code: def evalClassifier(individual):
      labels = classifier.predict(trainset[N_TRAIN:], individual)
      return sum(
          x == y for x, y in zip(labels, trainlabels[N_TRAIN:])
      ) / float(len(trainlabels[N_TRAIN:])), sum(individual) / float(
          classifier.ndim
      )
  
file_path: deap/examples/ga/evoknn.py
imports: import csv
  import random
  import numpy
  import knn
  from deap import algorithms
  from deap import base
  from deap import creator
  from deap import tools
  import knn
label_desc: Use image node_id 1 for calling a global function with example usage: evalClassifier(individual) and returns: unknown,  unknown