Add a calibration error statistic #126
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…ill equal CCS loss." string to be accurate
AlexTMallen
approved these changes
Mar 16, 2023
elk/training/reporter.py
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| @@ -190,7 +199,6 @@ def score(self, labels: Tensor, x_pos: Tensor, x_neg: Tensor) -> EvalResult: | |||
| # makes `num_variants` copies of each label, all within a single | |||
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| @@ -182,6 +184,13 @@ def score(self, labels: Tensor, x_pos: Tensor, x_neg: Tensor) -> EvalResult: | |||
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| pred_probs = self.predict(x_pos, x_neg) | |||
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So we are implicitly averaging over all the heads and variants? Also now just looking at this I'm not sure how this works when num_variants>1 and num_heads>1.
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No this doesn't average over heads and variants, we actually need to fully support the num_heads > 1 case, we don't right now
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Created
CalibrationErrorclass for computing the expected calibration error based on https://arxiv.org/abs/2012.08668. We use this to compute and log the ECE of the probe intrain.pyDepends on #124