From: Nate E TeBlunthuis Date: Tue, 10 Nov 2020 21:12:11 +0000 (-0800) Subject: Improvements to idf code X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/6edd1557491a0d08302ba7506dbccc36f620b5e1?ds=inline Improvements to idf code --- diff --git a/idf_authors.py b/idf_authors.py index 379de5a..92a4965 100644 --- a/idf_authors.py +++ b/idf_authors.py @@ -25,19 +25,19 @@ authors = authors.withColumn('author_id',f.monotonically_increasing_id()) # map terms to indexes in the tfs and the idfs -df = df.join(terms,on='author') +df = df.join(authors,on='author') -idf = idf.join(terms,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('term_id','tf_idf')).alias('tfidf_maps')) + +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.parquet('/gscratch/comdata/users/nathante/test_tfidf_authors.parquet',mode='overwrite',compression='snappy') +df.write.json('/gscratch/comdata/users/nathante/test_tfidf_authors.parquet',mode='overwrite',compression='snappy') diff --git a/idf_comments.py b/idf_comments.py index d29be80..b3e5624 100644 --- a/idf_comments.py +++ b/idf_comments.py @@ -1,58 +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") -max_subreddit_week_terms = df.groupby(['subreddit','week']).max('tf') -max_subreddit_week_terms = max_subreddit_week_terms.withColumnRenamed('max(tf)','sr_week_max_tf') - -df = df.join(max_subreddit_week_terms, ['subreddit','week']) +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} -df = df.withColumn("relative_tf", df.tf / df.sr_week_max_tf) +# 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')) -# group by term / week -idf = df.groupby(['term','week']).count() +max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique +max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf') -idf = idf.withColumnRenamed('count','idf') +df = df.join(max_subreddit_terms, on='subreddit') -# 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 -terms = idf.select('term').distinct() -terms = terms.withColumn('term_id',f.monotonically_increasing_id()) +df = df.withColumn("relative_tf", df.tf / df.sr_max_tf) +# group by term. term is unique +idf = df.groupby(['term']).count() -# print('collected terms') +N_docs = df.select('subreddit').distinct().count() -# terms = [t.term for t in terms] -# NTerms = len(terms) -# term_id_map = {term:i for i,term in enumerate(sorted(terms))} +idf = idf.withColumn('idf',f.log(N_docs/f.col('count'))) -# term_id_map = spark.sparkContext.broadcast(term_id_map) - -# print('term_id_map is broadcasted') +# 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 -# def map_term(x): -# return term_id_map.value[x] +# make subreddit ids +subreddits = df.select(['subreddit']).distinct() +subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit"))) -# map_term_udf = f.udf(map_term) +df = df.join(subreddits,on='subreddit') # map terms to indexes in the tfs and the idfs -df = df.join(terms,on='term') +df = df.join(terms,on='term') # subreddit-term-id is unique idf = idf.join(terms,on='term') -# join on subreddit/term/week to create tf/dfs indexed by term -df = df.join(idf, on=['term_id','week','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",df.relative_tf / df.sr_week_max_tf) - -df = df.groupby(['subreddit','week']).agg(f.collect_list(f.struct('term_id','tf_idf')).alias('tfidf_maps')) - -df = df.withColumn('tfidf_vec', f.map_from_entries('tfidf_maps')) +df = df.withColumn("tf_idf", (0.5 + (0.5 * df.relative_tf) * df.idf)) -# output: subreddit | week | tf/df -df.write.parquet('/gscratch/comdata/users/nathante/test_tfidf.parquet',mode='overwrite',compression='snappy') +df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')