from typing import IO, Optional, Union
import numpy as np
import scipy.optimize as opt
from ase import Atoms
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: Atoms,
logfile: Union[IO, str] = '-',
trajectory: Optional[str] = None,
callback_always: bool = False,
alpha: float = 70.0,
**kwargs,
):
"""Initialize object
Parameters
----------
atoms: :class:`~ase.Atoms`
The Atoms object to relax.
trajectory: str
Trajectory file used to store optimisation path.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
callback_always: bool
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.
kwargs : dict, optional
Extra arguments passed to
:class:`~ase.optimize.optimize.Optimizer`.
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory, **kwargs)
self.force_calls = 0
self.callback_always = callback_always
self.H0 = alpha
self.max_steps = 0
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.optimizable.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.get_potential_energy() / self.H0
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.optimizable.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.optimizable.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.
:meth:`callback`() can raise a :exc:`Converged` exception to signal the
optimisation is complete. This will be silently ignored by
:meth:`run`().
"""
if self.nsteps < self.max_steps:
self.nsteps += 1
f = self.optimizable.get_forces()
self.log(f)
self.call_observers()
if self.converged(f):
raise Converged
def run(self, fmax=0.05, steps=100000000):
self.fmax = fmax
try:
# As SciPy does not log the zeroth iteration, we do that manually
if self.nsteps == 0:
self.log()
self.call_observers()
self.max_steps = steps + self.nsteps
# Scale the problem as SciPy uses I as initial Hessian.
self.call_fmin(fmax / self.H0, steps)
except Converged:
pass
return self.converged()
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, fmax, steps):
raise NotImplementedError
[docs]
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')
[docs]
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 redefine 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: Atoms,
logfile: Union[IO, str] = '-',
trajectory: Optional[str] = None,
callback_always: bool = False,
**kwargs,
):
"""Initialize object
Parameters
----------
atoms: :class:`~ase.Atoms`
The Atoms object to relax.
trajectory: str
Trajectory file used to store optimisation path.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
callback_always: bool
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.
kwargs : dict, optional
Extra arguments passed to
:class:`~ase.optimize.optimize.Optimizer`.
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory, **kwargs)
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.optimizable.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.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.optimizable.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
return self.converged()
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, xtol, ftol, 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):
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):
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)