Apache spark Pyspark从CrossValidator中的每个子模型检索度量（AUC ROC）

使用crossValidator时，如何返回每个折叠/子模型的单个auc roc分数

文档表明collectSubModels=True应该保存所有模型，而不仅仅是最好的或平均的，但是在检查model.subModels之后，我找不到如何打印它们

下面的示例仅在缺少model.subModels.auccore时起作用

期望的结果是每一次折叠都有相应的分数，如[fold1:0.85，fold2:0.07，fold3:0.55]

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

#Creating test dataframe
training = spark.createDataFrame([
    (1,0,1),
    (1,0,0),
    (0,1,1),
    (0,1,0)], ["label", "feature1", "feature2"])

#Vectorizing features for modelling

assembler = VectorAssembler(inputCols=['feature1','feature2'],outputCol="features")
prepped = assembler.transform(training).select('label','features')

#setting variables and configuring CrossValidator

rf = RandomForestClassifier(labelCol="label", featuresCol="features")
params = ParamGridBuilder().build()
evaluator = BinaryClassificationEvaluator()
folds = 3

cv = CrossValidator(estimator=rf,
estimatorParamMaps=params,
evaluator=evaluator,
numFolds=folds,
collectSubModels=True
)

#Fitting model
model = cv.fit(prepped)

#Print Metrics
print(model)
print()
print(model.avgMetrics)
print()
print(model.subModels)

>>>>>Return:
>>>>>CrossValidatorModel_3a5c95c6d8d2
>>>>>()
>>>>>[0.8333333333333333]
>>>>>()
>>>>>[[RandomForestClassificationModel (uid=RandomForestClassifier_95da3a68af93) with 20 trees], >>>>>[RandomForestClassificationModel (uid=RandomForestClassifier_95da3a68af93) with 20 trees], >>>>>[RandomForestClassificationModel (uid=RandomForestClassifier_95da3a68af93) with 20 trees]]