Dsfdr numpy fix #312
Open
Dsfdr numpy fix #312
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There is a problem with the matrix multiplication used for for the fast mean calculation in dsFDR (using the default method='meandiff'). This results in small (1E-9) differences in means that should be the same. This problem is then propagated to the dsFDR calculation (that relies on exact equality for the ranking), which eventually leads to dsFDR not ignoring enough the features present in a very small number of samples (i.e. reducing the power).
To fix this, we normalize and round the means to 9 decimal points before performing the dsFDR routine.
Also added a new test to validate the correctness by comparing to the slower (and more exact) method=ca.dsfdr.meandiff