将两个 Spark mllib 管道连接在一起

Question

我有两个单独的 DataFrames，每个都有几个不同的处理阶段，我在管道中使用 mllib 转换器来处理。

我现在想将这两个管道连接在一起，保留每个 DataFrame 的特征（列）。

Scikit-learn 有 FeatureUnion class 来处理这个问题，我似乎找不到 mllib.

的等价物

我可以在一个管道的末尾添加一个自定义转换器阶段，将另一个管道生成的 DataFrame 作为属性并将其加入转换方法中，但这看起来很乱。

Answer 1

Pipeline 或 PipelineModel 是有效的 PipelineStages，因此可以组合成一个 Pipeline。例如：

from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler

df = spark.createDataFrame([
    (1.0, 0, 1, 1, 0),
    (0.0, 1, 0, 0, 1)
], ("label", "x1", "x2", "x3", "x4"))

pipeline1 = Pipeline(stages=[
    VectorAssembler(inputCols=["x1", "x2"], outputCol="features1")
])

pipeline2 = Pipeline(stages=[
    VectorAssembler(inputCols=["x3", "x4"], outputCol="features2")
])

你可以组合 Pipelines:

Pipeline(stages=[
    pipeline1, pipeline2, 
    VectorAssembler(inputCols=["features1", "features2"], outputCol="features")
]).fit(df).transform(df)

+-----+---+---+---+---+---------+---------+-----------------+
|label|x1 |x2 |x3 |x4 |features1|features2|features         |
+-----+---+---+---+---+---------+---------+-----------------+
|1.0  |0  |1  |1  |0  |[0.0,1.0]|[1.0,0.0]|[0.0,1.0,1.0,0.0]|
|0.0  |1  |0  |0  |1  |[1.0,0.0]|[0.0,1.0]|[1.0,0.0,0.0,1.0]|
+-----+---+---+---+---+---------+---------+-----------------+

或预装PipelineModels:

model1 = pipeline1.fit(df)
model2 = pipeline2.fit(df)

Pipeline(stages=[
    model1, model2, 
    VectorAssembler(inputCols=["features1", "features2"], outputCol="features")
]).fit(df).transform(df)

+-----+---+---+---+---+---------+---------+-----------------+
|label| x1| x2| x3| x4|features1|features2|         features|
+-----+---+---+---+---+---------+---------+-----------------+
|  1.0|  0|  1|  1|  0|[0.0,1.0]|[1.0,0.0]|[0.0,1.0,1.0,0.0]|
|  0.0|  1|  0|  0|  1|[1.0,0.0]|[0.0,1.0]|[1.0,0.0,0.0,1.0]|
+-----+---+---+---+---+---------+---------+-----------------+

所以我推荐的方法是预先加入数据，fit和transform一个整体DataFrame。

另请参阅：

将两个 Spark mllib 管道连接在一起

Join two Spark mllib pipelines together

python

scala

apache-spark

apache-spark-ml

apache-spark-mllib