text: import numpy as np
  import pytest
  from pandas.testing import assert_series_equal
  
  from evalml.model_family import ModelFamily
  from evalml.pipelines.components import TimeSeriesBaselineEstimator
  
  
  def test_time_series_baseline_regressor_init():
      baseline = TimeSeriesBaselineEstimator()
      assert baseline.model_family == ModelFamily.BASELINE
  
  
  def test_time_series_baseline_gap_negative():
      with pytest.raises(ValueError, match='gap value must be a positive integer.'):
          TimeSeriesBaselineEstimator(gap=-1)
  
  
  def test_time_series_baseline_y_is_None(X_y_regression):
      X, _ = X_y_regression
      clf = TimeSeriesBaselineEstimator()
      clf.fit(X, y=None)
      with pytest.raises(ValueError):
          clf.predict(X, y=None)
      with pytest.raises(ValueError):
          clf.predict_proba(X, y=None)
  
  
  def test_time_series_baseline(ts_data):
      X, y = ts_data
  
      clf = TimeSeriesBaselineEstimator(gap=1)
      clf.fit(X, y)
  
      assert_series_equal(y.astype("Int64"), clf.predict(X, y).to_series())
      np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
  
  
  def test_time_series_baseline_gap_0(ts_data):
      X, y = ts_data
  
      y_true = y.shift(periods=1)
  
      clf = TimeSeriesBaselineEstimator(gap=0)
      clf.fit(X, y)
  
      assert_series_equal(y_true, clf.predict(X, y).to_series())
      np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
  
  
  def test_time_series_baseline_no_X(ts_data):
      _, y = ts_data
  
      clf = TimeSeriesBaselineEstimator()
      clf.fit(X=None, y=y)
  
      assert_series_equal(y.astype("Int64"), clf.predict(X=None, y=y).to_series())
      np.testing.assert_allclose(clf.feature_importance, np.array([]))
  