class_docstr: Unit test class for testing scikit-learn converter.
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","."]
code: def test_pipeline_classifier_creation(self):
      input_names = self.scikit_data.feature_names
      p_classifier = PipelineClassifier(input_names, [1, 0])
      p_classifier.add_model(self.libsvm_spec)
  
      self.assertIsNotNone(p_classifier.spec)
      self.assertEqual(
          len(p_classifier.spec.pipelineClassifier.pipeline.models), 1
      )
  
      # Test the model class of the svm model
      spec = p_classifier.spec.pipelineClassifier.pipeline.models[0]
      self.assertIsNotNone(spec.description)
  
      # Test the interface class
      self.assertEqual(spec.description.predictedFeatureName, "target")
  
      # Test the inputs and outputs
      self.assertEqual(len(spec.description.output), 1)
      self.assertEqual(spec.description.output[0].name, "target")
      self.assertEqual(
          spec.description.output[0].type.WhichOneof("Type"),
          "int64Type",
      )
  
      for input_type in spec.description.input:
          self.assertEqual(
              input_type.type.WhichOneof("Type"), "doubleType"
          )
      self.assertEqual(
          sorted(input_names),
          sorted(map(lambda x: x.name, spec.description.input)),
      )
  
imports: import unittest
  import tempfile
  from coremltools.proto import FeatureTypes_pb2
  from coremltools._deps import _HAS_SKLEARN, _HAS_LIBSVM
  from coremltools.models.pipeline import PipelineRegressor, PipelineClassifier
  from coremltools.models.utils import evaluate_transformer
  import coremltools.models.datatypes
  from coremltools.models.feature_vectorizer import create_feature_vectorizer
label_desc: Use image node_id 2 for calling the LibSVMPipelineCreationTest obj's underlying member method code with example usage: obj.test_pipeline_classifier_creation() without return types