sourcecode/scoring/pflip_model.py [777:799]:
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  def fit(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    noteStatusHistory: pd.DataFrame,
    prescoringRaterModelOutput: pd.DataFrame,
  ) -> None:
    """Fit and evaluate a sklearn pipeline for predicting note status.

    Args:
      notes: pd.DataFrame
      ratings: pd.DataFrame
      noteStatusHistory: pd.DataFrame
      prescoringRaterModelOutput: pd.DataFrame

    Returns:
      sklearn pipeline covering containing full process of feature extraction, feature
      selection and prediction.
    """
    # Apply seed if necessary
    if self._seed is not None:
      logger.info(f"seeding pflip: {self._seed}")
      np.random.seed(self._seed)
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sourcecode/scoring/pflip_plus_model.py [1540:1562]:
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  def fit(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    noteStatusHistory: pd.DataFrame,
    prescoringRaterModelOutput: pd.DataFrame,
  ) -> None:
    """Fit and evaluate a sklearn pipeline for predicting note status.

    Args:
      notes: pd.DataFrame
      ratings: pd.DataFrame
      noteStatusHistory: pd.DataFrame
      prescoringRaterModelOutput: pd.DataFrame

    Returns:
      sklearn pipeline covering containing full process of feature extraction, feature
      selection and prediction.
    """
    # Apply seed if necessary
    if self._seed is not None:
      logger.info(f"seeding pflip: {self._seed}")
      np.random.seed(self._seed)
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