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archive/loss.py

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+def calc_loss(df):
+    """
+    Geometric-mean log error between prediction bands and golden bands.
+    Assumes all columns are strictly positive.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        Must contain the columns:
+            - 'pred_lower', 'pred_upper'
+            - 'golden_lower', 'golden_upper'
+
+    Returns
+    -------
+    float
+        Scalar loss value (the smaller, the better).
+    """
+    # Element-wise absolute log-ratios
+    loss_lower = np.abs(np.log(df["pred_lower"] / df["golden_lower"]))
+    loss_upper = np.abs(np.log(df["pred_upper"] / df["golden_upper"]))
+
+    # Average the two means, then exponentiate
+    loss = np.exp(0.5 * (loss_lower.mean() + loss_upper.mean()))
+    return loss