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).
My my_script.py
is:
from pyspark.mllib.util import MLUtils
from pyspark import SparkContext
sc = SparkContext("local", "Teste Original")
data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()
and I'm running using: ./spark-submit my_script.py
And I get the error:
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'
What I can't understand is that if I run:
data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()
directly inside PySpark shell, it works.
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|
## +---+---+
Not to mention you need a SQLContext
or SparkSession
to work with DataFrames
in the first place.