Comments (4)
Hey there. I've refactored the library with correctness in mind. I didn't look into performance. There are likely many parts low hanging fruits to improve. I need to spend some time looking into this, but I don't have much time right now 😔
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Ok I took a quick look, and I think I found one of these low hanging fruits. Basically, at one point we do a matrix multiplication between a diagonal matrix and a matrix. The diagonal matrix was not sparse, and therefore allocated too much memory. I've switched it to a sparse matrix.
@raihaan could you install the new version (0.8.1) and let me know if the issue persists? Many thanks.
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Updated to 0.8.1 and no issue with memory while running MCA. Have also tested with a larger dataset (282819x48), also no issues. Thank you for the quick response.
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Wonderful 🤟
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Related Issues (20)
- FAMD transform ValueError on qualitative data HOT 1
- 'MCA' object has no attribute '_check_is_fitted' with .transform HOT 2
- Not compatible with pandas 2.0.0 HOT 4
- Sklearn GridSearchCV: 'MCA' object has no attribute 'set_params' HOT 3
- Difficulty applying FMA with a group of categorical variables. HOT 1
- Handle_unknown in OneHotEncoder (FAMD) method HOT 5
- Information regarding Inverse MCA HOT 2
- MCA: plotting assumes raw dataset has already run get_dummies HOT 7
- prince.PCA vs. sklearn.decomposition.PCA? HOT 3
- UserWarning "pandas.DataFrame with sparse columns found" when running famd() HOT 1
- Feature idea: biplot HOT 2
- Compatibility with altair 5.0.x HOT 7
- Feature Idea: Weights HOT 3
- How to transform on new unseen test data? HOT 3
- CA.row_coordinates only works with pd.DataFrame HOT 5
- FAMD fit method not compatible with scikit-learn pipeline HOT 2
- MCA doesn't work with column transformers of sklearn HOT 2
- Bug in FAMD transform() HOT 1
- Support for sklearn Pipelines HOT 1
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