"""LBFGS refinement engine for cosym analysis."""
import logging
import scipy.optimize
import scitbx.lbfgs
from scitbx.array_family import flex
logger = logging.getLogger(__name__)
[docs]class lbfgs_with_curvs:
"""Minimise a target function using the LBFGS minimiser.
Implementation of an LBFGS minimiser using curvature information, according
to the interface defined by :mod:`scitbx.lbfgs`.
"""
[docs] def __init__(self, target, coords, use_curvatures=True, termination_params=None):
"""Initialise an lbfgs_with_curvs object.
Args:
target (dials.algorithms.target.Target): The target function to minimise.
coords (np.ndarray): The starting coordinates for minimisation.
use_curvatures (bool): Whether or not to use curvature information in the
minimisation. Defaults to True.
termination_params (scitbx.lbfgs.termination_parameters):
Override the default termination parameters for the minimisation.
"""
self.target = target
self.x = flex.double(coords)
self.f = None
self.g = None
if use_curvatures:
self.diag_mode = "always"
else:
self.diag_mode = None
self.minimizer = scitbx.lbfgs.run(
target_evaluator=self, termination_params=termination_params
)
self.coords = self.x.as_numpy_array()
[docs] def compute_functional_gradients_diag(self):
"""Compute the functional, gradients and diagonal.
Returns:
tuple: A tuple of the functional, gradients and diagonal, where the
diagonal is the reciprocal of the curvatures.
"""
f, g, curvs = self.compute_functional_gradients_and_curvatures()
# Curvatures of zero will cause a crash, because their inverse is taken.
assert curvs.all_gt(0.0)
diags = 1.0 / curvs
return f, g, diags
[docs] def compute_functional_gradients_and_curvatures(self):
"""Compute the functional, gradients and curvatures.
Returns:
tuple: A tuple of the functional, gradients and curvatures.
"""
x = self.x.as_numpy_array()
self.f = self.target.compute_functional(x)
self.g = self.target.compute_gradients(x)
self.c = self.target.curvatures(x)
return self.f, flex.double(self.g), flex.double(self.c)
[docs] def compute_functional_and_gradients(self):
"""Compute the functional and gradients.
Returns:
tuple: A tuple of the functional and gradients.
"""
x = self.x.as_numpy_array()
self.f = self.target.compute_functional(x)
self.g = self.target.compute_gradients(x)
return self.f, flex.double(self.g)
[docs] def callback_after_step(self, minimizer):
"""Log progress after each successful step of the minimisation."""
logger.debug("minimization step: f, iter, nfun:")
logger.debug(f"{self.f} {minimizer.iter()} {minimizer.nfun()}")
[docs]def minimize_scitbx_lbfgs(
target, coords, use_curvatures=True, max_iterations=100, max_calls=None
):
termination_params = scitbx.lbfgs.termination_parameters(
max_iterations=max_iterations,
max_calls=max_calls,
traditional_convergence_test=True,
traditional_convergence_test_eps=1,
drop_convergence_test_n_test_points=5,
drop_convergence_test_max_drop_eps=1.0e-5,
drop_convergence_test_iteration_coefficient=2,
)
result = lbfgs_with_curvs(
target,
coords,
use_curvatures=use_curvatures,
termination_params=termination_params,
)
return scipy.optimize.OptimizeResult(
fun=result.f, jac=result.g, x=result.coords, nfev=result.minimizer.nfun()
)
[docs]def minimize_scipy(
target, coords, method="L-BFGS-B", max_iterations=None, max_calls=None
):
"""Thin wrapper around scipy.optimize.minimize.
Args:
target (dials.algorithms.target.Target): The target function to minimise.
coords (np.array): The starting coordinates for
minimisation.
"""
options = {}
if max_iterations:
options.update(maxiter=max_iterations)
if max_calls:
options.update(maxfun=max_calls)
return scipy.optimize.minimize(
fun=target.compute_functional,
x0=coords,
jac=target.compute_gradients,
method=method,
options=options,
)
