'RDD' object has no attribute '_jdf

Question

I'm new in pyspark. I would like to perform some machine Learning on a text file.

from pyspark import Row
from pyspark.context import SparkContext
from pyspark.sql.session import SparkSession
from pyspark import SparkConf
sc = SparkContext
spark = SparkSession.builder.appName("ML").getOrCreate()

train_data = spark.read.text("20ng-train-all-terms.txt")
td= train_data.rdd #transformer df to rdd
tr_data= td.map(lambda line: line.split()).map(lambda words: Row(label=words[0],words=words[1:]))
from pyspark.ml.feature import CountVectorizer

vectorizer = CountVectorizer(inputCol ="words", outputCol="bag_of_words")
vectorizer_transformer = vectorizer.fit(td)

and for my last command, i obtain the error "AttributeError: 'RDD' object has no attribute '_jdf'

enter image description here

can anyone help me please. thank you

Answer 1

You shouldn't be using rdd with CountVectorizer. Instead you should try to form the array of words in the dataframe itself as

train_data = spark.read.text("20ng-train-all-terms.txt")

from pyspark.sql import functions as F
td= train_data.select(F.split("value", " ").alias("words")).select(F.col("words")[0].alias("label"), F.col("words"))

from pyspark.ml.feature import CountVectorizer
vectorizer = CountVectorizer(inputCol="words", outputCol="bag_of_words")
vectorizer_transformer = vectorizer.fit(td)

And then it should work so that you can call transform function as

vectorizer_transformer.transform(td).show(truncate=False)

Now, if you want to stick to the old style of converting to the rdd style then you have to modify certain lines of code. Following is the modified complete code (working) of yours

from pyspark import Row
from pyspark.context import SparkContext
from pyspark.sql.session import SparkSession
from pyspark import SparkConf
sc = SparkContext
spark = SparkSession.builder.appName("ML").getOrCreate()

train_data = spark.read.text("20ng-train-all-terms.txt")
td= train_data.rdd #transformer df to rdd
tr_data= td.map(lambda line: line[0].split(" ")).map(lambda words: Row(label=words[0], words=words[1:])).toDF()
from pyspark.ml.feature import CountVectorizer

vectorizer = CountVectorizer(inputCol="words", outputCol="bag_of_words")
vectorizer_transformer = vectorizer.fit(tr_data)

But I would suggest you to stick with dataframe way.