问题描述
我想对pyspark数据帧中的字符串列进行一些NLP分析。
df:
year month u_id rating_score p_id review
2010 09 tvwe 1 p_5 I do not like it because its size is not for me.
2011 11 frsa 1 p_7 I am allergic to the peanut elements.
2015 5 ybfd 1 p_2 It is a repeated one,please no more.
2016 7 tbfb 2 p_2 It is not good for my oil hair.
每个p_id代表一个项目。 每个u_id可能对每个项目都有一些评论。评论可能是几个字,一个句子或一个段落,甚至是表情符号。
我想找到物品被评为低或高的根本原因。 例如,有多少“ u_id”抱怨物品的尺寸,化学元素过敏或其他与物品功能有关的问题。
从How to iterate over rows in a DataFrame in Pandas,我了解到将数据帧转换为numpy数组,然后使用矢量化进行NLP分析,效率更高。
我正在尝试使用SparkNLP为年,月,u_id,p_id的每个注释提取形容词和名词短语。
我不确定如何应用numpy向量化以非常有效地做到这一点。
我的py3代码:
from sparknlp.pretrained import PretrainedPipeline
df = spark.sql('select year,month,u_id,p_id,comment from MY_DF where rating_score = 1 and isnull(comment) = false')
import numpy as np
trainseries = df['comment'].apply(lambda x : np.array(x.toArray())).as_matrix().reshape(-1,1)
text = np.apply_along_axis(lambda x : x[0],1,trainseries) # TypeError: 'Column' object is not callable
pipeline_dl = PretrainedPipeline('explain_document_dl',lang='en') #
result = pipeline_dl.fullAnnotate(text)
该代码不起作用。 我还需要在向量化中保留其他列(例如,年,月,u_id,p_id),并确保NLP分析结果可以与年,月,u_id,p_id保持一致。
我不喜欢这样 How to convert a pyspark dataframe column to numpy array,因为collect()太慢。
谢谢
解决方法
IIUC,您不需要Numpy(Spark内部处理矢量化),只需执行transform
,然后从结果数据框中选择并过滤适当的信息即可:
from sparknlp.pretrained import PretrainedPipeline
df = spark.sql('select year,month,u_id,p_id,comment from MY_DF where rating_score = 1 and isnull(comment) = false')
df1 = df.withColumnRenamed('comment','text')
pipeline_dl = PretrainedPipeline('explain_document_dl',lang='en')
result = pipeline_dl.transform(df1)
df_new = result.selectExpr(
*df1.columns,'transform(filter(pos,p -> p.result rlike "^(?:NN|JJ)"),x -> x.metadata.word) as words'
)
输出:
df_new.show(10,0)
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+
|years|month|u_id|rating_score|p_id|text |words |
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+
|2010 |09 |tvwe|1 |p_5 |I do not like it because its size is not for me.|[size] |
|2011 |11 |frsa|1 |p_7 |I am allergic to the peanut elements. |[allergic,peanut,elements]|
|2015 |5 |ybfd|1 |p_2 |It is a repeated one,please no more. |[more] |
|2016 |7 |tbfb|2 |p_2 |It is not good for my oil hair. |[good,oil,hair] |
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+
注意:
(1)result = pipeline.fullAnnotate(df,'comment')
是将comment
重命名为text
然后执行pipeline.transform(df1)
的快捷方式。 fullAnnotate的第一个参数可以是DataFrame,List或String。
(2)https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html中的POS标签列表