docstr: Evaluate using the best model after HPO.
  
  :param data: evaluation data, a pandas dataframe with Td rows,
      and 2 columns, with column 'ds' indicating date and column 'y' indicating value
      and Td is the time dimension
  :param metrics: A list contains metrics for test/valid data.
docstr_tok: ["Evaluate","using","the","best","model","after","HPO",".",":","param","data",":","evaluation","data",",","a","pandas","dataframe","with","Td","rows",",","and","2","columns",",","with","column","'ds","'","indicating","date","and","column","'","y","'","indicating","value","and","Td","is","the","time","dimension",":","param","metrics",":","A","list","contains","metrics","for","test\/valid","data","."]
code: def evaluate(self, data, metrics=["mse"]):
      """
      Evaluate using the best model after HPO.
  
      :param data: evaluation data, a pandas dataframe with Td rows,
          and 2 columns, with column 'ds' indicating date and column 'y' indicating value
          and Td is the time dimension
      :param metrics: A list contains metrics for test/valid data.
      """
      if data is None:
          raise ValueError("Input invalid data of None")
      if self.best_model.model is None:
          raise RuntimeError(
              "You must call fit or restore first before calling evaluate!"
          )
      return self.best_model.evaluate(target=data, metrics=metrics)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/model/auto_prophet.py
imports: import pandas
  from zoo.orca.automl.auto_estimator import AutoEstimator
  import zoo.orca.automl.hp
  from zoo.chronos.model.prophet import ProphetBuilder
label_desc: Use image node_id 4 for calling the AutoProphet obj's underlying member method code with example usage: obj.evaluate(data, metrics) and returns: self