text: """
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  royalty-free right to use, copy, and modify the software code provided by us
  ("Software Code"). You may not sublicense the Software Code or any use of it
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  data you may have shared with Microsoft in the creation of the Software Code.
  Unless applicable law gives you more rights, Microsoft reserves all other
  rights not expressly granted herein, whether by implication, estoppel or
  otherwise. ​
   ​
  THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
  OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
  MICROSOFT OR ITS LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
  BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
  IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE
  POSSIBILITY OF SUCH DAMAGE.
  """
  from azureml.core import Run
  import argparse
  import traceback
  from model_helper import get_model
  
  run = Run.get_context()
  
  # if you would like to run this script on a local computer
  # the following code is a good starting point for you
  # use
  # python -m evaluate.evaluate_model
  # in driver_training folder context
  
  # if (run.id.startswith('OfflineRun')):
  #     from dotenv import load_dotenv
  #     # For local development, set values in this section
  #     load_dotenv()
  #     sources_dir = os.environ.get("SOURCES_DIR_TRAIN")
  #     if (sources_dir is None):
  #         sources_dir = 'driver_training'
  #     path_to_util = os.path.join(".", sources_dir, "util")
  #     sys.path.append(os.path.abspath(path_to_util))  # NOQA: E402
  #     from model_helper import get_model
  #     workspace_name = os.environ.get("WORKSPACE_NAME")
  #     experiment_name = os.environ.get("EXPERIMENT_NAME")
  #     resource_group = os.environ.get("RESOURCE_GROUP")
  #     subscription_id = os.environ.get("SUBSCRIPTION_ID")
  #     tenant_id = os.environ.get("TENANT_ID")
  #     model_name = os.environ.get("MODEL_NAME")
  #     app_id = os.environ.get('SP_APP_ID')
  #     app_secret = os.environ.get('SP_APP_SECRET')
  #     build_id = os.environ.get('BUILD_BUILDID')
  #     # run_id useful to query previous runs
  #     run_id = "57fee47f-5ae8-441c-bc0c-d4c371f32d70"
  
  #     aml_workspace = Workspace.get(
  #         name=workspace_name,
  #         subscription_id=subscription_id,
  #         resource_group=resource_group
  #     )
  #     ws = aml_workspace
  #     exp = Experiment(ws, experiment_name)
  
  # comment the following three lines
  # if you would like to use Offline mode
  exp = run.experiment
  ws = run.experiment.workspace
  run_id = 'amlcompute'
  
  parser = argparse.ArgumentParser("evaluate")
  
  parser.add_argument(
      "--run_id",
      type=str,
      help="Training run ID",
  )
  parser.add_argument(
      "--model_name",
      type=str,
      help="Name of the Model",
      default="driver_training_model.pkl",
  )
  
  parser.add_argument(
      "--allow_run_cancel",
      type=str,
      help="Set this to false to avoid evaluation step from cancelling run after an unsuccessful evaluation",  # NOQA: E501
      default="true",
  )
  
  args = parser.parse_args()
  if (args.run_id is not None):
      run_id = args.run_id
  if (run_id == 'amlcompute'):
      run_id = run.parent.id
  model_name = args.model_name
  metric_eval_auc = "auc"
  metric_eval_f1 = "f1"
  
  allow_run_cancel = args.allow_run_cancel
  # Parameterize the matrices on which the models should be compared
  # Add golden data set on which all the model performance can be evaluated
  try:
      firstRegistration = False
      tag_name = 'experiment_name'
  
      model = get_model(
                  model_name=model_name,
                  tag_name=tag_name,
                  tag_value=exp.name,
                  aml_workspace=ws)
  
      if (model is not None):
          production_model_auc = 1
          production_model_f1 = 1
          if (metric_eval_auc in model.tags) and (metric_eval_f1 in model.tags):
              production_model_auc = float(model.tags[metric_eval_auc])
              production_model_f1 = float(model.tags[metric_eval_f1])
          new_model_auc = float(run.parent.get_metrics().get(metric_eval_auc))
          new_model_f1 = float(run.parent.get_metrics().get(metric_eval_f1) )
          if (production_model_auc is None or new_model_auc is None) and (production_model_f1 is None or new_model_f1 is None):
              print("Unable to find", metric_eval_auc, "and", metric_eval_f1, ", exiting evaluation")
              if((allow_run_cancel).lower() == 'true'):
                  run.parent.cancel()
          else:
              print(
                  "Current Production model AUC: {}. Current Production model F1 score: {}."
                  "New trained model auc: {}. New trained model F1 score: {}".format(
                      production_model_auc, new_model_auc, production_model_f1, new_model_f1
                  )
              )
  
          if (new_model_auc < production_model_auc) and (new_model_f1 < production_model_f1):
              print("New trained model performs better, "
                    "thus it should be registered")
          else:
              print("New trained model metric is worse than or equal to "
                    "production model so skipping model registration.")
              if((allow_run_cancel).lower() == 'true'):
                  run.parent.cancel()
      else:
          print("This is the first model, "
                "thus it should be registered")
  
  except Exception:
      traceback.print_exc(limit=None, file=None, chain=True)
      print("Something went wrong trying to evaluate. Exiting.")
      raise
  