added sklearn GLM #216
added sklearn GLM #216
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requirements-test.txt
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| @@ -1,9 +1,9 @@ | |||
| # Supported models | |||
| scikit-learn==0.22.2.post1 | |||
| scikit-learn==0.23.0 | |||
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Could not find a version that satisfies the requirement scikit-learn==0.23.0 (from -r requirements-test.txt (line 2)) (from versions: 0.9, 0.10, 0.11, 0.12, 0.12.1, 0.13, 0.13.1, 0.14, 0.14.1, 0.15.0b1, 0.15.0b2, 0.15.0, 0.15.1, 0.15.2, 0.16b1, 0.16.0, 0.16.1, 0.17b1, 0.17, 0.17.1, 0.18rc2, 0.18, 0.18.1, 0.18.2, 0.19b2, 0.19.0, 0.19.1, 0.19.2, 0.20rc1, 0.20.0, 0.20.1, 0.20.2, 0.20.3, 0.20.4, 0.21rc2, 0.21.0, 0.21.1, 0.21.2, 0.21.3, 0.22rc2.post1, 0.22rc3, 0.22, 0.22.1, 0.22.2.post1)
No matching distribution found for scikit-learn==0.23.0 (from -r requirements-test.txt (line 2))
https://pypi.org/project/scikit-learn/#files
I think that as scikit-learn doesn't provide Python 3.5 wheels anymore and Python 3.5 blocks us from better performance (#209) we can drop Python 3.5 support right now.
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Another one reason to drop support of Python 3.5:
/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/xgboost/__init__.py:28: FutureWarning: Python 3.5 support is deprecated; XGBoost will require Python 3.6+ in the near future. Consider upgrading to Python 3.6+.
FutureWarning)
UPD: Next release of XGBoost will support only Python >= 3.6: dmlc/xgboost#5338 (comment) and dmlc/xgboost#5715.
requirements-test.txt
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| xgboost==1.0.2 | ||
| lightgbm==2.3.1 | ||
| statsmodels==0.11.1 | ||
| git+git://github.com/scikit-learn-contrib/lightning.git@b96f9c674968496e854078163c8814049a7b9f43 | ||
| git+git://github.com/scikit-learn-contrib/lightning@refs/pull/142/head |
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scikit-learn-contrib/lightning#142
I don't believe that that my PR will be merged (at least soon), but this trick allows us to install the latest scikit-learn with lightning at Travis and run all our tests. At the same time, users are still able to install latest master of lightning with old scikit-learn (<0.23.0) and not face any problems with a such environment setup.
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Wow! They merged it! 🎉
| regression(linear_model.TweedieRegressor(power=0.0)), | ||
| regression(linear_model.TweedieRegressor(power=1.0)), | ||
| regression(linear_model.TweedieRegressor(power=1.5)), | ||
| regression(linear_model.TweedieRegressor(power=2.0)), | ||
| regression(linear_model.TweedieRegressor(power=3.0)), |
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Closed #214.