old_contents: 
  import numpy as np
  from sklearn import svm, datasets
  
  from darwin.pipeline import ClassificationPipeline
  
  
  def test_binary_classification_with_classification_pipeline():
      # generate the dataset
      n_samples=100
      n_features=20
      x, y = datasets.make_gaussian_quantiles(mean=None, cov=1.0, n_samples=n_samples,
                                              n_features=n_features, n_classes=2,
                                              shuffle=True, random_state=1)
  
      # another way to generate the data
      # x, y = datasets.make_hastie_10_2(n_samples=10, random_state=1)
  
      # -- test with darwin
      classifier_name='linsvm'
      cvmethod='10'
      n_feats = x.shape[1]
  
      pipe = ClassificationPipeline(n_feats=n_feats, clfmethod=classifier_name,
                                    cvmethod=cvmethod)
  
      results, metrics = pipe.cross_validation(x, y)
  
      assert(results is not None)
  
  
  # def test_
  #
  #     inst = instance.LearnerInstantiator()
  #     learner_item_name = 'LinearSVC'
  #     classifier, param_grid = inst.get_method_with_grid(learner_item_name)
  
  
  
new_contents: 
  import numpy as np
  from sklearn import svm, datasets
  
  from darwin.pipeline import ClassificationPipeline
  
  
  def test_binary_classification_with_classification_pipeline():
      # generate the dataset
      n_samples = 100
      n_features = 20
      x, y = datasets.make_gaussian_quantiles(mean=None, cov=1.0, n_samples=n_samples, n_features=n_features, n_classes=2,
                                              shuffle=True, random_state=1)
  
      # -- test with darwin
      classifier_name = 'rbfsvm' #'linsvm'
      cvmethod = '10'
      n_feats = x.shape[1]
  
      pipe = ClassificationPipeline(n_feats=n_feats, clfmethod=classifier_name, cvmethod=cvmethod)
      results, metrics = pipe.cross_validation(x, y)
      assert(results is not None)
  
      return results, metrics
  
  results, metrics = test_binary_classification_with_classification_pipeline()
  # def test_
  #
  #     inst = instance.LearnerInstantiator()
  #     learner_item_name = 'LinearSVC'
  #     classifier, param_grid = inst.get_method_with_grid(learner_item_name)
  
  
  
subject: clean code