old_contents: import pytest
  from sklearn.naive_bayes import MultinomialNB
  import wandb
  from wandb.sklearn import learning_curve, roc, confusion_matrix, precision_recall
  
  @pytest.fixture
  def dummy_classifier(request):
      nb = MultinomialNB()
      x_train = [[1,2],[1,2],[1,2],[1,2],[2,3],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4]]
      y_train = [0,0,0,0,0,1,1,1,1,1,1]
      nb.fit(x_train, y_train)
      x_test = [[4,5], [5,6]]
      y_test = [0,1]
      y_probas = nb.predict_proba(x_test)
      y_pred = nb.predict(x_test)
      return (nb, x_train, y_train, x_test, y_test, y_pred, y_probas)
  
  def test_learning_curve(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      lc_table = learning_curve(nb, x_train, y_train)
      assert(len(lc_table.value.value.data) == 10)
      assert(lc_table.value.value.data[0][0] == 'train')
      assert(lc_table.value.value.data[1][0] == 'test')
  
  def test_roc(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      lc_table = learning_curve(nb, x_train, y_train)
      r = roc(y_test, y_probas)
  
      assert(r.value.data[0] == [0, 0.0, 1.0])
  
  def test_confusion_matrix(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      cm = confusion_matrix(y_test, y_pred)
      assert(len(cm.value.data)==4)
      assert(cm.value.data[0]== [0,0,0])
  
  def test_precision_recall(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      pr = precision_recall(y_test, y_probas)
  
      assert(pr.value.data[0]== [0, 1.0, 1.0])
  
new_contents: import pytest
  from sklearn.naive_bayes import MultinomialNB
  import wandb
  from wandb.sklearn import learning_curve, roc, confusion_matrix, precision_recall
  
  @pytest.fixture
  def dummy_classifier(request):
      nb = MultinomialNB()
      x_train = [[1,2],[1,2],[1,2],[1,2],[2,3],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4]]
      y_train = [0,0,0,0,0,1,1,1,1,1,1]
      nb.fit(x_train, y_train)
      x_test = [[4,5], [5,6]]
      y_test = [0,1]
      y_probas = nb.predict_proba(x_test)
      y_pred = nb.predict(x_test)
      return (nb, x_train, y_train, x_test, y_test, y_pred, y_probas)
  
  def test_learning_curve(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      lc_table = learning_curve(nb, x_train, y_train)
      assert(len(lc_table.value.data) == 10)
      assert(lc_table.value.data[0][0] == 'train')
      assert(lc_table.value.data[1][0] == 'test')
  
  def test_roc(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      lc_table = learning_curve(nb, x_train, y_train)
      r = roc(y_test, y_probas)
  
      assert(r.value.data[0] == [0, 0.0, 0.0])
  
  def test_confusion_matrix(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      cm = confusion_matrix(y_test, y_pred)
      assert(len(cm.value.data)==4)
      assert(cm.value.data[0]== [0,0,0])
  
  def test_precision_recall(dummy_classifier):
      (nb, x_train, y_train, x_test, y_test, y_pred, y_probas) = dummy_classifier
      pr = precision_recall(y_test, y_probas)
  
      assert(pr.value.data[0]== [0, 1.0, 1.0])
  
subject: fix dem tests - round 2