sourcecode/scoring/pflip_model.py [818:843]:
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    logger.info("Training Results:")
    threshold, _, _, _ = self._evaluate_model(trainDataFrame)
    self._predictionThreshold = threshold
    logger.info("Validation Results:")
    self._evaluate_model(validationDataFrame, threshold=threshold)

  def serialize(self) -> bytes:
    """Return a serialized version of the PFlipModel object.

    Note that since the pflip pipeline includes CountVectorizer instances that have
    functions as parameters, joblib must be called from within this same module to be
    able to serialize the functions.

    Returns:
      bytes containing a serialized PFlipModel object
    """
    buffer = BytesIO()
    joblib.dump(self, buffer)
    return buffer.getvalue()

  def predict(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    noteStatusHistory: pd.DataFrame,
    prescoringRaterModelOutput: pd.DataFrame,
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sourcecode/scoring/pflip_plus_model.py [1606:1631]:
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    logger.info("Training Results:")
    threshold, _, _, _ = self._evaluate_model(trainDataFrame)
    self._predictionThreshold = threshold
    logger.info("Validation Results:")
    self._evaluate_model(validationDataFrame, threshold=threshold)

  def serialize(self) -> bytes:
    """Return a serialized version of the PFlipModel object.

    Note that since the pflip pipeline includes CountVectorizer instances that have
    functions as parameters, joblib must be called from within this same module to be
    able to serialize the functions.

    Returns:
      bytes containing a serialized PFlipModel object
    """
    buffer = BytesIO()
    joblib.dump(self, buffer)
    return buffer.getvalue()

  def predict(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    noteStatusHistory: pd.DataFrame,
    prescoringRaterModelOutput: pd.DataFrame,
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