问题描述
我正在尝试加载SVM文件并将其转换为DataFrame，因此我可以使用Spark中的ML模块(Pipeline ML).我刚刚在Ubuntu 14.04(未配置spark-env.sh)上安装了新的Spark 1.5.0.
I'm trying to load an SVM file and convert it to a DataFrame so I can use the ML module (Pipeline ML) from Spark.I've just installed a fresh Spark 1.5.0 on an Ubuntu 14.04 (no spark-env.sh configured).
我的my_script.py是:
from pyspark.mllib.util import MLUtils
from pyspark import SparkContext
sc = SparkContext("local", "Teste Original")
data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()
并且我正在使用:./spark-submit my_script.py
我得到了错误:
Traceback (most recent call last):
File "/home/fred-spark/spark-1.5.0-bin-hadoop2.6/pipeline_teste_original.py", line 34, in <module>
data = MLUtils.loadLibSVMFile(sc, "/home/fred-spark/svm_capture").toDF()
AttributeError: 'PipelinedRDD' object has no attribute 'toDF'
我不明白的是，如果我跑步:
What I can't understand is that if I run:
data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()
直接在PySpark外壳内，即可正常工作.
directly inside PySpark shell, it works.
推荐答案
toDF方法是一个猴子补丁在SparkSession(在1.x中为SQLContext构造函数)构造函数中执行，因此必须使用创建一个首先SQLContext(或SparkSession)
toDF method is a monkey patch executed inside SparkSession (SQLContext constructor in 1.x) constructor so to be able to use it you have to create a SQLContext (or SparkSession) first:
# SQLContext or HiveContext in Spark 1.x
from pyspark.sql import SparkSession
from pyspark import SparkContext
sc = SparkContext()
rdd = sc.parallelize([("a", 1)])
hasattr(rdd, "toDF")
## False
spark = SparkSession(sc)
hasattr(rdd, "toDF")
## True
rdd.toDF().show()
## +---+---+
## | _1| _2|
## +---+---+
## | a| 1|
## +---+---+
更不用说，首先需要SQLContext或SparkSession与DataFrames一起使用.
Not to mention you need a SQLContext or SparkSession to work with DataFrames in the first place.
这篇关于'PipelinedRDD'对象在PySpark中没有属性'toDF'的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！