特征抽取----Word2Vec

#导入相关的库

from pyspark.ml.feature import Word2Vec

from pyspark.sql import SparkSession

 

#配置spark

spark = SparkSession.builder.master("local").appName("Word2VecDemo").getOrCreate()

#创建三个词语序列，每个代表一个文档

documentDF = spark.createDataFrame([

("Hi I heard about Spark".split(" "), ),

("I wish Java could use case classes".split(" "), ),

("Logistic regression models are neat".split(" "), )

], ["text"])

#新建一个Word2Vec ，他是一个Estimator

word2Vec = Word2Vec(vectorSize =3, minCount=0, inputCol="text", outputCol="result")

#读入训练集，用fit() 方法生成Word2VecModel

model = word2Vec.fit(documentDF)

#利用生成的Word2VecModel转成特征向量

result =model.transform(documentDF)

for row in result.collect():

　　text, vector = row

　　print("Text: [%s] => \nVector: %s\n" % (", ".join(text), str(vector)))

 

关于超参数的设置: