Ngram/Tokenizer Transofmed Dataframe 上的 Pyspark VectorAssembler

Question

如果我有一个包含字段 ['did','doc'] 的数据框，例如

data = sc.parallelize(['This is a test',
                   'This is also a test',
                   'These sentence are tests',
                   'This tests these sentences'])\
         .zipWithIndex()\
         .map(lambda x: (x[1],x[0]))\
         .toDF(['did','doc'])
data.show()
+---+--------------------+--------------------+
|did|                 doc|               words|
+---+--------------------+--------------------+
|  0|      This is a test| [this, is, a, test]|
|  1| This is also a test|[this, is, also, ...|
|  2|These sentence ar...|[these, sentence,...|
|  3|This tests these ...|[this, tests, the...|
+---+--------------------+--------------------+

我对该文档进行了一些转换，例如标记和查找 2-gram：

data = Tokenizer(inputCol = 'doc',outputCol = 'words').transform(data)
data = NGram(n=2,inputCol = 'words',outputCol='grams').transform(data)
data.show()
+---+--------------------+--------------------+--------------------+
|did|                 doc|               words|               grams|
+---+--------------------+--------------------+--------------------+
|  0|      This is a test| [this, is, a, test]|[this is, is a, a...|
|  1| This is also a test|[this, is, also, ...|[this is, is also...|
|  2|These sentence ar...|[these, sentence,...|[these sentence, ...|
|  3|This tests these ...|[this, tests, the...|[this tests, test...|
+---+--------------------+--------------------+--------------------+

最后我想用 VectorAssembler 将两个 gram 和单词组合成一列特征：

data = VectorAssembler(inputCol=['words','grams'],
                       outputCol='features').transform(data)

然后我收到以下错误：

Py4JJavaError: An error occurred while calling o504.transform.
: java.lang.IllegalArgumentException: Data type ArrayType(StringType,true) is not supported.

因为 VectorAssembler 不喜欢使用字符串列表。为了解决这个问题，我可以将数据框放到一个 rdd 中，将 rdd 映射到适当的行，然后将其重新压缩到一个数据框中，一个 la

data = data.rdd.map(lambda x: Row(did = x['did'], 
           features = x['words']+x['grams'])) .toDF(['did','features'])

这对于这个小型数据集来说不是问题，但对于大型数据集来说却过于昂贵。

有没有比上述方法更有效地实现这一目标的方法？

Answer 1

您可以像这样使用 udf 创建 features 列

import pyspark.sql.functions as f
import pyspark.sql.types as t


udf_add = f.udf(lambda x,y: x+y, t.ArrayType(t.StringType()))
data.withColumn('features', udf_add('words','grams')).select('features').show()

[Row(features=['this', 'is', 'a', 'test', 'this is', 'is a', 'a test']),
Row(features=['this', 'is', 'also', 'a', 'test', 'this is', 'is also', 'also a', 'a test']),
Row(features=['these', 'sentence', 'are', 'tests', 'these sentence', 'sentence are', 'are tests']),
Row(features=['this', 'tests', 'these', 'sentences', 'this tests', 'tests these', 'these sentences'])]