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use static tfidf (not weekly) to create tfidf matrix

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Nathan TeBlunthuis 2024-12-28 20:00:53 -08:00
parent 7b5ac73b2c
commit f11d4cfc72
1 changed files with 6 additions and 4 deletions
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8
similarities/weekly_cosine_similarities.py
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@ -75,17 +75,19 @@ def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kw
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_submission_terms_tfidf.parquet')
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None, max_df=None, static_tfidf_path=None):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
conn = duckdb.connect()
subreddit_names = conn.execute(f"SELECT DISTINCT subreddit, subreddit_id from read_parquet('{tfidf_path}/*/*.parquet') ORDER BY subreddit_id;").df()
if static_tfidf_path is not None:
nterms = conn.execute(f"SELECT MAX({term_colname + '_id'}) as nterms FROM read_parquet('{static_tfidf_path}/*/*.parquet')").df()
else:
nterms = conn.execute(f"SELECT MAX({term_colname + '_id'}) as nterms FROM read_parquet('{tfidf_path}/*/*.parquet')").df()
nterms = nterms.nterms.values
print(nterms)
print(int(nterms[0]))
nterms = int(nterms[0])
weeks = conn.execute(f"SELECT DISTINCT week FROM read_parquet('{tfidf_path}/*/*.parquet')").df()
weeks = weeks.week.values
conn.close()