I encoded target using sklearn2pmml.preprocessing.CutTransformer and sklearn.preprocessing.LabelEncoder while training a LR model.
Here's my code:
from sklearn2pmml.preprocessing import CutTransformer
from sklearn.preprocessing.label import LabelEncoder
income_bins = [-np.inf, 10000, 50000, 100000, 300000, 500000, 1000000, 3000000, 5000000, 10000000, np.inf]
targetDiscretizer = PMMLPipeline([('target',
DataFrameMapper([
(['income'], [CutTransformer(bins=income_bins), LabelEncoder()])
])
)])
dataset['target_income_lvl'] = targetDiscretizer.fit_transform(dataset)
sklearn2pmml(targetDiscretizer, '../model/targetDiscretizer.pmml', with_repr=True )
But I get a bug saying:
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pandas._libs.interval.Interval)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:732)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:200)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:122)
at numpy.core.NDArrayUtil.readObject(NDArrayUtil.java:384)
at numpy.core.NDArrayUtil.access$700(NDArrayUtil.java:42)
at numpy.core.NDArrayUtil$TypeDescriptor.read(NDArrayUtil.java:542)
at numpy.core.NDArrayUtil.parseArray(NDArrayUtil.java:215)
at numpy.core.NDArrayUtil.parseData(NDArrayUtil.java:190)
at joblib.NumpyArrayWrapper.toArray(NumpyArrayWrapper.java:43)
at org.jpmml.sklearn.PickleUtil$1.dispatch(PickleUtil.java:88)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:122)
at org.jpmml.sklearn.PickleUtil.unpickle(PickleUtil.java:98)
at org.jpmml.sklearn.Main.run(Main.java:104)
at org.jpmml.sklearn.Main.main(Main.java:94)
Exception in thread "main" net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pandas._libs.interval.Interval)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:732)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:200)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:122)
at numpy.core.NDArrayUtil.readObject(NDArrayUtil.java:384)
at numpy.core.NDArrayUtil.access$700(NDArrayUtil.java:42)
at numpy.core.NDArrayUtil$TypeDescriptor.read(NDArrayUtil.java:542)
at numpy.core.NDArrayUtil.parseArray(NDArrayUtil.java:215)
at numpy.core.NDArrayUtil.parseData(NDArrayUtil.java:190)
at joblib.NumpyArrayWrapper.toArray(NumpyArrayWrapper.java:43)
at org.jpmml.sklearn.PickleUtil$1.dispatch(PickleUtil.java:88)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:122)
at org.jpmml.sklearn.PickleUtil.unpickle(PickleUtil.java:98)
at org.jpmml.sklearn.Main.run(Main.java:104)
at org.jpmml.sklearn.Main.main(Main.java:94)
I have not idea about this. Can anybody help me?
By default, the Java parser for Python pickle files does not know about non-standard CPython classes such as the pandas._libs.interval.Interval
. It needs to be taught by each CPython class separately. For example, there is a related bug report in SkLearn2PMML issue tracker: https://github.com/jpmml/sklearn2pmml/issues/115
The conversion should work if you (at least temporarily-) manage to suppress the generation of the pandas._libs.interval.Interval
object. The most likely source for this is auto-generated bin labels. So, try supplying bin labels explicitly using the labels
argument: CutTransformer(bins = income_bins, labels = income_bin_labels)
.
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