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+ Coverage 68.20% 68.23% +0.03%
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Files 1142 1142
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Branches 25378 25385 +7
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| var ml2 = new MLContext(42); | ||
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| pfi = ML.Regression.PermutationFeatureImportance(model, data); | ||
| pfiDict = ml2.Regression.PermutationFeatureImportance((ITransformer)model, data); |
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Why is this cast needed? Shouldn't model already be something that implements ITransformer?
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Yes. The issue is that the PFI method (of the same name) takes a ISingleFeaturePredictionTransformer<TModel>, and so the compiler defaults to that method instead of the method that just takes an ITransformer. model in this case is just a single estimator so its the actual type instead of ITransformer. Is there another way to work around that?
| // X4Rand: 3 | ||
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| // Make sure that PFI from the array and the dictionary both have the same value for each feature. | ||
| Assert.Equal(JsonConvert.SerializeObject(pfi[0]), JsonConvert.SerializeObject(pfiDict["X1"])); |
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One concern I have with testing like this is that if RegressionMetricsStatistics wasn't convertible to JSON, and just serialized to {} (an empty object), this assert would pass and not really verify anything.
Is there a way we can ensure that these MetricsStatistics classes serialize to JSON correctly if we are going to compare them this way?
Another option would be to write a simple "AssertStatisticsEquals" method that compared all the properties of 2 instances.
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Would that be better? I thought about doing that, but each type of statistics, Regression/Binary/Multiclass/Ranking, is a littler different so I would need different methods for each one. Thats why I ended up using this approach.
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I'm not sure of a good way to know if they serialize correctly. I have pretty much always assumed Newtonsoft.json would just work, but I'm sure there are cases where it doesn't. In this case it does as I manually checked...
Would just verifying its not an empty string or just empty {} be enough? Or would we need better verification. If its not, I can always just make an equals method for these objects. None of the stats ones have it so I could just make it for them all.
| Assert.Equal(3, MaxDeltaIndex(pfi, m => m.NegativePrecision.Mean)); | ||
| Assert.Equal(1, MinDeltaIndex(pfi, m => m.NegativePrecision.Mean)); | ||
| Assert.Equal(3, MaxDeltaIndex(pfi, m => m.NegativeRecall.Mean)); | ||
| Assert.Equal(0, MaxDeltaIndex(pfi, m => m.NegativeRecall.Mean)); |
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?? Is this change intentional? Your changes shouldn't have affected these numbers.
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This may be a good one to sync on, but the problem is that PFI changes the random state. So to make sure that both methods of PFI return the same results I reset the seed to 42 each time. Since that reset didn't happen before it slightly changed the results. Its possible I could find a seed though that would have the same results. Do you think that would be better:?
src/Microsoft.ML.Transforms/PermutationFeatureImportanceExtensions.cs
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This looks good. Should really help out our customers who want to use this API.
Nice work.
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Based on the feedback we received (thanks @houghj16 for all your work on that study!), our PFI API was too complex (and lets be honest, getting past the initial cast stops everyone...).
This PR fixes #5625 by adding a new PFI API that is much more user friendly in both how to call it, and how the results are returned. The added tests also make sure the output from the original API and the new API are identical if you match up the features correctly.
@JakeRadMSFT @briacht