feat: Narwhals for dataframe-agnostic codebase #671
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* feat: make ColumnDropped dataframe-agnostic * use narwhals[polars] in pyproject.toml, link to list of supported libraries * note that narwhals is used for cross-dataframe support * test refactor * docstrings --------- Co-authored-by: FBruzzesi <[email protected]>
* columnselector with test rufformatted * adding whitespace * fixed the fit and transform * removed intendation in examples * font:false
* make add_lags dataframe-agnostic * try getting tests to run? * patch: cvxpy 1.5.0 support (#663) --------- Co-authored-by: Francesco Bruzzesi <[email protected]>
* make regression outlier df-agnostic * need to use eager-only for this one * pass native to check_array * remove cudf, link to check_X_y
* make timegapsplit dataframe-agnostic * actually, include cuDF
* start all over * fixture working * wip * passing tests - again * pre-commit complaining * changed fixture on test_demographic_parity
* make pandas dtype selector df-agnostic * bump version * 3.8 compat * Update sklego/preprocessing/pandastransformers.py Co-authored-by: Francesco Bruzzesi <[email protected]> * fixup pyproject.toml * unify (and test!) error message * deprecate * update readme * undo contribution.md change --------- Co-authored-by: Francesco Bruzzesi <[email protected]>
koaning
reviewed
May 18, 2024
| @@ -4,6 +4,7 @@ on: | |||
| pull_request: | |||
| branches: | |||
| - main | |||
| - narwhals-development | |||
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Can this go away once we have it in main?
koaning
approved these changes
May 18, 2024
* feat: make grouped and hierarchical dataframe-agnostic * add pyarrow * narwhals grouped_transformer * grouped transformer eureka * hierarchical narwhalified * so close but so far * return series instead of DataFrame for y * grouped WIP * merge branch and fix grouped * future annotations * format * handling negative indices * solve conflicts
FBruzzesi
commented
May 24, 2024
Comment on lines
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| for C in [1, 0.5, 0.1, 0.05]: | ||
| fair = DemographicParityClassifier(covariance_threshold=None, sensitive_cols=["x1"], C=C).fit(X, y) | ||
| theta_norm = np.abs(np.sum(fair.estimators_[0].coef_)) | ||
| theta_norm = np.sum(np.abs(fair.estimators_[0].coef_)) |
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cc: @koaning here is the change for more stability.
In the meanwhile I am properly investigating to report it to the cvxpy folks
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Description
Partially solves #658 with a large effort in many contributions.
add_lagsdataframe-agnostic #661TypeSelector