“RDD”对象没有属性“_jdf”pyspark RDD

我是 pyspark 的新手。我想对文本文件执行一些机器学习。

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)

对于我的最后一个命令，我收到错误 “AttributeError：'RDD'对象没有属性'_jdf'

在此输入图像描述 https://i.stack.imgur.com/PlBOI.png

谁能帮助我吗？ 谢谢

你不应该使用rdd with CountVectorizer。相反，你应该尝试形成单词数组 in the dataframe本身作为

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)

然后它应该可以工作，这样你就可以打电话transform充当

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

现在，如果您想坚持转换为旧样式RDD风格那么你必须修改某些代码行。以下是您修改后的完整代码（工作）

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)

但我建议你坚持dataframe way.