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])
  
      # Grab out the relevant sets.
      comp = grid[complete, :]
      cand = grid[candidates, :]
      pend = grid[pending, :]
      vals = values[complete]
  
      # Identify constraint violations
      # Note that we'll treat NaNs and Infs as these values as well
      # as an optional user defined value
      idx = np.logical_and(vals != self.bad_value, np.isfinite(vals))
      goodvals = np.nonzero(idx)[0]
      badvals = np.nonzero(np.logical_not(idx))[0]
  
      log("Found %d constraint violating jobs" % (badvals.shape[0]))
  
      # There's no point regressing on one observation
      log("Received %d valid results" % (goodvals.shape[0]))
      if goodvals.shape[0] < 2:
          return int(candidates[0])
  
      labels = np.zeros(vals.shape[0])
      labels[goodvals] = 1
  
      if np.sum(labels) < 2:
          return int(candidates[0])
  
      # Perform the real initialization.
      if self.D == -1:
          self._real_init(
              grid.shape[1], values[complete], durations[complete]
          )
  
      # 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_constraint_hypers(comp, labels)
                  self.sample_hypers(comp[goodvals, :], vals[goodvals])
                  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_constraint_hypers(comp, labels)
              self.sample_hypers(comp[goodvals, :], vals[goodvals])
              if self.verbosity > 0:
                  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] constraint_mean: %.2f "
                      "constraint_amp: %.2f "
                      "constraint_gain: %.4f "
                      "constraint_min_ls: %.4f "
                      "constraint_max_ls: "
                      "%.4f"
                      % (
                          mcmc_iter + 1,
                          self.mcmc_iters,
                          self.constraint_mean,
                          np.sqrt(self.constraint_amp2),
                          self.constraint_gain,
                          np.min(self.constraint_ls),
                          np.max(self.constraint_ls),
                      )
                  )
          self.dump_hypers()
          comp_preds = np.zeros(labels.shape[0]).flatten()
  
          preds = self.pred_constraint_voilation(
              cand, comp, labels
          ).flatten()
          for ii in range(self.mcmc_iters):
              constraint_hyper = self.constraint_hyper_samples[ii]
              self.ff = self.ff_samples[ii]
              self.constraint_mean = constraint_hyper[0]
              self.constraint_gain = constraint_hyper[1]
              self.constraint_amp2 = constraint_hyper[2]
              self.constraint_ls = constraint_hyper[3]
              comp_preds += self.pred_constraint_voilation(
                  comp, comp, labels
              ).flatten()
          comp_preds = comp_preds / float(self.mcmc_iters)
          log(
              "Predicting %.2f%% constraint violations (%d/%d): "
              % (
                  np.mean(preds < 0.5) * 100,
                  np.sum(preds < 0.5),
                  preds.shape[0],
              )
          )
          if self.verbosity > 0:
              log(
                  "Prediction` %f%% train accuracy (%d/%d): "
                  % (
                      np.mean((comp_preds > 0.5) == labels),
                      np.sum((comp_preds > 0.5) == labels),
                      comp_preds.shape[0],
                  )
              )
  
          if self.visualize2D:
              delta = 0.025
              x = np.arange(0, 1.0, delta)
              y = np.arange(0, 1.0, delta)
              X, Y = np.meshgrid(x, y)
  
              cpreds = np.zeros((X.shape[0], X.shape[1]))
              predei = np.zeros((X.shape[0], X.shape[1]))
              predei2 = np.zeros((X.shape[0], X.shape[1]))
              for ii in range(self.mcmc_iters):
                  constraint_hyper = self.constraint_hyper_samples[ii]
                  self.ff = self.ff_samples[ii]
                  self.constraint_mean = constraint_hyper[0]
                  self.constraint_gain = constraint_hyper[1]
                  self.constraint_amp2 = constraint_hyper[2]
                  self.constraint_ls = constraint_hyper[3]
  
                  cpred = self.pred_constraint_voilation(
                      np.hstack(
                          (
                              X.flatten()[:, np.newaxis],
                              Y.flatten()[:, np.newaxis],
                          )
                      ),
                      comp,
                      labels,
                  )
                  pei = self.compute_constrained_ei(
                      comp,
                      pend,
                      np.hstack(
                          (
                              X.flatten()[:, np.newaxis],
                              Y.flatten()[:, np.newaxis],
                          )
                      ),
                      vals,
                      labels,
                  )
                  pei2 = self.compute_ei(
                      comp,
                      pend,
                      np.hstack(
                          (
                              X.flatten()[:, np.newaxis],
                              Y.flatten()[:, np.newaxis],
                          )
                      ),
                      vals,
                      labels,
                  )
  
                  cpreds += np.reshape(cpred, (X.shape[0], X.shape[1]))
                  predei += np.reshape(pei, (X.shape[0], X.shape[1]))
                  predei2 += np.reshape(pei2, (X.shape[0], X.shape[1]))
  
              plt.figure(1)
              plt.clf()
              cpreds = cpreds / float(self.mcmc_iters)
              CS = plt.contour(X, Y, cpreds)
              plt.clabel(CS, inline=1, fontsize=10)
              plt.plot(comp[labels == 0, 0], comp[labels == 0, 1], "rx")
              plt.plot(comp[labels == 1, 0], comp[labels == 1, 1], "bx")
              plt.title(
                  "Contours of Classification GP (Prob of not being a "
                  "constraint violation)"
              )
              plt.legend(
                  ("Constraint Violations", "Good points"), "lower left"
              )
              plt.savefig("constrained_ei_chooser_class_contour.pdf")
  
              plt.figure(2)
              plt.clf()
              predei = predei / float(self.mcmc_iters)
              CS = plt.contour(X, Y, predei)
              plt.clabel(CS, inline=1, fontsize=10)
              plt.plot(comp[labels == 0, 0], comp[labels == 0, 1], "rx")
              plt.plot(comp[labels == 1, 0], comp[labels == 1, 1], "bx")
              plt.title("Contours of EI*P(not violating constraint)")
              plt.legend(
                  ("Constraint Violations", "Good points"), "lower left"
              )
              plt.savefig(
                  "constrained_ei_chooser_eitimesprob_contour.pdf"
              )
  
              plt.figure(3)
              plt.clf()
              predei2 = predei2 / float(self.mcmc_iters)
              CS = plt.contour(X, Y, predei2)
              plt.clabel(CS, inline=1, fontsize=10)
              plt.plot(comp[labels == 0, 0], comp[labels == 0, 1], "rx")
              plt.plot(comp[labels == 1, 0], comp[labels == 1, 1], "bx")
              plt.title("Contours of EI")
              plt.legend(
                  ("Constraint Violations", "Good points"), "lower left"
              )
              plt.savefig("constrained_ei_chooser_ei_contour.pdf")
              # plt.show()
  
          # Pick the top candidates to optimize over
          overall_ei = self.ei_over_hypers(
              comp, pend, cand2, vals, labels
          )
          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))
  
          # Optimize each point in parallel
          pool = multiprocessing.Pool(self.grid_subset)
          results = [
              pool.apply_async(
                  optimize_pt,
                  args=(
                      c,
                      b,
                      comp,
                      pend,
                      vals,
                      labels,
                      copy.copy(self),
                  ),
              )
              for c in cand2
          ]
          for res in results:
              cand = np.vstack((cand, res.get(1024)))
          pool.close()
  
          # for i in range(0, cand2.shape[0]):
          #    log("Optimizing candidate %d/%d\n" %
          #                     (i+1, cand2.shape[0]))
          #    self.check_grad_ei(cand2[i,:], comp, pend, vals, labels)
          #    ret = spo.fmin_l_bfgs_b(self.grad_optimize_ei_over_hypers,
          #                            cand2[i,:].flatten(),
          #                            args=(comp,pend,vals,labels,True),
          #                            bounds=b, disp=0)
          #    cand2[i,:] = ret[0]
  
          cand = np.vstack((cand, cand2))
  
          overall_ei = self.ei_over_hypers(
              comp, pend, cand, vals, labels
          )
          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:
          log(
              "This Chooser module permits only slice sampling with > 0 "
              "samples."
          )
          raise Exception("mcmc_iters <= 0")
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import numpy
  import math
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import pickle
  import matplotlib.pyplot
  import multiprocessing
  import copy
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 5 for calling the GPConstrainedEIChooser obj's underlying member method code with example usage: obj.next(grid, values, durations, candidates, pending, complete) and returns: int, int, int, int, int,  cand