Question

我正在尝试提取我使用random forest classifier训练过的Pyspark模型的特征重要性。我参考以下文章以获得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，当我使用本文中介绍的方法时，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功能

Answer 1

这是因为CrossValidator模型没有功能重要性属性。

另一方面，RandomForest模型具有。

由于您正在使用Pipeline和CrossValidator来拟合数据，因此需要获得 best 拟合模型的基础阶段：

your_model = cvModel.bestModel.stages[2] # index of your RandomForestModel
var_imp = your_model.featureImportances

'CrossValidatorModel'对象没有属性'featureImportances'

1 个答案: