text: Title: Improve RankD Tests
  Body: Right now the Rank1D and Rank2D tests are very basic and can be improved using the new assert image similarity and pytest testing framework mechanisms. 
  
  ### Proposal/Issue
  
  The test matrix should against the following:
  
   - [x] replace `make_regression` dataset with `load_energy` and update those tests
   - [x] use `load_occupancy` for classification tests
   - [x] algorithms: pearson, covariance, spearman (2D) and shaprio (1D)
   - [x] 1D case - horizontal orientation
   - [x] 1D case - vertical orientation
   - [x] Test that an exception is raised for unrecognized algorithms
   - [x] test the underlying rank matrix is correct
  
  Unfortunately, we can't use `pytest.mark.parametrize` with visual test cases (yet), so we'll have to make individual tests for each. 
  
  ### Code Snippet
  
  Tests will look approximately like:
  
  ```python
  def test_rank2d_bad_algorithm(self):
      """"
      Assert that unknown algorithms raise exception
      """"
      with pytest.raises(YellowbrickValueError, match="unknown algorithm"):
           # do the thing 
  
  def test_rank2d_pearson_regression(self):
      """"
      Test Rank2D images similar with pearson scores on regression dataset 
      """"
      data = load_energy(return_dataset=True)
      oz = Rank2D(algorithm='pearson')
      oz.fit_transform(data) 
      npt.assert_array_equal(oz.ranks_, [[]])
      self.assert_images_similar(oz, tol=0.25) 
  ```
  
  ### Background
  
  See #68 and #429 
  