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Add Covariance and PearsonCorrelation metrics #1684
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joelgrus
approved these changes
Aug 29, 2018
| def __init__(self) -> None: | ||
| self._total_prediction_mean = 0.0 | ||
| self._total_label_mean = 0.0 | ||
| self._total_comoment = 0.0 |
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nit: it's very hard for my brain not to look at comoment and feel like someone made a typo in comment, which is jarring. would you consider co_moment instead? 😀
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(feel free to tell me I'm being a crazy person)
gabrielStanovsky
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Sep 7, 2018
This PR implements an online algorithm for calculating Covariance and the sample Pearson correlation coefficient. This was actually nontrivial, I mostly referenced the tensorflow [streaming_covariance metric](https://github.com/tensorflow/tensorflow/blob/4dcfddc5d12018a5a0fdca652b9221ed95e9eb23/tensorflow/contrib/metrics/python/ops/metric_ops.py#L3127-L3264) in implementing this. Their implementation is a vectorized version of the weighted algorithm [on this wikipedia page](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online) The tests simply ensure that the streaming Covariance and PearsonCorrelation match up with what numpy would calculate, which I believe is a reasonable correctness check.
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This PR implements an online algorithm for calculating Covariance and the sample Pearson correlation coefficient.
This was actually nontrivial, I mostly referenced the tensorflow streaming_covariance metric in implementing this. Their implementation is a vectorized version of the weighted algorithm on this wikipedia page
The tests simply ensure that the streaming Covariance and PearsonCorrelation match up with what numpy would calculate, which I believe is a reasonable correctness check.