from builtins import zip
import numpy as np
from threeML.minimizer.minimization import LocalMinimizer, FitFailed
from threeML.utils.differentiation import get_jacobian
import scipy.optimize
_SUPPORTED_ALGORITHMS = ["L-BFGS-B", "TNC", "SLSQP"]
[docs]class ScipyMinimizer(LocalMinimizer):
valid_setup_keys = ("tol", "algorithm")
def __init__(self, function, parameters, verbosity=10, setup_dict=None):
super(ScipyMinimizer, self).__init__(
function, parameters, verbosity, setup_dict
)
def _setup(self, user_setup_dict):
if user_setup_dict is None:
default_setup = {"algorithm": "L-BFGS-B", "tol": 0.0001}
self._setup_dict = default_setup
else:
if "algorithm" in user_setup_dict:
assert (
user_setup_dict["algorithm"] in _SUPPORTED_ALGORITHMS
), "Supported algorithms are %s" % (",".join(_SUPPORTED_ALGORITHMS))
# We can assume that the setup has been already checked against the setup_keys
for key in user_setup_dict:
self._setup_dict[key] = user_setup_dict[key]
# This cannot be part of a class, unfortunately, because of how PyGMO serialize objects
@staticmethod
def _check_bounds(x, minima, maxima):
for val, min_val, max_val in zip(x, minima, maxima):
if val < min_val or val > max_val:
return False
return True
def _minimize(self):
# Build initial point
x0 = []
bounds = []
minima = []
maxima = []
for i, (par_name, (cur_value, cur_delta, cur_min, cur_max)) in enumerate(
self._internal_parameters.items()
):
x0.append(cur_value)
# scipy's algorithms will always try to evaluate the function exactly at the boundaries, which will
# fail because the Jacobian is not defined there... let's fix this by using a slightly larger or smaller
# minimum and maximum within the scipy algorithm than the real boundaries (saved in minima and maxima)
minima.append(cur_min)
maxima.append(cur_max)
if cur_min is not None:
cur_min = cur_min + 0.00005 * abs(cur_min)
if cur_max is not None:
cur_max = cur_max - 0.00005 * abs(cur_max)
bounds.append((cur_min, cur_max))
def wrapper(x):
if not self._check_bounds(x, minima, maxima):
return np.inf
return self.function(*x)
def wrapper_2(*x):
return wrapper(x)
def jacobian(x):
if not self._check_bounds(x, minima, maxima):
return np.inf
jacv = get_jacobian(wrapper_2, x, minima, maxima)
return jacv
res = scipy.optimize.minimize(
wrapper,
np.array(x0),
method=self._setup_dict["algorithm"],
bounds=bounds,
jac=jacobian,
tol=self._setup_dict["tol"],
)
# Make sure the optimization worked
if not res.success:
raise FitFailed(
"Could not converge. Message from solver: %s (status: %i)"
% (res.message, res.status)
)
# Transform the result to numpy.array
best_fit_values = np.array(res.x)
return best_fit_values, float(res.fun)