82 lines
3.8 KiB
Python
82 lines
3.8 KiB
Python
from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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from pyspark.sql import Window
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import numpy as np
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import pyarrow
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import pyarrow.dataset as ds
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import pandas as pd
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import fire
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from itertools import islice, chain
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from pathlib import Path
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from similarities_helper import *
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from multiprocessing import Pool, cpu_count
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from functools import partial
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def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
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term = term_colname
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term_id = term + '_id'
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term_id_new = term + '_id_new'
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print(f"loading matrix: {week}")
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entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
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term_colname=term_colname,
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min_df=min_df,
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max_df=max_df,
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included_subreddits=included_subreddits,
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topN=topN,
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week=week)
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mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
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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'] = names.subreddit.values
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outfile = str(Path(outdir) / str(week))
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write_weekly_similarities(outfile, sims, week, names)
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def pull_weeks(batch):
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return set(batch.to_pandas()['week'])
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#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
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def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
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print(outfile)
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tfidf_ds = ds.dataset(tfidf_path)
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tfidf_ds = tfidf_ds.to_table(columns=["week"])
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batches = tfidf_ds.to_batches()
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with Pool(cpu_count()) as pool:
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weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
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weeks = sorted(weeks)
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# do this step in parallel if we have the memory for it.
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# should be doable with pool.map
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print(f"computing weekly similarities")
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week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
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with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
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list(pool.map(week_similarities_helper,weeks))
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def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
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return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
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outfile,
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'author',
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min_df,
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max_df,
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included_subreddits,
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topN)
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def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
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return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
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outfile,
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'term',
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min_df,
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max_df,
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included_subreddits,
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topN)
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if __name__ == "__main__":
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fire.Fire({'authors':author_cosine_similarities_weekly,
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'terms':term_cosine_similarities_weekly})
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