Fix tests warnings #1628
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Fix tests warnings #1628
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The function scipy.special.btdtri was deprecated in SciPy 1.12, and will be removed in SciPy 1.14. scipy.special.betaincinv should be a drop-in replacement.
datetime.datetime.utcnow() is deprecated because it is too easy of a footgun. Since we don't need timezone-aware objects, now() has the same behaviour (to the difference of the local time difference to UTC). The exact time used (or timezone-equivalent) has no impact on this test, the only requirements is for both calls to have the same timestamp.
Concatenation of empty dataframes causes a FutureWarning in pandas. None values in concatenation are ignored, as long as not all objects to concatenate are None.
In a sparse dataframe, all elements must have a sparse Dtype. Values added after the fact need to be converted.
MaxHalford
approved these changes
Nov 13, 2024
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Nice PR! Good job |
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Maybe it's worth reactivating the pytest warnings? |
Sorry, I don't understand what you mean by this. The warnings are active and displayed at the end of every Pytest run, this is how I realised there were so many. |
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No my bad. They're deactivated for me by default, which is why I didn't notice them 👍 |
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River's test suite raises an important quantity of warnings, and their number may hide defects in new code. Most warnings are created by a few pieces of code run many times. This PR aims to greatly reduce the number of warnings in the tests.
Before: 209139 warnings
After : 17 warnings
Most warnings left are caused by libraries like NumPy and Scikit-learn.
Four of the remaining warnings are a deprecation :
pickle,copy, anddeepcopysupport will be removed fromitertoolsin Python 3.14. This may warrant an action.This PR also fixes a semi-bug:
In
naives_bayes.BernoulliNB.joint_log_likelihood_many(), theif hasattr(X, "sparse")path was never used (at least in the tests) because sparse dataframes were completed with non-sparse values, becoming non-sparse.Thankfully, both types of dataframes have the same behaviour so nothing bad happened and only a warning was issued by Pandas.
In removing the warning, I restored the intended behaviour (or the one I believe was intended).