Python OptimizeResult.nfev方法代码示例

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]
def scipy_graduate_walk(*args, **kwargs):
    """Scipy-compatible graduate_walk function wrapper.

    parameters:
        args[0]: target, function to be minimized
        args[1]: x0, starting point for minimization
        dx=1e-8: step in change of the point
        dx_start=0.1: starting value for dx step. Must be bigger that dx.
        dx_step=0.1: change of dx on each iteration. Should be less than 1.
        diagonal=False: defines directions for point movements. See
                generate_all_directions
                generate_nondiagonal_directions
            for more information.
        bounds=None: list of bounds for the movement
                [[min, max], [min, max], ...]
            if set to None, bounds are ignored
        ytol=1e-8: relative tolerance for search stop. See graduate_walk for
            more info.
    returns:
        OptimizeResult() object with properly set x, fun, nfev.
            success is always set to True, status to 1
    """
    target = args[0]
    x0 = args[1]
    dx = kwargs['dx'] if 'dx' in list(kwargs.keys()) else 1e-8
    dx_start = kwargs['dx_start'] if 'dx_start' in list(kwargs.keys()) else 0.1
    dx_step = kwargs['dx_step'] if 'dx_step' in list(kwargs.keys()) else 0.1
    if 'diagonal' in list(kwargs.keys()) and kwargs['diagonal']:
        directions = generate_all_directions(len(x0))
    else:
        directions = generate_nondiagonal_directions(len(x0))
    if 'bounds' in list(kwargs.keys()) and kwargs['bounds'] is not None:
        bounds = Bounds(kwargs['bounds'])
    else:
        bounds = None
    ytol_rel = kwargs['ytol_rel'] if 'ytol_rel' in list(kwargs.keys()) else 1e-8

    res = graduate_walk(target, x0, dx, directions, dx_start, dx_step,
                        bounds=bounds, ytol_rel=ytol_rel)

    answ = OptimizeResult()
    answ.x = res['x0']
    answ.fun = res['fval']
    answ.success = True
    answ.status = 1
    answ.nfev = res['fnval']
    return answ

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]
    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.population_energies[:] = self._calculate_population_energies(
                self.population)
            self._promote_lowest_energy()

        # do the optimisation.
        for nit in xrange(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]))

            # should the solver terminate?
            convergence = self.convergence

            if (self.callback and
                    self.callback(self._scale_parameters(self.population[0]),
                                  convergence=self.tol / convergence) is True):

                warning_flag = True
                status_message = ('callback function requested stop early '
                                  'by returning True')
                break

            if np.any(np.isinf(self.population_energies)):
                intol = False
            else:
                intol = (np.std(self.population_energies) <=
                         self.atol +
                         self.tol * np.abs(np.mean(self.population_energies)))
            if warning_flag or intol:
                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:
            result = minimize(self.func,
                              np.copy(DE_result.x),
                              method='L-BFGS-B',
                              bounds=self.limits.T)

            self._nfev += result.nfev
            DE_result.nfev = self._nfev

            if result.fun < DE_result.fun:
                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)

        return DE_result

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]
    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, then OptimizeResult also contains the ``hess_inv`` and
            ``jac`` attributes.
        """

        nfev, nit, warning_flag = 0, 0, False
        status_message = _status_message['success']

        # calculate energies to start with
        for index, candidate in enumerate(self.population):
            parameters = self._scale_parameters(candidate)
            self.population_energies[index] = self.func(parameters,
                                                        *self.args)
            nfev += 1

            if nfev > self.maxfun:
                warning_flag = True
                status_message = _status_message['maxfev']
                break

        minval = np.argmin(self.population_energies)

        # put the lowest energy into the best solution position.
        lowest_energy = self.population_energies[minval]
        self.population_energies[minval] = self.population_energies[0]
        self.population_energies[0] = lowest_energy

        self.population[[0, minval], :] = self.population[[minval, 0], :]

        if warning_flag:
            return OptimizeResult(
                           x=self.x,
                           fun=self.population_energies[0],
                           nfev=nfev,
                           nit=nit,
                           message=status_message,
                           success=(warning_flag != True))

        # do the optimisation.
        for nit in range(1, self.maxiter + 1):
            if self.dither is not None:
                self.scale = self.random_number_generator.rand(
                ) * (self.dither[1] - self.dither[0]) + self.dither[0]
            for candidate in range(np.size(self.population, 0)):
                if nfev > self.maxfun:
                    warning_flag = True
                    status_message = _status_message['maxfev']
                    break

                trial = self._mutate(candidate)
                self._ensure_constraint(trial)
                parameters = self._scale_parameters(trial)

                energy = self.func(parameters, *self.args)
                nfev += 1

                if energy < self.population_energies[candidate]:
                    self.population[candidate] = trial
                    self.population_energies[candidate] = energy

                    if energy < self.population_energies[0]:
                        self.population_energies[0] = energy
                        self.population[0] = trial

            # stop when the fractional s.d. of the population is less than tol
            # of the mean energy
            convergence = (np.std(self.population_energies) /
                           np.abs(np.mean(self.population_energies) +
                                  _MACHEPS))

            if self.disp:
                print("differential_evolution step %d: f(x)= %g"
                      % (nit,
                         self.population_energies[0]))

            if (self.callback and
                    self.callback(self._scale_parameters(self.population[0]),
                                  convergence=self.tol / convergence) is True):

                warning_flag = True
                status_message = ('callback function requested stop early '
                                  'by returning True')
                break

            if convergence < self.tol or warning_flag:
                break

        else:
            status_message = _status_message['maxiter']
#.........这里部分代码省略.........

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]

#.........这里部分代码省略.........
        Package for R. The R Journal, Volume 5/1 (2013).
    .. [6] Mullen, K. Continuous Global Optimization in R. Journal of
        Statistical Software, 60(6), 1 - 45, (2014). DOI:10.18637/jss.v060.i06

    Examples
    --------
    The following example is a 10-dimensional problem, with many local minima.
    The function involved is called Rastrigin
    (https://en.wikipedia.org/wiki/Rastrigin_function)

    >>> from scipy.optimize import dual_annealing
    >>> func = lambda x: np.sum(x*x - 10*np.cos(2*np.pi*x)) + 10*np.size(x)
    >>> lw = [-5.12] * 10
    >>> up = [5.12] * 10
    >>> ret = dual_annealing(func, None, bounds=list(zip(lw, up)), seed=1234)
    >>> print("global minimum: xmin = {0}, f(xmin) = {1:.6f}".format(
    ...       ret.x, ret.fun))
    global minimum: xmin = [-4.26437714e-09 -3.91699361e-09 -1.86149218e-09 -3.97165720e-09
     -6.29151648e-09 -6.53145322e-09 -3.93616815e-09 -6.55623025e-09
    -6.05775280e-09 -5.00668935e-09], f(xmin) = 0.000000

    """
    if x0 is not None and not len(x0) == len(bounds):
        raise ValueError('Bounds size does not match x0')

    lu = list(zip(*bounds))
    lower = np.array(lu[0])
    upper = np.array(lu[1])
    # Check that restart temperature ratio is correct
    if restart_temp_ratio <= 0. or restart_temp_ratio >= 1.:
        raise ValueError('Restart temperature ratio has to be in range (0, 1)')
    # Checking bounds are valid
    if (np.any(np.isinf(lower)) or np.any(np.isinf(upper)) or np.any(
            np.isnan(lower)) or np.any(np.isnan(upper))):
        raise ValueError('Some bounds values are inf values or nan values')
    # Checking that bounds are consistent
    if not np.all(lower < upper):
        raise ValueError('Bounds are note consistent min < max')

    # Wrapper for the objective function
    func_wrapper = ObjectiveFunWrapper(func, maxfun, *args)
    # Wrapper fot the minimizer
    minimizer_wrapper = LocalSearchWrapper(
        bounds, func_wrapper, **local_search_options)
    # Initialization of RandomState for reproducible runs if seed provided
    rand_state = check_random_state(seed)
    # Initialization of the energy state
    energy_state = EnergyState(lower, upper, callback)
    energy_state.reset(func_wrapper, rand_state, x0)
    # Minimum value of annealing temperature reached to perform
    # re-annealing
    temperature_restart = initial_temp * restart_temp_ratio
    # VisitingDistribution instance
    visit_dist = VisitingDistribution(lower, upper, visit, rand_state)
    # Strategy chain instance
    strategy_chain = StrategyChain(accept, visit_dist, func_wrapper,
                               minimizer_wrapper, rand_state, energy_state)
    # Run the search loop
    need_to_stop = False
    iteration = 0
    message = []
    t1 = np.exp((visit - 1) * np.log(2.0)) - 1.0
    while(not need_to_stop):
        for i in range(maxiter):
            # Compute temperature for this step
            s = float(i) + 2.0
            t2 = np.exp((visit - 1) * np.log(s)) - 1.0
            temperature = initial_temp * t1 / t2
            iteration += 1
            if iteration >= maxiter:
                message.append("Maximum number of iteration reached")
                need_to_stop = True
                break
            # Need a re-annealing process?
            if temperature < temperature_restart:
                energy_state.reset(func_wrapper, rand_state)
                break
            # starting strategy chain
            val = strategy_chain.run(i, temperature)
            if val is not None:
                message.append(val)
                need_to_stop = True
                break
            # Possible local search at the end of the strategy chain
            if not no_local_search:
                val = strategy_chain.local_search()
                if val is not None:
                    message.append(val)
                    need_to_stop = True
                    break

    # Return the OptimizeResult
    res = OptimizeResult()
    res.x = energy_state.xbest
    res.fun = energy_state.ebest
    res.nit = iteration
    res.nfev = func_wrapper.nfev
    res.njev = func_wrapper.ngev
    res.message = message
    return res

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]
def optimize_stiefel(func, X0, args=(), tau_max=.5, max_it=1, tol=1e-6,
                     disp=False, tau_find_freq=100):
    """
    Optimize a function over a Stiefel manifold.

    :param func: Function to be optimized
    :param X0: Initial point for line search
    :param tau_max: Maximum step size
    :param max_it: Maximum number of iterations
    :param tol: Tolerance criteria to terminate line search
    :param disp: Choose whether to display output
    :param args: Extra arguments passed to the function
    """
    tol = float(tol)
    assert tol > 0, 'Tolerance must be positive'
    max_it = int(max_it)
    assert max_it > 0, 'The maximum number of iterations must be a positive '\
                       + 'integer'
    tau_max = float(tau_max)
    assert tau_max > 0, 'The parameter `tau_max` must be positive.'
    k = 0
    X = X0.copy()
    nit = 0
    nfev = 0
    success = False
    if disp:
        print 'Stiefel Optimization'.center(80)
        print '{0:4s} {1:11s} {2:5s}'.format('It', 'F', '(F - F_old) / F_old')
        print '-' * 30

    
    ls_func = LSFunc()
    ls_func.func = func
    decrease_tau = False
    tau_max0 = tau_max
    while nit <= max_it:
        nit += 1
        F, G = func(X, *args)
        F_old = F
        nfev += 1
        A = compute_A(G, X)
        ls_func.A = A
        ls_func.X = X
        ls_func.func_args = args
        ls_func.tau_max = tau_max
        increased_tau = False
        if nit == 1 or decrease_tau or nit % tau_find_freq == 0:
            # Need to minimize ls_func with respect to each argument
            tau_init = np.linspace(-10, 0., 3)[:, None]
            tau_d = np.linspace(-10, 0., 50)[:, None]
            tau_all, F_all = pybgo.minimize(ls_func, tau_init, tau_d, fixed_noise=1e-16,
                    add_at_least=1, tol=1e-2, scale=True,
                    train_every=1)[:2]
            nfev += tau_all.shape[0]
            idx = np.argmin(F_all)
            tau = np.exp(tau_all[idx, 0]) * tau_max
            if tau_max - tau <= 1e-6:
                tau_max = 1.2 * tau_max
                if disp:
                    print 'increasing tau_max to {0:1.5e}'.format(tau_max)
                    increased_tau = True
            if decrease_tau:
                tau_max = .8 * tau_max
                if disp:
                    print 'decreasing max_tau to {0:1.5e}'.format(tau_max)
                decrease_tau = False
            F = F_all[idx, 0]
        else:
            F = ls_func([np.log(tau /  tau_max)])
        delta_F = (F_old - F) / np.abs(F_old)
        if delta_F < 0:
            if disp:
                print '*** backtracking'
            nit -= 1
            decrease_tau = True
            continue
        X_old = X
        X = Y_func(tau, X, A)
        if disp:
            print '{0:4s} {1:1.5e} {2:5e} tau = {3:1.3e}, tau_max = {4:1.3e}'.format(
             str(nit).zfill(4), F, delta_F, tau, tau_max)
        if delta_F <= tol:
            if disp:
                print '*** Converged ***'
            success = True
            break
    res = OptimizeResult()
    res.tau_max = tau_max
    res.X = X
    res.nfev = nfev
    res.nit = nit
    res.fun = F
    res.success = success
    return res

# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import nfev [as 别名]
    def solve(self):
        nfev, nit, warning_flag = 0, 0, False
        status_message = _status_message['success']

        # calculate energies to start with
        for index, candidate in enumerate(self.population):
            parameters = self._scale_parameters(candidate)
            self.population_energies[index] = self.func(parameters,
                                                        *self.args)
            nfev += 1

            if nfev > self.maxfun:
                warning_flag = True
                status_message = _status_message['maxfev']
                break

        minval = np.argmin(self.population_energies)

        # put the lowest energy into the best solution position.
        lowest_energy = self.population_energies[minval]
        self.population_energies[minval] = self.population_energies[0]
        self.population_energies[0] = lowest_energy

        self.population[[0, minval], :] = self.population[[minval, 0], :]

        if warning_flag:
            return OptimizeResult(
                           x=self.x,
                           fun=self.population_energies[0],
                           nfev=nfev,
                           nit=nit,
                           message=status_message,
                           success=(warning_flag is not True))

        # do the optimisation.
        start_time = time.time()
        nit = 0
        while nit < self.maxiter + 1:
            nit += 1
            if start_time + self.max_execution_time < time.time():
                warning_flag = True
                status_message = 'Max execution time reached'
                break

            if self.dither is not None:
                self.scale = self.random_number_generator.rand(
                ) * (self.dither[1] - self.dither[0]) + self.dither[0]
            for candidate in range(np.size(self.population, 0)):
                if nfev > self.maxfun:
                    warning_flag = True
                    status_message = _status_message['maxfev']
                    break

                trial = self._mutate(candidate)
                self._ensure_constraint(trial)
                parameters = self._scale_parameters(trial)

                energy = self.func(parameters, *self.args)
                nfev += 1

                if energy < self.population_energies[candidate]:
                    self.population[candidate] = trial
                    self.population_energies[candidate] = energy

                    if energy < self.population_energies[0]:
                        self.population_energies[0] = energy
                        self.population[0] = trial

            # stop when the fractional s.d. of the population is less than tol
            # of the mean energy
            convergence = (np.std(self.population_energies) /
                           np.abs(np.mean(self.population_energies) +
                                  _MACHEPS))

            if self.disp:
                print("differential_evolution step %d: f(x)= %g"
                      % (nit,
                         self.population_energies[0]))

            if (self.callback and
                    self.callback(self._scale_parameters(self.population[0]),
                                  convergence=self.tol / convergence) is True):

                warning_flag = True
                status_message = ('callback function requested stop early '
                                  'by returning True')
                break

            if convergence < self.tol or warning_flag:
                break

        else:
            status_message = _status_message['maxiter']
            warning_flag = True

        DE_result = OptimizeResult(
            x=self.x,
            fun=self.population_energies[0],
            nfev=nfev,
            nit=nit,
#.........这里部分代码省略.........