'CrossValidatorModel' 对象没有属性 'featureImportances'

Question

我正在尝试提取random forest classifier我使用Pyspark. 我参考了下面的文章来获得我训练的随机森林模型的特征重要性分数。

PySpark 和 MLLib：随机森林特征的重要性

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

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

由于您正在使用PipelineandCrossValidator来拟合数据，因此您需要获得最佳拟合模型的基础阶段：

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