Comments (5)
It should happen when the underlying JPMML-SkLearn library reaches the 1.0
version.
from sklearn2pmml.
Any update on adding into pip?
from sklearn2pmml.
Update on this? Thanks.
from sklearn2pmml.
Please, just upload it. PyPI is not a collection of finished work. It's just a place where modules are made available.
from sklearn2pmml.
Also, jpmml-sklearn is >= 1.0 since half a year now
from sklearn2pmml.
Related Issues (20)
- Unpickling error: invalid pickle opcode: 64 HOT 3
- Support for `numpy.datetime64` data type(s) HOT 23
- Failure to create a pmml file when using CountVectorizer with analyzer = 'char' HOT 1
- sklearn2pmml fails to create pmml for XGBoost model HOT 1
- Patching 0.51.1 for unknown categorical variables HOT 4
- Postprocessing the probabilities of an XGBoost Model HOT 3
- Link Estimator with prefit=True doesn't work
- Support for the `OneHotEncoder.infrequent_categories_` attribute HOT 1
- 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
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from sklearn2pmml.