code: def next(
      self, grid, values, durations, candidates, pending, complete
  ):
      # Don't bother using fancy GP stuff at first.
      if complete.shape[0] < 2:
          return int(candidates[0])
  
      # Perform the real initialization.
      if self.D == -1:
          self._real_init(
              grid.shape[1], values[complete], durations[complete]
          )
  
      # Grab out the relevant sets.
      comp = grid[complete, :]
      cand = grid[candidates, :]
      pend = grid[pending, :]
      vals = values[complete]
      durs = durations[complete]
  
      # Bring time into the log domain before we do anything
      # to maintain strict positivity
      durs = np.log(durs)
  
      # Spray a set of candidates around the min so far
      numcand = cand.shape[0]
      best_comp = np.argmin(vals)
      cand2 = np.vstack(
          (
              np.random.randn(10, comp.shape[1]) * 0.001
              + comp[best_comp, :],
              cand,
          )
      )
  
      if self.mcmc_iters > 0:
          # Possibly burn in.
          if self.needs_burnin:
              for mcmc_iter in range(self.burnin):
                  self.sample_hypers(comp, vals, durs)
                  log(
                      "BURN %d/%d] mean: %.2f  amp: %.2f "
                      "noise: %.4f  min_ls: %.4f  max_ls: %.4f"
                      % (
                          mcmc_iter + 1,
                          self.burnin,
                          self.mean,
                          np.sqrt(self.amp2),
                          self.noise,
                          np.min(self.ls),
                          np.max(self.ls),
                      )
                  )
              self.needs_burnin = False
  
          # Sample from hyperparameters.
          # Adjust the candidates to hit ei/sec peaks
          self.hyper_samples = []
          for mcmc_iter in range(self.mcmc_iters):
              self.sample_hypers(comp, vals, durs)
              log(
                  "%d/%d] mean: %.2f  amp: %.2f  noise: %.4f "
                  "min_ls: %.4f  max_ls: %.4f"
                  % (
                      mcmc_iter + 1,
                      self.mcmc_iters,
                      self.mean,
                      np.sqrt(self.amp2),
                      self.noise,
                      np.min(self.ls),
                      np.max(self.ls),
                  )
              )
  
              log(
                  "%d/%d] time_mean: %.2fs time_amp: %.2f  time_noise: %.4f "
                  "time_min_ls: %.4f  time_max_ls: %.4f"
                  % (
                      mcmc_iter + 1,
                      self.mcmc_iters,
                      np.exp(self.time_mean),
                      np.sqrt(self.time_amp2),
                      np.exp(self.time_noise),
                      np.min(self.time_ls),
                      np.max(self.time_ls),
                  )
              )
          self.dump_hypers()
  
          # Pick the top candidates to optimize over
          overall_ei = self.ei_over_hypers(
              comp, pend, cand2, vals, durs
          )
          inds = np.argsort(np.mean(overall_ei, axis=1))[
              -self.grid_subset :
          ]
          cand2 = cand2[inds, :]
  
          # Adjust the candidates to hit ei peaks
          b = []  # optimization bounds
          for i in range(0, cand.shape[1]):
              b.append((0, 1))
  
          for i in range(0, cand2.shape[0]):
              log(
                  "Optimizing candidate %d/%d" % (i + 1, cand2.shape[0])
              )
              ret = spo.fmin_l_bfgs_b(
                  self.grad_optimize_ei_over_hypers,
                  cand2[i, :].flatten(),
                  args=(comp, vals, durs, True),
                  bounds=b,
                  disp=0,
              )
              cand2[i, :] = ret[0]
  
          cand = np.vstack((cand, cand2))
  
          overall_ei = self.ei_over_hypers(comp, pend, cand, vals, durs)
          best_cand = np.argmax(np.mean(overall_ei, axis=1))
          self.dump_hypers()
          if best_cand >= numcand:
              return (int(numcand), cand[best_cand, :])
  
          return int(candidates[best_cand])
  
      else:
          # Optimize hyperparameters
          self.optimize_hypers(comp, vals, durs)
  
          log(
              "mean: %f  amp: %f  noise: %f "
              "min_ls: %f  max_ls: %f"
              % (
                  self.mean,
                  np.sqrt(self.amp2),
                  self.noise,
                  np.min(self.ls),
                  np.max(self.ls),
              )
          )
  
          # Pick the top candidates to optimize over
          ei = self.compute_ei_per_s(comp, pend, cand2, vals, durs)
          inds = np.argsort(np.mean(overall_ei, axis=1))[
              -self.grid_subset :
          ]
          cand2 = cand2[inds, :]
  
          # Adjust the candidates to hit ei peaks
          b = []  # optimization bounds
          for i in range(0, cand.shape[1]):
              b.append((0, 1))
  
          for i in range(0, cand2.shape[0]):
              log(
                  "Optimizing candidate %d/%d" % (i + 1, cand2.shape[0])
              )
              ret = spo.fmin_l_bfgs_b(
                  self.grad_optimize_ei,
                  cand2[i, :].flatten(),
                  args=(comp, vals, durs, True),
                  bounds=b,
                  disp=0,
              )
              cand2[i, :] = ret[0]
  
          cand = np.vstack((cand, cand2))
          ei = self.compute_ei_per_s(comp, pend, cand, vals, durs)
  
          best_cand = np.argmax(ei)
          self.dump_hypers()
  
          if best_cand >= numcand:
              return (int(numcand), cand[best_cand, :])
  
          return int(candidates[best_cand])
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import numpy
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import pickle
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 5 for calling the GPEIperSecChooser obj's underlying member method code with example usage: obj.next(grid, values, durations, candidates, pending, complete) and returns: int, int, int, int,  cand, int,  cand