X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/6edd1557491a0d08302ba7506dbccc36f620b5e1..772f3a8fbd54c9d2c5e0d10889c272f44fef127a:/tfidf_comments.py

diff --git a/tfidf_comments.py b/tfidf_comments.py
new file mode 100644
index 0000000..b3e5624
--- /dev/null
+++ b/tfidf_comments.py
@@ -0,0 +1,53 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+
+## TODO:need to exclude automoderator / bot posts.
+## TODO:need to exclude better handle hyperlinks. 
+
+spark = SparkSession.builder.getOrCreate()
+df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp")
+
+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.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')