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Then I close #1 or what is the plan? |
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#1 seems more of an issue than a PR... |
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# This is the 1st commit message: Add Ridge class for linear models. # This is the commit message #2: Reduce the number of combinations tested on CI (#5) # This is the commit message #3: add standard scaler with tests # This is the commit message #4: small fixes, added TODOs to linear model # This is the commit message #5: format example and other files # This is the commit message #6: add tensor map to pickable dictionary object # This is the commit message #7: format example and other files # This is the commit message #8: Added tests # This is the commit message #9: skeleton for ridge test # This is the commit message #10: Added a shape test for Ridge + more utils # This is the commit message #11: fix type to int # This is the commit message #12: Added numerically stable solver # This is the commit message #13: Add tests for ridge solver: vs exact results # This is the commit message #14: Add test: Infinite regularization + predict # This is the commit message #15: changing temporray TensorMap member variables to dicts to allow saving of model as torchscript # This is the commit message #16: Add test: consistent scaling of weights
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Closed by #4 |
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Hi! This is a WIP, not open for official review yet.
This is a branch dedicated to laying the groundwork for sklearn-type models in equisolve, starting with a simple linear model. Currently I have it so that each key will lead to a different regressor, but I do need some datasets to test this. @curiosity54 do you have anything? Tests should provide coverage for the very few use-cases that I have test datasets for. Documentation forthcoming once we decide on structure.