AttributeError:“PipelineModel”对象没有属性“fitMultiple”

Question

我试图使用pyspark、CrossValidator和BinaryClassificationEvaluator、CrossValidator调优一个随机林模型，但是当我这样做时，我得到了一个错误。这是我的密码

from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline

# Create a spark RandomForestClassifier using all default parameters.
# Create a training, and testing df
training_df, testing_df = raw_data_df.randomSplit([0.6, 0.4])

# build a pipeline for analysis
va = VectorAssembler().setInputCols(training_df.columns[0:110:]).setOutputCol('features')

# featuresCol="features"
rf = RandomForestClassifier(labelCol="quality")

# Train the model and calculate the AUC using a BinaryClassificationEvaluator
rf_pipeline = Pipeline(stages=[va, rf]).fit(training_df)

bce = BinaryClassificationEvaluator(labelCol="quality")

# Check AUC before tuning
bce.evaluate(rf_pipeline.transform(testing_df))


from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

paramGrid = ParamGridBuilder().build()

crossValidator = CrossValidator(estimator=rf_pipeline, 
                          estimatorParamMaps=paramGrid, 
                          evaluator=bce, 
                          numFolds=3)

model = crossValidator.fit(training_df)

它将抛出以下错误：

AttributeError: 'PipelineModel' object has no attribute 'fitMultiple'

如何解决此问题

Answer 1

CrossValidator估计器采用管道对象，而不是管道模型

请检查此示例以供参考- https://github.com/apache/spark/blob/master/examples/src/main/python/ml/cross_validator.py

您的代码应该修改如下

1. 创建管道

rf_pipe = Pipeline(stages=[va, rf])

1. 将该管道用作crossvalidator中的估计器

crossValidator = CrossValidator(estimator=rf_pipe, 
                          estimatorParamMaps=paramGrid, 
                          evaluator=bce, 
                          numFolds=3)

所有-

....

# Train the model and calculate the AUC using a BinaryClassificationEvaluator
rf_pipe = Pipeline(stages=[va, rf])
rf_pipeline = rf_pipe.fit(training_df)

...

crossValidator = CrossValidator(estimator=**rf_pipe**, 
                          estimatorParamMaps=paramGrid, 
                          evaluator=bce, 
                          numFolds=3)

model = crossValidator.fit(training_df)