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Codecov Report
@@ Coverage Diff @@
## main #3051 +/- ##
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+ Coverage 99.7% 99.8% +0.1%
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Files 312 312
Lines 30344 30340 -4
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- Hits 30252 30249 -3
+ Misses 92 91 -1
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| def test_mcc_catches_warnings(): | ||
| y_true = [1, 0, 1, 1] | ||
| y_predicted = [0, 0, 0, 0] | ||
| with pytest.warns(RuntimeWarning) as record: |
| pandas>=1.3.0 | ||
| scipy>=1.5.0 | ||
| scikit-learn>=0.24.0,<1.0 | ||
| scikit-learn>=0.24.0 |
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Note:
scikit-learn >1.0 will require scikit-optimize>=0.9.0 because modules / objects get shuffled around, causing the following:
evalml/pipelines/components/estimators/classifiers/logistic_regression_classifier.py:4: in <module>
from skopt.space import Real
../evalml_venv/lib/python3.7/site-packages/skopt/__init__.py:55: in <module>
from .searchcv import BayesSearchCV
../evalml_venv/lib/python3.7/site-packages/skopt/searchcv.py:16: in <module>
from sklearn.utils.fixes import MaskedArray
E ImportError: cannot import name 'MaskedArray' from 'sklearn.utils.fixes' (/Users/angela.lin/Desktop/evalml_venv/lib/python3.7/site-packages/sklearn/utils/fixes.py)```
Our tests, which automatically install the latest version of scikit-optimize (0.9.0) will thus not break, so maybe we don't need to set our requirement of scikit-optimize>=0.9.0 but open to discussion if it feels like we should.
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How did you run into this?
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My local environment uses scikit-optimize==0.8.1 so as soon as I tried using scikit-learn 1.0, things broke 🥲
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I see. I'm wondering if this will affect users in some way/if we need to update the requirements file in light of this. Did you just install scikit-learn or did you do pip install -r requirements.txt ?
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I just installed scikit-learn! Our tests didn't break because they call pip install -r requirements.txt and in a clean environment, 0.9.0 will be installed.
This is a known issue in the scikit-optimize repo where it seems like the community solution is to either use scikit-learn<=0.24.2 or use scikit-optimize>=0.9.0: scikit-optimize/scikit-optimize#569
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Cool, makes sense to me!
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Would it be simpler to make the core requirements use scikit-learn >= 1.0.0 versus having support for both but test coverage for the later version?
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Hmm, I can be persuaded otherwise but I think this aligns with what we've currently been doing aka every time our dependencies come out with a new version, we pin until we can fix the tests to support the latest version--and thats the version that we run on!
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| @pytest.mark.parametrize("data_type", ["np", "pd", "ww"]) | ||
| @pytest.mark.parametrize("perplexity", [0, 4.6, 100]) | ||
| @pytest.mark.parametrize("learning_rate", [100.0, -15, 0]) |
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Negative and zero values are no longer accepted: https://github.com/scikit-learn/scikit-learn/blob/0d378913be6d7e485b792ea36e9268be31ed52d0/sklearn/manifold/_t_sne.py#L816
angela97lin
changed the title
angela97lin
requested review from
bchen1116,
chukarsten,
dsherry,
eccabay,
freddyaboulton and
jeremyliweishih
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@angela97lin This looks good to me!
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LGTM! Is there a reason we chose to support >=0.24 versus >=1.0.0?
| pandas>=1.3.0 | ||
| scipy>=1.5.0 | ||
| scikit-learn>=0.24.0,<1.0 | ||
| scikit-learn>=0.24.0 |
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Would it be simpler to make the core requirements use scikit-learn >= 1.0.0 versus having support for both but test coverage for the later version?
3 participants
Opening up a branch, enabling
scikit-learnv1.0, and seeing what breaks :)