class_docstr: A unittest class for testing the Threshold Attack.
  
  This module tests the Threshold Attack.
  
  | Paper link:
      https://arxiv.org/abs/1906.06026
class_docstr_tok: ["A","unittest","class","for","testing","the","Threshold","Attack",".","This","module","tests","the","Threshold","Attack",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1906.06026"]
docstr: Test with the Threshold Attack
  :return:
docstr_tok: ["Test","with","the","Threshold","Attack",":","return",":"]
code: def _test_attack(self, classifier, x_test, y_test, targeted):
      """
      Test with the Threshold Attack
      :return:
      """
      x_test_original = x_test.copy()
  
      if targeted:
          # Generate random target classes
          class_y_test = np.argmax(y_test, axis=1)
          nb_classes = np.unique(class_y_test).shape[0]
          np.random.seed(seed=487)
          targets = np.random.randint(nb_classes, size=self.n_test)
          for i in range(self.n_test):
              if class_y_test[i] == targets[i]:
                  targets[i] -= 1
      else:
          targets = y_test
  
      for es in [
          1
      ]:  # Option 0 is not easy to reproduce reliably, we should consider it at a later time
          df = ThresholdAttack(
              classifier,
              th=128,
              es=es,
              max_iter=10,
              targeted=targeted,
              verbose=False,
          )
          x_test_adv = df.generate(x_test_original, targets)
  
          np.testing.assert_raises(
              AssertionError,
              np.testing.assert_array_equal,
              x_test,
              x_test_adv,
          )
          self.assertFalse((0.0 == x_test_adv).all())
  
          y_pred = get_labels_np_array(classifier.predict(x_test_adv))
  
          accuracy = (
              np.sum(
                  np.argmax(y_pred, axis=1)
                  == np.argmax(self.y_test_mnist, axis=1)
              )
              / self.n_test
          )
          logger.info(
              "Accuracy on adversarial examples: %.2f%%",
              (accuracy * 100),
          )
  
      # Check that x_test has not been modified by attack and classifier
      self.assertAlmostEqual(
          float(np.max(np.abs(x_test_original - x_test))),
          0.0,
          delta=0.00001,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from art.attacks.evasion.pixel_threshold import ThresholdAttack
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import get_labels_np_array
  from tests.utils import TestBase
  from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
  from tests.attacks.utils import backend_test_classifier_type_check_fail
label_desc: Use image node_id 8 for calling the TestThresholdAttack obj's underlying member method code with example usage: obj._test_attack(classifier, x_test, y_test, targeted) without return types