text: from unittest import TestCase
  
  import numpy as np
  import pytest
  from sklearn.model_selection import train_test_split
  
  from diffprivlib.models.naive_bayes import GaussianNB
  from diffprivlib.utils import global_seed, PrivacyLeakWarning, DiffprivlibCompatibilityWarning, BudgetError
  
  
  class TestGaussianNB(TestCase):
      def test_not_none(self):
          clf = GaussianNB(epsilon=1, bounds=(0, 1))
          self.assertIsNotNone(clf)
  
      def test_zero_epsilon(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
          clf = GaussianNB(epsilon=0, bounds=(0, 1))
  
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
      def test_neg_epsilon(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
          clf = GaussianNB(epsilon=-1, bounds=(0, 1))
  
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
      def test_sample_weight_warning(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]))
          w = abs(np.random.randn(10))
  
          with self.assertWarns(DiffprivlibCompatibilityWarning):
              clf.fit(X, y, sample_weight=w)
  
      def test_mis_ordered_bounds(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 1], [1, 0]))
  
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
      def test_no_bounds(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
          clf = GaussianNB()
  
          with self.assertWarns(PrivacyLeakWarning):
              clf.fit(X, y)
  
          self.assertIsNotNone(clf)
  
      def test_missing_bounds(self):
          X = np.random.random((10, 3))
          y = np.random.randint(2, size=10)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]))
  
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
      def test_noisy_count(self):
          y = np.random.randint(20, size=10000)
          actual_counts = np.array([(y == y_i).sum() for y_i in np.unique(y)])
  
          clf = GaussianNB(epsilon=3)
          noisy_counts = clf._noisy_class_counts(y)
          self.assertEqual(y.shape[0], noisy_counts.sum())
          self.assertFalse(np.all(noisy_counts == actual_counts))
  
          clf = GaussianNB(epsilon=float("inf"))
          noisy_counts = clf._noisy_class_counts(y)
          self.assertEqual(y.shape[0], noisy_counts.sum())
          self.assertTrue(np.all(noisy_counts == actual_counts))
  
      @pytest.mark.filterwarnings('ignore: numpy.ufunc size changed')
      def test_different_results(self):
          from sklearn.naive_bayes import GaussianNB as sk_nb
          from sklearn import datasets
  
          global_seed(12345)
          dataset = datasets.load_iris()
  
          x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=.2)
  
          bounds = ([4.3, 2.0, 1.0, 0.1], [7.9, 4.4, 6.9, 2.5])
  
          clf_dp = GaussianNB(epsilon=1.0, bounds=bounds)
          clf_non_private = sk_nb()
  
          for clf in [clf_dp, clf_non_private]:
              clf.fit(x_train, y_train)
  
          # Todo: remove try...except when sklearn v1.0 is required
          try:
              nonprivate_var = clf_non_private.var_
          except AttributeError:
              nonprivate_var = clf_non_private.sigma_
  
          theta_diff = (clf_dp.theta_ - clf_non_private.theta_) ** 2
          self.assertGreater(theta_diff.sum(), 0)
  
          var_diff = (clf_dp.var_ - nonprivate_var) ** 2
          self.assertGreater(var_diff.sum(), 0)
  
      @pytest.mark.filterwarnings('ignore: numpy.ufunc size changed')
      def test_with_iris(self):
          global_seed(12345)
          from sklearn import datasets
          dataset = datasets.load_iris()
  
          x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=.2)
  
          bounds = ([4.3, 2.0, 1.0, 0.1], [7.9, 4.4, 6.9, 2.5])
  
          clf = GaussianNB(epsilon=5.0, bounds=bounds)
          clf.fit(x_train, y_train)
  
          accuracy = clf.score(x_test, y_test)
          counts = clf.class_count_.copy()
          self.assertGreater(accuracy, 0.45)
  
          clf.partial_fit(x_train, y_train)
          new_counts = clf.class_count_
          self.assertEqual(np.sum(new_counts), np.sum(counts) * 2)
  
      def test_accountant(self):
          from diffprivlib.accountant import BudgetAccountant
          acc = BudgetAccountant()
  
          x_train = np.random.random((10, 2))
          y_train = np.random.randint(2, size=10)
  
          clf = GaussianNB(epsilon=1.0, bounds=(0, 1), accountant=acc)
          clf.fit(x_train, y_train)
          self.assertEqual((1, 0), acc.total())
  
          with BudgetAccountant(1.5, 0) as acc2:
              clf = GaussianNB(epsilon=1.0, bounds=(0, 1))
              clf.fit(x_train, y_train)
              self.assertEqual((1, 0), acc2.total())
  
              with self.assertRaises(BudgetError):
                  clf.fit(x_train, y_train)
  
      def test_priors(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]), priors=(0.75, 0.25))
          self.assertIsNotNone(clf.fit(X, y))
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]), priors=(1,))
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]), priors=(0.5, 0.7))
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]), priors=(-0.5, 1.5))
          with self.assertRaises(ValueError):
              clf.fit(X, y)
  
      def test_refit(self):
          X = np.random.random((10, 2))
          y = np.random.randint(2, size=10)
  
          clf = GaussianNB(epsilon=1, bounds=([0, 0], [1, 1]))
          clf.fit(X, y)
  
          X2 = np.random.random((10, 3))
          clf.bounds = ([0, 0, 0], [1, 1, 1])
  
          with self.assertRaises(ValueError):
              clf.partial_fit(X2, y)
  