add note to try other tf normalization strategies.

[cdsc_reddit.git] / tfidf_authors.py

diff --git a/tfidf_authors.py b/tfidf_authors.py
deleted file mode 100644 (file)
index 92a4965..0000000
--- a/tfidf_authors.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-
-spark = SparkSession.builder.getOrCreate()
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
-
-max_subreddit_week_authors = df.groupby(['subreddit','week']).max('tf')
-max_subreddit_week_authors = max_subreddit_week_authors.withColumnRenamed('max(tf)','sr_week_max_tf')
-
-df = df.join(max_subreddit_week_authors, ['subreddit','week'])
-
-df = df.withColumn("relative_tf", df.tf / df.sr_week_max_tf)
-
-# group by term / week
-idf = df.groupby(['author','week']).count()
-
-idf = idf.withColumnRenamed('count','idf')
-
-# output: term | week | df
-#idf.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
-
-# collect the dictionary to make a pydict of terms to indexes
-authors = idf.select('author').distinct()
-authors = authors.withColumn('author_id',f.monotonically_increasing_id())
-
-
-# map terms to indexes in the tfs and the idfs
-df = df.join(authors,on='author')
-
-idf = idf.join(authors,on='author')
-
-# join on subreddit/term/week to create tf/dfs indexed by term
-df = df.join(idf, on=['author_id','week','author'])
-
-# agg terms by subreddit to make sparse tf/df vectors
-df = df.withColumn("tf_idf",df.relative_tf / df.sr_week_max_tf)
- 
-df = df.groupby(['subreddit','week']).agg(f.collect_list(f.struct('author_id','tf_idf')).alias('tfidf_maps'))
- 
-df = df.withColumn('tfidf_vec', f.map_from_entries('tfidf_maps'))
-
-# output: subreddit | week | tf/df
-df.write.json('/gscratch/comdata/users/nathante/test_tfidf_authors.parquet',mode='overwrite',compression='snappy')