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Fix pearson correlation.py #3101
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Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the denominator is assigned a value of 1.
Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the denominator is assigned a value of 1.
Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the pearson_r is assigned a value of 0.
Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the pearson_r is assigned a value of 0.
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Thanks for the bug fix! Does the test you added fail without the changes that you made?
There are some minor pylint things to fix (you can see them here: http://build.allennlp.org/viewLog.html?buildId=18000&buildTypeId=AllenNLP_AllenNLPPullRequests&tab=buildLog). After that, this should be good to merge.
JackKuo666
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Jul 30, 2019
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The reason for fixing this bug is that in some input data, a batch's predictions or labels may be exactly the same. Then when the pearson_correlation.py is called, it will appear that the predictions_variance or labels_variance of the batch is 0 when calculating the variance, resulting in:
ZeroDivisionError: float division by zero
The two tests are separated because: in most cases, the data is constructed like predictions_1; but in a few cases, for example: predictions_2, the data of such a batch is exactly the same.
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@matt-gardner please review |
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@matt-gardner can you take another look at this? |
matt-gardner
approved these changes
Aug 20, 2019
reiyw
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Nov 12, 2019
* fix bug: ZeroDivisionError: float division by zero Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the denominator is assigned a value of 1. * fix bug: ZeroDivisionError: float division by zero Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the denominator is assigned a value of 1. * fix bug: ZeroDivisionError: float division by zero Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the pearson_r is assigned a value of 0. * fix bug: ZeroDivisionError: float division by zero Since the input tensor may be, for example, a tensor ([[0.,0.,0.,0.], [0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the pearson_r is assigned a value of 0. * fix some pylint things fix some pylint things * Update pearson_correlation.py * Update pearson_correlation_test.py * Update pearson_correlation_test.py
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Fixes #3102. Since the input tensor may be, for example(a batch of label), a tensor ([[0.,0.,0.,0.],
[0.,0.,0.,0. ]), there will be a case where (math.sqrt(predictions_variance) or math.sqrt(labels_variance)) is zero, so a judgment is added here to prevent the denominator from being zero. If it is zero, the denominator is assigned a value of 1.