old_contents: import numpy as np
  from sklearn.model_selection import train_test_split
  
  from ngboost import NGBClassifier, NGBRegressor
  from ngboost.distns import Bernoulli
  
  np.random.seed(1)
  
  
  def test_classification():
      from sklearn.datasets import load_breast_cancer
      from sklearn.metrics import roc_auc_score
      data, target = load_breast_cancer(True)
      x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                          test_size=0.2,
                                                          random_state=42)
      ngb = NGBClassifier(Dist=Bernoulli, verbose=False)
      ngb.fit(x_train, y_train)
      preds = ngb.predict(x_test)
      score = roc_auc_score(y_test, preds)
      assert score >= 0.95
  
  
  def test_regression():
      from sklearn.datasets import load_boston
      from sklearn.metrics import mean_squared_error
      data, target = load_boston(True)
      x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                          test_size=0.2,
                                                          random_state=42)
      ngb = NGBRegressor(verbose=False)
      ngb.fit(x_train, y_train)
      preds = ngb.predict(x_test)
      score = mean_squared_error(y_test, preds)
      assert score <= 8.0
  
new_contents: import numpy as np
  from sklearn.model_selection import train_test_split
  
  from ngboost import NGBClassifier, NGBRegressor
  from ngboost.distns import Bernoulli
  
  np.random.seed(1)
  
  
  def test_classification():
      from sklearn.datasets import load_breast_cancer
      from sklearn.metrics import roc_auc_score, log_loss
      data, target = load_breast_cancer(True)
      x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                          test_size=0.2,
                                                          random_state=42)
      ngb = NGBClassifier(Dist=Bernoulli, verbose=False)
      ngb.fit(x_train, y_train)
      preds = ngb.predict(x_test)
      score = roc_auc_score(y_test, preds)
      assert score >= 0.95
  
      preds = ngb.predict_proba(x_test)
      score = log_loss(y_test, preds)
      assert score <= 0.20
  
      score = ngb.score(x_test, y_test)
      assert score <= 0.20
  
      dist = ngb.pred_dist(x_test)
      assert isinstance(dist, Bernoulli)
  
      preds = ngb.dist_to_prediction(dist)
      score = roc_auc_score(y_test, preds)
      assert score >= 0.95
  
  
  def test_regression():
      from sklearn.datasets import load_boston
      from sklearn.metrics import mean_squared_error
      data, target = load_boston(True)
      x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                          test_size=0.2,
                                                          random_state=42)
      ngb = NGBRegressor(verbose=False)
      ngb.fit(x_train, y_train)
      preds = ngb.predict(x_test)
      score = mean_squared_error(y_test, preds)
      assert score <= 8.0
  
subject: add test, classification predict method