How to use the tfx.proto.evaluator_pb2.FeatureSlicingSpec function in tfx

schema=infer_schema.outputs['schema'])
  transform = Transform(
      examples=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=taxi_module_file)
  trainer = Trainer(
      module_file=taxi_module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10),
      eval_args=trainer_pb2.EvalArgs(num_steps=5))
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=os.path.join(pipeline_root, 'model_serving'))))

  return [
      example_gen, statistics_gen, infer_schema, validate_stats, transform,
      trainer, model_analyzer, model_validator, pusher
  ]

module_file=module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

'model_blessing':
          ChannelParameter(
              type=standard_artifacts.ModelBlessing, optional=True),
  }
  OUTPUTS = {
      'inference_result':
          ChannelParameter(type=standard_artifacts.InferenceResult),
  }


class EvaluatorSpec(ComponentSpec):
  """Evaluator component spec."""

  PARAMETERS = {
      'feature_slicing_spec':
          ExecutionParameter(type=evaluator_pb2.FeatureSlicingSpec),
      # This parameter is experimental: its interface and functionality may
      # change at any time.
      'fairness_indicator_thresholds':
          ExecutionParameter(type=List[float], optional=True),
  }
  INPUTS = {
      'examples': ChannelParameter(type=standard_artifacts.Examples),
      # TODO(b/139281215): this will be renamed to 'model' in the future.
      'model_exports': ChannelParameter(type=standard_artifacts.Model),
  }
  OUTPUTS = {
      'output': ChannelParameter(type=standard_artifacts.ModelEvaluation),
  }
  # TODO(b/139281215): these input / output names will be renamed in the future.
  # These compatibility aliases are provided for forwards compatibility.
  _INPUT_COMPATIBILITY_ALIASES = {

module_file=module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['output'],
      transform_output=transform.outputs['transform_output'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['output'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['output'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = Pusher(
      model_export=trainer.outputs['output'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

module_file=_taxi_module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=_taxi_module_file,
      examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # This custom component serves as a bridge between pipeline and human model
  # reviewers to enable review-and-push workflow in model development cycle. It
  # utilizes Slack API to send message to user-defined Slack channel with model
  # URI info and wait for go / no-go decision from the same Slack channel:
  #   * To approve the model, users need to reply the thread sent out by the bot
  #     started by SlackComponent with 'lgtm' or 'approve'.
  #   * To reject the model, users need to reply the thread sent out by the bot
  #     started by SlackComponent with 'decline' or 'reject'.

trainer = Trainer(
      custom_executor_spec=executor_spec.ExecutorClassSpec(
          ai_platform_trainer_executor.Executor),
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
      custom_config={'ai_platform_training_args': ai_platform_training_args})

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to  Google Cloud AI Platform if check passed.
  pusher = Pusher(
      custom_executor_spec=executor_spec.ExecutorClassSpec(
          ai_platform_pusher_executor.Executor),
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      custom_config={'ai_platform_serving_args': ai_platform_serving_args})

module_file=module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=hello.outputs['output_data'],
      model=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=hello.outputs['output_data'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

# Uses user-provided Python function that implements a model using TF-Learn
  # to train a model on Google Cloud AI Platform.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
  )

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to  Google Cloud AI Platform if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

input_data=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=taxi_module_file,
  )
  trainer = Trainer(
      module_file=taxi_module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_output=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10),
      eval_args=trainer_pb2.EvalArgs(num_steps=5),
  )
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(
          specs=[
              evaluator_pb2.SingleSlicingSpec(
                  column_for_slicing=['trip_start_hour']
              )
          ]
      ),
  )
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model']
  )

  # Hack: ensuring push_destination can be correctly parameterized and interpreted.
  # pipeline root will be specified as a dsl.PipelineParam with the name
  # pipeline-root, see:
  # https://github.com/tensorflow/tfx/blob/1c670e92143c7856f67a866f721b8a9368ede385/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L226
  _pipeline_root_param = dsl.PipelineParam(name='pipeline-root')