X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/c0da8f4dbfc62b3b8af0212281eb219d14079d1e..6baa08889b2f46c14f2baa5e3d2136cf165b1673:/tf_comments.py?ds=sidebyside

diff --git a/tf_comments.py b/tf_comments.py
index 211647e..526bac2 100755
--- a/tf_comments.py
+++ b/tf_comments.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
+import pandas as pd
 import pyarrow as pa
 import pyarrow.dataset as ds
 import pyarrow.parquet as pq
 from itertools import groupby, islice, chain
 import fire
 from collections import Counter
-import pandas as pd
 import os
 import datetime
 import re
@@ -22,7 +22,6 @@ urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-
 # 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/")
 
@@ -31,8 +30,9 @@ def weekly_tf(partition, mwe_pass = 'first'):
 
     ngram_output = partition.replace("parquet","txt")
 
-    if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
-        os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
+    if mwe_pass == 'first':
+        if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
+            os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
     
     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
 
@@ -161,21 +161,26 @@ def weekly_tf(partition, mwe_pass = 'first'):
         while True:
 
             chunk = islice(outrows,outchunksize)
-            chunk = (c for c in chunk if c.subreddit is not None)
+            chunk = (c for c in chunk if c[1] is not None)
             pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
-
             author_pddf = pddf.loc[pddf.is_token == False, schema.names]
             pddf = pddf.loc[pddf.is_token == True, schema.names]
             author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
             author_pddf = author_pddf.loc[:,author_schema.names]
-
             table = pa.Table.from_pandas(pddf,schema=schema)
             author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
-            if table.shape[0] == 0:
+            do_break = True
+
+            if table.shape[0] != 0:
+                writer.write_table(table)
+                do_break = False
+            if author_table.shape[0] != 0:
+                author_writer.write_table(author_table)
+                do_break = False
+
+            if do_break:
                 break
-            writer.write_table(table)
-            author_writer.write_table(author_table)
-            
+
         writer.close()
         author_writer.close()
 
@@ -185,7 +190,7 @@ def gen_task_list(mwe_pass='first'):
     with open("tf_task_list",'w') as outfile:
         for f in files:
             if f.endswith(".parquet"):
-                outfile.write(f"python3 tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
+                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
 
 if __name__ == "__main__":
     fire.Fire({"gen_task_list":gen_task_list,