[英]Pyspark ML pipeline error when fitting pipeline
我第一次从三个pyspark.ml.feature(tokenizer,CV,idf)构建了一条管道,所有丁字裤都运行良好,但是第二次尝试告诉我Py4JJavaError:调用o175.fit时发生错误。 有谁知道这个错误的原因是什么谢谢
import findspark
findspark.init()
import pyspark.sql.types as typ
import pyspark as ps
from pyspark.sql import SparkSession
import pandas as pd
import numpy as np
import warnings
from pyspark.sql import SQLContext
sparkSession = SparkSession.builder \
.master("local[2]") \
.appName("Pyspark Sentiment") \
.getOrCreate()
df = sparkSession.read.load('data/Microblog_Trialdata.csv',
format='com.databricks.spark.csv',
header='true',
inferSchema='true')
df=df.select("sentiment score","spans")
(train_set, val_set, test_set) = df.randomSplit([0.6, 0.2, 0.2], seed = 42)
from pyspark.ml.feature import HashingTF, IDF, Tokenizer ,CountVectorizer
from pyspark.ml.feature import StringIndexer
from pyspark.ml import Pipeline
tokenizer = Tokenizer(inputCol="spans", outputCol="words")
CV = CountVectorizer(vocabSize=2**11, inputCol="words", outputCol='cv_')
idf = IDF(inputCol='cv_', outputCol="features", minDocFreq=5) #minDocFreq:
remove sparse terms
#model=CV.fit(data)
#vo=model.vocabulary
#print(type(vo))
pipeline = Pipeline(stages=[tokenizer, CV, idf])
pipelineFit = pipeline.fit(train_set)
train_df = pipelineFit.transform(train_set)
val_df = pipelineFit.transform(val_set)
train_df.select("cv_").show(5,truncate=False)
train_df.show(5)
在train_set中但在val_set中看不到的单词可能导致错误。 Count Vectorizer具有handleInvalid选项来解决此问题。
# this will ignore not seen words
CV = CountVectorizer(vocabSize=2**11, inputCol="words", outputCol='cv_',handleInvalid='skip')
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.