class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","is","split","into","data",",","validation_data",">",">",">","model","=","ProphetForecaster","(","changepoint_prior_scale=0.05",",","seasonality_mode='additive","'",")",">",">",">","model.fit","(","data",",","validation_data",")",">",">",">","predict_result","=","model.predict","(","horizon=24",")"]
docstr: Evaluate using a trained forecaster.
  
  :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.
  
  :return: A list of evaluation results. Calculation results for each metrics.
docstr_tok: ["Evaluate","using","a","trained","forecaster",".",":","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",".",":","return",":","A","list","of","evaluation","results",".","Calculation","results","for","each","metrics","."]
code: def evaluate(self, data, metrics=["mse"]):
      """
      Evaluate using a trained forecaster.
  
      :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.
  
      :return: A list of evaluation results. Calculation results for each metrics.
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if data is None:
          invalidInputError(False, "Input invalid data of None")
      if self.internal.model is None:
          invalidInputError(
              False,
              "You must call fit or restore first before calling evaluate!",
          )
      return self.internal.evaluate(target=data, metrics=metrics)
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/prophet_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.prophet import ProphetModel
label_desc: Use image node_id 5 for calling the ProphetForecaster obj's underlying member method code with example usage: obj.evaluate(data, metrics) and returns: self