answer["X"]
LAST_MVALS = mvals.copy()
return Answer
