CODEWITHSUNDEEP
pythonpysparkpipelineapache-spark-ml
YaleBD
YaleBD

Reputation: 163

PySpark: combining output of two VectorAssemblers

Using pyspark, ​I have created two VectorAssemblers, the first with multiple numeric columns ('colA', 'colB', 'colC'), and the second with multiple categorical columns ('colD', 'colE', I applied OneHotEncoder on each column).

I could create these VectorAssemblers separately. How can I combine the outputs into a single vector column (so that I can feed it into a Xgboost model)?

I tried the following, but got "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)

Upvotes: 4

Views: 1142

Answers (1)

YaleBD
YaleBD

Reputation: 163

Solved it! Just use another VectorAssembler (at the end) before the pipeline:

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

Upvotes: 5

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