Refactor tfidf code to for code resuse.

similarities_helper.py

@@ -0,0 +1,116 @@
+from pyspark.sql import Window
+from pyspark.sql import functions as f
+from enum import Enum
+from pyspark.mllib.linalg.distributed import CoordinateMatrix
+
+class tf_weight(Enum):
+    MaxTF = 1
+    Norm05 = 2
+
+
+def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    if min_df is None:
+        min_df = 0.1 * len(included_subreddits)
+
+    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
+    tfidf = tfidf.cache()
+
+    # reset the subreddit ids
+    sub_ids = tfidf.select('subreddit_id').distinct()
+    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
+    tfidf = tfidf.join(sub_ids,'subreddit_id')
+
+    # only use terms in at least min_df included subreddits
+    new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
+#    new_count = new_count.filter(f.col('new_count') >= min_df)
+    tfidf = tfidf.join(new_count,term_id,how='inner')
+    
+    # reset the term ids
+    term_ids = tfidf.select([term_id]).distinct()
+    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
+    tfidf = tfidf.join(term_ids,term_id)
+
+    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
+    # tfidf = tfidf.withColumnRenamed("idf","idf_old")
+    # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
+    tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
+
+    # step 1 make an rdd of entires
+    # sorted by (dense) spark subreddit id
+    #    entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
+ 
+    n_partitions = int(len(included_subreddits)*2 / 5)
+
+    entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
+
+    # put like 10 subredis in each partition
+
+    # step 2 make it into a distributed.RowMatrix
+    coordMat = CoordinateMatrix(entries)
+
+    coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
+
+    # this needs to be an IndexedRowMatrix()
+    mat = coordMat.toRowMatrix()
+
+    #goal: build a matrix of subreddit columns and tf-idfs rows
+    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
+
+    return (sim_dist, tfidf)
+
+
+def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+
+    term = term_colname
+    term_id = term + '_id'
+    # 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()
+
+    # add a little smoothing to the idf
+    idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
+
+    # 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
+    
+    if tf_family == tf_weight.MaxTF:
+        df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
+    else: # tf_fam = tf_weight.Norm05
+        df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
+
+    return df
+
+

tfidf_authors.py

@@ -1,43 +1,19 @@
-from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
+from similarities_helper import build_tfidf_dataset
 
+## 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_authors.parquet_temp/")
 
-max_subreddit_week_authors = df.groupby(['subreddit','week']).max('tf')
+df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/part-00000-d61007de-9cbe-4970-857f-b9fd4b35b741-c000.snappy.parquet")
-max_subreddit_week_authors = max_subreddit_week_authors.withColumnRenamed('max(tf)','sr_week_max_tf')
 
-df = df.join(max_subreddit_week_authors, ['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.filter(df.author != '[deleted]')
+df = df.filter(df.author != 'AutoModerator')
 
-df = df.withColumn("relative_tf", df.tf / df.sr_week_max_tf)
+df = build_tfidf_dataset(df, include_subs, 'author')
 
-# group by term / week
+df.cache()
-idf = df.groupby(['author','week']).count()
 
-idf = idf.withColumnRenamed('count','idf')
+df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet',mode='overwrite',compression='snappy')
-
-# 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')

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 similarities_helper import build_tfidf_dataset
 
 ## 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}
 
-# aggregate counts by week. now subreddit-term is distinct
+df = build_tfidf_dataset(df, include_subs, 'term')
-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')