text: import numpy as np
  import pandas as pd
  import pytest
  from pandas.testing import assert_series_equal
  
  from evalml.model_family import ModelFamily
  from evalml.pipelines.components import BaselineRegressor
  
  
  def test_baseline_init():
      baseline = BaselineRegressor()
      assert baseline.parameters["strategy"] == "mean"
      assert baseline.model_family == ModelFamily.BASELINE
  
  
  def test_baseline_invalid_strategy():
      with pytest.raises(ValueError):
          BaselineRegressor(strategy="unfortunately invalid strategy")
  
  
  def test_baseline_y_is_None(X_y_regression):
      X, _ = X_y_regression
      with pytest.raises(ValueError):
          BaselineRegressor().fit(X, y=None)
  
  
  def test_baseline_mean(X_y_regression):
      X, y = X_y_regression
      mean = y.mean()
      clf = BaselineRegressor()
  
      fitted = clf.fit(X, y)
      assert isinstance(fitted, BaselineRegressor)
  
      expected_predictions = pd.Series([mean] * len(X))
      predictions = clf.predict(X)
      assert_series_equal(expected_predictions, predictions.to_series())
      np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
  
  
  def test_baseline_median(X_y_regression):
      X, y = X_y_regression
      median = np.median(y)
      clf = BaselineRegressor(strategy="median")
      clf.fit(X, y)
  
      expected_predictions = pd.Series([median] * len(X))
      predictions = clf.predict(X)
      assert_series_equal(expected_predictions, predictions.to_series())
      np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
  