Apache spark 用Spark将句子编码为序列模型
我正在进行文本分类,并使用Apache spark 用Spark将句子编码为序列模型,apache-spark,parallel-processing,pyspark,text-classification,Apache Spark,Parallel Processing,Pyspark,Text Classification,我正在进行文本分类,并使用pyspark.ml.feature.Tokenizer标记文本。但是,CountVectorizer将标记化的单词列表转换为单词包模型,而不是序列模型 假设我们有以下带有列id和文本的DataFrame: id | texts ----|---------- 0 | Array("a", "b", "c") 1 | Array("a", "b", "b", "c", "a") each row in texts is a document of type A
pyspark.ml.feature.Tokenizer
标记文本。但是,CountVectorizer
将标记化的单词列表转换为单词包模型,而不是序列模型
假设我们有以下带有列id和文本的DataFrame:
id | texts
----|----------
0 | Array("a", "b", "c")
1 | Array("a", "b", "b", "c", "a")
each row in texts is a document of type Array[String]. Invoking fit of CountVectorizer produces a CountVectorizerModel with vocabulary (a, b, c). Then the output column “vector” after transformation contains:
id | texts | vector
----|---------------------------------|---------------
0 | Array("a", "b", "c") | (3,[0,1,2],[1.0,1.0,1.0])
1 | Array("a", "b", "b", "c", "a") | (3,[0,1,2],[2.0,2.0,1.0])
我想要的是(第1行)
那么,我是否可以编写自定义函数来并行运行编码呢?或者除了使用spark,还有其他库可以并行执行吗?您可以使用
StringIndexer
和explode
:
df = spark_session.createDataFrame([
Row(id=0, texts=["a", "b", "c"]),
Row(id=1, texts=["a", "b", "b", "c", "a"])
])
data = df.select("id", explode("texts").alias("texts"))
indexer = StringIndexer(inputCol="texts", outputCol="indexed", stringOrderType="alphabetAsc")
indexer\
.fit(data)\
.transform(data)\
.groupBy("id")\
.agg(collect_list("texts").alias("texts"), collect_list("indexed").alias("vector"))\
.show(20, False)
输出:
+---+---------------+-------------------------+
|id |texts |vector |
+---+---------------+-------------------------+
|0 |[a, b, c] |[0.0, 1.0, 2.0] |
|1 |[a, b, b, c, a]|[0.0, 1.0, 1.0, 2.0, 0.0]|
+---+---------------+-------------------------+
+---+---------------+-------------------------+
|id |texts |vector |
+---+---------------+-------------------------+
|0 |[a, b, c] |[0.0, 1.0, 2.0] |
|1 |[a, b, b, c, a]|[0.0, 1.0, 1.0, 2.0, 0.0]|
+---+---------------+-------------------------+