Added baselines for the TestDatasets.breastCancerPipeMissing dataset, and their unit tests

[mstfbl]

Related to Issue #4681 and PR #4695.

This PR adds the baseline figures for the TestDatasets.breastCancerPipeMissing dataset (which is the same dataset as TestDatasets.breastCancerPipe but without missing values due to the NAHandle transformer), and adds these baseline figures to the required tests in TestPredictors.cs.

The tests that these baselines satisfy are:

• LightGBMClassificationTest,
• GossLightGBMTest,
• DartLightGBMTest,
• FastTreeBinaryClassificationTest,
• FastTreeHighMinDocsTest
• MulticlassNaiveBayes

These tests are also originally the only unit tests that utilize TestDatasets.breastCancerPipe.

Since the TestDatasets.breastCancerPipeMissing dataset does not contain missing ? values, the change in PR #4695 of allowing CursOpt.AllFeatures will not impact the test cases mentioned above.

**Edit: ** In the last sentence above, I mean that since there are no missing values in TestDatasets.breastCancerPipeMissing, these datasets are better suited to test these LightGbm tests as CursOpt.AllFeatures combined with the HandleMissingValues flag in LightGbmTrainerBase.cs introduce multiple modifiers to the case of handling missing values, which this TestDatasets.breastCancerPipeMissing dataset is not impacted by.

[antoniovs1029]

Hi, @mstfbl . Can you please explain here why these baselines are needed, if the ones with missing values already exist? Specially since, as you've stated, these baselines won't actually test the changes introduced in your other PR.

[mstfbl]

Hi @antoniovs1029 , I wasn't as clear as I could have been in the original post. There I meant that the change made in PR #4695 with CursOpt.AllFeatures won't break the tests with these baselines, as CursOpt.AllFeatures includes a peculiar case with the handling of missing values. I am trying to say that these baselines are a better set of metrics to test these LightGbm tests.

In addition, they are a second set of baselines that are tested in the already-existing set of LightGbm tests, and I think having extra cases to test isn't a bad thing at all.

[ganik]

This is not right way to go. Add unit test in your original PR #4695. Take any already generated model file and add it to your PR. Also add just one (for one dataset) established baseline. Load the model in Unit test and run with the new LightGbm code, compare the baselines.

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In reply to: 582580955 [](ancestors = 582580955)

[ganik]

🕐

revoking review

[ganik]

🕐