74 lines
2.9 KiB
Plaintext
74 lines
2.9 KiB
Plaintext
from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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import pandas as pd
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import fire
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from pathlib import Path
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from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
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def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
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spark = SparkSession.builder.getOrCreate()
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conf = spark.sparkContext.getConf()
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print(outfile)
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print(exclude_phrases)
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tfidf = spark.read.parquet(infile)
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if included_subreddits is None:
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included_subreddits = select_topN_subreddits(topN)
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else:
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included_subreddits = set(open(included_subreddits))
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if exclude_phrases == True:
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tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
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print("creating temporary parquet with matrix indicies")
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tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits)
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tfidf = spark.read.parquet(tempdir.name)
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subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
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subreddit_names = subreddit_names.sort_values("subreddit_id_new")
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subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
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spark.stop()
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print("loading matrix")
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mat = read_tfidf_matrix(tempdir.name, term_colname)
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print('computing similarities')
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sims = column_similarities(mat)
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del mat
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sims = pd.DataFrame(sims.todense())
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sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
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sims['subreddit'] = subreddit_names.subreddit.values
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p = Path(outfile)
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output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
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output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
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output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
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sims.to_feather(outfile)
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tempdir.cleanup()
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def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
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return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
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'term',
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outfile,
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min_df,
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included_subreddits,
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topN,
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exclude_phrases)
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def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
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return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
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'author',
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outfile,
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min_df,
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included_subreddits,
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topN,
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exclude_phrases=False)
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if __name__ == "__main__":
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fire.Fire({'term':term_cosine_similarities,
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'author':author_cosine_similarities})
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