python - PySpark:组合两个 VectorAssembler 的输出

Question

我使用 pyspark 创建了两个 VectorAssembler，第一个具有多个数字列('colA'、'colB'、'colC')，第二个具有多个分类列('colD'、'colE'、I在每一列上应用 OneHotEncoder)。

我可以单独创建这些 VectorAssembler。如何将输出组合成一个向量列(以便我可以将其输入 Xgboost 模型)？

我尝试了以下方法，但出现“TypeError: can only concatenate str (not "list") to str”

# my dataframe with all columns is df

# VectorAssembler 1: with 3 numeric columns 
numeric_cols = ['colA', 'colB', 'colC']
assembler = VectorAssembler(
    inputCols= numeric_cols,
    outputCol="numericFeatures"
)


# VectorAssembler 2: with 2 categorical columns
categ_cols = ['colD', 'colE']
indexers = [
    StringIndexer(inputCol=c, outputCol="{0}_indexed".format(c))
    for c in categ_cols
]
encoders = [
    OneHotEncoder(
        inputCol=indexer.getOutputCol(),
        outputCol="{0}_encoded".format(indexer.getOutputCol())) 
    for indexer in indexers
]
assemblerCateg = VectorAssembler(
    inputCols = [encoder.getOutputCol() for encoder in encoders],
    outputCol = "categFeatures"
)


pipeline = Pipeline(stages = [assembler] + indexers + encoders + [assemblerCateg])
df2 = pipeline.fit(df).transform(df)

Answer 1

解决了!只需在管道之前使用另一个 VectorAssembler(最后):

assemblerAll = VectorAssembler(inputCols= ["numericFeatures", "categFeatures"], outputCol="allFeatures")
pipeline = Pipeline(stages = [assembler] + indexers + encoders + [assemblerCateg] + [assemblerAll])