content: import pickle
  from io import BytesIO
  import numpy as np
  import scipy.sparse
  
  from sklearn.datasets import load_digits, load_iris
  
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import cross_val_score
  
  from sklearn.externals.six.moves import zip
  from sklearn.utils.testing import assert_almost_equal
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import assert_array_almost_equal
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_greater
  
  from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
  
  # Data is just 6 separable points in the plane
  X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
  y = np.array([1, 1, 1, 2, 2, 2])
  
  # A bit more random tests
  rng = np.random.RandomState(0)
  X1 = rng.normal(size=(10, 3))
  y1 = (rng.normal(size=(10)) > 0).astype(np.int)
  
  # Data is 6 random integer points in a 100 dimensional space classified to
  # three classes.
  X2 = rng.randint(5, size=(6, 100))
  y2 = np.array([1, 1, 2, 2, 3, 3])
  
  
  def test_gnb():
      # Gaussian Naive Bayes classification.
      # This checks that GaussianNB implements fit and predict and returns
      # correct values for a simple toy dataset.
  
      clf = GaussianNB()
      y_pred = clf.fit(X, y).predict(X)
      assert_array_equal(y_pred, y)
  
      y_pred_proba = clf.predict_proba(X)
      y_pred_log_proba = clf.predict_log_proba(X)
      assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
  
      # Test whether label mismatch between target y and classes raises
      # an Error
      # FIXME Remove this test once the more general partial_fit tests are merged
      assert_raises(ValueError, GaussianNB().partial_fit, X, y, classes=[0, 1])
  
  
  def test_gnb_prior():
      # Test whether class priors are properly set.
      clf = GaussianNB().fit(X, y)
      assert_array_almost_equal(np.array([3, 3]) / 6.0,
                                clf.class_prior_, 8)
      clf.fit(X1, y1)
      # Check that the class priors sum to 1
      assert_array_almost_equal(clf.class_prior_.sum(), 1)
  
  
  def test_gnb_sample_weight():
      """Test whether sample weights are properly used in GNB. """
      # Sample weights all being 1 should not change results
      sw = np.ones(6)
      clf = GaussianNB().fit(X, y)
      clf_sw = GaussianNB().fit(X, y, sw)
  
      assert_array_almost_equal(clf.theta_, clf_sw.theta_)
      assert_array_almost_equal(clf.sigma_, clf_sw.sigma_)
  
      # Fitting twice with half sample-weights should result
      # in same result as fitting once with full weights
      sw = rng.rand(y.shape[0])
      clf1 = GaussianNB().fit(X, y, sample_weight=sw)
      clf2 = GaussianNB().partial_fit(X, y, classes=[1, 2], sample_weight=sw / 2)
      clf2.partial_fit(X, y, sample_weight=sw / 2)
  
      assert_array_almost_equal(clf1.theta_, clf2.theta_)
      assert_array_almost_equal(clf1.sigma_, clf2.sigma_)
  
      # Check that duplicate entries and correspondingly increased sample
      # weights yield the same result
      ind = rng.randint(0, X.shape[0], 20)
      sample_weight = np.bincount(ind, minlength=X.shape[0])
  
      clf_dupl = GaussianNB().fit(X[ind], y[ind])
      clf_sw = GaussianNB().fit(X, y, sample_weight)
  
      assert_array_almost_equal(clf_dupl.theta_, clf_sw.theta_)
      assert_array_almost_equal(clf_dupl.sigma_, clf_sw.sigma_)
  
  
  def test_discrete_prior():
      # Test whether class priors are properly set.
      for cls in [BernoulliNB, MultinomialNB]:
          clf = cls().fit(X2, y2)
          assert_array_almost_equal(np.log(np.array([2, 2, 2]) / 6.0),
                                    clf.class_log_prior_, 8)
  
  
  def test_mnnb():
      # Test Multinomial Naive Bayes classification.
      # This checks that MultinomialNB implements fit and predict and returns
      # correct values for a simple toy dataset.
  
      for X in [X2, scipy.sparse.csr_matrix(X2)]:
          # Check the ability to predict the learning set.
          clf = MultinomialNB()
          assert_raises(ValueError, clf.fit, -X, y2)
          y_pred = clf.fit(X, y2).predict(X)
  
          assert_array_equal(y_pred, y2)
  
          # Verify that np.log(clf.predict_proba(X)) gives the same results as
          # clf.predict_log_proba(X)
          y_pred_proba = clf.predict_proba(X)
          y_pred_log_proba = clf.predict_log_proba(X)
          assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
  
          # Check that incremental fitting yields the same results
          clf2 = MultinomialNB()
          clf2.partial_fit(X[:2], y2[:2], classes=np.unique(y2))
          clf2.partial_fit(X[2:5], y2[2:5])
          clf2.partial_fit(X[5:], y2[5:])
  
          y_pred2 = clf2.predict(X)
          assert_array_equal(y_pred2, y2)
  
          y_pred_proba2 = clf2.predict_proba(X)
          y_pred_log_proba2 = clf2.predict_log_proba(X)
          assert_array_almost_equal(np.log(y_pred_proba2), y_pred_log_proba2, 8)
          assert_array_almost_equal(y_pred_proba2, y_pred_proba)
          assert_array_almost_equal(y_pred_log_proba2, y_pred_log_proba)
  
          # Partial fit on the whole data at once should be the same as fit too
          clf3 = MultinomialNB()
          clf3.partial_fit(X, y2, classes=np.unique(y2))
  
          y_pred3 = clf3.predict(X)
          assert_array_equal(y_pred3, y2)
          y_pred_proba3 = clf3.predict_proba(X)
          y_pred_log_proba3 = clf3.predict_log_proba(X)
          assert_array_almost_equal(np.log(y_pred_proba3), y_pred_log_proba3, 8)
          assert_array_almost_equal(y_pred_proba3, y_pred_proba)
          assert_array_almost_equal(y_pred_log_proba3, y_pred_log_proba)
  
  
  def check_partial_fit(cls):
      clf1 = cls()
      clf1.fit([[0, 1], [1, 0]], [0, 1])
  
      clf2 = cls()
      clf2.partial_fit([[0, 1], [1, 0]], [0, 1], classes=[0, 1])
      assert_array_equal(clf1.class_count_, clf2.class_count_)
      assert_array_equal(clf1.feature_count_, clf2.feature_count_)
  
      clf3 = cls()
      clf3.partial_fit([[0, 1]], [0], classes=[0, 1])
      clf3.partial_fit([[1, 0]], [1])
      assert_array_equal(clf1.class_count_, clf3.class_count_)
      assert_array_equal(clf1.feature_count_, clf3.feature_count_)
  
  
  def test_discretenb_partial_fit():
      for cls in [MultinomialNB, BernoulliNB]:
          yield check_partial_fit, cls
  
  
  def test_gnb_partial_fit():
      clf = GaussianNB().fit(X, y)
      clf_pf = GaussianNB().partial_fit(X, y, np.unique(y))
      assert_array_almost_equal(clf.theta_, clf_pf.theta_)
      assert_array_almost_equal(clf.sigma_, clf_pf.sigma_)
      assert_array_almost_equal(clf.class_prior_, clf_pf.class_prior_)
  
      clf_pf2 = GaussianNB().partial_fit(X[0::2, :], y[0::2], np.unique(y))
      clf_pf2.partial_fit(X[1::2], y[1::2])
      assert_array_almost_equal(clf.theta_, clf_pf2.theta_)
      assert_array_almost_equal(clf.sigma_, clf_pf2.sigma_)
      assert_array_almost_equal(clf.class_prior_, clf_pf2.class_prior_)
  
  
  def test_discretenb_pickle():
      # Test picklability of discrete naive Bayes classifiers
  
      for cls in [BernoulliNB, MultinomialNB, GaussianNB]:
          clf = cls().fit(X2, y2)
          y_pred = clf.predict(X2)
  
          store = BytesIO()
          pickle.dump(clf, store)
          clf = pickle.load(BytesIO(store.getvalue()))
  
          assert_array_equal(y_pred, clf.predict(X2))
  
          if cls is not GaussianNB:
              # TODO re-enable me when partial_fit is implemented for GaussianNB
  
              # Test pickling of estimator trained with partial_fit
              clf2 = cls().partial_fit(X2[:3], y2[:3], classes=np.unique(y2))
              clf2.partial_fit(X2[3:], y2[3:])
              store = BytesIO()
              pickle.dump(clf2, store)
              clf2 = pickle.load(BytesIO(store.getvalue()))
              assert_array_equal(y_pred, clf2.predict(X2))
  
  
  def test_input_check_fit():
      # Test input checks for the fit method
      for cls in [BernoulliNB, MultinomialNB, GaussianNB]:
          # check shape consistency for number of samples at fit time
          assert_raises(ValueError, cls().fit, X2, y2[:-1])
  
          # check shape consistency for number of input features at predict time
          clf = cls().fit(X2, y2)
          assert_raises(ValueError, clf.predict, X2[:, :-1])
  
  
  def test_input_check_partial_fit():
      for cls in [BernoulliNB, MultinomialNB]:
          # check shape consistency
          assert_raises(ValueError, cls().partial_fit, X2, y2[:-1],
                        classes=np.unique(y2))
  
          # classes is required for first call to partial fit
          assert_raises(ValueError, cls().partial_fit, X2, y2)
  
          # check consistency of consecutive classes values
          clf = cls()
          clf.partial_fit(X2, y2, classes=np.unique(y2))
          assert_raises(ValueError, clf.partial_fit, X2, y2,
                        classes=np.arange(42))
  
          # check consistency of input shape for partial_fit
          assert_raises(ValueError, clf.partial_fit, X2[:, :-1], y2)
  
          # check consistency of input shape for predict
          assert_raises(ValueError, clf.predict, X2[:, :-1])
  
  
  def test_discretenb_predict_proba():
      # Test discrete NB classes' probability scores
  
      # The 100s below distinguish Bernoulli from multinomial.
      # FIXME: write a test to show this.
      X_bernoulli = [[1, 100, 0], [0, 1, 0], [0, 100, 1]]
      X_multinomial = [[0, 1], [1, 3], [4, 0]]
  
      # test binary case (1-d output)
      y = [0, 0, 2]   # 2 is regression test for binary case, 02e673
      for cls, X in zip([BernoulliNB, MultinomialNB],
                        [X_bernoulli, X_multinomial]):
          clf = cls().fit(X, y)
          assert_equal(clf.predict(X[-1:]), 2)
          assert_equal(clf.predict_proba([X[0]]).shape, (1, 2))
          assert_array_almost_equal(clf.predict_proba(X[:2]).sum(axis=1),
                                    np.array([1., 1.]), 6)
  
      # test multiclass case (2-d output, must sum to one)
      y = [0, 1, 2]
      for cls, X in zip([BernoulliNB, MultinomialNB],
                        [X_bernoulli, X_multinomial]):
          clf = cls().fit(X, y)
          assert_equal(clf.predict_proba(X[0:1]).shape, (1, 3))
          assert_equal(clf.predict_proba(X[:2]).shape, (2, 3))
          assert_almost_equal(np.sum(clf.predict_proba([X[1]])), 1)
          assert_almost_equal(np.sum(clf.predict_proba([X[-1]])), 1)
          assert_almost_equal(np.sum(np.exp(clf.class_log_prior_)), 1)
          assert_almost_equal(np.sum(np.exp(clf.intercept_)), 1)
  
  
  def test_discretenb_uniform_prior():
      # Test whether discrete NB classes fit a uniform prior
      # when fit_prior=False and class_prior=None
  
      for cls in [BernoulliNB, MultinomialNB]:
          clf = cls()
          clf.set_params(fit_prior=False)
          clf.fit([[0], [0], [1]], [0, 0, 1])
          prior = np.exp(clf.class_log_prior_)
          assert_array_equal(prior, np.array([.5, .5]))
  
  
  def test_discretenb_provide_prior():
      # Test whether discrete NB classes use provided prior
  
      for cls in [BernoulliNB, MultinomialNB]:
          clf = cls(class_prior=[0.5, 0.5])
          clf.fit([[0], [0], [1]], [0, 0, 1])
          prior = np.exp(clf.class_log_prior_)
          assert_array_equal(prior, np.array([.5, .5]))
  
          # Inconsistent number of classes with prior
          assert_raises(ValueError, clf.fit, [[0], [1], [2]], [0, 1, 2])
          assert_raises(ValueError, clf.partial_fit, [[0], [1]], [0, 1],
                        classes=[0, 1, 1])
  
  
  def test_discretenb_provide_prior_with_partial_fit():
      # Test whether discrete NB classes use provided prior
      # when using partial_fit
  
      iris = load_iris()
      iris_data1, iris_data2, iris_target1, iris_target2 = train_test_split(
          iris.data, iris.target, test_size=0.4, random_state=415)
  
      for cls in [BernoulliNB, MultinomialNB]:
          for prior in [None, [0.3, 0.3, 0.4]]:
              clf_full = cls(class_prior=prior)
              clf_full.fit(iris.data, iris.target)
              clf_partial = cls(class_prior=prior)
              clf_partial.partial_fit(iris_data1, iris_target1,
                                      classes=[0, 1, 2])
              clf_partial.partial_fit(iris_data2, iris_target2)
              assert_array_almost_equal(clf_full.class_log_prior_,
                                        clf_partial.class_log_prior_)
  
  
  def test_sample_weight_multiclass():
      for cls in [BernoulliNB, MultinomialNB]:
          # check shape consistency for number of samples at fit time
          yield check_sample_weight_multiclass, cls
  
  
  def check_sample_weight_multiclass(cls):
      X = [
          [0, 0, 1],
          [0, 1, 1],
          [0, 1, 1],
          [1, 0, 0],
      ]
      y = [0, 0, 1, 2]
      sample_weight = np.array([1, 1, 2, 2], dtype=np.float64)
      sample_weight /= sample_weight.sum()
      clf = cls().fit(X, y, sample_weight=sample_weight)
      assert_array_equal(clf.predict(X), [0, 1, 1, 2])
  
      # Check sample weight using the partial_fit method
      clf = cls()
      clf.partial_fit(X[:2], y[:2], classes=[0, 1, 2],
                      sample_weight=sample_weight[:2])
      clf.partial_fit(X[2:3], y[2:3], sample_weight=sample_weight[2:3])
      clf.partial_fit(X[3:], y[3:], sample_weight=sample_weight[3:])
      assert_array_equal(clf.predict(X), [0, 1, 1, 2])
  
  
  def test_sample_weight_mnb():
      clf = MultinomialNB()
      clf.fit([[1, 2], [1, 2], [1, 0]],
              [0, 0, 1],
              sample_weight=[1, 1, 4])
      assert_array_equal(clf.predict([[1, 0]]), [1])
      positive_prior = np.exp(clf.intercept_[0])
      assert_array_almost_equal([1 - positive_prior, positive_prior],
                                [1 / 3., 2 / 3.])
  
  
  def test_coef_intercept_shape():
      # coef_ and intercept_ should have shapes as in other linear models.
      # Non-regression test for issue #2127.
      X = [[1, 0, 0], [1, 1, 1]]
      y = [1, 2]  # binary classification
  
      for clf in [MultinomialNB(), BernoulliNB()]:
          clf.fit(X, y)
          assert_equal(clf.coef_.shape, (1, 3))
          assert_equal(clf.intercept_.shape, (1,))
  
  
  def test_check_accuracy_on_digits():
      # Non regression test to make sure that any further refactoring / optim
      # of the NB models do not harm the performance on a slightly non-linearly
      # separable dataset
      digits = load_digits()
      X, y = digits.data, digits.target
      binary_3v8 = np.logical_or(digits.target == 3, digits.target == 8)
      X_3v8, y_3v8 = X[binary_3v8], y[binary_3v8]
  
      # Multinomial NB
      scores = cross_val_score(MultinomialNB(alpha=10), X, y, cv=10)
      assert_greater(scores.mean(), 0.86)
  
      scores = cross_val_score(MultinomialNB(alpha=10), X_3v8, y_3v8, cv=10)
      assert_greater(scores.mean(), 0.94)
  
      # Bernoulli NB
      scores = cross_val_score(BernoulliNB(alpha=10), X > 4, y, cv=10)
      assert_greater(scores.mean(), 0.83)
  
      scores = cross_val_score(BernoulliNB(alpha=10), X_3v8 > 4, y_3v8, cv=10)
      assert_greater(scores.mean(), 0.92)
  
      # Gaussian NB
      scores = cross_val_score(GaussianNB(), X, y, cv=10)
      assert_greater(scores.mean(), 0.77)
  
      scores = cross_val_score(GaussianNB(), X_3v8, y_3v8, cv=10)
      assert_greater(scores.mean(), 0.86)
  
  
  def test_feature_log_prob_bnb():
      # Test for issue #4268.
      # Tests that the feature log prob value computed by BernoulliNB when
      # alpha=1.0 is equal to the expression given in Manning, Raghavan,
      # and Schuetze's "Introduction to Information Retrieval" book:
      # http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
  
      X = np.array([[0, 0, 0], [1, 1, 0], [0, 1, 0], [1, 0, 1], [0, 1, 0]])
      Y = np.array([0, 0, 1, 2, 2])
  
      # Fit Bernoulli NB w/ alpha = 1.0
      clf = BernoulliNB(alpha=1.0)
      clf.fit(X, Y)
  
      # Manually form the (log) numerator and denominator that
      # constitute P(feature presence | class)
      num = np.log(clf.feature_count_ + 1.0)
      denom = np.tile(np.log(clf.class_count_ + 2.0), (X.shape[1], 1)).T
  
      # Check manual estimate matches
      assert_array_equal(clf.feature_log_prob_, (num - denom))
  
  
  def test_bnb():
      # Tests that BernoulliNB when alpha=1.0 gives the same values as
      # those given for the toy example in Manning, Raghavan, and
      # Schuetze's "Introduction to Information Retrieval" book:
      # http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
  
      # Training data points are:
      # Chinese Beijing Chinese (class: China)
      # Chinese Chinese Shanghai (class: China)
      # Chinese Macao (class: China)
      # Tokyo Japan Chinese (class: Japan)
  
      # Features are Beijing, Chinese, Japan, Macao, Shanghai, and Tokyo
      X = np.array([[1, 1, 0, 0, 0, 0],
                    [0, 1, 0, 0, 1, 0],
                    [0, 1, 0, 1, 0, 0],
                    [0, 1, 1, 0, 0, 1]])
  
      # Classes are China (0), Japan (1)
      Y = np.array([0, 0, 0, 1])
  
      # Fit BernoulliBN w/ alpha = 1.0
      clf = BernoulliNB(alpha=1.0)
      clf.fit(X, Y)
  
      # Check the class prior is correct
      class_prior = np.array([0.75, 0.25])
      assert_array_almost_equal(np.exp(clf.class_log_prior_), class_prior)
  
      # Check the feature probabilities are correct
      feature_prob = np.array([[0.4, 0.8, 0.2, 0.4, 0.4, 0.2],
                               [1/3.0, 2/3.0, 2/3.0, 1/3.0, 1/3.0, 2/3.0]])
      assert_array_almost_equal(np.exp(clf.feature_log_prob_), feature_prob)
  
      # Testing data point is:
      # Chinese Chinese Chinese Tokyo Japan
      X_test = np.array([[0, 1, 1, 0, 0, 1]])
  
      # Check the predictive probabilities are correct
      unnorm_predict_proba = np.array([[0.005183999999999999,
                                        0.02194787379972565]])
      predict_proba = unnorm_predict_proba / np.sum(unnorm_predict_proba)
      assert_array_almost_equal(clf.predict_proba(X_test), predict_proba)
  
  
  def test_naive_bayes_scale_invariance():
      # Scaling the data should not change the prediction results
      iris = load_iris()
      X, y = iris.data, iris.target
      labels = [GaussianNB().fit(f * X, y).predict(f * X)
                for f in [1E-10, 1, 1E10]]
      assert_array_equal(labels[0], labels[1])
      assert_array_equal(labels[1], labels[2])
  