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Fix calibration docstring escape warning #1149

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Fix calibration docstring escape warning #1149

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6 changes: 3 additions & 3 deletions pyhealth/metrics/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ def _ECE_classwise(prob:np.ndarray, label_onehot:np.ndarray, bins=20, threshold=
return summs, class_losses

def ece_confidence_multiclass(prob:np.ndarray, label:np.ndarray, bins=20, adaptive=False):
"""Expected Calibration Error (ECE).
r"""Expected Calibration Error (ECE).

We group samples into 'bins' basing on the top-class prediction.
Then, we compute the absolute difference between the average top-class prediction and
Expand All @@ -108,7 +108,7 @@ def ece_confidence_multiclass(prob:np.ndarray, label:np.ndarray, bins=20, adapti
It could be expressed by the following formula, with :math:`B_m` denoting the m-th bin:

.. math::
ECE = \\sum_{m=1}^M \\frac{|B_m|}{N} |acc(B_m) - conf(B_m)|
ECE = \sum_{m=1}^M \frac{|B_m|}{N} |acc(B_m) - conf(B_m)|

Example:
>>> pred = np.asarray([[0.2, 0.2, 0.6], [0.2, 0.31, 0.49], [0.1, 0.1, 0.8]])
Expand All @@ -119,7 +119,7 @@ def ece_confidence_multiclass(prob:np.ndarray, label:np.ndarray, bins=20, adapti
Explanation of the example: The bins are [0, 0.5] and (0.5, 1].
In the first bin, we have one sample with top-class prediction of 0.49, and its
accuracy is 0. In the second bin, we have average confidence of 0.7 and average
accuracy of 1. Thus, the ECE is :math:`\\frac{1}{3} \cdot 0.49 + \\frac{2}{3}\cdot 0.3=0.3633`.
accuracy of 1. Thus, the ECE is :math:`\frac{1}{3} \cdot 0.49 + \frac{2}{3}\cdot 0.3=0.3633`.

Args:
prob (np.ndarray): (N, C)
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