小编aam*_*irr的帖子

我试图绘制具有列名称的某些基于树的模型的特征重要性.我正在使用Pyspark.

既然我有文本分类变量和数字变量,我不得不使用类似这样的管道方法 -

1. 使用字符串索引器来索引字符串列
2. 为所有列使用一个热编码器

3. 使用vectorassembler创建包含特征向量的要素列

   步骤1,2,3 的文档中的一些示例代码-

   from pyspark.ml import Pipeline
   from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, 
   VectorAssembler
   categoricalColumns = ["workclass", "education", "marital_status", 
   "occupation", "relationship", "race", "sex", "native_country"]
    stages = [] # stages in our Pipeline
    for categoricalCol in categoricalColumns:
       # Category Indexing with StringIndexer
       stringIndexer = StringIndexer(inputCol=categoricalCol, 
       outputCol=categoricalCol + "Index")
       # Use OneHotEncoder to convert categorical variables into binary 
       SparseVectors
       # encoder = OneHotEncoderEstimator(inputCol=categoricalCol + "Index", 
       outputCol=categoricalCol + "classVec")
       encoder = OneHotEncoderEstimator(inputCols= 
       [stringIndexer.getOutputCol()], outputCols=[categoricalCol + "classVec"])
       # …

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