Use numpy divide in explained variance #1691
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anargyri
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miguelgfierro,
gramhagen,
loomlike and
wutaomsft
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miguelgfierro
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Mar 31, 2022
| @@ -158,7 +159,8 @@ def exp_var(self): | |||
| 0 | |||
| ] | |||
| var2 = self.y_pred_true.selectExpr("variance(label)").collect()[0][0] | |||
| return 1 - var1 / var2 | |||
| # numpy divide is more tolerant to var2 being zero | |||
| return 1 - np.divide(var1, var2) | |||
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I'm looking at the test here: https://github.com/microsoft/recommenders/blob/main/tests/unit/recommenders/evaluation/test_spark_evaluation.py#L239, should we maybe add the edge case for when var2 is zero?
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Maybe, although it is a very unusual case in practice (everything in the data is rated equally).
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Description
Useful in cases when all true labels are the same and hence explained variance should equal minus infinity.
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staging branchand not tomain branch.