【发布时间】：2020-06-05 23:26:26
【问题描述】：
我想使用 sklearn2pmml 将我的 XGBoost 模型保存为 pmml。我将 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的，只有 3 列连续数据，我在 Databricks 中运行我的笔记本并将我的 Spark 数据帧转换为熊猫数据帧。下面代码sn-p
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，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)根据这篇帖子https://github.com/jpmml/sklearn2pmml/issues/197，我认为这可能是 Sklearn 和 sklearn2pmml 之间的版本不兼容问题，但我认为我安装的版本应该没问题。关于这是怎么回事的任何想法？提前致谢
【问题讨论】：
-
此问题已在 SkLearn2PMML 版本 0.55.0 中得到修复。