class_docstr: Unit test class for testing scikit-learn converter.
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","."]
code: def test_unseen_or_no_features(self):
      D1 = [{"camelot": 0, "spamalot": 1}]
      D2 = [{}, {"nothing": 21}]
  
      for sparse in (True, False):
          for dtype in (int, np.float32, np.int16):
              for sort in (True, False):
                  v = DictVectorizer(
                      sparse=sparse, dtype=dtype, sort=sort
                  )
                  v = v.fit(D1)
                  self._test_conversion(D2, v)
  
file_path: coremltools/coremltools/test/sklearn_tests/test_dict_vectorizer.py
imports: import unittest
  import numpy
  import numpy.random
  import pandas
  import coremltools
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier, evaluate_transformer
label_desc: Use image node_id 3 for calling the DictVectorizerScikitTest obj's underlying member method code with example usage: obj.test_unseen_or_no_features() without return types