apache-spark - “CrossValidatorModel”对象没有属性 'featureImportances'

Question

我正在尝试提取使用 Pyspark 训练过的随机森林分类器模型的特征重要性。我引用了下面的文章来获取我训练的随机森林模型的特征重要性得分。

PySpark & MLLib: Random Forest Feature Importances

但是，当我使用本文中描述的方法时，出现以下错误

'CrossValidatorModel' object has no attribute 'featureImportances'

这是我用来训练模型的代码

cols = new_data.columns
stages = []
label_stringIdx = StringIndexer(inputCol = 'Bought_Fibre', outputCol = 'label')
stages += [label_stringIdx]
numericCols = new_data.schema.names[1:-1]
assembler = VectorAssembler(inputCols=numericCols, outputCol="features")
stages += [assembler]

pipeline = Pipeline(stages = stages)
pipelineModel = pipeline.fit(new_data)
new_data.fillna(0, subset=cols)
new_data = pipelineModel.transform(new_data)
new_data.fillna(0, subset=cols)
new_data.printSchema()


train_initial, test = new_data.randomSplit([0.7, 0.3], seed = 1045)
train_initial.groupby('label').count().toPandas()
test.groupby('label').count().toPandas()

train_sampled = train_initial.sampleBy("label", fractions={0: 0.1, 1: 1.0}, seed=0)
train_sampled.groupBy("label").count().orderBy("label").show()



labelIndexer = StringIndexer(inputCol='label',
                             outputCol='indexedLabel').fit(train_sampled)

featureIndexer = VectorIndexer(inputCol='features',
                               outputCol='indexedFeatures',
                               maxCategories=2).fit(train_sampled)

from pyspark.ml.classification import RandomForestClassifier
rf_model = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")

labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel",
                               labels=labelIndexer.labels)


pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf_model, labelConverter])

paramGrid = ParamGridBuilder() \
    .addGrid(rf_model.numTrees, [ 200, 400,600,800,1000]) \
    .addGrid(rf_model.impurity,['entropy','gini']) \
    .addGrid(rf_model.maxDepth,[2,3,4,5]) \
    .build()

crossval = CrossValidator(estimator=pipeline,
                          estimatorParamMaps=paramGrid,
                          evaluator=BinaryClassificationEvaluator(),
                          numFolds=5)    


train_model = crossval.fit(train_sampled)

请帮助解决上述错误并帮助提取特征

Answer 1

这是因为 CrossValidatorModel 没有特征重要性属性，但 RandomForestModel 模型有。

由于您使用Pipeline和CrossValidator来拟合数据，因此您需要获得最佳拟合的底层阶段型号:

# '2' is the index of your RandomForestModel inside of the Pipeline
your_model = cvModel.bestModel.stages[2] 
var_imp = your_model.featureImportances