如何將ML VectorUDT功能從.mllib轉換爲.ml類型

Question

在線性迴歸簡單示例中使用版本2.0.0中的pySpark ML API時，出現新ML庫的錯誤。

的代碼是：

from pyspark.sql import SQLContext 
sqlContext =SQLContext(sc) 
from pyspark.mllib.linalg import Vectors 

data=sc.parallelize(([1,2],[2,4],[3,6],[4,8])) 
def f2Lp(inStr): 
    return (float(inStr[0]), Vectors.dense(inStr[1])) 
Lp = data.map(f2Lp) 
testDF=sqlContext.createDataFrame(Lp,["label","features"]) 
(trainingData, testData) = testDF.randomSplit([0.8,0.2]) 
from pyspark.ml.regression import LinearRegression 
lr=LinearRegression() 
model=lr.fit(trainingData) 

和錯誤：

IllegalArgumentException: u'requirement failed: Column features must be of type org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7 but was actually org.apache.spark.mllib.linalg.VectorUDT@f71b0bce.' 

我應該如何變換，從.mllib載體功能.ml類型？

Answer 1

從Spark2.0使用

from pyspark.ml.linalg import Vectors, VectorUDT 

，而不是

from pyspark.mllib.linalg import Vectors, VectorUDT