From: Nathan TeBlunthuis <nathante@uw.edu>
Date: Wed, 6 Apr 2022 18:11:11 +0000 (-0700)
Subject: git-annex in
X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/197518a222a321a8027c3dc5a4121350c47d0779?ds=inline;hp=-c

git-annex in
---

197518a222a321a8027c3dc5a4121350c47d0779
diff --git a/datasets/checkpoint_parallelsql.sbatch b/datasets/checkpoint_parallelsql.sbatch
deleted file mode 100644
index dd61e65..0000000
--- a/datasets/checkpoint_parallelsql.sbatch
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-## parallel_sql_job.sh
-#SBATCH --job-name=tf_subreddit_comments
-## Allocation Definition
-#SBATCH --account=comdata-ckpt
-#SBATCH --partition=ckpt
-## Resources
-## Nodes. This should always be 1 for parallel-sql.
-#SBATCH --nodes=1    
-## Walltime (12 hours)
-#SBATCH --time=12:00:00
-## Memory per node
-#SBATCH --mem=32G
-#SBATCH --cpus-per-task=4
-#SBATCH --ntasks=1
-#SBATCH -D /gscratch/comdata/users/nathante/cdsc-reddit
-source ./bin/activate
-module load parallel_sql
-echo $(which perl)
-conda list pyarrow
-which python3
-#Put here commands to load other modules (e.g. matlab etc.)
-#Below command means that parallel_sql will get tasks from the database
-#and run them on the node (in parallel). So a 16 core node will have
-#16 tasks running at one time.
-parallel-sql --sql -a parallel --exit-on-term --jobs 4
diff --git a/datasets/comments_2_parquet.sh b/datasets/comments_2_parquet.sh
index 56ecc4d..d61eb65 100755
--- a/datasets/comments_2_parquet.sh
+++ b/datasets/comments_2_parquet.sh
@@ -1,10 +1,10 @@
+#!/usr/bin/env bash
 ## needs to be run by hand since i don't have a nice way of waiting on a parallel-sql job to complete 
 
-#!/usr/bin/env bash
 echo "#!/usr/bin/bash" > job_script.sh
 #echo "source $(pwd)/../bin/activate" >> job_script.sh
 echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
 
-srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 --pty job_script.sh
+srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 --pty job_script.sh
 
 start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py
diff --git a/datasets/comments_2_parquet_part1.py b/datasets/comments_2_parquet_part1.py
index d3c7b7c..6960986 100755
--- a/datasets/comments_2_parquet_part1.py
+++ b/datasets/comments_2_parquet_part1.py
@@ -1,12 +1,15 @@
 #!/usr/bin/env python3
+import os
 import json
 from datetime import datetime
 from multiprocessing import Pool
 from itertools import islice
-from helper import find_dumps, open_fileset
+from helper import open_input_file, find_dumps
 import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
+from pathlib import Path
+import fire
 
 def parse_comment(comment, names= None):
     if names is None:
@@ -46,70 +49,63 @@ def parse_comment(comment, names= None):
 
 #    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
 
-dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
-
-files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
-
-pool = Pool(28)
-
-stream = open_fileset(files)
-
-N = int(1e4)
-
-rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
-
-schema = pa.schema([
-    pa.field('id', pa.string(), nullable=True),
-    pa.field('subreddit', pa.string(), nullable=True),
-    pa.field('link_id', pa.string(), nullable=True),
-    pa.field('parent_id', pa.string(), nullable=True),
-    pa.field('created_utc', pa.timestamp('ms'), nullable=True),
-    pa.field('author', pa.string(), nullable=True),
-    pa.field('ups', pa.int64(), nullable=True),
-    pa.field('downs', pa.int64(), nullable=True),
-    pa.field('score', pa.int64(), nullable=True),
-    pa.field('edited', pa.bool_(), nullable=True),
-    pa.field('time_edited', pa.timestamp('ms'), nullable=True),
-    pa.field('subreddit_type', pa.string(), nullable=True),
-    pa.field('subreddit_id', pa.string(), nullable=True),
-    pa.field('stickied', pa.bool_(), nullable=True),
-    pa.field('is_submitter', pa.bool_(), nullable=True),
-    pa.field('body', pa.string(), nullable=True),
-    pa.field('error', pa.string(), nullable=True),
-])
-
-from pathlib import Path
-p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
-
-if not p.is_dir():
-    if p.exists():
-        p.unlink()
-    p.mkdir()
-
-else:
-    list(map(Path.unlink,p.glob('*')))
-
-part_size = int(1e7)
-part = 1
-n_output = 0
-writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
-
-while True:
-    if n_output > part_size:
-        if part > 1:
-            writer.close()
-
-        part = part + 1
-        n_output = 0
-    
-        writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
-
-    n_output += N
-    chunk = islice(rows,N)
-    pddf = pd.DataFrame(chunk, columns=schema.names)
-    table = pa.Table.from_pandas(pddf,schema=schema)
-    if table.shape[0] == 0:
-        break
-    writer.write_table(table)
-
+def parse_dump(partition):
+
+    dumpdir = f"/gscratch/comdata/raw_data/reddit_dumps/comments/{partition}"
+
+    stream = open_input_file(dumpdir)
+    rows = map(parse_comment, stream)
+
+    schema = pa.schema([
+        pa.field('id', pa.string(), nullable=True),
+        pa.field('subreddit', pa.string(), nullable=True),
+        pa.field('link_id', pa.string(), nullable=True),
+        pa.field('parent_id', pa.string(), nullable=True),
+        pa.field('created_utc', pa.timestamp('ms'), nullable=True),
+        pa.field('author', pa.string(), nullable=True),
+        pa.field('ups', pa.int64(), nullable=True),
+        pa.field('downs', pa.int64(), nullable=True),
+        pa.field('score', pa.int64(), nullable=True),
+        pa.field('edited', pa.bool_(), nullable=True),
+        pa.field('time_edited', pa.timestamp('ms'), nullable=True),
+        pa.field('subreddit_type', pa.string(), nullable=True),
+        pa.field('subreddit_id', pa.string(), nullable=True),
+        pa.field('stickied', pa.bool_(), nullable=True),
+        pa.field('is_submitter', pa.bool_(), nullable=True),
+        pa.field('body', pa.string(), nullable=True),
+        pa.field('error', pa.string(), nullable=True),
+    ])
+
+    p = Path("/gscratch/comdata/output/temp/reddit_comments.parquet")
+    p.mkdir(exist_ok=True,parents=True)
+
+    N=10000
+    with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet",
+                          schema=schema,
+                          compression='snappy',
+                          flavor='spark') as writer:
+
+        while True:
+            chunk = islice(rows,N)
+            pddf = pd.DataFrame(chunk, columns=schema.names)
+            table = pa.Table.from_pandas(pddf,schema=schema)
+            if table.shape[0] == 0:
+                break
+            writer.write_table(table)
+
+        writer.close()
+
+
+def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/comments", overwrite=True):
+    files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
+    with open("comments_task_list.sh",'w') as of:
+        for fpath in files:
+            partition = os.path.split(fpath)[1]
+            if (not Path(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
+                of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
+
+
+if __name__ == '__main__':
+    fire.Fire({'parse_dump':parse_dump,
+              'gen_task_list':gen_task_list})
 
diff --git a/datasets/comments_2_parquet_part2.py b/datasets/comments_2_parquet_part2.py
index 0d5cc9e..1031c68 100755
--- a/datasets/comments_2_parquet_part2.py
+++ b/datasets/comments_2_parquet_part2.py
@@ -2,12 +2,19 @@
 
 # spark script to make sorted, and partitioned parquet files 
 
+import pyspark
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 
 spark = SparkSession.builder.getOrCreate()
 
-df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2",compression='snappy')
+conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
+conf = conf.set("spark.sql.shuffle.partitions",2000)
+conf = conf.set('spark.sql.crossJoin.enabled',"true")
+conf = conf.set('spark.debug.maxToStringFields',200)
+sc = spark.sparkContext
+
+df = spark.read.parquet("/gscratch/comdata/output/temp/reddit_comments.parquet",compression='snappy')
 
 df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
 df = df.drop('subreddit')
@@ -21,9 +28,9 @@ df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
 df = df.repartition('subreddit')
 df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
 df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_subreddit.parquet_new", mode='overwrite', compression='snappy')
+df2.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
 
 df = df.repartition('author')
 df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
 df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
-df3.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_author.parquet_new", mode='overwrite',compression='snappy')
+df3.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')
diff --git a/datasets/helper.py b/datasets/helper.py
index 8f1dfe2..db28628 100644
--- a/datasets/helper.py
+++ b/datasets/helper.py
@@ -24,8 +24,7 @@ def open_fileset(files):
     for fh in files:
         print(fh)
         lines = open_input_file(fh)
-        for line in lines:
-            yield line
+        yield from lines
 
 def open_input_file(input_filename):
     if re.match(r'.*\.7z$', input_filename):
@@ -39,7 +38,7 @@ def open_input_file(input_filename):
     elif re.match(r'.*\.xz', input_filename):
         cmd = ["xzcat",'-dk', '-T 20',input_filename]
     elif re.match(r'.*\.zst',input_filename):
-        cmd = ['zstd','-dck', input_filename]
+        cmd = ['/kloneusr/bin/zstd','-dck', input_filename,  '--memory=2048MB --stdout']
     elif re.match(r'.*\.gz',input_filename):
         cmd = ['gzip','-dc', input_filename]
     try:
diff --git a/datasets/job_script.sh b/datasets/job_script.sh
index d90b618..5b5a7d3 100755
--- a/datasets/job_script.sh
+++ b/datasets/job_script.sh
@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/users/nathante/subreddit_term_similarity_weekly_5000.parquet --topN=5000
-stop-all.sh
+singularity exec  /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 comments_2_parquet_part2.py 
+singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
diff --git a/datasets/submissions_2_parquet.sh b/datasets/submissions_2_parquet.sh
old mode 100644
new mode 100755
index f133069..81a5753
--- a/datasets/submissions_2_parquet.sh
+++ b/datasets/submissions_2_parquet.sh
@@ -1,8 +1,8 @@
+#!/usr/bin/env bash
 ## this should be run manually since we don't have a nice way to wait on parallel_sql jobs
 
-#!/usr/bin/env bash
 
-./parse_submissions.sh
+srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 $(pwd)/submissions_2_parquet_part1.py gen_task_list
 
 start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py
 
diff --git a/datasets/submissions_2_parquet_part1.py b/datasets/submissions_2_parquet_part1.py
index 16d1988..77ae09f 100755
--- a/datasets/submissions_2_parquet_part1.py
+++ b/datasets/submissions_2_parquet_part1.py
@@ -3,26 +3,23 @@
 # two stages:
 # 1. from gz to arrow parquet (this script) 
 # 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
-
 from datetime import datetime
-from multiprocessing import Pool
+from pathlib import Path
 from itertools import islice
 from helper import find_dumps, open_fileset
 import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
-import simdjson
 import fire
 import os
-
-parser = simdjson.Parser()
+import json
 
 def parse_submission(post, names = None):
     if names is None:
         names = ['id','author','subreddit','title','created_utc','permalink','url','domain','score','ups','downs','over_18','has_media','selftext','retrieved_on','num_comments','gilded','edited','time_edited','subreddit_type','subreddit_id','subreddit_subscribers','name','is_self','stickied','quarantine','error']
 
     try:
-        post = parser.parse(post)
+        post = json.loads(post)
     except (ValueError) as e:
         #        print(e)
         #        print(post)
@@ -92,8 +89,7 @@ def parse_dump(partition):
         pa.field('quarantine',pa.bool_(),nullable=True),
         pa.field('error',pa.string(),nullable=True)])
 
-    if not os.path.exists("/gscratch/comdata/output/temp/reddit_submissions.parquet/"):
-        os.mkdir("/gscratch/comdata/output/temp/reddit_submissions.parquet/")
+    Path("/gscratch/comdata/output/temp/reddit_submissions.parquet/").mkdir(exist_ok=True,parents=True)
 
     with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_submissions.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer:
         while True:
@@ -108,7 +104,7 @@ def parse_dump(partition):
 
 def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/submissions"):
     files = list(find_dumps(dumpdir,base_pattern="RS_20*.*"))
-    with open("parse_submissions_task_list",'w') as of:
+    with open("submissions_task_list.sh",'w') as of:
         for fpath in files:
             partition = os.path.split(fpath)[1]
             of.write(f'python3 submissions_2_parquet_part1.py parse_dump {partition}\n')
diff --git a/dumps/check_comments_shas.py b/dumps/check_comments_shas.py
index dd428be..e59a7b8 100755
--- a/dumps/check_comments_shas.py
+++ b/dumps/check_comments_shas.py
@@ -8,7 +8,7 @@ import hashlib
 shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
 #shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
 
-shasums = shasums1 + shasums2
+shasums = shasums1 
 dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
 
 for l in shasums.strip().split('\n'):
diff --git a/ngrams/run_tf_jobs.sh b/ngrams/run_tf_jobs.sh
index 0e7d5dd..9ff590f 100755
--- a/ngrams/run_tf_jobs.sh
+++ b/ngrams/run_tf_jobs.sh
@@ -1,8 +1,6 @@
 #!/usr/bin/env bash
-module load parallel_sql
+
 source ./bin/activate
 python3 tf_comments.py gen_task_list
-psu --del --Y
-cat tf_task_list | psu --load
 
 for job in $(seq 1 50); do sbatch checkpoint_parallelsql.sbatch; done;
diff --git a/ngrams/sort_tf_comments.py b/ngrams/sort_tf_comments.py
index abb097e..d9c3e2c 100644
--- a/ngrams/sort_tf_comments.py
+++ b/ngrams/sort_tf_comments.py
@@ -2,12 +2,17 @@
 
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
+import fire
 
-spark = SparkSession.builder.getOrCreate()
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/")
+def main(inparquet, outparquet, colname):
+    spark = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(inparquet)
 
-df = df.repartition(2000,'term')
-df = df.sort(['term','week','subreddit'])
-df = df.sortWithinPartitions(['term','week','subreddit'])
+    df = df.repartition(2000,colname)
+    df = df.sort([colname,'week','subreddit'])
+    df = df.sortWithinPartitions([colname,'week','subreddit'])
 
-df.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
+    df.write.parquet(outparquet,mode='overwrite',compression='snappy')
+
+if __name__ == '__main__':
+    fire.Fire(main)
diff --git a/ngrams/tf_comments.py b/ngrams/tf_comments.py
index a40e5d9..f472eeb 100755
--- a/ngrams/tf_comments.py
+++ b/ngrams/tf_comments.py
@@ -14,21 +14,29 @@ from nltk.util import ngrams
 import string
 from random import random
 from redditcleaner import clean
+from pathlib import Path
 
 # compute term frequencies for comments in each subreddit by week
-def weekly_tf(partition, mwe_pass = 'first'):
-    dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
-    if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
-        os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
+def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', input_dir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
 
-    if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
-        os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
+    dataset = ds.dataset(Path(input_dir)/partition, format='parquet')
+    outputdir = Path(outputdir)
+    samppath = outputdir / "reddit_comment_ngrams_10p_sample"
+
+    if not samppath.exists():
+        samppath.mkdir(parents=True, exist_ok=True)
 
     ngram_output = partition.replace("parquet","txt")
 
+    if excluded_users is not None:
+        excluded_users = set(map(str.strip,open(excluded_users)))
+        df = df.filter(~ (f.col("author").isin(excluded_users)))
+
+
+    ngram_path = samppath / ngram_output
     if mwe_pass == 'first':
-        if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
-            os.remove(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}")
+        if ngram_path.exists():
+            ngram_path.unlink()
     
     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
 
@@ -62,8 +70,10 @@ def weekly_tf(partition, mwe_pass = 'first'):
 
     subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
 
+    mwe_path = outputdir / "multiword_expressions.feather"
+
     if mwe_pass != 'first':
-        mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
+        mwe_dataset = pd.read_feather(mwe_path)
         mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
         mwe_phrases = list(mwe_dataset.phrase)
         mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
@@ -115,7 +125,7 @@ def weekly_tf(partition, mwe_pass = 'first'):
             for sentence in sentences:
                 if random() <= 0.1:
                     grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
-                    with open(f'/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
+                    with open(ngram_path,'a') as gram_file:
                         for ng in grams:
                             gram_file.write(' '.join(ng) + '\n')
                 for token in sentence:
@@ -149,8 +159,15 @@ def weekly_tf(partition, mwe_pass = 'first'):
     outrows = tf_comments(subreddit_weeks)
 
     outchunksize = 10000
-
-    with pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
+    
+    termtf_outputdir = (outputdir / "comment_terms")
+    termtf_outputdir.mkdir(parents=True, exist_ok=True)
+    authortf_outputdir = (outputdir / "comment_authors")
+    authortf_outputdir.mkdir(parents=True, exist_ok=True)    
+    termtf_path = termtf_outputdir / partition
+    authortf_path = authortf_outputdir / partition
+    with pq.ParquetWriter(termtf_path, schema=schema, compression='snappy', flavor='spark') as writer, \
+         pq.ParquetWriter(authortf_path, schema=author_schema, compression='snappy', flavor='spark') as author_writer:
     
         while True:
 
@@ -179,12 +196,12 @@ def weekly_tf(partition, mwe_pass = 'first'):
         author_writer.close()
 
 
-def gen_task_list(mwe_pass='first'):
+def gen_task_list(mwe_pass='first', outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
     files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
-    with open("tf_task_list",'w') as outfile:
+    with open(tf_task_list,'w') as outfile:
         for f in files:
             if f.endswith(".parquet"):
-                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
+                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
 
 if __name__ == "__main__":
     fire.Fire({"gen_task_list":gen_task_list,
diff --git a/ngrams/top_comment_phrases.py b/ngrams/top_comment_phrases.py
old mode 100644
new mode 100755
index 031cba5..ff1c4f0
--- a/ngrams/top_comment_phrases.py
+++ b/ngrams/top_comment_phrases.py
@@ -1,58 +1,69 @@
+#!/usr/bin/env python3
 from pyspark.sql import functions as f
 from pyspark.sql import Window
 from pyspark.sql import SparkSession
 import numpy as np
+import fire
+from pathlib import Path
 
-spark = SparkSession.builder.getOrCreate()
-df = spark.read.text("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
 
-df = df.withColumnRenamed("value","phrase")
+def main(ngram_dir="/gscratch/comdata/output/reddit_ngrams"):
+    spark = SparkSession.builder.getOrCreate()
+    ngram_dir = Path(ngram_dir)
+    ngram_sample = ngram_dir / "reddit_comment_ngrams_10p_sample"
+    df = spark.read.text(str(ngram_sample))
 
-# count phrase occurrances
-phrases = df.groupby('phrase').count()
-phrases = phrases.withColumnRenamed('count','phraseCount')
-phrases = phrases.filter(phrases.phraseCount > 10)
+    df = df.withColumnRenamed("value","phrase")
 
+    # count phrase occurrances
+    phrases = df.groupby('phrase').count()
+    phrases = phrases.withColumnRenamed('count','phraseCount')
+    phrases = phrases.filter(phrases.phraseCount > 10)
 
-# count overall
-N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
+    # count overall
+    N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
 
-print(f'analyzing PMI on a sample of {N} phrases') 
-logN = np.log(N)
-phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
+    print(f'analyzing PMI on a sample of {N} phrases') 
+    logN = np.log(N)
+    phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
 
-# count term occurrances
-phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
-terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
+    # count term occurrances
+    phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
+    terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
 
-win = Window.partitionBy('term')
-terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
-terms = terms.withColumnRenamed('count','termCount')
-terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
+    win = Window.partitionBy('term')
+    terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
+    terms = terms.withColumnRenamed('count','termCount')
+    terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
 
-terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
-terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
-terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
+    terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
+    terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
+    terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
 
-# join phrases to term counts
+    # join phrases to term counts
 
 
-df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
+    df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
 
-df = df.sort(['phrasePWMI'],descending=True)
-df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
-df.write.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/",mode='overwrite',compression='snappy')
+    df = df.sort(['phrasePWMI'],descending=True)
+    df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
 
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/")
+    pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
+    df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
 
-df.write.csv("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.csv/",mode='overwrite',compression='none')
+    df = spark.read.parquet(str(pwmi_dir))
 
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet")
-df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
+    df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
 
-# choosing phrases occurring at least 3500 times in the 10% sample (35000 times) and then with a PWMI of at least 3 yeids about 65000 expressions.
-#
-df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
-df = df.toPandas()
-df.to_feather("/gscratch/comdata/users/nathante/reddit_multiword_expressions.feather")
-df.to_csv("/gscratch/comdata/users/nathante/reddit_multiword_expressions.csv")
+    df = spark.read.parquet(str(pwmi_dir))
+    df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
+
+    # choosing phrases occurring at least 3500 times in the 10% sample (35000 times) and then with a PWMI of at least 3 yeids about 65000 expressions.
+    #
+    df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
+    df = df.toPandas()
+    df.to_feather(ngram_dir / "multiword_expressions.feather")
+    df.to_csv(ngram_dir / "multiword_expressions.csv")
+
+if __name__ == '__main__':
+    fire.Fire(main)
diff --git a/similarities/Makefile b/similarities/Makefile
index f578fd5..963192d 100644
--- a/similarities/Makefile
+++ b/similarities/Makefile
@@ -1,8 +1,10 @@
+
 #all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
-srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
-srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
-base_data=/gscratch/comdata/output
-similarity_data=${base_data}/reddit_similarity
+# srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+# srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
+srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40 
+srun_huge=srun -p compute-hugemem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40 
+similarity_data=/gscratch/scrubbed/comdata/reddit_similarity
 tfidf_data=${similarity_data}/tfidf
 tfidf_weekly_data=${similarity_data}/tfidf_weekly
 similarity_weekly_data=${similarity_data}/weekly
@@ -10,7 +12,10 @@ lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
 
 lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
 
-all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
+
+all: ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather
+
+#all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
 
 #${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet  ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
 
@@ -18,103 +23,106 @@ all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.pa
 
 # all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
 
-${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
+${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms.parquet
+	 ${srun} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
 
 ${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
+	 ${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
 
 ${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
+	 ${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
 
 ${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
+	 ${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200 --inpath=$<
 
 ${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
+	 ${srun_huge} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000 --inpath=$<
 
 ${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
+	 ${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000 --inpath=$<
 
 ${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
+	 ${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000 --inpath=$<
 
 ${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
+	 ${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
 
 ${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
+	 ${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
 
-${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000
+${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000 --inpath=$<
 
-${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
+${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
+	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
 
 ${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
+	 ${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
 
 ${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
+	 ${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
 
 ${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
+	 ${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
 
 ${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
+	 ${srun} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
 
 ${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
+	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
+
+${similarity_data}/subreddits_by_num_comments_nonsfw.csv:
+	start_spark_and_run.sh 3 top_subreddits_by_comments.py
 
-${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather 
+${tfidf_data}/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py terms --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_terms_100k.parquet
 
-${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather
+${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py terms --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_terms_30k.feather
 
-${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather
+${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py terms --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_terms_10k.feather
 
-${tfidf_data}/comment_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.feather
+${tfidf_data}/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
 
-${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet
+${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py authors --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_authors_10k.parquet
 
-${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.parquet
+${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+#	mkdir -p ${tfidf_data}/
+	start_spark_and_run.sh 3 tfidf.py authors --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_authors_30k.parquet
 
-${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
+${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	start_spark_and_run.sh 3 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
 
 ${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
-	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
+	start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=100000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
 
-${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
+${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
 
-${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
+${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
 
 ${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
+	 ${srun} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_terms_100k.parquet
 
-${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
+${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
+	 ${srun} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
 
-${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
+${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
+	 ${srun} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
 
-${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
+,${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
+	 ${srun} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
 
-# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
+# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv
 # 	start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
 
 # /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet 
diff --git a/similarities/job_script.sh b/similarities/job_script.sh
index 0c37103..1158ff0 100755
--- a/similarities/job_script.sh
+++ b/similarities/job_script.sh
@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname):7077 top_subreddits_by_comments.py 
-singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
+singularity exec  /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 tfidf.py authors --topN=100000 --inpath=/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet --outpath=/gscratch/scrubbed/comdata/reddit_similarity/tfidf/comment_authors_100k.parquet
+singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
diff --git a/similarities/lsi_similarities.py b/similarities/lsi_similarities.py
index eb89f55..493755f 100644
--- a/similarities/lsi_similarities.py
+++ b/similarities/lsi_similarities.py
@@ -5,19 +5,20 @@ from similarities_helper import *
 #from similarities_helper import similarities, lsi_column_similarities
 from functools import partial
 
-inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
-term_colname='term'
-outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
-n_components=[10,50,100]
-included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-n_iter=5
-random_state=1968
-algorithm='arpack'
-topN = None
-from_date=None
-to_date=None
-min_df=None
-max_df=None
+# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
+# term_colname='term'
+# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
+# n_components=[10,50,100]
+# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+# n_iter=5
+# random_state=1968
+# algorithm='arpack'
+# topN = None
+# from_date=None
+# to_date=None
+# min_df=None
+# max_df=None
+
 def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
     print(n_components,flush=True)
 
@@ -62,7 +63,7 @@ def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/t
                             n_components=n_components
                                )
 
-def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968):
+def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
     return lsi_similarities(inpath,
                             'author',
                             outfile,
diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index d97e519..03c10b2 100644
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -262,6 +262,7 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
 
     lsimat = mod.transform(tfidfmat.T)
     if lsi_model_save is not None:
+        Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
         pickle.dump(mod, open(lsi_model_save,'wb'))
 
     sims_list = []
diff --git a/similarities/tfidf.py b/similarities/tfidf.py
index 01b0b20..bbae528 100644
--- a/similarities/tfidf.py
+++ b/similarities/tfidf.py
@@ -4,7 +4,7 @@ from pyspark.sql import functions as f
 from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
 
 def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
-    spark = SparkSession.builder.getOrCreate()y
+    spark = SparkSession.builder.getOrCreate()
 
     df = spark.read.parquet(inpath)
 
diff --git a/similarities/top_subreddits_by_comments.py b/similarities/top_subreddits_by_comments.py
index ff9293c..9a4d7d3 100644
--- a/similarities/top_subreddits_by_comments.py
+++ b/similarities/top_subreddits_by_comments.py
@@ -17,7 +17,7 @@ df = df.filter(~df.subreddit.like("u_%"))
 df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
 
 df = df.join(prop_nsfw,on='subreddit')
-#df = df.filter(df.prop_nsfw < 0.5)
+df = df.filter(df.prop_nsfw < 0.5)
 
 win = Window.orderBy(f.col('n_comments').desc())
 df = df.withColumn('comments_rank', f.rank().over(win))
@@ -26,4 +26,4 @@ df = df.toPandas()
 
 df = df.sort_values("n_comments")
 
-df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False)
+df.to_csv('/gscratch/scrubbed/comdata/reddit_similarity/subreddits_by_num_comments_nonsfw.csv', index=False)