Reuse code for term and author cosine similarity.

[cdsc_reddit.git] / tfidf_comments.py

diff --git a/tfidf_comments.py b/tfidf_comments.py
index b3e5624a3a5ae6e8585a45e9ffe4ee756a1cde2c..9e1a437d417df955b6ea5f84c9f5c9b2b87b2136 100644 (file)
--- a/tfidf_comments.py
+++ b/tfidf_comments.py
@@ -1,6 +1,7 @@
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window

+from similarities_helper import build_tfidf_dataset

 
 ## TODO:need to exclude automoderator / bot posts.
 ## TODO:need to exclude better handle hyperlinks. 
 
 ## TODO:need to exclude automoderator / bot posts.
 ## TODO:need to exclude better handle hyperlinks. 


@@ -11,43 +12,6 @@ df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parq
 include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
 include_subs = {s.strip('\n') for s in include_subs}
 
 include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
 include_subs = {s.strip('\n') for s in include_subs}
 

-# aggregate counts by week. now subreddit-term is distinct
-df = df.filter(df.subreddit.isin(include_subs))
-df = df.groupBy(['subreddit','term']).agg(f.sum('tf').alias('tf'))
-
-max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
-max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
-
-df = df.join(max_subreddit_terms, on='subreddit')
-
-df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
-
-# group by term. term is unique
-idf = df.groupby(['term']).count()
-
-N_docs = df.select('subreddit').distinct().count()
-
-idf = idf.withColumn('idf',f.log(N_docs/f.col('count')))
-
-# collect the dictionary to make a pydict of terms to indexes
-terms = idf.select('term').distinct() # terms are distinct
-terms = terms.withColumn('term_id',f.row_number().over(Window.orderBy("term"))) # term ids are distinct
-
-# make subreddit ids
-subreddits = df.select(['subreddit']).distinct()
-subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
-
-df = df.join(subreddits,on='subreddit')
-
-# map terms to indexes in the tfs and the idfs
-df = df.join(terms,on='term') # subreddit-term-id is unique
-
-idf = idf.join(terms,on='term')
-
-# join on subreddit/term to create tf/dfs indexed by term
-df = df.join(idf, on=['term_id','term'])
-
-# agg terms by subreddit to make sparse tf/df vectors
-df = df.withColumn("tf_idf", (0.5 + (0.5 * df.relative_tf) * df.idf))
+df = build_tfidf_dataset(df, include_subs, 'term')

 
 df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')
 
 df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')