Quellcode für qiskit.algorithms.optimizers.optimizer
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Optimizer interface"""
from typing import Dict, Any, Union, Callable, Optional, Tuple, List
import warnings
from enum import IntEnum
import logging
from abc import ABC, abstractmethod
import numpy as np
from qiskit.algorithms.algorithm_result import AlgorithmResult
logger = logging.getLogger(__name__)
POINT = Union[float, np.ndarray]
[Doku]class OptimizerResult(AlgorithmResult):
"""The result of an optimization routine."""
def __init__(self) -> None:
super().__init__()
self._x = None # pylint: disable=invalid-name
self._fun = None
self._jac = None
self._nfev = None
self._njev = None
self._nit = None
@property
def x(self) -> Optional[POINT]:
"""The final point of the minimization."""
return self._x
@x.setter
def x(self, x: Optional[POINT]) -> None:
"""Set the final point of the minimization."""
self._x = x
@property
def fun(self) -> Optional[float]:
"""The final value of the minimization."""
return self._fun
@fun.setter
def fun(self, fun: Optional[float]) -> None:
"""Set the final value of the minimization."""
self._fun = fun
@property
def jac(self) -> Optional[POINT]:
"""The final gradient of the minimization."""
return self._jac
@jac.setter
def jac(self, jac: Optional[POINT]) -> None:
"""Set the final gradient of the minimization."""
self._jac = jac
@property
def nfev(self) -> Optional[int]:
"""The total number of function evaluations."""
return self._nfev
@nfev.setter
def nfev(self, nfev: Optional[int]) -> None:
"""Set the total number of function evaluations."""
self._nfev = nfev
@property
def njev(self) -> Optional[int]:
"""The total number of gradient evaluations."""
return self._njev
@njev.setter
def njev(self, njev: Optional[int]) -> None:
"""Set the total number of gradient evaluations."""
self._njev = njev
@property
def nit(self) -> Optional[int]:
"""The total number of iterations."""
return self._njev
@nit.setter
def nit(self, nit: Optional[int]) -> None:
"""Set the total number of iterations."""
self._nit = nit
[Doku]class OptimizerSupportLevel(IntEnum):
"""Support Level enum for features such as bounds, gradient and initial point"""
# pylint: disable=invalid-name
not_supported = 0 # Does not support the corresponding parameter in optimize()
ignored = 1 # Feature can be passed as non None but will be ignored
supported = 2 # Feature is supported
required = 3 # Feature is required and must be given, None is invalid
[Doku]class Optimizer(ABC):
"""Base class for optimization algorithm."""
@abstractmethod
def __init__(self):
"""
Initialize the optimization algorithm, setting the support
level for _gradient_support_level, _bound_support_level,
_initial_point_support_level, and empty options.
"""
self._gradient_support_level = self.get_support_level()["gradient"]
self._bounds_support_level = self.get_support_level()["bounds"]
self._initial_point_support_level = self.get_support_level()["initial_point"]
self._options = {}
self._max_evals_grouped = 1
[Doku] @abstractmethod
def get_support_level(self):
"""Return support level dictionary"""
raise NotImplementedError
[Doku] def set_options(self, **kwargs):
"""
Sets or updates values in the options dictionary.
The options dictionary may be used internally by a given optimizer to
pass additional optional values for the underlying optimizer/optimization
function used. The options dictionary may be initially populated with
a set of key/values when the given optimizer is constructed.
Args:
kwargs (dict): options, given as name=value.
"""
for name, value in kwargs.items():
self._options[name] = value
logger.debug("options: %s", self._options)
# pylint: disable=invalid-name
[Doku] @staticmethod
def gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1):
"""
We compute the gradient with the numeric differentiation in the parallel way,
around the point x_center.
Args:
x_center (ndarray): point around which we compute the gradient
f (func): the function of which the gradient is to be computed.
epsilon (float): the epsilon used in the numeric differentiation.
max_evals_grouped (int): max evals grouped
Returns:
grad: the gradient computed
"""
forig = f(*((x_center,)))
grad = []
ei = np.zeros((len(x_center),), float)
todos = []
for k in range(len(x_center)):
ei[k] = 1.0
d = epsilon * ei
todos.append(x_center + d)
ei[k] = 0.0
counter = 0
chunk = []
chunks = []
length = len(todos)
# split all points to chunks, where each chunk has batch_size points
for i in range(length):
x = todos[i]
chunk.append(x)
counter += 1
# the last one does not have to reach batch_size
if counter == max_evals_grouped or i == length - 1:
chunks.append(chunk)
chunk = []
counter = 0
for chunk in chunks: # eval the chunks in order
parallel_parameters = np.concatenate(chunk)
todos_results = f(parallel_parameters) # eval the points in a chunk (order preserved)
if isinstance(todos_results, float):
grad.append((todos_results - forig) / epsilon)
else:
for todor in todos_results:
grad.append((todor - forig) / epsilon)
return np.array(grad)
[Doku] @staticmethod
def wrap_function(function, args):
"""
Wrap the function to implicitly inject the args at the call of the function.
Args:
function (func): the target function
args (tuple): the args to be injected
Returns:
function_wrapper: wrapper
"""
def function_wrapper(*wrapper_args):
return function(*(wrapper_args + args))
return function_wrapper
@property
def setting(self):
"""Return setting"""
ret = f"Optimizer: {self.__class__.__name__}\n"
params = ""
for key, value in self.__dict__.items():
if key[0] == "_":
params += f"-- {key[1:]}: {value}\n"
ret += f"{params}"
return ret
@property
def settings(self) -> Dict[str, Any]:
"""The optimizer settings in a dictionary format.
The settings can for instance be used for JSON-serialization (if all settings are
serializable, which e.g. doesn't hold per default for callables), such that the
optimizer object can be reconstructed as
.. code-block::
settings = optimizer.settings
# JSON serialize and send to another server
optimizer = OptimizerClass(**settings)
"""
raise NotImplementedError("The settings method is not implemented per default.")
# TODO make abstract after the deprecation period
# @abstractmethod
[Doku] def minimize(
self,
fun: Callable[[POINT], float],
x0: POINT,
jac: Optional[Callable[[POINT], POINT]] = None,
bounds: Optional[List[Tuple[float, float]]] = None,
) -> OptimizerResult:
"""Minimize the scalar function.
Args:
fun: The scalar function to minimize.
x0: The initial point for the minimization.
jac: The gradient of the scalar function ``fun``.
bounds: Bounds for the variables of ``fun``. This argument might be ignored if the
optimizer does not support bounds.
Returns:
The result of the optimization, containing e.g. the result as attribute ``x``.
"""
raise NotImplementedError()
[Doku] def optimize(
self,
num_vars,
objective_function, # pylint: disable=unused-argument
gradient_function=None,
variable_bounds=None,
initial_point=None,
):
"""
Perform optimization.
Args:
num_vars (int) : Number of parameters to be optimized.
objective_function (callable) : A function that
computes the objective function.
gradient_function (callable) : A function that
computes the gradient of the objective function, or
None if not available.
variable_bounds (list[(float, float)]) : List of variable
bounds, given as pairs (lower, upper). None means
unbounded.
initial_point (numpy.ndarray[float]) : Initial point.
Returns:
point, value, nfev
point: is a 1D numpy.ndarray[float] containing the solution
value: is a float with the objective function value
nfev: number of objective function calls made if available or None
Raises:
ValueError: invalid input
"""
warnings.warn(
f"The {self.__class__.__name__}.optimize method is deprecated as of Qiskit Terra "
"0.19.0 and will be removed no sooner than 3 months after the release date. "
f"Instead, use the {self.__class__.__name__}.minimize method, which mimics the "
"signature of scipy.optimize.minimize.",
DeprecationWarning,
stacklevel=2,
)
if initial_point is not None and len(initial_point) != num_vars:
raise ValueError("Initial point does not match dimension")
if variable_bounds is not None and len(variable_bounds) != num_vars:
raise ValueError("Variable bounds not match dimension")
has_bounds = False
if variable_bounds is not None:
# If *any* value is *equal* in bounds array to None then the does *not* have bounds
has_bounds = not np.any(np.equal(variable_bounds, None))
if gradient_function is None and self.is_gradient_required:
raise ValueError("Gradient is required but None given")
if not has_bounds and self.is_bounds_required:
raise ValueError("Variable bounds is required but None given")
if initial_point is None and self.is_initial_point_required:
raise ValueError("Initial point is required but None given")
if gradient_function is not None and self.is_gradient_ignored:
logger.debug(
"WARNING: %s does not support gradient function. It will be ignored.",
self.__class__.__name__,
)
if has_bounds and self.is_bounds_ignored:
logger.debug(
"WARNING: %s does not support bounds. It will be ignored.", self.__class__.__name__
)
if initial_point is not None and self.is_initial_point_ignored:
logger.debug(
"WARNING: %s does not support initial point. It will be ignored.",
self.__class__.__name__,
)
@property
def gradient_support_level(self):
"""Returns gradient support level"""
return self._gradient_support_level
@property
def is_gradient_ignored(self):
"""Returns is gradient ignored"""
return self._gradient_support_level == OptimizerSupportLevel.ignored
@property
def is_gradient_supported(self):
"""Returns is gradient supported"""
return self._gradient_support_level != OptimizerSupportLevel.not_supported
@property
def is_gradient_required(self):
"""Returns is gradient required"""
return self._gradient_support_level == OptimizerSupportLevel.required
@property
def bounds_support_level(self):
"""Returns bounds support level"""
return self._bounds_support_level
@property
def is_bounds_ignored(self):
"""Returns is bounds ignored"""
return self._bounds_support_level == OptimizerSupportLevel.ignored
@property
def is_bounds_supported(self):
"""Returns is bounds supported"""
return self._bounds_support_level != OptimizerSupportLevel.not_supported
@property
def is_bounds_required(self):
"""Returns is bounds required"""
return self._bounds_support_level == OptimizerSupportLevel.required
@property
def initial_point_support_level(self):
"""Returns initial point support level"""
return self._initial_point_support_level
@property
def is_initial_point_ignored(self):
"""Returns is initial point ignored"""
return self._initial_point_support_level == OptimizerSupportLevel.ignored
@property
def is_initial_point_supported(self):
"""Returns is initial point supported"""
return self._initial_point_support_level != OptimizerSupportLevel.not_supported
@property
def is_initial_point_required(self):
"""Returns is initial point required"""
return self._initial_point_support_level == OptimizerSupportLevel.required
[Doku] def print_options(self):
"""Print algorithm-specific options."""
for name in sorted(self._options):
logger.debug("%s = %s", name, str(self._options[name]))
[Doku] def set_max_evals_grouped(self, limit):
"""Set max evals grouped"""
self._max_evals_grouped = limit