docstr: Evaluate on the prediction results. We predict horizon time-points ahead the input data
  in fit_eval before evaluation, where the horizon length equals the second dimension size of
  target.
  :param data: Prophet predicts the horizon steps foreward from the training data.
      So data should be None as it is not used.
  :param target: target for evaluation. A dataframe with 2 columns, where column 'ds'
         indicating date and column 'y' indicating target.
  :param metrics: a list of metrics in string format
  :return: a list of metric evaluation results
docstr_tok: ["Evaluate","on","the","prediction","results",".","We","predict","horizon","time-points","ahead","the","input","data","in","fit_eval","before","evaluation",",","where","the","horizon","length","equals","the","second","dimension","size","of","target",".",":","param","data",":","Prophet","predicts","the","horizon","steps","foreward","from","the","training","data",".","So","data","should","be","None","as","it","is","not","used",".",":","param","target",":","target","for","evaluation",".","A","dataframe","with","2","columns",",","where","column","'ds'","indicating","date","and","column","'","y","'","indicating","target",".",":","param","metrics",":","a","list","of","metrics","in","string","format",":","return",":","a","list","of","metric","evaluation","results"]
code: def evaluate(self, target, data=None, metrics=["mse"]):
      """
      Evaluate on the prediction results. We predict horizon time-points ahead the input data
      in fit_eval before evaluation, where the horizon length equals the second dimension size of
      target.
      :param data: Prophet predicts the horizon steps foreward from the training data.
          So data should be None as it is not used.
      :param target: target for evaluation. A dataframe with 2 columns, where column 'ds'
             indicating date and column 'y' indicating target.
      :param metrics: a list of metrics in string format
      :return: a list of metric evaluation results
      """
      if data is not None:
          raise ValueError("We don't support input data currently")
      if target is None:
          raise ValueError("Input invalid target of None")
      if self.model is None:
          raise Exception(
              "Needs to call fit_eval or restore first before calling evaluate"
          )
  
      target_pred = self.model.predict(target)
      return [
          Evaluator.evaluate(
              m, target.y.values, target_pred.yhat.values
          )
          for m in metrics
      ]
  
imports: import json
  import numpy
  from prophet import Prophet
  from prophet.serialize import model_to_json, model_from_json
  from prophet.diagnostics import performance_metrics
  from prophet.diagnostics import cross_validation
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
label_desc: Use image node_id 6 for calling the ProphetModel obj's underlying member method code with example usage: obj.evaluate(target, data, metrics) and returns: unknown