class_docstr: Unit test class for testing scikit-learn converter.
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","."]
docstr: Tests conversion of a scikit KNeighborsClassifier with a valid distance metric.
docstr_tok: ["Tests","conversion","of","a","scikit","KNeighborsClassifier","with","a","valid","distance","metric","."]
code: def test_conversion_distance_function_good(self):
      """Tests conversion of a scikit KNeighborsClassifier with a valid distance metric."""
      scikit_model = KNeighborsClassifier(
          algorithm="brute", metric="euclidean"
      )
      scikit_model.fit(self.iris_X, self.iris_y)
      coreml_model = sklearn.convert(
          scikit_model, "single_input", "single_output"
      )
      coreml_spec = coreml_model.get_spec()
      self.assertIsNotNone(coreml_spec)
      self.assertTrue(
          coreml_spec.kNearestNeighborsClassifier.nearestNeighborsIndex.HasField(
              "squaredEuclideanDistance"
          )
      )
  
      # Minkowski metric with p=2 is equivalent to the squared Euclidean distance
      scikit_model = KNeighborsClassifier(
          algorithm="brute", metric="minkowski", p=2
      )
      scikit_model.fit(self.iris_X, self.iris_y)
      coreml_spec = coreml_model.get_spec()
      self.assertIsNotNone(coreml_spec)
      self.assertTrue(
          coreml_spec.kNearestNeighborsClassifier.nearestNeighborsIndex.HasField(
              "squaredEuclideanDistance"
          )
      )
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/test/sklearn_tests/test_k_neighbors_classifier.py
imports: import unittest
  import numpy
  from scipy import sparse
  from coremltools._deps import _HAS_SKLEARN
label_desc: Use image node_id 9 for calling the KNeighborsClassifierScikitTest obj's underlying member method code with example usage: obj.test_conversion_distance_function_good() without return types