glmpca-py
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willtownes
generalized principal component analysis (GLM-PCA) implemented in python
To help users pre-filter features and remove those that are not informative (so as to speed computation of glmpca), provide the function to compute deviance for each feature and rank...
`pip3 install glmpca` installs the package with no complaints but `import glmpca` gives an empty wrapper: ``` >>> dir(glmpca) ['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__'] >>> from...
Consider changing the packaging to the [poetry](https://python-poetry.org/) system.
Hey Will, in the R package, sparse matrices are supported under some circumstances. As far as I saw, the python version does not make use of sparsity yet - do...
If sometimes happens that there is a huge increase in loss in a single step. Increasing the penalty changes the objective function, but it may also be possible that the...
This is a straight translation from R. I’d recommend making this pythonic: - Don’t use mathy variable names or dict parameters. Use expressive names in lower case, especially for the...
I run my analysis in small pipelined chunks of commands on a server. This means that if there is an error, all the sequence of commands I am running will...
Are the factors ordered in some way? For classical PCA, PCs are canonically defined and ordered by variance explained. I noticed from a small experiment that the loadings appear to...
It would be straightforward to include the Gaussian family, which would allow an alternative algorithm for computing PCA. In particular this may help scale PCA to out-of-memory datasets when we...
Currently (due to its R origin) the glmpca method is implemented with frequent indexing along columns of various matrices. This is because R stores data in column-major order so it...
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generalized principal component analysis (GLM-PCA) implemented in python