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

# 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)))

except ImportError:
    # Train using a deprecated flag.
    trainer = Trainer(
        module_file=_taxi_utils,
        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),
        custom_config={'cmle_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['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 destination if check passed.
  try:
    from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor  # pylint: disable=g-import-not-at-top
    # Deploy the model on Google Cloud AI Platform. This requires TFX >=0.14.
    pusher = Pusher(
        executor_class=ai_platform_pusher_executor.Executor,
        model_export=trainer.outputs['output'],

module_file=module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=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=1000),
      eval_args=trainer_pb2.EvalArgs(num_steps=500))

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

  # 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)))

  return pipeline.Pipeline(

# Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=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=1000),
      eval_args=trainer_pb2.EvalArgs(num_steps=500))

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

  # 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)))

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,

# 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.
  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'.
  slack_validator = SlackComponent(

# Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=_taxi_utils,
      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),
      custom_config={'cmle_training_args': _cmle_training_args})

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(  # pylint: disable=unused-variable
      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)))