使用sklearn2pmml将XGBoost分类器写入pmml时出错

Question

我想使用sklearn2pmml将XGBoost模型另存为pmml。我正在将Python V3.7.3与Sklearn 0.20.3和sklearn2pmml V0.53.0一起使用。我的数据主要是二进制数据，只有3列连续数据，我在Databricks中运行我的笔记本，然后将Spark数据框转换为pandas数据框。下面的代码段

import xgboost as xgb

from sklearn_pandas import DataFrameMapper
from sklearn.compose import ColumnTransformer

from sklearn2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
from sklearn2pmml.decoration import ContinuousDomain
from sklearn.preprocessing import StandardScaler

X = pdf[continuous_features + numericCols]
y = pdf["Label"]


mapper = DataFrameMapper(
  [([cont_column], [ContinuousDomain(), StandardScaler()]) for cont_column in continuous_features] +
  [([c for c in numericCols], None)] # no transformation
)

clf = xgb.XGBClassifier(objective='multi:softprob',eval_metric='auc',num_class = 2,
                        n_jobs =6,max_delta_step=1, min_child_weight=14, gamma=1.5, subsample = 0.8,
                        colsample_bytree = 0.5, max_depth=10, learning_rate = 0.1)


pipeline = PMMLPipeline([
  ("mapper", mapper),
  ("estimator", clf)
])

pipeline.fit(X,y.values.reshape(-1,))

sklearn2pmml(pipeline, "xgb_V1.pmml", with_repr = True)

管道适合数据，并使用pipeline.score（X，y）和pipeline.predict（X）生成分数和预测，但是当我尝试将其写入pmml时，出现以下错误：

Standard output is empty
Standard error:
Feb 21, 2020 1:53:30 PM org.jpmml.sklearn.Main run
INFO: Parsing PKL..
Feb 21, 2020 1:53:30 PM org.jpmml.sklearn.Main run
INFO: Parsed PKL in 47 ms.
Feb 21, 2020 1:53:30 PM org.jpmml.sklearn.Main run
INFO: Converting..
Feb 21, 2020 1:53:30 PM sklearn2pmml.pipeline.PMMLPipeline initTargetFields
WARNING: Attribute 'sklearn2pmml.pipeline.PMMLPipeline.target_fields' is not set. Assuming y as the name of the target field
Feb 21, 2020 1:53:30 PM org.jpmml.sklearn.Main run
SEVERE: Failed to convert
java.lang.IllegalArgumentException: Attribute 'xgboost.sklearn.XGBClassifier._le' has an unsupported value (Python class xgboost.compat.XGBoostLabelEncoder)
	at org.jpmml.sklearn.CastFunction.apply(CastFunction.java:45)
	at org.jpmml.sklearn.PyClassDict.get(PyClassDict.java:82)
	at sklearn.LabelEncoderClassifier.getLabelEncoder(LabelEncoderClassifier.java:40)
	at sklearn.LabelEncoderClassifier.getClasses(LabelEncoderClassifier.java:34)
	at sklearn.ClassifierUtil.getClasses(ClassifierUtil.java:32)
	at sklearn2pmml.pipeline.PMMLPipeline.encodePMML(PMMLPipeline.java:133)
	at org.jpmml.sklearn.Main.run(Main.java:145)
	at org.jpmml.sklearn.Main.main(Main.java:94)
Caused by: java.lang.ClassCastException: Cannot cast net.razorvine.pickle.objects.ClassDict to sklearn.preprocessing.LabelEncoder
	at java.lang.Class.cast(Class.java:3369)
	at org.jpmml.sklearn.CastFunction.apply(CastFunction.java:43)
	... 7 more

Exception in thread "main" java.lang.IllegalArgumentException: Attribute 'xgboost.sklearn.XGBClassifier._le' has an unsupported value (Python class xgboost.compat.XGBoostLabelEncoder)
	at org.jpmml.sklearn.CastFunction.apply(CastFunction.java:45)
	at org.jpmml.sklearn.PyClassDict.get(PyClassDict.java:82)
	at sklearn.LabelEncoderClassifier.getLabelEncoder(LabelEncoderClassifier.java:40)
	at sklearn.LabelEncoderClassifier.getClasses(LabelEncoderClassifier.java:34)
	at sklearn.ClassifierUtil.getClasses(ClassifierUtil.java:32)
	at sklearn2pmml.pipeline.PMMLPipeline.encodePMML(PMMLPipeline.java:133)
	at org.jpmml.sklearn.Main.run(Main.java:145)
	at org.jpmml.sklearn.Main.main(Main.java:94)
Caused by: java.lang.ClassCastException: Cannot cast net.razorvine.pickle.objects.ClassDict to sklearn.preprocessing.LabelEncoder
	at java.lang.Class.cast(Class.java:3369)
	at org.jpmml.sklearn.CastFunction.apply(CastFunction.java:43)

根据这篇文章https://github.com/jpmml/sklearn2pmml/issues/197，我认为这可能是Sklearn和sklearn2pmml之间的版本不兼容的问题，但是我认为我安装的版本应该可以。关于这是怎么回事的任何想法？在此先感谢

Answer 1

这可能是XGBoost软件包的版本问题。 SkLearn2PMML软件包期望标签编码器（XGBClassifier._le属性）是“普通”的Scikit-Learn标签编码器类（sklearn.preprocessing.(label|_label).LabelEncoder），但在您的情况下，它有所不同（xgboost.compat.XGBoostLabelEncoder）。

xgboost.compat.XGBoostLabelEncoder引入了哪个XGBOost软件包版本？它要么很旧，要么很新。

无论如何，请通过JPMML-SkLearn项目here打开功能请求，以解决此问题。