Comments (3)
vruusmann
commented on August 20, 2024
This warning appeared after you upgraded from SkLearn 0.17 to 0.18?
The sklearn2pmml package does not import or use any functionality from the sklearn.cross_validation module. It follows that this warning is emitted by SkLearn internal workings, and there's nothing that I can do to make it go away.
Perhaps the SkLearn library has some sort of global configuration option/flag, where you can adjust your preferred warning level.
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maxnoe
commented on August 20, 2024
Ah, sorry. The import happens in sklearn_pandas not in sklearn2pmml.
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vruusmann
commented on August 20, 2024
Could be happening in several places.
At least we have it "documented" now. Quick Googling didn't reveal any easy options for silencing those warnings.
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Related Issues (20)
- Revert PMML file to Python source code HOT 1
- Error when using `TargetEncoder` within `DataFrameMapper` within pipeline HOT 6
- Missing dependency `dill` from `setup.py`. HOT 7
- Support for Python power operator `**` HOT 1
- Assembling a pipeline from pre-fitted components HOT 3
- looking for support on quantile_forest for RandomForestQuantileRegressor HOT 2
- Failing to convert `CalibratedClassifierCV` plus `XGBClassifier` combination HOT 12
- Effectively debugging XGBoost pipelines (mis-matching predictions between Python and (J)PMML) HOT 14
- Revisit the missing value handling of custom transformers HOT 1
- Support for AgglomerativeClustering HOT 2
- `ExpressionTransformer` should try to rectify feature type information HOT 5
- `Alias` object has no attribute `transformer_` HOT 8
- Storing LinearSVC as coefficients instead of support vectors HOT 1
- Reshape transformation results from 2-D to 1-D (column vectors) HOT 1
- sklearn2pmml does not work with sklearn version 1.3.0 and newer HOT 6
- sklearn2pmml does not work with xgboost >= 2.0.0 HOT 9
- LookupTransformer TypeError when default value is not exactly the type from the mapping HOT 2
- xgb 生成的pkl 转pmml 失败 HOT 1
- Can sklearn2pmml just for variable transformations be used? HOT 1
- PyPMML is making systematically off predictions with XGBoost PMML documents? HOT 16
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