Update SciKit-Learn Library Requirements #57
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Where calculations.py called functions from sklearn like "cross_val_score" and "mean_absolute_error", pandas data frames were being given as inputs to those functions. I am not sure what Sklearn's new update did not like about it, but changing those pandas data frames to numpy arrays with .to_numpy() fixed the problem and gave the correct values.
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Side note: The issue was with the pandas series, but if you use .astype("int64") or .astype("float"), or anything like that it fixes the issue too. Sklearn just needed us to declare the dtype of the pandas series. |
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Interesting, thank you. Why did you decide for the general |
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@fwetdb It looks like Sklearn handles numpy arrays differently than pandas dataframes. I digged as far as I could into the ppscore library, and found those two functions, cross_val_score and mean_absolute_error, that were causing the error. When I changed the dataframes to numpy, the problem was fixed, so I assume something about the way sklearn handles pd dataframes changed. Without looking into the new code for sklearn i cant give you an answer to that. I only decided on that first because I could not seem to get pandas dataframes to work for a bit, but once I declared their datatype it worked fine. |
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Great, thank you a lot @JDM288 |
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