Better tests for l2 alpha scaling between different sklearn estimators #17
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pierrenodet
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tfjgeorge
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Feb 21, 2025
| coef, | ||
| intercept, | ||
| loss=loss, | ||
| regul=estimator.alpha * batch_size / X.shape[0], |
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lol wtf ? Do we inherit this from sklearn's black magic ?
| @@ -25,23 +25,25 @@ | |||
| from sklearn.metrics import log_loss, mean_squared_error | |||
| from sklearn.model_selection import LeaveOneOut | |||
| from sklearn.preprocessing import LabelBinarizer, StandardScaler | |||
| from statsmodels.genmod import families | |||
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should it be added somewhere in the config files as a required dependency for testing ?
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it's written in the pyproject.toml, when running hatch test, it's going to download additional test-time dependencies. It's done in a hatch centric way, for a more generic way we could introduce a dependency-group https://docs.astral.sh/uv/concepts/projects/dependencies/#development-dependencies
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Hope the new tests in test_linear.py will be enough