Calculate new column in spark Dataframe, crossing a tokens list column in df1 with a text column in df2 with pyspark

问题

I am using spark 2.4.5 and I need to calculate the sentiment score from a token list column (MeaningfulWords column) of df1, according to the words in df2 (spanish sentiment dictionary). In df1 I must create a new column with the scores list of tokens and another column with the mean of scores (sum of scores / count words) of each record. If any token in the list (df1) is not in the dictionary (df2), zero is scored.

The Dataframes looks like this:

df1.select("ID","MeaningfulWords").show(truncate=True, n=5)
+------------------+------------------------------+
|                ID|               MeaningfulWords|
+------------------+------------------------------+
|abcde00000qMQ00001|[casa, alejado, buen, gusto...|
|abcde00000qMq00002|[clientes, contentos, servi...|
|abcde00000qMQ00003|                 [resto, bien]|
|abcde00000qMQ00004|[mal, servicio, no, antiend...|
|abcde00000qMq00005|[gestion, adecuada, proble ...|
+------------------+------------------------------+

df2.show(5)
+-----+----------+
|score|      word|
+-----+----------+
| 1.68|abandonado|
| 3.18|    abejas|
|  2.8|    aborto|
| 2.46| abrasador|
| 8.13|    abrazo|
+-----+----------+

The new columns to add in df1, should look like this:

+------------------+---------------------+
|         MeanScore|            ScoreList|
+------------------+---------------------+
|              2.95|[3.10, 2.50, 1.28,...|
|              2.15|[1.15, 3.50, 2.75,...|
|              2.75|[4.20, 1.00, 1.75,...|
|              3.25|[3.25, 2.50, 3.20,...|
|              3.15|[2.20, 3.10, 1.28,...|
+------------------+---------------------+

I have reviewed some options using .join, but using columns with different data types gives error. I have also tried converting the Dataframes to RDD and calling a function:

def map_words_to_values(review_words, dict_df):
return [dict_df[word] for word in review_words if word in dict_df]

RDD1=swRemoved.rdd.map(list) 
RDD2=Dict_df.rdd.map(list)

reviewsRDD_dict_values = RDD1.map(lambda tupple: (tupple[0], map_words_to_values(tupple[1], RDD2)))
reviewsRDD_dict_values.take(3)

But with this option I get the error:

PicklingError: Could not serialize object: Exception: It appears that you are attempting to broadcast an RDD or reference an RDD from an action or transformation. RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(lambda x: rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.

I have found some examples to score text using afinn library. But it doesn't works with spanish text.

I wanna try to utilize native functions of pyspark instead of using udfs to avoid affect the performance, if it's possible. But I'm a begginer in spark and I would like to find the spark way to do that.

回答1:

You could do this by first joining using array_contains word, then groupBy with aggregations of first, collect_list, and mean.(spark2.4+)

welcome to SO

df1.show()

#+------------------+----------------------------+
#|ID                |MeaningfulWords             |
#+------------------+----------------------------+
#|abcde00000qMQ00001|[casa, alejado, buen, gusto]|
#|abcde00000qMq00002|[clientes, contentos, servi]|
#|abcde00000qMQ00003|[resto, bien]               |
#+------------------+----------------------------+

df2.show()

#+-----+---------+
#|score|     word|
#+-----+---------+
#| 1.68|     casa|
#|  2.8|  alejado|
#| 1.03|     buen|
#| 3.68|    gusto|
#| 0.68| clientes|
#|  2.1|contentos|
#| 2.68|    servi|
#| 1.18|    resto|
#| 1.98|     bien|
#+-----+---------+


from pyspark.sql import functions as F
df1.join(df2, F.expr("""array_contains(MeaningfulWords,word)"""),'left')\
   .groupBy("ID").agg(F.first("MeaningfulWords").alias("MeaningfullWords")\
                      ,F.collect_list("score").alias("ScoreList")\
                      ,F.mean("score").alias("MeanScore"))\
                      .show(truncate=False)

#+------------------+----------------------------+-----------------------+------------------+
#|ID                |MeaningfullWords            |ScoreList              |MeanScore         |
#+------------------+----------------------------+-----------------------+------------------+
#|abcde00000qMQ00003|[resto, bien]               |[1.18, 1.98]           |1.58              |
#|abcde00000qMq00002|[clientes, contentos, servi]|[0.68, 2.1, 2.68]      |1.8200000000000003|
#|abcde00000qMQ00001|[casa, alejado, buen, gusto]|[1.68, 2.8, 1.03, 3.68]|2.2975            |
#+------------------+----------------------------+-----------------------+------------------+

来源：https://stackoverflow.com/questions/61687997/calculate-new-column-in-spark-dataframe-crossing-a-tokens-list-column-in-df1-wi