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Merge branch 'master' of code:cdsc_reddit into excise_reindex

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Nathan TeBlunthuis 2021-08-03 15:02:08 -07:00
commit 2d21ff1137
4 changed files with 26 additions and 22 deletions
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2
dumps/pull_pushshift_comments.sh
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@ -8,7 +8,5 @@ wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base
wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
# starting in 2020 we use daily dumps not monthly dumps
wget -r --no-parent -A 'RC_202*.gz' -U $user_agent -P $output_dir -nd -nc $base_url/daily/
./check_comments_shas.py ./check_comments_shas.py

22
similarities/cosine_similarities.py
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@ -2,46 +2,48 @@ import pandas as pd
import fire import fire
from pathlib import Path from pathlib import Path
from similarities_helper import similarities, column_similarities from similarities_helper import similarities, column_similarities
from functools import partial
def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'): def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname) return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
# change so that these take in an input as an optional argument (for speed, but also for idf).
def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
return cosine_similarities(infile,
'term', 'term',
outfile, outfile,
min_df, min_df,
max_df, max_df,
included_subreddits, included_subreddits,
topN, topN,
exclude_phrases,
from_date, from_date,
to_date to_date
) )
def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None): def author_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', return cosine_similarities(infile,
'author', 'author',
outfile, outfile,
min_df, min_df,
max_df, max_df,
included_subreddits, included_subreddits,
topN, topN,
exclude_phrases=False,
from_date=from_date, from_date=from_date,
to_date=to_date to_date=to_date
) )
def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None): def author_tf_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', return cosine_similarities(infile,
'author', 'author',
outfile, outfile,
min_df, min_df,
max_df, max_df,
included_subreddits, included_subreddits,
topN, topN,
exclude_phrases=False,
from_date=from_date, from_date=from_date,
to_date=to_date, to_date=to_date,
tfidf_colname='relative_tf' tfidf_colname='relative_tf'

2
similarities/similarities_helper.py
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@ -32,7 +32,7 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
if included_subreddits is None: if included_subreddits is None:
included_subreddits = select_topN_subreddits(topN) included_subreddits = select_topN_subreddits(topN)
else: else:
included_subreddits = set(open(included_subreddits)) included_subreddits = set(map(str.strip,map(str.lower,open(included_subreddits))))
ds_filter = ds.field("subreddit").isin(included_subreddits) ds_filter = ds.field("subreddit").isin(included_subreddits)

22
similarities/tfidf.py
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@ -11,7 +11,7 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
df = df.filter(~ f.col(term_colname).isin(exclude)) df = df.filter(~ f.col(term_colname).isin(exclude))
if included_subreddits is not None: if included_subreddits is not None:
include_subs = list(open(included_subreddits)) include_subs = set(map(str.strip,map(str.lower, open(included_subreddits))))
else: else:
include_subs = select_topN_subreddits(topN) include_subs = select_topN_subreddits(topN)
@ -27,40 +27,44 @@ def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddi
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits) return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet', def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=25000): topN=25000,
included_subreddits=None):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet", return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath, outpath,
topN, topN,
'author', 'author',
['[deleted]','AutoModerator'], ['[deleted]','AutoModerator'],
included_subreddits=None included_subreddits=included_subreddits
) )
def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet', def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=25000): topN=25000,
included_subreddits=None):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet", return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath, outpath,
topN, topN,
'term', 'term',
[], [],
included_subreddits=None included_subreddits=included_subreddits
) )
def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet', def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=25000): topN=25000,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet", return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath, outpath,
topN, topN,
'author', 'author',
['[deleted]','AutoModerator'], ['[deleted]','AutoModerator'],
included_subreddits=None included_subreddits=included_subreddits
) )
def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=25000): topN=25000,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet", return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
@ -68,7 +72,7 @@ def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf
topN, topN,
'term', 'term',
[], [],
included_subreddits=None included_subreddits=included_subreddits
) )