无法使用网格搜索检索 bestModel答案

【问题标题】：Unable to retrieve bestModel using Grid search无法使用网格搜索检索 bestModel
【发布时间】：2025-12-19 14:50:10
【问题描述】：

我正在使用下面的代码来获得回归模型的最佳拟合并得到一个错误：

# Creating parameter grid
params = ParamGridBuilder()

# Adding grids for two parameters
params = params.addGrid(regression.regParam, [0.01, 0.1, 1.0, 10.0]) \
               .addGrid(regression.elasticNetParam, [0.0, 0.5, 1.0])

# Building the parameter grid
params = params.build()
print('Number of models to be tested: ', len(params))

# Creating cross-validator
cv = CrossValidator(estimator=pipeline, estimatorParamMaps=params, evaluator=evaluator, numFolds=5)

from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit, CrossValidator
from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Get the best model from cross validation
best_model = cv.bestModel

错误是：

AttributeError                            Traceback (most recent 
call last)
<ipython-input-449-f7d43e2cf76b> in <module>
  3 
  4 # Get the best model from cross validation
 ----> 5 best_model = cv.bestModel
  6 
  7 # Look at the stages in the best model

AttributeError: 'CrossValidator' object has no attribute 'bestModel'

用于获取最佳模型参数的 CrossValidator 没有返回经过训练的模型！！

【问题讨论】：

标签： python machine-learning pyspark apache-spark-mllib grid-search

【解决方案1】：

在访问bestModel 属性之前，您必须先拟合并分配 CV 模型；改编docs中的示例：

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

dataset = spark.createDataFrame(
    [(Vectors.dense([0.0]), 0.0),
     (Vectors.dense([0.4]), 1.0),
     (Vectors.dense([0.5]), 0.0),
     (Vectors.dense([0.6]), 1.0),
     (Vectors.dense([1.0]), 1.0)] * 10,
    ["features", "label"])

lr = LogisticRegression()
grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
    parallelism=2)

在这个阶段请求cv.bestModel会得到

AttributeError                            Traceback (most recent call last)
<command-388275196191991> in <module>
----> 1 cv.bestModel

AttributeError: 'CrossValidator' object has no attribute 'bestModel'

就像你的情况。

先拟合并分配：

cvModel = cv.fit(dataset)
cvModel.bestModel
# result:
LogisticRegressionModel: uid = LogisticRegression_f9c9ea282e32, numClasses = 2, numFeatures = 1

【讨论】：