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1340 lines
56 KiB
Python
1340 lines
56 KiB
Python
"""
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differential_evolution: The differential evolution global optimization algorithm
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Added by Andrew Nelson 2014
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"""
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import warnings
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import numpy as np
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from scipy.optimize import OptimizeResult, minimize
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from scipy.optimize.optimize import _status_message
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from scipy._lib._util import check_random_state, MapWrapper
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from scipy.optimize._constraints import (Bounds, new_bounds_to_old,
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NonlinearConstraint, LinearConstraint)
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from scipy.sparse import issparse
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__all__ = ['differential_evolution']
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_MACHEPS = np.finfo(np.float64).eps
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def differential_evolution(func, bounds, args=(), strategy='best1bin',
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maxiter=1000, popsize=15, tol=0.01,
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mutation=(0.5, 1), recombination=0.7, seed=None,
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callback=None, disp=False, polish=True,
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init='latinhypercube', atol=0, updating='immediate',
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workers=1, constraints=()):
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"""Finds the global minimum of a multivariate function.
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Differential Evolution is stochastic in nature (does not use gradient
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methods) to find the minimum, and can search large areas of candidate
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space, but often requires larger numbers of function evaluations than
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conventional gradient-based techniques.
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The algorithm is due to Storn and Price [1]_.
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Parameters
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----------
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func : callable
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The objective function to be minimized. Must be in the form
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``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
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and ``args`` is a tuple of any additional fixed parameters needed to
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completely specify the function.
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bounds : sequence or `Bounds`, optional
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Bounds for variables. There are two ways to specify the bounds:
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1. Instance of `Bounds` class.
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2. ``(min, max)`` pairs for each element in ``x``, defining the finite
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lower and upper bounds for the optimizing argument of `func`. It is
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required to have ``len(bounds) == len(x)``. ``len(bounds)`` is used
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to determine the number of parameters in ``x``.
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args : tuple, optional
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Any additional fixed parameters needed to
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completely specify the objective function.
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strategy : str, optional
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The differential evolution strategy to use. Should be one of:
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- 'best1bin'
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- 'best1exp'
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- 'rand1exp'
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- 'randtobest1exp'
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- 'currenttobest1exp'
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- 'best2exp'
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- 'rand2exp'
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- 'randtobest1bin'
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- 'currenttobest1bin'
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- 'best2bin'
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- 'rand2bin'
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- 'rand1bin'
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The default is 'best1bin'.
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maxiter : int, optional
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The maximum number of generations over which the entire population is
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evolved. The maximum number of function evaluations (with no polishing)
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is: ``(maxiter + 1) * popsize * len(x)``
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popsize : int, optional
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A multiplier for setting the total population size. The population has
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``popsize * len(x)`` individuals (unless the initial population is
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supplied via the `init` keyword).
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tol : float, optional
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Relative tolerance for convergence, the solving stops when
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``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
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where and `atol` and `tol` are the absolute and relative tolerance
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respectively.
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mutation : float or tuple(float, float), optional
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The mutation constant. In the literature this is also known as
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differential weight, being denoted by F.
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If specified as a float it should be in the range [0, 2].
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If specified as a tuple ``(min, max)`` dithering is employed. Dithering
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randomly changes the mutation constant on a generation by generation
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basis. The mutation constant for that generation is taken from
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``U[min, max)``. Dithering can help speed convergence significantly.
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Increasing the mutation constant increases the search radius, but will
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slow down convergence.
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recombination : float, optional
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The recombination constant, should be in the range [0, 1]. In the
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literature this is also known as the crossover probability, being
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denoted by CR. Increasing this value allows a larger number of mutants
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|
to progress into the next generation, but at the risk of population
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stability.
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seed : {int, `~np.random.RandomState`, `~np.random.Generator`}, optional
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If `seed` is not specified the `~np.random.RandomState` singleton is
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used.
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If `seed` is an int, a new ``RandomState`` instance is used,
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seeded with seed.
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If `seed` is already a ``RandomState`` or a ``Generator`` instance,
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then that object is used.
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Specify `seed` for repeatable minimizations.
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disp : bool, optional
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Prints the evaluated `func` at every iteration.
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callback : callable, `callback(xk, convergence=val)`, optional
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A function to follow the progress of the minimization. ``xk`` is
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the current value of ``x0``. ``val`` represents the fractional
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value of the population convergence. When ``val`` is greater than one
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the function halts. If callback returns `True`, then the minimization
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is halted (any polishing is still carried out).
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polish : bool, optional
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If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
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|
method is used to polish the best population member at the end, which
|
|
can improve the minimization slightly. If a constrained problem is
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|
being studied then the `trust-constr` method is used instead.
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init : str or array-like, optional
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Specify which type of population initialization is performed. Should be
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|
one of:
|
|
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- 'latinhypercube'
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- 'random'
|
|
- array specifying the initial population. The array should have
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shape ``(M, len(x))``, where M is the total population size and
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len(x) is the number of parameters.
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`init` is clipped to `bounds` before use.
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The default is 'latinhypercube'. Latin Hypercube sampling tries to
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maximize coverage of the available parameter space. 'random'
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initializes the population randomly - this has the drawback that
|
|
clustering can occur, preventing the whole of parameter space being
|
|
covered. Use of an array to specify a population subset could be used,
|
|
for example, to create a tight bunch of initial guesses in an location
|
|
where the solution is known to exist, thereby reducing time for
|
|
convergence.
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atol : float, optional
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|
Absolute tolerance for convergence, the solving stops when
|
|
``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
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where and `atol` and `tol` are the absolute and relative tolerance
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respectively.
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updating : {'immediate', 'deferred'}, optional
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If ``'immediate'``, the best solution vector is continuously updated
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within a single generation [4]_. This can lead to faster convergence as
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trial vectors can take advantage of continuous improvements in the best
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|
solution.
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With ``'deferred'``, the best solution vector is updated once per
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generation. Only ``'deferred'`` is compatible with parallelization, and
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the `workers` keyword can over-ride this option.
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.. versionadded:: 1.2.0
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workers : int or map-like callable, optional
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If `workers` is an int the population is subdivided into `workers`
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sections and evaluated in parallel
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(uses `multiprocessing.Pool <multiprocessing>`).
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Supply -1 to use all available CPU cores.
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Alternatively supply a map-like callable, such as
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`multiprocessing.Pool.map` for evaluating the population in parallel.
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This evaluation is carried out as ``workers(func, iterable)``.
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This option will override the `updating` keyword to
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``updating='deferred'`` if ``workers != 1``.
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Requires that `func` be pickleable.
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.. versionadded:: 1.2.0
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constraints : {NonLinearConstraint, LinearConstraint, Bounds}
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Constraints on the solver, over and above those applied by the `bounds`
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kwd. Uses the approach by Lampinen [5]_.
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.. versionadded:: 1.4.0
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Returns
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-------
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res : OptimizeResult
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The optimization result represented as a `OptimizeResult` object.
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Important attributes are: ``x`` the solution array, ``success`` a
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Boolean flag indicating if the optimizer exited successfully and
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``message`` which describes the cause of the termination. See
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`OptimizeResult` for a description of other attributes. If `polish`
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was employed, and a lower minimum was obtained by the polishing, then
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OptimizeResult also contains the ``jac`` attribute.
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If the eventual solution does not satisfy the applied constraints
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``success`` will be `False`.
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Notes
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-----
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Differential evolution is a stochastic population based method that is
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useful for global optimization problems. At each pass through the population
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the algorithm mutates each candidate solution by mixing with other candidate
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solutions to create a trial candidate. There are several strategies [2]_ for
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creating trial candidates, which suit some problems more than others. The
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'best1bin' strategy is a good starting point for many systems. In this
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strategy two members of the population are randomly chosen. Their difference
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is used to mutate the best member (the 'best' in 'best1bin'), :math:`b_0`,
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so far:
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.. math::
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b' = b_0 + mutation * (population[rand0] - population[rand1])
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A trial vector is then constructed. Starting with a randomly chosen ith
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parameter the trial is sequentially filled (in modulo) with parameters from
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``b'`` or the original candidate. The choice of whether to use ``b'`` or the
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original candidate is made with a binomial distribution (the 'bin' in
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'best1bin') - a random number in [0, 1) is generated. If this number is
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less than the `recombination` constant then the parameter is loaded from
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``b'``, otherwise it is loaded from the original candidate. The final
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parameter is always loaded from ``b'``. Once the trial candidate is built
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its fitness is assessed. If the trial is better than the original candidate
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then it takes its place. If it is also better than the best overall
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candidate it also replaces that.
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To improve your chances of finding a global minimum use higher `popsize`
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values, with higher `mutation` and (dithering), but lower `recombination`
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values. This has the effect of widening the search radius, but slowing
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|
convergence.
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By default the best solution vector is updated continuously within a single
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iteration (``updating='immediate'``). This is a modification [4]_ of the
|
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original differential evolution algorithm which can lead to faster
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convergence as trial vectors can immediately benefit from improved
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solutions. To use the original Storn and Price behaviour, updating the best
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solution once per iteration, set ``updating='deferred'``.
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.. versionadded:: 0.15.0
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Examples
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--------
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Let us consider the problem of minimizing the Rosenbrock function. This
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function is implemented in `rosen` in `scipy.optimize`.
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>>> from scipy.optimize import rosen, differential_evolution
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>>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
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>>> result = differential_evolution(rosen, bounds)
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>>> result.x, result.fun
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(array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)
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Now repeat, but with parallelization.
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>>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
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>>> result = differential_evolution(rosen, bounds, updating='deferred',
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... workers=2)
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>>> result.x, result.fun
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(array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)
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Let's try and do a constrained minimization
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>>> from scipy.optimize import NonlinearConstraint, Bounds
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>>> def constr_f(x):
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... return np.array(x[0] + x[1])
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>>>
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>>> # the sum of x[0] and x[1] must be less than 1.9
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>>> nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
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>>> # specify limits using a `Bounds` object.
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>>> bounds = Bounds([0., 0.], [2., 2.])
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>>> result = differential_evolution(rosen, bounds, constraints=(nlc),
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... seed=1)
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>>> result.x, result.fun
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(array([0.96633867, 0.93363577]), 0.0011361355854792312)
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Next find the minimum of the Ackley function
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(https://en.wikipedia.org/wiki/Test_functions_for_optimization).
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>>> from scipy.optimize import differential_evolution
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>>> import numpy as np
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>>> def ackley(x):
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... arg1 = -0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2))
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... arg2 = 0.5 * (np.cos(2. * np.pi * x[0]) + np.cos(2. * np.pi * x[1]))
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... return -20. * np.exp(arg1) - np.exp(arg2) + 20. + np.e
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>>> bounds = [(-5, 5), (-5, 5)]
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>>> result = differential_evolution(ackley, bounds)
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>>> result.x, result.fun
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(array([ 0., 0.]), 4.4408920985006262e-16)
|
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|
References
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----------
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.. [1] Storn, R and Price, K, Differential Evolution - a Simple and
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Efficient Heuristic for Global Optimization over Continuous Spaces,
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Journal of Global Optimization, 1997, 11, 341 - 359.
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.. [2] http://www1.icsi.berkeley.edu/~storn/code.html
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.. [3] http://en.wikipedia.org/wiki/Differential_evolution
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.. [4] Wormington, M., Panaccione, C., Matney, K. M., Bowen, D. K., -
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Characterization of structures from X-ray scattering data using
|
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genetic algorithms, Phil. Trans. R. Soc. Lond. A, 1999, 357,
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2827-2848
|
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.. [5] Lampinen, J., A constraint handling approach for the differential
|
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evolution algorithm. Proceedings of the 2002 Congress on
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Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). Vol. 2. IEEE,
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2002.
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|
"""
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# using a context manager means that any created Pool objects are
|
|
# cleared up.
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with DifferentialEvolutionSolver(func, bounds, args=args,
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strategy=strategy,
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maxiter=maxiter,
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popsize=popsize, tol=tol,
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mutation=mutation,
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recombination=recombination,
|
|
seed=seed, polish=polish,
|
|
callback=callback,
|
|
disp=disp, init=init, atol=atol,
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|
updating=updating,
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|
workers=workers,
|
|
constraints=constraints) as solver:
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ret = solver.solve()
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return ret
|
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|
|
|
|
class DifferentialEvolutionSolver(object):
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|
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"""This class implements the differential evolution solver
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|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
The objective function to be minimized. Must be in the form
|
|
``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
|
|
and ``args`` is a tuple of any additional fixed parameters needed to
|
|
completely specify the function.
|
|
bounds : sequence or `Bounds`, optional
|
|
Bounds for variables. There are two ways to specify the bounds:
|
|
1. Instance of `Bounds` class.
|
|
2. ``(min, max)`` pairs for each element in ``x``, defining the finite
|
|
lower and upper bounds for the optimizing argument of `func`. It is
|
|
required to have ``len(bounds) == len(x)``. ``len(bounds)`` is used
|
|
to determine the number of parameters in ``x``.
|
|
args : tuple, optional
|
|
Any additional fixed parameters needed to
|
|
completely specify the objective function.
|
|
strategy : str, optional
|
|
The differential evolution strategy to use. Should be one of:
|
|
|
|
- 'best1bin'
|
|
- 'best1exp'
|
|
- 'rand1exp'
|
|
- 'randtobest1exp'
|
|
- 'currenttobest1exp'
|
|
- 'best2exp'
|
|
- 'rand2exp'
|
|
- 'randtobest1bin'
|
|
- 'currenttobest1bin'
|
|
- 'best2bin'
|
|
- 'rand2bin'
|
|
- 'rand1bin'
|
|
|
|
The default is 'best1bin'
|
|
|
|
maxiter : int, optional
|
|
The maximum number of generations over which the entire population is
|
|
evolved. The maximum number of function evaluations (with no polishing)
|
|
is: ``(maxiter + 1) * popsize * len(x)``
|
|
popsize : int, optional
|
|
A multiplier for setting the total population size. The population has
|
|
``popsize * len(x)`` individuals (unless the initial population is
|
|
supplied via the `init` keyword).
|
|
tol : float, optional
|
|
Relative tolerance for convergence, the solving stops when
|
|
``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
|
|
where and `atol` and `tol` are the absolute and relative tolerance
|
|
respectively.
|
|
mutation : float or tuple(float, float), optional
|
|
The mutation constant. In the literature this is also known as
|
|
differential weight, being denoted by F.
|
|
If specified as a float it should be in the range [0, 2].
|
|
If specified as a tuple ``(min, max)`` dithering is employed. Dithering
|
|
randomly changes the mutation constant on a generation by generation
|
|
basis. The mutation constant for that generation is taken from
|
|
U[min, max). Dithering can help speed convergence significantly.
|
|
Increasing the mutation constant increases the search radius, but will
|
|
slow down convergence.
|
|
recombination : float, optional
|
|
The recombination constant, should be in the range [0, 1]. In the
|
|
literature this is also known as the crossover probability, being
|
|
denoted by CR. Increasing this value allows a larger number of mutants
|
|
to progress into the next generation, but at the risk of population
|
|
stability.
|
|
seed : {int, `~np.random.RandomState`, `~np.random.Generator`}, optional
|
|
If `seed` is not specified the `~np.random.RandomState` singleton is
|
|
used.
|
|
If `seed` is an int, a new ``RandomState`` instance is used,
|
|
seeded with seed.
|
|
If `seed` is already a ``RandomState`` or a ``Generator`` instance,
|
|
then that object is used.
|
|
Specify `seed` for repeatable minimizations.
|
|
disp : bool, optional
|
|
Prints the evaluated `func` at every iteration.
|
|
callback : callable, `callback(xk, convergence=val)`, optional
|
|
A function to follow the progress of the minimization. ``xk`` is
|
|
the current value of ``x0``. ``val`` represents the fractional
|
|
value of the population convergence. When ``val`` is greater than one
|
|
the function halts. If callback returns `True`, then the minimization
|
|
is halted (any polishing is still carried out).
|
|
polish : bool, optional
|
|
If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
|
|
method is used to polish the best population member at the end, which
|
|
can improve the minimization slightly. If a constrained problem is
|
|
being studied then the `trust-constr` method is used instead.
|
|
maxfun : int, optional
|
|
Set the maximum number of function evaluations. However, it probably
|
|
makes more sense to set `maxiter` instead.
|
|
init : str or array-like, optional
|
|
Specify which type of population initialization is performed. Should be
|
|
one of:
|
|
|
|
- 'latinhypercube'
|
|
- 'random'
|
|
- array specifying the initial population. The array should have
|
|
shape ``(M, len(x))``, where M is the total population size and
|
|
len(x) is the number of parameters.
|
|
`init` is clipped to `bounds` before use.
|
|
|
|
The default is 'latinhypercube'. Latin Hypercube sampling tries to
|
|
maximize coverage of the available parameter space. 'random'
|
|
initializes the population randomly - this has the drawback that
|
|
clustering can occur, preventing the whole of parameter space being
|
|
covered. Use of an array to specify a population could be used, for
|
|
example, to create a tight bunch of initial guesses in an location
|
|
where the solution is known to exist, thereby reducing time for
|
|
convergence.
|
|
atol : float, optional
|
|
Absolute tolerance for convergence, the solving stops when
|
|
``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
|
|
where and `atol` and `tol` are the absolute and relative tolerance
|
|
respectively.
|
|
updating : {'immediate', 'deferred'}, optional
|
|
If `immediate` the best solution vector is continuously updated within
|
|
a single generation. This can lead to faster convergence as trial
|
|
vectors can take advantage of continuous improvements in the best
|
|
solution.
|
|
With `deferred` the best solution vector is updated once per
|
|
generation. Only `deferred` is compatible with parallelization, and the
|
|
`workers` keyword can over-ride this option.
|
|
workers : int or map-like callable, optional
|
|
If `workers` is an int the population is subdivided into `workers`
|
|
sections and evaluated in parallel
|
|
(uses `multiprocessing.Pool <multiprocessing>`).
|
|
Supply `-1` to use all cores available to the Process.
|
|
Alternatively supply a map-like callable, such as
|
|
`multiprocessing.Pool.map` for evaluating the population in parallel.
|
|
This evaluation is carried out as ``workers(func, iterable)``.
|
|
This option will override the `updating` keyword to
|
|
`updating='deferred'` if `workers != 1`.
|
|
Requires that `func` be pickleable.
|
|
constraints : {NonLinearConstraint, LinearConstraint, Bounds}
|
|
Constraints on the solver, over and above those applied by the `bounds`
|
|
kwd. Uses the approach by Lampinen.
|
|
"""
|
|
|
|
# Dispatch of mutation strategy method (binomial or exponential).
|
|
_binomial = {'best1bin': '_best1',
|
|
'randtobest1bin': '_randtobest1',
|
|
'currenttobest1bin': '_currenttobest1',
|
|
'best2bin': '_best2',
|
|
'rand2bin': '_rand2',
|
|
'rand1bin': '_rand1'}
|
|
_exponential = {'best1exp': '_best1',
|
|
'rand1exp': '_rand1',
|
|
'randtobest1exp': '_randtobest1',
|
|
'currenttobest1exp': '_currenttobest1',
|
|
'best2exp': '_best2',
|
|
'rand2exp': '_rand2'}
|
|
|
|
__init_error_msg = ("The population initialization method must be one of "
|
|
"'latinhypercube' or 'random', or an array of shape "
|
|
"(M, N) where N is the number of parameters and M>5")
|
|
|
|
def __init__(self, func, bounds, args=(),
|
|
strategy='best1bin', maxiter=1000, popsize=15,
|
|
tol=0.01, mutation=(0.5, 1), recombination=0.7, seed=None,
|
|
maxfun=np.inf, callback=None, disp=False, polish=True,
|
|
init='latinhypercube', atol=0, updating='immediate',
|
|
workers=1, constraints=()):
|
|
|
|
if strategy in self._binomial:
|
|
self.mutation_func = getattr(self, self._binomial[strategy])
|
|
elif strategy in self._exponential:
|
|
self.mutation_func = getattr(self, self._exponential[strategy])
|
|
else:
|
|
raise ValueError("Please select a valid mutation strategy")
|
|
self.strategy = strategy
|
|
|
|
self.callback = callback
|
|
self.polish = polish
|
|
|
|
# set the updating / parallelisation options
|
|
if updating in ['immediate', 'deferred']:
|
|
self._updating = updating
|
|
|
|
# want to use parallelisation, but updating is immediate
|
|
if workers != 1 and updating == 'immediate':
|
|
warnings.warn("differential_evolution: the 'workers' keyword has"
|
|
" overridden updating='immediate' to"
|
|
" updating='deferred'", UserWarning)
|
|
self._updating = 'deferred'
|
|
|
|
# an object with a map method.
|
|
self._mapwrapper = MapWrapper(workers)
|
|
|
|
# relative and absolute tolerances for convergence
|
|
self.tol, self.atol = tol, atol
|
|
|
|
# Mutation constant should be in [0, 2). If specified as a sequence
|
|
# then dithering is performed.
|
|
self.scale = mutation
|
|
if (not np.all(np.isfinite(mutation)) or
|
|
np.any(np.array(mutation) >= 2) or
|
|
np.any(np.array(mutation) < 0)):
|
|
raise ValueError('The mutation constant must be a float in '
|
|
'U[0, 2), or specified as a tuple(min, max)'
|
|
' where min < max and min, max are in U[0, 2).')
|
|
|
|
self.dither = None
|
|
if hasattr(mutation, '__iter__') and len(mutation) > 1:
|
|
self.dither = [mutation[0], mutation[1]]
|
|
self.dither.sort()
|
|
|
|
self.cross_over_probability = recombination
|
|
|
|
# we create a wrapped function to allow the use of map (and Pool.map
|
|
# in the future)
|
|
self.func = _FunctionWrapper(func, args)
|
|
self.args = args
|
|
|
|
# convert tuple of lower and upper bounds to limits
|
|
# [(low_0, high_0), ..., (low_n, high_n]
|
|
# -> [[low_0, ..., low_n], [high_0, ..., high_n]]
|
|
if isinstance(bounds, Bounds):
|
|
self.limits = np.array(new_bounds_to_old(bounds.lb,
|
|
bounds.ub,
|
|
len(bounds.lb)),
|
|
dtype=float).T
|
|
else:
|
|
self.limits = np.array(bounds, dtype='float').T
|
|
|
|
if (np.size(self.limits, 0) != 2 or not
|
|
np.all(np.isfinite(self.limits))):
|
|
raise ValueError('bounds should be a sequence containing '
|
|
'real valued (min, max) pairs for each value'
|
|
' in x')
|
|
|
|
if maxiter is None: # the default used to be None
|
|
maxiter = 1000
|
|
self.maxiter = maxiter
|
|
if maxfun is None: # the default used to be None
|
|
maxfun = np.inf
|
|
self.maxfun = maxfun
|
|
|
|
# population is scaled to between [0, 1].
|
|
# We have to scale between parameter <-> population
|
|
# save these arguments for _scale_parameter and
|
|
# _unscale_parameter. This is an optimization
|
|
self.__scale_arg1 = 0.5 * (self.limits[0] + self.limits[1])
|
|
self.__scale_arg2 = np.fabs(self.limits[0] - self.limits[1])
|
|
|
|
self.parameter_count = np.size(self.limits, 1)
|
|
|
|
self.random_number_generator = check_random_state(seed)
|
|
|
|
# default population initialization is a latin hypercube design, but
|
|
# there are other population initializations possible.
|
|
# the minimum is 5 because 'best2bin' requires a population that's at
|
|
# least 5 long
|
|
self.num_population_members = max(5, popsize * self.parameter_count)
|
|
|
|
self.population_shape = (self.num_population_members,
|
|
self.parameter_count)
|
|
|
|
self._nfev = 0
|
|
if isinstance(init, str):
|
|
if init == 'latinhypercube':
|
|
self.init_population_lhs()
|
|
elif init == 'random':
|
|
self.init_population_random()
|
|
else:
|
|
raise ValueError(self.__init_error_msg)
|
|
else:
|
|
self.init_population_array(init)
|
|
|
|
# infrastructure for constraints
|
|
# dummy parameter vector for preparing constraints, this is required so
|
|
# that the number of constraints is known.
|
|
x0 = self._scale_parameters(self.population[0])
|
|
|
|
self.constraints = constraints
|
|
self._wrapped_constraints = []
|
|
|
|
if hasattr(constraints, '__len__'):
|
|
# sequence of constraints, this will also deal with default
|
|
# keyword parameter
|
|
for c in constraints:
|
|
self._wrapped_constraints.append(_ConstraintWrapper(c, x0))
|
|
else:
|
|
self._wrapped_constraints = [_ConstraintWrapper(constraints, x0)]
|
|
|
|
self.constraint_violation = np.zeros((self.num_population_members, 1))
|
|
self.feasible = np.ones(self.num_population_members, bool)
|
|
|
|
self.disp = disp
|
|
|
|
def init_population_lhs(self):
|
|
"""
|
|
Initializes the population with Latin Hypercube Sampling.
|
|
Latin Hypercube Sampling ensures that each parameter is uniformly
|
|
sampled over its range.
|
|
"""
|
|
rng = self.random_number_generator
|
|
|
|
# Each parameter range needs to be sampled uniformly. The scaled
|
|
# parameter range ([0, 1)) needs to be split into
|
|
# `self.num_population_members` segments, each of which has the following
|
|
# size:
|
|
segsize = 1.0 / self.num_population_members
|
|
|
|
# Within each segment we sample from a uniform random distribution.
|
|
# We need to do this sampling for each parameter.
|
|
samples = (segsize * rng.uniform(size=self.population_shape)
|
|
|
|
# Offset each segment to cover the entire parameter range [0, 1)
|
|
+ np.linspace(0., 1., self.num_population_members,
|
|
endpoint=False)[:, np.newaxis])
|
|
|
|
# Create an array for population of candidate solutions.
|
|
self.population = np.zeros_like(samples)
|
|
|
|
# Initialize population of candidate solutions by permutation of the
|
|
# random samples.
|
|
for j in range(self.parameter_count):
|
|
order = rng.permutation(range(self.num_population_members))
|
|
self.population[:, j] = samples[order, j]
|
|
|
|
# reset population energies
|
|
self.population_energies = np.full(self.num_population_members,
|
|
np.inf)
|
|
|
|
# reset number of function evaluations counter
|
|
self._nfev = 0
|
|
|
|
def init_population_random(self):
|
|
"""
|
|
Initializes the population at random. This type of initialization
|
|
can possess clustering, Latin Hypercube sampling is generally better.
|
|
"""
|
|
rng = self.random_number_generator
|
|
self.population = rng.uniform(size=self.population_shape)
|
|
|
|
# reset population energies
|
|
self.population_energies = np.full(self.num_population_members,
|
|
np.inf)
|
|
|
|
# reset number of function evaluations counter
|
|
self._nfev = 0
|
|
|
|
def init_population_array(self, init):
|
|
"""
|
|
Initializes the population with a user specified population.
|
|
|
|
Parameters
|
|
----------
|
|
init : np.ndarray
|
|
Array specifying subset of the initial population. The array should
|
|
have shape (M, len(x)), where len(x) is the number of parameters.
|
|
The population is clipped to the lower and upper bounds.
|
|
"""
|
|
# make sure you're using a float array
|
|
popn = np.asfarray(init)
|
|
|
|
if (np.size(popn, 0) < 5 or
|
|
popn.shape[1] != self.parameter_count or
|
|
len(popn.shape) != 2):
|
|
raise ValueError("The population supplied needs to have shape"
|
|
" (M, len(x)), where M > 4.")
|
|
|
|
# scale values and clip to bounds, assigning to population
|
|
self.population = np.clip(self._unscale_parameters(popn), 0, 1)
|
|
|
|
self.num_population_members = np.size(self.population, 0)
|
|
|
|
self.population_shape = (self.num_population_members,
|
|
self.parameter_count)
|
|
|
|
# reset population energies
|
|
self.population_energies = np.full(self.num_population_members,
|
|
np.inf)
|
|
|
|
# reset number of function evaluations counter
|
|
self._nfev = 0
|
|
|
|
@property
|
|
def x(self):
|
|
"""
|
|
The best solution from the solver
|
|
"""
|
|
return self._scale_parameters(self.population[0])
|
|
|
|
@property
|
|
def convergence(self):
|
|
"""
|
|
The standard deviation of the population energies divided by their
|
|
mean.
|
|
"""
|
|
if np.any(np.isinf(self.population_energies)):
|
|
return np.inf
|
|
return (np.std(self.population_energies) /
|
|
np.abs(np.mean(self.population_energies) + _MACHEPS))
|
|
|
|
def converged(self):
|
|
"""
|
|
Return True if the solver has converged.
|
|
"""
|
|
if np.any(np.isinf(self.population_energies)):
|
|
return False
|
|
|
|
return (np.std(self.population_energies) <=
|
|
self.atol +
|
|
self.tol * np.abs(np.mean(self.population_energies)))
|
|
|
|
def solve(self):
|
|
"""
|
|
Runs the DifferentialEvolutionSolver.
|
|
|
|
Returns
|
|
-------
|
|
res : OptimizeResult
|
|
The optimization result represented as a ``OptimizeResult`` object.
|
|
Important attributes are: ``x`` the solution array, ``success`` a
|
|
Boolean flag indicating if the optimizer exited successfully and
|
|
``message`` which describes the cause of the termination. See
|
|
`OptimizeResult` for a description of other attributes. If `polish`
|
|
was employed, and a lower minimum was obtained by the polishing,
|
|
then OptimizeResult also contains the ``jac`` attribute.
|
|
"""
|
|
nit, warning_flag = 0, False
|
|
status_message = _status_message['success']
|
|
|
|
# The population may have just been initialized (all entries are
|
|
# np.inf). If it has you have to calculate the initial energies.
|
|
# Although this is also done in the evolve generator it's possible
|
|
# that someone can set maxiter=0, at which point we still want the
|
|
# initial energies to be calculated (the following loop isn't run).
|
|
if np.all(np.isinf(self.population_energies)):
|
|
self.feasible, self.constraint_violation = (
|
|
self._calculate_population_feasibilities(self.population))
|
|
|
|
# only work out population energies for feasible solutions
|
|
self.population_energies[self.feasible] = (
|
|
self._calculate_population_energies(
|
|
self.population[self.feasible]))
|
|
|
|
self._promote_lowest_energy()
|
|
|
|
# do the optimization.
|
|
for nit in range(1, self.maxiter + 1):
|
|
# evolve the population by a generation
|
|
try:
|
|
next(self)
|
|
except StopIteration:
|
|
warning_flag = True
|
|
if self._nfev > self.maxfun:
|
|
status_message = _status_message['maxfev']
|
|
elif self._nfev == self.maxfun:
|
|
status_message = ('Maximum number of function evaluations'
|
|
' has been reached.')
|
|
break
|
|
|
|
if self.disp:
|
|
print("differential_evolution step %d: f(x)= %g"
|
|
% (nit,
|
|
self.population_energies[0]))
|
|
|
|
if self.callback:
|
|
c = self.tol / (self.convergence + _MACHEPS)
|
|
warning_flag = bool(self.callback(self.x, convergence=c))
|
|
if warning_flag:
|
|
status_message = ('callback function requested stop early'
|
|
' by returning True')
|
|
|
|
# should the solver terminate?
|
|
if warning_flag or self.converged():
|
|
break
|
|
|
|
else:
|
|
status_message = _status_message['maxiter']
|
|
warning_flag = True
|
|
|
|
DE_result = OptimizeResult(
|
|
x=self.x,
|
|
fun=self.population_energies[0],
|
|
nfev=self._nfev,
|
|
nit=nit,
|
|
message=status_message,
|
|
success=(warning_flag is not True))
|
|
|
|
if self.polish:
|
|
polish_method = 'L-BFGS-B'
|
|
|
|
if self._wrapped_constraints:
|
|
polish_method = 'trust-constr'
|
|
|
|
constr_violation = self._constraint_violation_fn(DE_result.x)
|
|
if np.any(constr_violation > 0.):
|
|
warnings.warn("differential evolution didn't find a"
|
|
" solution satisfying the constraints,"
|
|
" attempting to polish from the least"
|
|
" infeasible solution", UserWarning)
|
|
|
|
result = minimize(self.func,
|
|
np.copy(DE_result.x),
|
|
method=polish_method,
|
|
bounds=self.limits.T,
|
|
constraints=self.constraints)
|
|
|
|
self._nfev += result.nfev
|
|
DE_result.nfev = self._nfev
|
|
|
|
# Polishing solution is only accepted if there is an improvement in
|
|
# cost function, the polishing was successful and the solution lies
|
|
# within the bounds.
|
|
if (result.fun < DE_result.fun and
|
|
result.success and
|
|
np.all(result.x <= self.limits[1]) and
|
|
np.all(self.limits[0] <= result.x)):
|
|
DE_result.fun = result.fun
|
|
DE_result.x = result.x
|
|
DE_result.jac = result.jac
|
|
# to keep internal state consistent
|
|
self.population_energies[0] = result.fun
|
|
self.population[0] = self._unscale_parameters(result.x)
|
|
|
|
if self._wrapped_constraints:
|
|
DE_result.constr = [c.violation(DE_result.x) for
|
|
c in self._wrapped_constraints]
|
|
DE_result.constr_violation = np.max(
|
|
np.concatenate(DE_result.constr))
|
|
DE_result.maxcv = DE_result.constr_violation
|
|
if DE_result.maxcv > 0:
|
|
# if the result is infeasible then success must be False
|
|
DE_result.success = False
|
|
DE_result.message = ("The solution does not satisfy the"
|
|
" constraints, MAXCV = " % DE_result.maxcv)
|
|
|
|
return DE_result
|
|
|
|
def _calculate_population_energies(self, population):
|
|
"""
|
|
Calculate the energies of a population.
|
|
|
|
Parameters
|
|
----------
|
|
population : ndarray
|
|
An array of parameter vectors normalised to [0, 1] using lower
|
|
and upper limits. Has shape ``(np.size(population, 0), len(x))``.
|
|
|
|
Returns
|
|
-------
|
|
energies : ndarray
|
|
An array of energies corresponding to each population member. If
|
|
maxfun will be exceeded during this call, then the number of
|
|
function evaluations will be reduced and energies will be
|
|
right-padded with np.inf. Has shape ``(np.size(population, 0),)``
|
|
"""
|
|
num_members = np.size(population, 0)
|
|
nfevs = min(num_members,
|
|
self.maxfun - num_members)
|
|
|
|
energies = np.full(num_members, np.inf)
|
|
|
|
parameters_pop = self._scale_parameters(population)
|
|
try:
|
|
calc_energies = list(self._mapwrapper(self.func,
|
|
parameters_pop[0:nfevs]))
|
|
energies[0:nfevs] = np.squeeze(calc_energies)
|
|
except (TypeError, ValueError) as e:
|
|
# wrong number of arguments for _mapwrapper
|
|
# or wrong length returned from the mapper
|
|
raise RuntimeError(
|
|
"The map-like callable must be of the form f(func, iterable), "
|
|
"returning a sequence of numbers the same length as 'iterable'"
|
|
) from e
|
|
|
|
self._nfev += nfevs
|
|
|
|
return energies
|
|
|
|
def _promote_lowest_energy(self):
|
|
# swaps 'best solution' into first population entry
|
|
|
|
idx = np.arange(self.num_population_members)
|
|
feasible_solutions = idx[self.feasible]
|
|
if feasible_solutions.size:
|
|
# find the best feasible solution
|
|
idx_t = np.argmin(self.population_energies[feasible_solutions])
|
|
l = feasible_solutions[idx_t]
|
|
else:
|
|
# no solution was feasible, use 'best' infeasible solution, which
|
|
# will violate constraints the least
|
|
l = np.argmin(np.sum(self.constraint_violation, axis=1))
|
|
|
|
self.population_energies[[0, l]] = self.population_energies[[l, 0]]
|
|
self.population[[0, l], :] = self.population[[l, 0], :]
|
|
self.feasible[[0, l]] = self.feasible[[l, 0]]
|
|
self.constraint_violation[[0, l], :] = (
|
|
self.constraint_violation[[l, 0], :])
|
|
|
|
def _constraint_violation_fn(self, x):
|
|
"""
|
|
Calculates total constraint violation for all the constraints, for a given
|
|
solution.
|
|
|
|
Parameters
|
|
----------
|
|
x : ndarray
|
|
Solution vector
|
|
|
|
Returns
|
|
-------
|
|
cv : ndarray
|
|
Total violation of constraints. Has shape ``(M,)``, where M is the
|
|
number of constraints (if each constraint function only returns one
|
|
value)
|
|
"""
|
|
return np.concatenate([c.violation(x) for c in self._wrapped_constraints])
|
|
|
|
def _calculate_population_feasibilities(self, population):
|
|
"""
|
|
Calculate the feasibilities of a population.
|
|
|
|
Parameters
|
|
----------
|
|
population : ndarray
|
|
An array of parameter vectors normalised to [0, 1] using lower
|
|
and upper limits. Has shape ``(np.size(population, 0), len(x))``.
|
|
|
|
Returns
|
|
-------
|
|
feasible, constraint_violation : ndarray, ndarray
|
|
Boolean array of feasibility for each population member, and an
|
|
array of the constraint violation for each population member.
|
|
constraint_violation has shape ``(np.size(population, 0), M)``,
|
|
where M is the number of constraints.
|
|
"""
|
|
num_members = np.size(population, 0)
|
|
if not self._wrapped_constraints:
|
|
# shortcut for no constraints
|
|
return np.ones(num_members, bool), np.zeros((num_members, 1))
|
|
|
|
parameters_pop = self._scale_parameters(population)
|
|
|
|
constraint_violation = np.array([self._constraint_violation_fn(x)
|
|
for x in parameters_pop])
|
|
feasible = ~(np.sum(constraint_violation, axis=1) > 0)
|
|
|
|
return feasible, constraint_violation
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *args):
|
|
return self._mapwrapper.__exit__(*args)
|
|
|
|
def _accept_trial(self, energy_trial, feasible_trial, cv_trial,
|
|
energy_orig, feasible_orig, cv_orig):
|
|
"""
|
|
Trial is accepted if:
|
|
* it satisfies all constraints and provides a lower or equal objective
|
|
function value, while both the compared solutions are feasible
|
|
- or -
|
|
* it is feasible while the original solution is infeasible,
|
|
- or -
|
|
* it is infeasible, but provides a lower or equal constraint violation
|
|
for all constraint functions.
|
|
|
|
This test corresponds to section III of Lampinen [1]_.
|
|
|
|
Parameters
|
|
----------
|
|
energy_trial : float
|
|
Energy of the trial solution
|
|
feasible_trial : float
|
|
Feasibility of trial solution
|
|
cv_trial : array-like
|
|
Excess constraint violation for the trial solution
|
|
energy_orig : float
|
|
Energy of the original solution
|
|
feasible_orig : float
|
|
Feasibility of original solution
|
|
cv_orig : array-like
|
|
Excess constraint violation for the original solution
|
|
|
|
Returns
|
|
-------
|
|
accepted : bool
|
|
|
|
"""
|
|
if feasible_orig and feasible_trial:
|
|
return energy_trial <= energy_orig
|
|
elif feasible_trial and not feasible_orig:
|
|
return True
|
|
elif not feasible_trial and (cv_trial <= cv_orig).all():
|
|
# cv_trial < cv_orig would imply that both trial and orig are not
|
|
# feasible
|
|
return True
|
|
|
|
return False
|
|
|
|
def __next__(self):
|
|
"""
|
|
Evolve the population by a single generation
|
|
|
|
Returns
|
|
-------
|
|
x : ndarray
|
|
The best solution from the solver.
|
|
fun : float
|
|
Value of objective function obtained from the best solution.
|
|
"""
|
|
# the population may have just been initialized (all entries are
|
|
# np.inf). If it has you have to calculate the initial energies
|
|
if np.all(np.isinf(self.population_energies)):
|
|
self.feasible, self.constraint_violation = (
|
|
self._calculate_population_feasibilities(self.population))
|
|
|
|
# only need to work out population energies for those that are
|
|
# feasible
|
|
self.population_energies[self.feasible] = (
|
|
self._calculate_population_energies(
|
|
self.population[self.feasible]))
|
|
|
|
self._promote_lowest_energy()
|
|
|
|
if self.dither is not None:
|
|
self.scale = self.random_number_generator.uniform(self.dither[0],
|
|
self.dither[1])
|
|
|
|
if self._updating == 'immediate':
|
|
# update best solution immediately
|
|
for candidate in range(self.num_population_members):
|
|
if self._nfev > self.maxfun:
|
|
raise StopIteration
|
|
|
|
# create a trial solution
|
|
trial = self._mutate(candidate)
|
|
|
|
# ensuring that it's in the range [0, 1)
|
|
self._ensure_constraint(trial)
|
|
|
|
# scale from [0, 1) to the actual parameter value
|
|
parameters = self._scale_parameters(trial)
|
|
|
|
# determine the energy of the objective function
|
|
if self._wrapped_constraints:
|
|
cv = self._constraint_violation_fn(parameters)
|
|
feasible = False
|
|
energy = np.inf
|
|
if not np.sum(cv) > 0:
|
|
# solution is feasible
|
|
feasible = True
|
|
energy = self.func(parameters)
|
|
self._nfev += 1
|
|
else:
|
|
feasible = True
|
|
cv = np.atleast_2d([0.])
|
|
energy = self.func(parameters)
|
|
self._nfev += 1
|
|
|
|
# compare trial and population member
|
|
if self._accept_trial(energy, feasible, cv,
|
|
self.population_energies[candidate],
|
|
self.feasible[candidate],
|
|
self.constraint_violation[candidate]):
|
|
self.population[candidate] = trial
|
|
self.population_energies[candidate] = energy
|
|
self.feasible[candidate] = feasible
|
|
self.constraint_violation[candidate] = cv
|
|
|
|
# if the trial candidate is also better than the best
|
|
# solution then promote it.
|
|
if self._accept_trial(energy, feasible, cv,
|
|
self.population_energies[0],
|
|
self.feasible[0],
|
|
self.constraint_violation[0]):
|
|
self._promote_lowest_energy()
|
|
|
|
elif self._updating == 'deferred':
|
|
# update best solution once per generation
|
|
if self._nfev >= self.maxfun:
|
|
raise StopIteration
|
|
|
|
# 'deferred' approach, vectorised form.
|
|
# create trial solutions
|
|
trial_pop = np.array(
|
|
[self._mutate(i) for i in range(self.num_population_members)])
|
|
|
|
# enforce bounds
|
|
self._ensure_constraint(trial_pop)
|
|
|
|
# determine the energies of the objective function, but only for
|
|
# feasible trials
|
|
feasible, cv = self._calculate_population_feasibilities(trial_pop)
|
|
trial_energies = np.full(self.num_population_members, np.inf)
|
|
|
|
# only calculate for feasible entries
|
|
trial_energies[feasible] = self._calculate_population_energies(
|
|
trial_pop[feasible])
|
|
|
|
# which solutions are 'improved'?
|
|
loc = [self._accept_trial(*val) for val in
|
|
zip(trial_energies, feasible, cv, self.population_energies,
|
|
self.feasible, self.constraint_violation)]
|
|
loc = np.array(loc)
|
|
self.population = np.where(loc[:, np.newaxis],
|
|
trial_pop,
|
|
self.population)
|
|
self.population_energies = np.where(loc,
|
|
trial_energies,
|
|
self.population_energies)
|
|
self.feasible = np.where(loc,
|
|
feasible,
|
|
self.feasible)
|
|
self.constraint_violation = np.where(loc[:, np.newaxis],
|
|
cv,
|
|
self.constraint_violation)
|
|
|
|
# make sure the best solution is updated if updating='deferred'.
|
|
# put the lowest energy into the best solution position.
|
|
self._promote_lowest_energy()
|
|
|
|
return self.x, self.population_energies[0]
|
|
|
|
next = __next__
|
|
|
|
def _scale_parameters(self, trial):
|
|
"""Scale from a number between 0 and 1 to parameters."""
|
|
return self.__scale_arg1 + (trial - 0.5) * self.__scale_arg2
|
|
|
|
def _unscale_parameters(self, parameters):
|
|
"""Scale from parameters to a number between 0 and 1."""
|
|
return (parameters - self.__scale_arg1) / self.__scale_arg2 + 0.5
|
|
|
|
def _ensure_constraint(self, trial):
|
|
"""Make sure the parameters lie between the limits."""
|
|
mask = np.where((trial > 1) | (trial < 0))
|
|
trial[mask] = self.random_number_generator.uniform(size=mask[0].shape)
|
|
|
|
def _mutate(self, candidate):
|
|
"""Create a trial vector based on a mutation strategy."""
|
|
trial = np.copy(self.population[candidate])
|
|
|
|
rng = self.random_number_generator
|
|
|
|
fill_point = rng.choice(self.parameter_count)
|
|
|
|
if self.strategy in ['currenttobest1exp', 'currenttobest1bin']:
|
|
bprime = self.mutation_func(candidate,
|
|
self._select_samples(candidate, 5))
|
|
else:
|
|
bprime = self.mutation_func(self._select_samples(candidate, 5))
|
|
|
|
if self.strategy in self._binomial:
|
|
crossovers = rng.uniform(size=self.parameter_count)
|
|
crossovers = crossovers < self.cross_over_probability
|
|
# the last one is always from the bprime vector for binomial
|
|
# If you fill in modulo with a loop you have to set the last one to
|
|
# true. If you don't use a loop then you can have any random entry
|
|
# be True.
|
|
crossovers[fill_point] = True
|
|
trial = np.where(crossovers, bprime, trial)
|
|
return trial
|
|
|
|
elif self.strategy in self._exponential:
|
|
i = 0
|
|
crossovers = rng.uniform(size=self.parameter_count)
|
|
crossovers = crossovers < self.cross_over_probability
|
|
while (i < self.parameter_count and crossovers[i]):
|
|
trial[fill_point] = bprime[fill_point]
|
|
fill_point = (fill_point + 1) % self.parameter_count
|
|
i += 1
|
|
|
|
return trial
|
|
|
|
def _best1(self, samples):
|
|
"""best1bin, best1exp"""
|
|
r0, r1 = samples[:2]
|
|
return (self.population[0] + self.scale *
|
|
(self.population[r0] - self.population[r1]))
|
|
|
|
def _rand1(self, samples):
|
|
"""rand1bin, rand1exp"""
|
|
r0, r1, r2 = samples[:3]
|
|
return (self.population[r0] + self.scale *
|
|
(self.population[r1] - self.population[r2]))
|
|
|
|
def _randtobest1(self, samples):
|
|
"""randtobest1bin, randtobest1exp"""
|
|
r0, r1, r2 = samples[:3]
|
|
bprime = np.copy(self.population[r0])
|
|
bprime += self.scale * (self.population[0] - bprime)
|
|
bprime += self.scale * (self.population[r1] -
|
|
self.population[r2])
|
|
return bprime
|
|
|
|
def _currenttobest1(self, candidate, samples):
|
|
"""currenttobest1bin, currenttobest1exp"""
|
|
r0, r1 = samples[:2]
|
|
bprime = (self.population[candidate] + self.scale *
|
|
(self.population[0] - self.population[candidate] +
|
|
self.population[r0] - self.population[r1]))
|
|
return bprime
|
|
|
|
def _best2(self, samples):
|
|
"""best2bin, best2exp"""
|
|
r0, r1, r2, r3 = samples[:4]
|
|
bprime = (self.population[0] + self.scale *
|
|
(self.population[r0] + self.population[r1] -
|
|
self.population[r2] - self.population[r3]))
|
|
|
|
return bprime
|
|
|
|
def _rand2(self, samples):
|
|
"""rand2bin, rand2exp"""
|
|
r0, r1, r2, r3, r4 = samples
|
|
bprime = (self.population[r0] + self.scale *
|
|
(self.population[r1] + self.population[r2] -
|
|
self.population[r3] - self.population[r4]))
|
|
|
|
return bprime
|
|
|
|
def _select_samples(self, candidate, number_samples):
|
|
"""
|
|
obtain random integers from range(self.num_population_members),
|
|
without replacement. You can't have the original candidate either.
|
|
"""
|
|
idxs = list(range(self.num_population_members))
|
|
idxs.remove(candidate)
|
|
self.random_number_generator.shuffle(idxs)
|
|
idxs = idxs[:number_samples]
|
|
return idxs
|
|
|
|
|
|
class _FunctionWrapper(object):
|
|
"""
|
|
Object to wrap user cost function, allowing picklability
|
|
"""
|
|
def __init__(self, f, args):
|
|
self.f = f
|
|
self.args = [] if args is None else args
|
|
|
|
def __call__(self, x):
|
|
return self.f(x, *self.args)
|
|
|
|
|
|
class _ConstraintWrapper(object):
|
|
"""Object to wrap/evaluate user defined constraints.
|
|
|
|
Very similar in practice to `PreparedConstraint`, except that no evaluation
|
|
of jac/hess is performed (explicit or implicit).
|
|
|
|
If created successfully, it will contain the attributes listed below.
|
|
|
|
Parameters
|
|
----------
|
|
constraint : {`NonlinearConstraint`, `LinearConstraint`, `Bounds`}
|
|
Constraint to check and prepare.
|
|
x0 : array_like
|
|
Initial vector of independent variables.
|
|
|
|
Attributes
|
|
----------
|
|
fun : callable
|
|
Function defining the constraint wrapped by one of the convenience
|
|
classes.
|
|
bounds : 2-tuple
|
|
Contains lower and upper bounds for the constraints --- lb and ub.
|
|
These are converted to ndarray and have a size equal to the number of
|
|
the constraints.
|
|
"""
|
|
def __init__(self, constraint, x0):
|
|
self.constraint = constraint
|
|
|
|
if isinstance(constraint, NonlinearConstraint):
|
|
def fun(x):
|
|
return np.atleast_1d(constraint.fun(x))
|
|
elif isinstance(constraint, LinearConstraint):
|
|
def fun(x):
|
|
if issparse(constraint.A):
|
|
A = constraint.A
|
|
else:
|
|
A = np.atleast_2d(constraint.A)
|
|
return A.dot(x)
|
|
elif isinstance(constraint, Bounds):
|
|
def fun(x):
|
|
return x
|
|
else:
|
|
raise ValueError("`constraint` of an unknown type is passed.")
|
|
|
|
self.fun = fun
|
|
|
|
lb = np.asarray(constraint.lb, dtype=float)
|
|
ub = np.asarray(constraint.ub, dtype=float)
|
|
|
|
f0 = fun(x0)
|
|
m = f0.size
|
|
|
|
if lb.ndim == 0:
|
|
lb = np.resize(lb, m)
|
|
if ub.ndim == 0:
|
|
ub = np.resize(ub, m)
|
|
|
|
self.bounds = (lb, ub)
|
|
|
|
def __call__(self, x):
|
|
return np.atleast_1d(self.fun(x))
|
|
|
|
def violation(self, x):
|
|
"""How much the constraint is exceeded by.
|
|
|
|
Parameters
|
|
----------
|
|
x : array-like
|
|
Vector of independent variables
|
|
|
|
Returns
|
|
-------
|
|
excess : array-like
|
|
How much the constraint is exceeded by, for each of the
|
|
constraints specified by `_ConstraintWrapper.fun`.
|
|
"""
|
|
ev = self.fun(np.asarray(x))
|
|
|
|
excess_lb = np.maximum(self.bounds[0] - ev, 0)
|
|
excess_ub = np.maximum(ev - self.bounds[1], 0)
|
|
|
|
return excess_lb + excess_ub
|