/usr/share/pyshared/ase/optimize/sciopt.py is in python-ase 3.6.0.2515-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | import numpy as np
try:
import scipy.optimize as opt
except ImportError:
pass
from ase.optimize.optimize import Optimizer
class Converged(Exception):
pass
class OptimizerConvergenceError(Exception):
pass
class SciPyOptimizer(Optimizer):
"""General interface for SciPy optimizers
Only the call to the optimizer is still needed
"""
def __init__(self, atoms, logfile='-', trajectory=None,
callback_always=False, alpha=70.0):
"""Initialize object
Parameters:
callback_always: book
Should the callback be run after each force call (also in the
linesearch)
alpha: float
Initial guess for the Hessian (curvature of energy surface). A
conservative value of 70.0 is the default, but number of needed
steps to converge might be less if a lower value is used. However,
a lower value also means risk of instability.
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
self.force_calls = 0
self.callback_always = callback_always
self.H0 = alpha
def x0(self):
"""Return x0 in a way SciPy can use
This class is mostly usable for subclasses wanting to redefine the
parameters (and the objective function)"""
return self.atoms.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.atoms.set_positions(x.reshape(-1, 3))
# Scale the problem as SciPy uses I as initial Hessian.
return self.atoms.get_potential_energy() / self.H0
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.atoms.set_positions(x.reshape(-1, 3))
self.force_calls += 1
if self.callback_always:
self.callback(x)
# Remember that forces are minus the gradient!
# Scale the problem as SciPy uses I as initial Hessian.
return - self.atoms.get_forces().reshape(-1) / self.H0
def callback(self, x):
"""Callback function to be run after each iteration by SciPy
This should also be called once before optimization starts, as SciPy
optimizers only calls it after each iteration, while ase optimizers
call something similar before as well.
"""
f = self.atoms.get_forces()
self.log(f)
self.call_observers()
if self.converged(f):
raise Converged
self.nsteps += 1
def run(self, fmax=0.05, steps=100000000):
self.fmax = fmax
# As SciPy does not log the zeroth iteration, we do that manually
self.callback(None)
try:
# Scale the problem as SciPy uses I as initial Hessian.
self.call_fmin(fmax / self.H0, steps)
except Converged:
pass
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, fmax, steps):
raise NotImplementedError
class SciPyFminCG(SciPyOptimizer):
"""Non-linear (Polak-Ribiere) conjugate gradient algorithm"""
def call_fmin(self, fmax, steps):
output = opt.fmin_cg(self.f,
self.x0(),
fprime=self.fprime,
#args=(),
gtol=fmax * 0.1, #Should never be reached
norm=np.inf,
#epsilon=
maxiter=steps,
full_output=1,
disp=0,
#retall=0,
callback=self.callback
)
warnflag = output[-1]
if warnflag == 2:
raise OptimizerConvergenceError('Warning: Desired error not necessarily achieved ' \
'due to precision loss')
class SciPyFminBFGS(SciPyOptimizer):
"""Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)"""
def call_fmin(self, fmax, steps):
output = opt.fmin_bfgs(self.f,
self.x0(),
fprime=self.fprime,
#args=(),
gtol=fmax * 0.1, #Should never be reached
norm=np.inf,
#epsilon=1.4901161193847656e-08,
maxiter=steps,
full_output=1,
disp=0,
#retall=0,
callback=self.callback
)
warnflag = output[-1]
if warnflag == 2:
raise OptimizerConvergenceError('Warning: Desired error not necessarily achieved' \
'due to precision loss')
class SciPyGradientlessOptimizer(Optimizer):
"""General interface for gradient less SciPy optimizers
Only the call to the optimizer is still needed
Note: If you redefien x0() and f(), you don't even need an atoms object.
Redefining these also allows you to specify an arbitrary objective
function.
XXX: This is still a work in progress
"""
def __init__(self, atoms, logfile='-', trajectory=None,
callback_always=False):
"""Parameters:
callback_always: book
Should the callback be run after each force call (also in the
linesearch)
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
self.function_calls = 0
self.callback_always = callback_always
def x0(self):
"""Return x0 in a way SciPy can use
This class is mostly usable for subclasses wanting to redefine the
parameters (and the objective function)"""
return self.atoms.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.atoms.set_positions(x.reshape(-1, 3))
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
return self.atoms.get_potential_energy()
def callback(self, x):
"""Callback function to be run after each iteration by SciPy
This should also be called once before optimization starts, as SciPy
optimizers only calls it after each iteration, while ase optimizers
call something similar before as well.
"""
# We can't assume that forces are available!
#f = self.atoms.get_forces()
#self.log(f)
self.call_observers()
#if self.converged(f):
# raise Converged
self.nsteps += 1
def run(self, ftol=0.01, xtol=0.01, steps=100000000):
self.xtol = xtol
self.ftol = ftol
# As SciPy does not log the zeroth iteration, we do that manually
self.callback(None)
try:
# Scale the problem as SciPy uses I as initial Hessian.
self.call_fmin(xtol, ftol, steps)
except Converged:
pass
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, fmax, steps):
raise NotImplementedError
class SciPyFmin(SciPyGradientlessOptimizer):
"""Nelder-Mead Simplex algorithm
Uses only function calls.
XXX: This is still a work in progress
"""
def call_fmin(self, xtol, ftol, steps):
output = opt.fmin(self.f,
self.x0(),
#args=(),
xtol=xtol,
ftol=ftol,
maxiter=steps,
#maxfun=None,
#full_output=1,
disp=0,
#retall=0,
callback=self.callback
)
class SciPyFminPowell(SciPyGradientlessOptimizer):
"""Powell's (modified) level set method
Uses only function calls.
XXX: This is still a work in progress
"""
def __init__(self, *args, **kwargs):
"""Parameters:
direc: float
How much to change x to initially. Defaults to 0.04.
"""
direc = kwargs.pop('direc', None)
SciPyGradientlessOptimizer.__init__(self, *args, **kwargs)
if direc is None:
self.direc = np.eye(len(self.x0()), dtype=float) * 0.04
else:
self.direc = np.eye(len(self.x0()), dtype=float) * direc
def call_fmin(self, xtol, ftol, steps):
output = opt.fmin_powell(self.f,
self.x0(),
#args=(),
xtol=xtol,
ftol=ftol,
maxiter=steps,
#maxfun=None,
#full_output=1,
disp=0,
#retall=0,
callback=self.callback,
direc=self.direc
)
|