content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
  #  or implied. See the License for the specific language governing
  #  permissions and limitations under the License.
  
  from steps.importer import importer
  from steps.profiler import ge_profiler_step
  from steps.splitter import splitter
  from steps.validator import ge_validate_test_step, ge_validate_train_step
  
  from zenml import pipeline
  from zenml.config import DockerSettings
  from zenml.integrations.constants import GREAT_EXPECTATIONS, SKLEARN
  
  docker_settings = DockerSettings(
      required_integrations=[SKLEARN, GREAT_EXPECTATIONS]
  )
  
  
  @pipeline(enable_cache=False, settings={"docker": docker_settings})
  def validation_pipeline():
      """Data validation pipeline for Great Expectations.
  
      The pipeline imports data from a source, then splits it into training
      and validation sets. The Great Expectations profiler step is used to
      generate the expectation suite (i.e. validation rules) based on the
      schema ans statistical properties of the training dataset.
  
      Next, that generated expectation suite is used to validate both the training
      dataset and the validation dataset.
      """
      imported_data = importer()
      train, test = splitter(imported_data)
      ge_profiler_step(train)
      ge_validate_train_step(train, after="great_expectations_profiler_step")
      ge_validate_test_step(test, after="great_expectations_profiler_step")
  