kats/models/metalearner/get_metadata.py [379:397]:
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    def _calc_mse(
        self,
        training_inputs: np.ndarray,
        predictions: np.ndarray,
        truth: np.ndarray,
        diffs: np.ndarray,
    ) -> float:
        logging.info("Calculating MSE")
        return ((diffs) ** 2).mean()

    def _calc_rmse(
        self,
        training_inputs: np.ndarray,
        predictions: np.ndarray,
        truth: np.ndarray,
        diffs: np.ndarray,
    ) -> float:
        logging.info("Calculating RMSE")
        return np.sqrt(self._calc_mse(training_inputs, predictions, truth, diffs))
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kats/utils/backtesters.py [238:260]:
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    def _calc_mse(
        self,
        training_inputs: np.ndarray,
        predictions: np.ndarray,
        truth: np.ndarray,
        diffs: np.ndarray,
    ) -> float:
        """Calculates MSE error."""

        logging.info("Calculating MSE")
        return ((diffs) ** 2).mean()

    def _calc_rmse(
        self,
        training_inputs: np.ndarray,
        predictions: np.ndarray,
        truth: np.ndarray,
        diffs: np.ndarray,
    ) -> float:
        """Calculates RMSE error."""

        logging.info("Calculating RMSE")
        return np.sqrt(self._calc_mse(training_inputs, predictions, truth, diffs))
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