Fix handling single class in chunk for CBPE #384
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The `confusion_matrix` function used in various CBPE metrics returns values for each class/label present in the input. For binary classification this means we expect 4 values (TP, FP, FN, TN). However if only one class is represented in the input, the function will only return one value. This commit addresses that failure case by explicitly providing the expected labels to the `confusion_matrix` function. Currently these values are hard-coded for binary classification, but we may want to derive them from the input later on if we were to support string-based pass/fail classes.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #384 +/- ##
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+ Coverage 78.52% 78.67% +0.15%
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Files 110 110
Lines 8562 8567 +5
Branches 1522 1523 +1
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+ Hits 6723 6740 +17
+ Misses 1476 1468 -8
+ Partials 363 359 -4 ☔ View full report in Codecov by Sentry. |
nnansters
approved these changes
May 2, 2024
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This PR fixes an error when calculating business value, confusion matrix & specificity for binary classification problems where a chunk only contains 1 class.
Previously this would fail with:
This happens because the
sklearn.metrics.confusion_matrixfunction NannyML uses internally bases its output on the number of classes present in the input. If only a single class is present, only 1 value is returned where we normally expect 4 for a binary classification problem. This PR resolves this by explicitly providing the expected classes in thelabelsargument. These expected classes are currently hard-coded as[0, 1]but we may want to change this to derive values from the input if/when we support string-based classes for binary classification.Additionally, this PR resolves an issue with F1 sampling error calculation when there are no positive cases present in the input. This previously resulted in a
ZeroDivisionError. Now it resolves theNaNsampling error.