old_contents: import pytest
  
  @pytest.fixture(scope="module")
  def dists_scores():
  	from ngboost.distns import Bernoulli, k_categorical, Normal, LogNormal, Exponential
  	from ngboost.scores import LogScore, CRPScore
  	return {
  		Bernoulli:[LogScore],
  		k_categorical(4): [LogScore], 
  		Normal: [LogScore, CRPScore], 
  		LogNormal: [LogScore, CRPScore], 
  		Exponential: [LogScore, CRPScore]
  		}
  
  @pytest.fixture(scope="module")
  def learners():
  	from sklearn.tree import DecisionTreeRegressor
  	return [
  		DecisionTreeRegressor(criterion='friedman_mse', max_depth=5),
  		DecisionTreeRegressor(criterion='friedman_mse', max_depth=3)
  	]
  
  class TestRegDistns():
  	@pytest.fixture(scope="class")
  	def reg_dists(dists_scores):
  		from ngboost.distns import RegressionDistn
  		return [dist for dist in dists_scores.keys() if isinstance(dist, RegressionDistn)]
  
  	@pytest.fixture(scope="class")
  	def reg_data():
  		from sklearn.datasets import load_boston
  		from sklearn.model_selection import train_test_split
  		X, Y = load_boston(True)
  		return train_test_split(X, Y, test_size=0.2)
  
  	def test_which_reg_dists(reg_dists):
  		from ngboost.distns import Normal, LogNormal, Exponential
  		assert set([Normal, LogNormal, Exponential]) == set(reg_dists)
  
  	def test_dists(dists_scores, learners, reg_dists, reg_data):
  		X_reg_train, X_reg_test, Y_reg_train, Y_reg_test = reg_data
  		for Dist in reg_dists:
  			for Score in dists_scores[Dist]:
  				for Learner in learners:
  					ngb = NGBRegressor(Dist=Dist, Score=Score, Base=Learner, verbose=False)
  					ngb.fit(X_reg_train, Y_reg_train)
  					y_pred = ngb.predict(X_reg_test)
  					y_dist = ngb.pred_dist(X_reg_test)
  
  # test slicing and ._params
new_contents: import pytest
  
  @pytest.fixture(scope="module")
  def dists_scores():
  	from ngboost.distns import Bernoulli, k_categorical, Normal, LogNormal, Exponential
  	from ngboost.scores import LogScore, CRPScore
  	return {
  		Bernoulli:[LogScore],
  		k_categorical(4): [LogScore], 
  		Normal: [LogScore, CRPScore], 
  		LogNormal: [LogScore, CRPScore], 
  		Exponential: [LogScore, CRPScore]
  		}
  
  @pytest.fixture(scope="module")
  def learners():
  	from sklearn.tree import DecisionTreeRegressor
  	return [
  		DecisionTreeRegressor(criterion='friedman_mse', max_depth=5),
  		DecisionTreeRegressor(criterion='friedman_mse', max_depth=3)
  	]
  
  class TestRegDistns():
  	@pytest.fixture(scope="class")
  	def reg_dists(dists_scores):
  		from ngboost.distns import RegressionDistn
  		return [dist for dist in dists_scores.keys() if isinstance(dist, RegressionDistn)]
  
  	@pytest.fixture(scope="class")
  	def reg_data():
  		from sklearn.datasets import load_boston
  		from sklearn.model_selection import train_test_split
  		X, Y = load_boston(True)
  		return train_test_split(X, Y, test_size=0.2)
  
  	def test_which_reg_dists(reg_dists):
  		from ngboost.distns import Normal, LogNormal, Exponential
  		assert set([Normal, LogNormal, Exponential]) == set(reg_dists)
  
  	def test_dists(dists_scores, learners, reg_dists, reg_data):
  		from ngboost import NGBRegressor
  		X_reg_train, X_reg_test, Y_reg_train, Y_reg_test = reg_data
  		for Dist in reg_dists:
  			for Score in dists_scores[Dist]:
  				for Learner in learners:
  					ngb = NGBRegressor(Dist=Dist, Score=Score, Base=Learner, verbose=False)
  					ngb.fit(X_reg_train, Y_reg_train)
  					y_pred = ngb.predict(X_reg_test)
  					y_dist = ngb.pred_dist(X_reg_test)
  
  # test slicing and ._params
subject: Update test_distns.py