idf_authors.py

   1 from pyspark.sql import functions as f
   2 from pyspark.sql import SparkSession
   3
   4 spark = SparkSession.builder.getOrCreate()
   5 df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
   6
   7 max_subreddit_week_authors = df.groupby(['subreddit','week']).max('tf')
   8 max_subreddit_week_authors = max_subreddit_week_authors.withColumnRenamed('max(tf)','sr_week_max_tf')
   9
  10 df = df.join(max_subreddit_week_authors, ['subreddit','week'])
  11
  12 df = df.withColumn("relative_tf", df.tf / df.sr_week_max_tf)
  13
  14 # group by term / week
  15 idf = df.groupby(['author','week']).count()
  16
  17 idf = idf.withColumnRenamed('count','idf')
  18
  19 # output: term | week | df
  20 #idf.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
  21
  22 # collect the dictionary to make a pydict of terms to indexes
  23 authors = idf.select('author').distinct()
  24 authors = authors.withColumn('author_id',f.monotonically_increasing_id())
  25
  26
  27 # map terms to indexes in the tfs and the idfs
  28 df = df.join(terms,on='author')
  29
  30 idf = idf.join(terms,on='author')
  31
  32 # join on subreddit/term/week to create tf/dfs indexed by term
  33 df = df.join(idf, on=['author_id','week','author'])
  34
  35 # agg terms by subreddit to make sparse tf/df vectors
  36 df = df.withColumn("tf_idf",df.relative_tf / df.sr_week_max_tf)
  37
  38 df = df.groupby(['subreddit','week']).agg(f.collect_list(f.struct('term_id','tf_idf')).alias('tfidf_maps'))
  39
  40 df = df.withColumn('tfidf_vec', f.map_from_entries('tfidf_maps'))
  41
  42 # output: subreddit | week | tf/df
  43 df.write.parquet('/gscratch/comdata/users/nathante/test_tfidf_authors.parquet',mode='overwrite',compression='snappy')