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 or callable function with format
         it signature should be func(y_true, y_pred), where y_true and y_pred are numpy
         ndarray. The function should return a float value as evaluation result.
  
  :return: a list of metric evaluation results
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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 or callable function with format
             it signature should be func(y_true, y_pred), where y_true and y_pred are numpy
             ndarray. The function should return a float value as evaluation result.
  
      :return: a list of metric evaluation results
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if data is not None:
          invalidInputError(
              False, "We don't support input data currently"
          )
      if target is None:
          invalidInputError(False, "Input invalid target of None")
      if self.model is None:
          invalidInputError(
              False,
              "Needs to call fit_eval or restore first before calling evaluate",
          )
  
      target_pred = self.model.predict(target)
      return Evaluator.evaluate(
          metrics,
          target.y.values,
          target_pred.yhat.values,
          aggregate="mean",
      )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/model/prophet.py
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 bigdl.chronos.metric.forecast_metrics import Evaluator
label_desc: Use image node_id 7 for calling the ProphetModel obj's underlying member method code with example usage: obj.evaluate(target, data, metrics) and returns: Evaluator